Innovations in Smart Cities Applications Volume 5

Smart city has been a subject of great interest in research and practice. Since its first appearance in early 1998, the Smart City concept is still unclear in terms of context and perspective. The aim of this article is to track the evolution of this emergent field of research between 1997 and 2020 through a systematic literature review based on the Theory method.

We describe a platform that utilizes the power of human computation to provide improved services to citizens in a smart city context. The platform enables users to report problems to authorities and facilitates communication between parties to work on solutions to encountered problems. An important aim is to involve citizens in the city decision-making process. System users can be registered who provide necessary information directly or through their social networking accounts. Alternatively, users can proceed as guests and use the system anonymously, without registration. For registered users the profile information and use history can be used to strengthen the validity/trust of their contributions. Users can perform several actions on the platform including: reporting a problem using username or anonymously, voting on reports submitted by others, reviewing the status of their reports and responding to requests to perform specific tasks on their travel route. We describe the system and focus on human computation aspects, including: user privacy issues, data collection methods, data reliability and data quality assessment, user engagement incentives and reward system, models of interaction between the user and the system (Push/Pull) and mention frameworks used for system implementation and testing. Although focused on citizen services, the platform is flexible and extensible to tasks in other areas.

The aim of this study is to discuss the concept of “Ecological Smart City” through the “Kepez-Santral Smart Urban Transformation Project”, which includes Kepez and Santral neighborhoods of Antalya’s Kepez District. Known as shantytowns, this area, which contains unhealthy and unstable buildings, was declared a ‘Risky Zone’ within the scope of the “Law on Disaster Relief Areas No. 6306” with the decision of the Council of Ministers dated 24.11.2014 and numbered 7041 and published in the Official Gazette. The practices of the new urban transformation in the concept of “Ecological Smart City”, which was started with the joint decision of more than 3000 landowners living in unhealthy conditions, still continue today. Kepez-Santral Smart Urban Transformation Project, which was prepared under the leadership of Antalya Metropolitan Municipality and received international award and grant support with the European Union MAtchUP project, was started as a pilot project in the fields of smart city, smart transportation and smart housing in Antalya. In addition to the benefits such as demolishing dangerous and risky urban areas and solving property problems, the Project aims to develop a smart area that uses green, livable and innovative technologies. In this study, the technologies used in the housing blocks to be smart within the scope of the MAtchUP project, the anticipated energy consumption and the opportunities it will provide to the users have been evaluated. In this context, a comparison is made between the old and new buildings in terms of residents’ comfort, heating-cooling systems, energy savings, etc. Consequently, in the study, the dimensions of the Ecological Smart City concept are revealed and analyzed through the introduction of the project.

While the rapid and intense urbanization processes taking place at the global level are transforming the urban landscape at the same speed, they have led to new policy searches in the spatial context at the stage of solving the increasing needs/problems. In this context, one of the most important approaches that come to the fore is the smart city. This approach, which offers technological solutions to the increasing urban problems of metropolitan cities, also offers important spatial opportunities for the sustainability of the urban landscape. In this study, the current status and potentials of smart city applications at the neighborhood scale were determined by drawing a methodical framework (analysis-synthesis-suggestion) based on the smart city concept and containing urban landscape planning goals and objectives in the example of Etiler Neighborhood, which is a part of Beşiktaş district in Istanbul. In the proposal phase, some spatial suggestions were made at the neighborhood scale based on the situations determined in the analysis and synthesis phase. In this direction, smart city targets and strategies that will contribute to future urban landscape planning studies have been determined.

COVID-19 pandemic has caused reduction of tourist arrival, and thus it has resulted in crisis of tourism sector in Special Region of Yogyakarta. The government has implemented health protocols in accordance to Decree of the Minister of Health No. HK.01.07/MENKES/382/2020 as an effort to revive the tourism sector while minimizing the spread of COVID-19. The information regarding the regulation can be conveyed by utilizing ICT, one of them is through Visiting Jogja Mobile Application. This study aimed to identify the provision of information on the Visiting Jogja Mobile Application regarding restrictions on tourism activities and identify the utilization of the application among people who carry out tourism activities during the COVID-19 pandemic. This study used qualitative research methods. Primary data were collected through in-depth interviews and online questionnaires. Secondary data were obtained from literature studies and browsing applications with smartphones. All of the data were analyzed by qualitative and quantitative descriptive analysis. The results show that the application provides information and helps implement 4 of the 8 health protocols through its features. Utilization of applications by tourists is still low both from system management and survey results. Therefore, branding is still needed about the application to the public.

The practice of benchmarking logistics performance is taken seriously by contemporary organizations because of all the advantages it presents knowing that they have a very big impact on international trade. For example, the benchmarking process helps minimize the gap between an organization’s vision and mission. At a fundamental level, each organization has goals to achieve. This requires that the organization present certain levels of performance in order to achieve these organizational objectives. However, it often happens that the organization presents a level of performance which is not up to this standardized level of performance. Benchmarking helps the organization to understand the level of performance it gives in relation to the performance standard it has set to achieve these organizational objectives. Thus, if the achievement of the organizational objective is the vision, the mission consists in taking measures to achieve this objective by making performance equivalent to the performance standard. Benchmarking therefore makes it possible to minimize the gap between vision and mission.Benchmarking business performance also helps reduce the cost of service in the long run. Indeed, performance benchmarking contributes to the process of quantitative measurement of the organization’s performance. Unnecessary service overheads can be eliminated. In addition, as benchmarking contributes to improving the quality of service, it also allows an organization to continuously improve the quality of its performance, by developing the trust of customers in the organization as well as in chain stakeholders value of the organization.This paper should focus on the basics of benchmarking in the logistics industry, particularly in Morocco, by presenting a brief overview of the concept of Benchmarking in the field of logistics. It is followed by an examination of the advantages of benchmarking in logistics for an organization and an elaboration of the logistics sector in Morocco which will include a discussion on its main players, competition and key statistics.

Companies are facing important challenges related to markets’ internationalization, regulatory restrictions and fierce competition especially during the COVID19 crisis. They should embrace change and be agile in order to prevent risks and seize opportunities quickly and efficiently. In this article, we examine how artificial intelligence (AI) can help companies to enhance their organizational agility. Based on two systematic literature reviews on the subject, we identified the weaknesses on relying only on a digital enabler or human resource (HR) practices. Thus, we propose a Framework integrating artificial intelligence and human practices in order to help companies in their efforts towards agility. This latter allows companies to adapt to new regulations in the society, to customers’ expectations and to environmental changes. Ultimately, agile companies can ensure a sustainable performance.

With the development of technologies, digital commerce is increasing in many sectors today. The concept of digital commerce has gained more importance in recent years due to the pandemic. Stock photography, which is a branch of digital commerce and passive income model, is a sector that has no development in Turkey and benefits from foreign-sourced services. This study aims to find a domestic solution to this issue and make a study that will facilitate the work of digital content producers. In order to carry out these studies, it is necessary to follow and implement the websites and the innovations that come with web 2.0. In addition, the software used forms the basis of every detail that serves its purpose. The fact that the software used in stock photography is flexible and has a structure that meets the needs increases the work’s applicability. In the globalizing world, giving importance to such issues has become as important as health and education. The state of development in countries’ economies is such an essential issue that it can direct these issues worldwide. Working on this issue will contribute significantly to developing countries and digital commerce in terms of stock photography. Especially in this study, evaluations will be made regarding the place and application areas of stock photography as digital commerce in Turkey.

Decision making engine based on the classification of the data stream in real time is the main challenge of the Smart City Application (SCA). Fuzzy logic system (FLS) can be used as such engine if the data stream dimension will be significantly reduced. The two-stage Computing with Words (CWW) approach overcomes the dimensionality problem by using abstraction engine at the first stage, which maps the meaning of stream data from sensors to the meaning of a few words. In this article the CWW approach is extended by a Short-Term Memory (STM) model that stores a time sequence of events in the form of fuzzy characteristics of words of a high level of abstraction. The STM footprint model is supported by the footprint blur algorithm, in which calculations are performed with blurring of information about events with time expiration. The classification is proposed to be performed by a fuzzy assessment of the proximity of the STM footprint and the prototype of the class of the flow of events. An example of the use of fuzzy classification of the flow of events model is given.

Subjective tender evaluation and contract award in public procurement is prevalent in various contexts. This has contributed to low quality of goods, services and projects. Successful implementation of building projects is heavily impacted by taking the right decision during tendering processes. Manning tender procedures can be complex and uncertain, involving coordination of numerous tasks and persons with different priorities and objectives. Bias and inconsistent decisions are inevitable if the decision-making process is wholly dependent on intuition, subjective judgement, or emotions. In making transparent decision and beneficial competition tendering, there is need for a flexible tool that could facilitate fair decision making. The purpose of this research was to present a model of an IT solution integrating the concepts of supervised machine learning techniques in the context of tender evaluation in public procurement. Independent variables used as inputs included “Experience”, “Equipment capacity”, “Professionalism”, and “Number of Personnel”. A set criteria was used to determine the values of the variables based on the documents submitted by applicants. The model combines the values of these attributes and determines the category of the entity as either “PASS” or “FAIL”. J48 decision tree classifier was used for this classification problem. This algorithm was preferred due to its relatively simple model among other benefits stated herein. The dataset was divided into test data and training data for the model. The performance appraisal of the model was based on the accuracy of the classification, the precision, recall ratio, ROC curve and the F-Measure. The model was proven to be impressively accurate with an accuracy of 91.1765% while the precision obtained was 0.857. The recall ratio was 1 and an F-measure of 0.923.

Microlending involves giving small loans to people in need. Usually, these loans are issued to entrepreneurs or those who need extra cash to either expand or for personal use. Digital lending is becoming a leading source of credit especially to low-income citizens with minimal or no financial footprints in various parts of the world. It has quickly become the default way for lenders to service loan requests from borrowers due to the convenience it brings about as well as the increased number of requests that can be processed compared to the traditional way that required quite an amount of paper work. As the number of lending companies grows, there is the need to standardize the credit scoring process and maintain an updated credit activity log for every user. This ensures that lenders are always aware of any other unsettled debts a borrower might have and provides them with the most recent information to assess the risk they face by lending to a borrower. The proposed solution consists of a credit scoring neural network-based algorithm composed of a single input layer, a single hidden layer and an output layer of one neuron, and a representational state transfer (REST) based web service allows lenders to submit details of loans they have approved and issued to a borrower. The information is used to generate and keep track of the user’s credit score and amount of risk lenders face should they consider lending to the user. Agile development methodology was used to develop robust credit scoring prototype and Android mobile application. The final prototype was tested to ensure that the requirements were met and the functionality working as required.

The diversity and problems of rural study areas specific to the geography of Turkey lead different fields of expertise to work together. In this sense, sinkholes are becoming a more and more current problem area. The Plateau of Obruk is a very interesting place in terms of smart and sustainable rural settlements as well as remote sensing and photogrammetry areas due to the collapsed areas that have become evident in recent years. A hazard mapping is urgently needed. In addition, a region-specific disaster early warning system should be designed. Regional planning need to be updated with new researches. It is clear that the traditional excessive use of agricultural water is a source of problems in the region, and this situation should be reconsidered and smart rural sustainability should be ensured. Otherwise, there will be serious losses in the productivity of the region, which is counted as a granary. The main purpose of the study will be to provide support for smart and sustainable rural settlements in the future, which will allow interdisciplinary issues and collaborations on the problem areas of the type. A working draft model is suggested for new common related interdisciplineary studies.

In recent years, people have often settled in suburban communities around large cities. Mobility is usually identified with one’s own means of transport, which provides access to work or shopping. In the era of urban development, it is important to analyze the impact of various features on the safety of city and region inhabitants. With help come modern theories from computer science and statistics that can be applied in practice through formalized notation. Then, it becomes possible, in the long perspective, to increase the safety of road users. The aim of this paper is to analyze the influence of various characteristics based on a large testing ground (all national roads in the Świętokrzyskie Voivodeship). The Bayesian regression model for road accidents was evaluated based on actual data of road incidents that occurred in Poland, in the Świętokrzyskie Voivodeship, in 1999–2012. Logistic regression analysis was used to create the Bayesian model. The a posteriori distributions of the random variables of the model were obtained using a sampling Monte Carlo method based on Markov chains. In addition, the paper presents the selected possibilities of using the SAS system in supporting data analysis.

A primary concern of Intelligent Traffic Management Systems (ITMSs) is to collect the required traffic data. Vehicle position is one of the most important data types to manage traffic effectively. In this regard, Global Positioning System (GPS) receivers are widely used; however, their estimation accuracy is affected by several parameters, such as signal blockage. Map-matching is one of the most popular approaches to dealing with this challenge. In this study, we investigated the performance of map-matching software and found that it cannot locate the vehicle effectively if the positional data are too noisy. This paper aims to propose a new methodology by integrating cross-GPS validation, interpolation, best-fit, and map-matching techniques to enhance the vehicle localization performance in the presence of GPS signal noise and investigate the methodology with real traffic data from a metropolitan area. Our evaluations indicate that the proposed methodology can significantly improve vehicle self-localization performance.

Population growth trends in cities have made urban mobility even more difficult. Problems arise in mobility systems, which become more difficult with the increase in population, in issues such as private vehicle ownership, traffic jams, and environmental pollution. Considering that the urban population will increase rapidly, unplanned urbanization is expected to increase the problems in urban mobility. The urban mobility system requires changes to solve these problems. In the context of the smart city approach, integrating technological innovations into urban mobility systems helps to improve the environment and life quality. Smart mobility reveals different alternative modes of mobility. One of these is shared micromobility.In this study, it is aimed to create an integrated framework for smart cities, smart mobility and shared micromobility. However, micromobility has many effects on smart cities. In this context, the effect of micro-mobility shared in the study is limited to the environment. Shared micromobility is an innovative sustainable mode of transport that can replace short-distance travel and has the potential to provide environmental benefits. However, as a result of the studies examined, it is revealed that the environmental effects of shared micromobility are not clear and do not create the expected environmental impact. Therefore, the right strategies should be developed in order to reveal the expected environmental effects of shared micromobility services. With the effective policies developed, the carbon footprint of urban mobility can be reduced.

Nowadays, road surface condition monitoring is a challenging problem that cannot be addressed with traditional techniques. In this paper we propose an architecture for monitoring the condition of road surfaces based on the paradigm of Mobile Crowdsensing. First, a surface detection module extracts high level features from raw data, indicating the presence of hazards. Then, in order to make the system resilient to attacks, the system exploits a reputation module to identify malicious users and filter out unreliable data. Finally, a truth discovery module aggregates the resulting information to obtain the desired truth values. Experiments carried out on a real world dataset prove the resilience of the proposed system to different attacks and the accuracy achieved.

The main reason for congestion in traffic is unnecessary waiting time at intersections. Economic and environmental improvement can be directly achieved by reducing the waiting time at the junction points. The controller, in which the signaling values are calculated by taking the vehicles and pedestrians into consideration in social terms, needs to be developed. Maximum flow in traffic at a single junction can cause a bottleneck at the next junction. Therefore, junction signaling times should be calculated in relation to each other for a certain region and line. Rule and learning based methods cannot respond to multiple intersections and unlikely situations. In order to provide maximum flow dynamically, a new Graph algorithm based on deep learning is needed.In this study, a deep learning-based Graph method has been proposed in which rule and learning-based methods will be used against unlikely situations and to eliminate the single junction bottleneck disadvantage.

Supply Chain Management (SCM) is further revolutionized by several major innovations especially in the field of integration of new information technologies. This is notably the case of Just Walk Out, developed by Amazon in the field of distribution. This new concept, combining an arsenal of sophisticated technologies, allows virtual customers labeling, checking items in the store, reverse echolocation of the customer by his or her location and analysis of the customer’s journey, etc.So, how do Amazon’s store customers perceive the impact of Just Walk Out technology on their shopping experience and on the store supply chain? This is the main question of this research. Once the concepts of Service Supply Chain Management (SSCM) and Logistics distribution chain have been defined, by identifying the specificities of SSCM we will present the principle of Amazon Just Walk Out, focusing on its technical and managerial dimension, and its impact on SCM in terms of the digital supply chain and the shopping experience. Finally, we will explore, through a netnographic approach, how customers perceive this information technology on their purchasing experience. The analysis of the results reveals a certain enthusiasm for this technology, in terms of handling, convenience and freedom, but at the price of a feeling of embarrassment and doubt because of tracking and the over-control generated.

Anomaly detection in urban big datasets is getting wide attention with the presence of different and various urban big data sources. Urban anomaly detection is an important application area because discovered anomalies in urban areas would provide essential information about urban areas and their utilization, especially human mobility analytics and traffic condition monitoring. In the literature, there are several studies performed for urban anomaly detection using taxi trajectory datasets, such as events detection, regional urban anomaly detection and traffic incident detection. In this study, anomaly detection in regional mobility utilization of daily taxi trajectory datasets is performed based on DBSCAN clustering algorithm. A new algorithm and a threshold value are proposed to detect taxi regions as normal and anomalous for both incoming and outgoing taxi trip records. Experiments are performed on New York taxi trajectory big dataset and the experimental results show that proposed algorithm is efficient on detecting regions as normal or anomalous based on daily taxi trip record counts.

Most path planning approaches (global, reactive, or hybrid) existing in literature don’t consider the transfer of experience concept between agents operating in the same environment. A mobile robot executes any planning method to reach its goal, at the time of navigation, it explores the environment, detects the location of obstacles, makes its calculations to choose the best decisions. All this experience gained by the robot will be wasted, and if a new robot has just joined the environment, it has to repeat the entire planning process without benefiting from past experiences. Our contribution consists in: the use of a Holonic Multi-Agent system for modeling the two abstraction levels of the proposed system, an enhanced version of Ant Colony Optimization is used, where the ants are considered as autonomous and heterogeneous robots’ agents. Each ant in the system executes a distinct planning method to reach its goal, on its return path, it puts some pheromones in its experience memory table, which will be transferred to the master agent which incarnates the collective experiences of all agents that have passed through the platform. The simulation and comparison results have confirmed that using the proposed system, the execution time is significantly decreased and the robot safely achieves its goal.

This work is devoted to studying steady-state flow and heat transfer coupled by conduction, convection, and surface radiation in an unvented Trombe wall subjected to solar flux. The lateral surfaces exchange heat with the indoor and outdoor environments, while the horizontal surfaces are assumed to be adiabatic. The finite volume method was used to solve the conservation equations. The radiative transfer was determined by the radiosity method. The effects of some relevant control parameters are investigated. Namely: the emissivities of the lateral air layer surfaces and the thicknesses of the glazing and the air gap. The results obtained show that the emissivities affect the useful and lost fluxes. However, the thicknesses of the glazing and the air layer do not alter these quantities.

Energy management system (EMS) algorithms and strategies are improved to make sure power continuity in all circumstances, minimizing energy production cost and protect grid components from being damaged. Energy management presents a viable solution to issues relating to the energy sector, such as rising demand, rising energy costs, sustainable supply, and environmental impact. The approaches performing energy management strategies, solution algorithms, and systems simulations to overcome many problems in low voltage distribution systems. Furthermore, in this paper some techniques and methodologies are considered to improve energy management of off-grid power systems with microgrid. The reviewed works in this paper cover the various structures of off-grid hybrid microgrids. The most common technologies and strategies have been used in the field of power management, in addition, providing of some future research directions.

The interaction between surface radiation and natural convection in a square cavity containing a heat-generating elliptical body is investigated numerically. The cavity is filled with air $$(Pr=0.71)$$ ( P r = 0.71 ) and cooled via its right vertical wall by a uniform temperature $${T}_{C}=293\,\mathrm{K}$$ T C = 293 K . The other walls are kept adiabatic. The finite volume method and the discrete ordinate method are used to solve differential equations. The effect of the Rayleigh number $$({10}^{3}\le Ra\le {10}^{7})$$ ( 10 3 ≤ R a ≤ 10 7 ) , the elliptical cylinder inclination angle $$0^\circ \le \gamma \le 180$$ 0 ∘ ≤ γ ≤ 180 , and surface emissivity $$\varepsilon\,(0$$ ε ( 0 or $$1)$$ 1 ) on dynamic and thermal performance was discussed. The solid–fluid thermal conductivity ratio is fixed at $$1$$ 1 . The obtained results show that the presence of surface radiation causes a good cooling of the heat-generating cylinder. In addition, an optimal tilting angle of the heated elliptic body for minimum temperature in the cavity is found.

In recent years, earth-abundant element-based solar cells have attracted the interest of researchers due to their excellent optoelectrical properties and green status, as well as their performance in a variety of other fields, such as photovoltaic technology. Copper zinc tin sulfide-selenide (CZTSSe) is a promising candidate for low-cost and non-toxic elements. In this work, we have used the SCAPS-1D simulator to investigate the performance of CZTSSe solar cells. The effect of device parameters such as the hole concentration and mobility, absorber layer band gap energy, and CdS buffer layer thickness have been examined. Furthermore, the impact of the work function of the solar cell’s back contact on improving the photovoltaic parameters of the CZTSSe cell device has been analyzed. The optimized structure with a work function of 5.65 eV (Pt) as a back contact shows the highest efficiency of 30.82%, a fill factor of 77.23%, an open-circuit voltage of 0.8664 V, and a short-circuit current density of 46.06072 mA/cm2. These findings indicate that it is possible to achieve high performance CZTSSe solar cells by optimizing the structure parameters.

The objective of this research project is to develop a robust, efficient control for controlling and monitoring the photovoltaic connected to the grid system. This work focuses on improving the performance of the photovoltaic system, by using advanced control algorithms, mainly the Backstepping controller for DC/DC power interface control.The backstepping technique can continuously operates the PV at maximum power point (MPP) under different and aggressive weather conditions. This controller allows the injection of a sinusoidal output current synchronized to the network with high quality of energy injected into the network mainly the rise time, the convergence, the settling time, and the steady-state error compared with the reference voltage curve. The asymptotic stability of the system will be proved by using Lyapunov stability criteria. A comparison of the reproduced energy using the perturbation and Observation (P & O) algorithm to backstepping results is highlighted. The aforementioned propositions will be validated by simulation under the Matlab/Simulink environment.

States are making regulations with the aim of reducing carbon emissions to zero by 2050. Buildings, which are responsible for a quarter of the energy consumed in the world, are within the scope of these regulations. Generally, policies developed cover new buildings. Since buildings with heritage value have a large share in the world, especially in Europe, they should not be separated from the policies developed. Heritage buildings will be important in achieving future energy targets with energy performance improvements. For this reason, it is necessary to increase the thermal performance of buildings with heritage value. Reinforcement in heritage buildings is usually carried out in the building envelope. However, this improvement process is quite difficult. Because the improvements should be carried out by considering the heritage values. The recent use of BIM-based systems has contributed positively to this process. All interventions to be performed on the HBIM model obtained from point clouds can be calculated and evaluated in advance. In this study, parametric performance calculations were performed over the BIM-based software Revit, taking into account the type and thickness of the insulation material over the HBIM model. The study was carried out on the Traditional Turkish House located in Ermenek district of Karaman province in Turkey. According to the results of the study, it was determined that the energy efficiency increased by 33% on average.

Many factors affect underground cables, including the temperature distribution surrounding the cable, the depth of the cable, the thermal resistivity of the soil, and the material the cable is backfilled. The study and analysis of these factors are exploited as much as possible to carry the maximum possible current through the power transmission cable. Calculations were made for single power cables with a flat configuration at a burial depth (0.8 and 1) meters, (0.8 and 1) km/w soil resistivity, and two types of backfill materials: cement-sand mixture backfill (CSB) and thermal backfill for the Aluminum conductor. The proposed model can determine the temperature distribution in the soil, thermal backfill, and around cables. The results essentially show that appropriate thermal backfill and spatial geometric characteristics are not only useful for reducing conductor temperature, but also for securing a specific cost metric at the same time being of exceptional importance to take full advantage of cable ampacity.

The global economy was adversely affected due to the contraction in international trade volume during the epidemic. The health systems of many countries are on the verge of collapse. In the study, phone calls were made with the managers of 5 companies providing natural gas and mechanical infrastructure services in Istanbul, and the multi-criteria decision-making method was used. As a result of the findings, it was determined that companies operating simultaneously in many sectors with different customer types were not affected by the epidemic. In contrast, companies serving individual customers in a single area were negatively affected by the Covid-19 global epidemic, and one of the companies stopped its activities. While there is no shrinkage in industrial customers’ demands, companies serving only individual customers faced great challenges. This study aims to determine how the Covid-19 global epidemic affects companies providing natural gas and mechanical infrastructure services in Turkey. In this context, from the study, it was concluded that companies operating in the mechanical and natural gas infrastructure sector, which concentrate on a single field, were adversely affected by the global epidemic.

Programming a humanoid robot for educational purpose is a demanding task for a beginner with little experience. Several studies are available in which humanoid robots such as NAO, are used in educational settings to move, recognize objects and hold conversations similar to a human. These studies usually incorporate third party libraries and advanced deep-learning methods making it difficult for a beginner to follow. This paper aims to work as a getting-started guide for someone starting out with programming the NAO robot using Choregraphe and the Python SDK. In this study, NAO robot is used to implement four scenarios based on - dialog, movement, object recognition and obstacle avoidance - using the available components that come with the robot. The paper focuses on comparing the Choregraphe and NAO Python SDK during this process by considering the advantages and limitations of both approaches. The results show that both Choregraphe and the Python SDK have their nuances and their usage depends on the use case. However, for a beginner just starting out, Choregraphe is easier to get things done without writing a single line of code. Python, on the other hand is useful for low-level functionalities and provides rather more flexibility.

Software ecosystem represents all the businesses, parties, and their interactions for the software product of mutual interest. Functioning in a software ecosystem is tough for entities involved due to varying interactions with other entities specially for companies functioning in multiple vendor ecosystem. This paper explores state of research about software ecosystems. Also, paper outlines some research gaps, and defines research questions that need to be addressed. Our findings suggest that existing guidelines are inadequate for addressing the needs and challenges faced by companies in multivendor ecosystem.

To produce working software deliverables, geographically distributed teams are increasingly adopting Global Software Development (GSD). This enables companies to decrease software development cost and time, access to skillful resources, increase product quality, and the benefit of the 24-hour development model. However, due to geographical, temporal, socio-cultural, and organizational distances, GSD projects face multiple challenges that impede attaining the intended goals and severely impact project planning. Agile planning is an approach, traditionally conducted in a collocated environment, that allows fast-reacting, project progress review, detecting development bottlenecks, and decreasing the misunderstanding between developers and customers. In a distributed setting, agile planning remains severely impacted by the shortage of face-to-face communication. Therefore it’s necessary to consider the team distribution and resulted challenges to adopt a beneficial agile planning process for GSD. Based on different research papers related to Agile Planning of Global Software Development, this paper focuses on the pre-planning phase. It describes the application of this phase in the GSD context, and defines the various activities included as well as all the used repositories.

In today's fast-changing environment, in order to create much more stable and complex software programs. With the emergence of Machine Learning, many companies are increasingly embracing this revolutionary approach, both in terms of growth and maintenance, to reduce software costs. As the size of applications increases in terms of functionality, when designing test cases, Software Quality Prediction is becoming more complex. Since the software measurement mechanism in a constant cycle has several benefits, namely reliable project cost estimation, process improvement and product quality compliance, it is vital to try further analysis of software metrics in order to implement the use of machine learning in software quality prediction. This research aimed at building two models which is Software Defect Prediction Model (SDPM) which will be used to predict defects in software and Software Maintainability Prediction Model (SMPM) which will be used for Software Maintainability. Different classifiers, namely Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks have been considered and then evaluated using different metrics such as Accuracy, Precision, Recall and Area Under the Curve (AUC). The two models have successfully been evaluated and Decision Tree has been chosen as compared to other classifiers which tends to perform much better for both models. These models have been eventually been deployed as web services. Finally a framework based on a set of guidelines that can be used to improve software quality has been devised.

E-commerce has become an integral part of citizens’ lives, due to the ease of use as well as the Covid_19 constraints that has limited direct interactions between people. In order to extend e-commerce, it is necessary to improve payment systems by allowing more comfort and security to citizens. This paper introduces a new payment system using a digital wallet supported by telephone operators. This solution integrates daily financial transactions and puts an end to the liquidity problem, which has become a major concern for many citizens in several countries. The creditor-debtor transaction is done by an exchange of information using a QR code. The payment process is carried out in real time with an acceptable level of security.

Today, television is considered indispensable in the home. Connected to the Internet, television has become intelligent and offers new services to users. A large number of programs are offered every day and it is difficult for the user to make a choice, especially for parents who have to choose what their children can watch. Moreover, the choice of the program becomes even more difficult if there are several people watching television. Many research studies have proposed TV recommendation systems to meet users’ preferences. But considering only user preferences is not enough to ensure adequate content, especially for children. In this article, we want to support parents in choosing content that is safe and appropriate for their children. We propose an extension of the TV recommendation solution QSimTV by adding a keywords filter and movie rating in the recommendation process.

Companies are evolving in an increasingly complex environment, characterised by frequent severe competitive pressure. Strong technological and economic evolution requires an acceleration of information flows, a transformation of operating methods, as well as an obligation to anticipate, innovate, and then make effective decision. In this article, we propose a business intelligence web portal called XEW 2.0 (Xplor EveryWhere). It acts as an effective system for all the treatments useful to the process of monitoring within client companies. Indeed, monitoring the technological environment of a company is one of the most promising methods that allow us to innovate and promote the development of a company.

In recent years, several efforts are being made to integrate IoT technology in various fields, including the health sector. Except that several problems arise, starting with the colossal amount of data generated that require significant computing resources where the need to use a combination of cloud and fog computing to optimize latency which is an important criterion for E-health applications. An even more important issue for such a system is the level of security and the mechanisms applied to ensure the confidentiality of the data as well as the preservation of the privacy of the users, because in a world where personal data is a manageable resource, the security and confidentiality of the data remain aspects of high importance. We have designed a platform for remote medical monitoring of patients by collecting, securing and protecting the end-to-end data transfer via encryption mechanisms, as well as user authentication.

Diabetic retinopathy is a severe retinal disease that can blur or distort the vision of the patient. It is one of the leading causes of blindness. Early detection of diabetic retinopathy can significantly help in the treatment. The recent development in the field of AI and especially Deep learning provides ambitious solutions that can be exploited to predict, forecast and diagnose several diseases in their early phases. This work aims towards finding an automatic way to classify a given set of retina images in order to detect the diabetic retinopathy. Deep learning concepts have been used with a convolutional neural network (CNN) algorithm to build a multi-classification model that can detect and classify disease levels automatically. In this study, a CNN architecture has been applied with several parameters on a dataset of diabetic retinopathy with different structures. At the current stage of this work, obtained results are highly encouraging.

The adaptive filter is a variation of the digital filtering technique. This form of filter, which does not resemble the classical filtering technique, consists of three basic elements. These elements are collector element, weighting (multiplication) element and a digital filter structure. A system, which has these elements, can make the necessary change in the filter characteristics depending on the environmental media by changing the filter coefficients. Breathing corresponds to the movement of the thorax and lungs and to volume and pressure changes that occur successive in these organs. Respiratory rate (RR) means the respiratory frequency per minute. Since the RR is used to detect and monitor the serious diseases, designing a respiratory rate detection system by means of using adaptive filtering is considered one of the important issues. In this study, a system that detects respiratory rate has been implemented. In this system, there are adaptive filtering, speech boundaries detection algorithm in the sound signal, two stethoscopes whose internal part is placed a microphone and a MATLAB GUI interface design. One of the microphones is placed to upper part of the trachea and the other is placed to upper part of the heart. The sound signals coming from the stethoscope on the heart are used as a noise source and a preprocessing is carried out by means of making free from this noise the sound signals received from the microphone placed on the trachea. After this pre-processing, a clean breathing sound signal is reached by means of making free the heart sounds from the stethoscope placed on the trachea at the adaptive filter output. Inhalation and exhalation time intervals can be determined by running the speech boundaries detection algorithm on this clean breathing sound signal. The respiratory rate is obtained by using these determined time intervals.

This paper presents an automatic classification technique for the detection of cardiac arrhythmias from ECG signals. With cardiac arrhythmias being one of the leading causes of death in the world, accurate and early detection of beat abnormalities can significantly reduce mortality rates. ECG signals are vastly used by physicians for diagnosing heart problems and abnormalities as a result of its simplicity and non-invasive nature. The aim of this study is to determine the most accurate combination of feature extraction methods and SVM (Support Vector Machine) kernel classifier that will produce the best results on ECG signals obtained from the MIT-BIH Arrhythmia Database. SVM classifiers with four different kernels (linear, polynomial, radial basis, and sigmoid) were used to classify different features extracted from the four feature selection methods; Random Forests, XGBoost, Principal Component Analysis, and Convolutional Neural Networks. The CNN-SVM classifier produced the best results overall, with the polynomial kernel achieving the maximum accuracy of 99.2%, the best sensitivity 92.40% from the radial basis kernel, and best specificity of 98.92% from the linear kernel. The high classification accuracy obtained is comparable to or even better than other approaches in literature.

The Internet-of-Things (IoT) has taken over the business spectrum and its applications vary widely from agriculture, and healthcare, to transportation etc. A hospital environment can be very stressful, especially for senior citizens and children. With the ever-increasing world population, the conventional patient-doctor appointment has lost its effectiveness. Hence smart healthcare becomes very important. Smart healthcare can be implemented at all levels, starting from temperature monitoring for babies to monitoring vital signs in the elderly. The IoT healthcare application is a complex fusion of a variety of technologies such as wireless network, embedded, sensor and connectivity. This diversity leads to a variety of quality measurement model, which makes the process of measuring quality more challengeable, less accurate, and less applicable. In this research, different quality models for IoT systems have been studied and compared regarding the quality factors. Besides, we discussed the importance, requirements and applications of smart healthcare and we defined quality attributes for evaluating IoT healthcare applications by considering the impacts of the identified characteristics on the quality of IoT applications.

The COVID-19 pandemic has become a great challenge for healthcare systems due to the urgent need for ICUs that exceeded their capacity. Determining critical patients that require ICU transfer early will be valuable in optimising ICU resources and triage the patients. We propose a ML-based approach to predict ICU requirement within COVID-19 patients based on clinical data. A Mexican dataset of 7078737 cases and 38 attributes was considered in this paper. We trained four models MLP, DT, RF, and GB, on 70% of the data with five fold cross-validation and tested using the remaining 30%. Classification accuracies obtained were 97.72%, 97.14%, 99.06%, and 99.28%, respectively. Feature importance analysis based on GB model showed that age, hypertension, diabetes, obesity, pneumonia, days between symptoms onset and hospitalisation, location of the care unit, and private and public insurance are the main important factors. The latter factors highlight the importance of rapid and good quality care.

COVID-19 is a fatal global pandemic that have been spread throughout the world rapidly. Based on the global statistics, the confirmed cases has reached 662 million cases at the mid of June 2021. Typically, COVID-19 is transmitted when a healthy person is closed contact with the infected person via the respiratory droplet or saliva. With the reopening of the academic institution in Malaysia, the formation of new clusters become more seriously. As until 21st April 2021, there are total of 83 COVID-19 clusters is reported that related to the education sector since early January 2021. The students may come back to the campus for conducting academic activities. Therefore, this study is proposed to analyze the COVID-19 infection inside the campus using ABM. The designed ABM model has three different settings, which are lecture room, laboratory and office. Students are the agents of the simulation. The factors of COVID-19 infection are social distance, ventilation condition of the room and exposure time of contact. The ABM model allows the users to analyze the effect of number of people and social distance towards COVID-19 infection. Based on the preliminary analysis, office has the highest risk, followed by lecture room and laboratory. For generating less than 25% of new infected people, the students should maintain at least 1.8 m of social distance. Through the model, the administrators can use to plan the classroom and laboratory to the students. This paper suggests to extend the research by analyzing other rooms in the campus.

Predicting forest fire risks constitutes a significant component of territorial risk management and combat strategies. It plays a major role in resource allocation, in mitigation and recovery efforts as well as in anticipating landscape deterioration around urban areas that create an ecological balance at the territorial scale. The purpose of this study is to develop a smart predictive system of seasonal forest fire risk using a machine learning approach. To achieve this aim, data related to 2,130 forest fire events that occurred between 1997 and 2014 were used. Furthermore, biophysical characteristics over the study area were completely processed and retrieved from in-situ measurements; and from time series of MODIS and Landsat (TM, ETM+ and OLI/TIRS) satellite imagery. These data sources served to represent 5 groups of variables, namely Rainfall, Wind, Evapotranspiration, Normalized Difference Vegetation Index (NDVI) and Water Balance; in total, these variable groupings were structured into 39 elemental variables according to the month of the year. The Random Forests algorithm was used to find the best-fit link between theses predictors and the target variable of seasonal forest fire risk.The trained model exhibited a good predictive ability (83% of accuracy, p-value = 0.013). It showed that precipitations, mainly those of the wintry period, have a strong influence on fire occurrence and seasonal severity in the fire season.Accordingly, the developed model allows to predict seasonal risk according to the winter precipitation and to anticipate forest fire risks at a very early stage as well as their impact on improving socio-ecosystem services and territorial resilience.

A scientific research project in epidemiology is a work that aims to present a solid and relevant research idea and in most cases, the analysis of the results requires a good study of the environment studied. Spatial epidemiology can be used to describe spatial variations in the study area and the risk factors for a disease in a given population, such as the rate of poverty or urbanization.With this in mind, the purpose of this research was to determine the value of GIS in describing the study area by creating a database containing several of the parameters in a geographic information system (GIS). This database was then exploited by a spatial and thematic analysis of some risk factors in the selected study area (prefecture of meknes), which allows an interpretation of the results, especially for epidemiological studies.The results obtained show the undeniable place of GIS in the representation of data containing geographical (prefectures), socio-economic (population and urbanization) and environmental (rivers) parameters on easy to interpret thematic maps.

Turkey is one of the countries that is already greatly affected by climate change. Geodesign is an emerging planning approach based on GIS that can address climate. It uses a participatory approach and is very adaptable to changed requirements. In this study, development scenarios for Harran district located in the Southeastern Anatolian Project (GAP) one of the world's biggest irrigation projects are presented. These scenarios show how the big potential in the tourism sector building on the region's huge archaeological heritage and the agricultural sector can be deployed to the benefit of a rapidly growing population and carbon sequestration can be achieved.During the Geodesign process, different scenarios are created by the different interest groups. The scenarios are compared with each other and using tools of the web based GeodesignHub software until a solution was found that satisfied the needs of all groups. The design of three different scenarios for a time horizon until 2035 and 2050 was required: 1) A non-adopter scenario, which presumed that current unsuitable practices would continue, 2) An early-adopter scenario, in which new environmentally friendly technologies would replace unsuitable practices immediately and 3) A late-adopter scenario, in which such technologies would be deployed with a delayed start. Results show that for 2050 under optimum conditions 23 386 tons of carbon sequestration in Harran district and 12 400 000 tons at the national level could be achieved for the agricultural sector alone.

Istanbul is the most populous city in Turkey. The population, which was approximately 5.5 million in 1985, has reached 15.5 million in 2020. Population growth is the most important factor behind human activities that put pressure on the environment. An increasing population means depletion of limited resources, increasing environmental problems, and rapid urbanization. In parallel with the increase in population and urbanization, there has also been an increase in demand for housing, leading to new residential areas in almost every district of Istanbul. This study examined the transformation from vegetation areas to residential areas between 1985 and 2020 in a selected region in Buyukcekmece, one of the 39 districts of Istanbul. The relationship between land use and land cover (LULC) change in the area and Land Surface Temperature (LST) change caused by urbanization was analyzed. It is seen that the built-up area has increased from 57.1 ha to 781.4 ha in 35 years. In every five years, an increase in surface temperatures was determined in parallel with increasing urbanization, and this increase was determined as about 5.4℃ from 1985 to 2020. Also, when the temperature data of the Buyukcekmece Meteorological station is analyzed, it is seen that there has been an increase of approximately 2 ºC in air temperatures in the last five years. In addition, movements were observed in the stability of structures in rapid urbanization areas after analyzing with the PSI time series InSAR method. The main causes were determined as construction sites around the buildings and geological conditions of the ground, which are triggered by urbanization.

In the present work, the assessment of drinking water quality was carried out through a monitoring of heavy metals in the treated and consumed waters in the city of Fes (Morocco). Monthly sampling was conducted for a period of 24 months between January 2016 and December 2019. Nine parameters were evaluated: pH, T (°C), Turbidity (NTU), Al, Fe, Cu, Mn, Al2(SO4)3 and CaO. Indexing approaches have been applied by calculating the Heavy Metal Pollution Index (HPI) and Metal Index (MI) for the assessment of influence of heavy metals on the overall quality of water. The obtained results for heavy metals are in good agreement with World Health Organization (WHO) standards. Though the aluminum concentration remains in the limits set by WHO, yet it shows a major contribution in the indices. This has been verified by the statistical analysis which demonstrates fair correlations between aluminum, HPI (r = 0.9) and MI (r = 0.77). Aluminum showed the important influence of seasonal change in the year as well as the doses of reagents injected during the treatment process on the concentration of aluminum is detailed.

It is clear that by no means, natural or manmade disasters can be fully prevented. Disaster strikes countries causing tremendous destruction, impacting every aspect of the countries. Mauritius being a small island is not spared of natural hazards which can be in different forms. However, the negative effects can be mitigated and ICT can be used in all the phases of the Disaster Management process. In a current situation characterized by the attempt of the world to combat the invisible enemy which is that of COVID-19, it is obvious that preparedness and risk reduction are key concepts. The same concepts are primordial in disaster risk reduction. This research advocates the use of a mobile App, Envirolarm, to help the citizens of Mauritius to act in circumstances of disasters by providing to them useful and timely information. This research also highlights some scenarios about how ICT can be used to better prepare the Island of Mauritius against natural hazards. The findings show that Mauritius is not currently using technology to its fullest in order to deal with the consequences of various disasters. It can also be said that advancement in ICT in the form of Internet, remote sensing, and satellite communication can help a lot in the management of disasters.

This paper presents a study on the mechanical characterization of laterite used as a building material. In Senegal, laterite has been used in construction on a semi-industrial scale since the 1990s through projects aimed at promoting local materials that can contribute to the energy efficiency of buildings.The work carried out, limited to the compression strength test, showed the possibility of using laterite and a composite (laterite + peanut shell) with satisfaction of established normative requirements governing the use of raw land in construction. The formulation BTS10 -12, 5–4 gives a compressive strength of 2,05 MPa for a minimum requirement of 2 MPa according to ARS 674:1996 and ARS 675:1996. The results obtained already indicate that the use of the peanut shell, from an environmental point of view, is of interest in managing bio-sourced waste by using it as a building material. The second interest of this work was to improve the thermal insulation of the building with this material; to be confirmed by studies of thermal characterization of the composite (laterite + peanut shell) that we have in perspective.

The goal of the presented research was to evaluate the heavy metal detection and potential ecological risks in Lake’s water. Here are the concentrations of heavy metals, which include Cu, Zn, Mn, Fe, Pb and As in the water which was studied. Samples for analysis were taken from six sites of the Sidi Boughaba Lake (Mehdia, Morocco). Our results showed that the mean metal concentrations in lake water were 0.472 for Cu, 0.657 for As, 1.02 for Fe, 0.28 for Mn, 0.076 for Pb and 0.091 for Zn. The existence of these metallic elements can be of natural origin as long as there are no direct pollutants discharged into this environment.

The focus of this article is to give an overview of inland water bodies (lakes, dams and lagoons) and surface water bodies (rivers and wetlands), as well as the various groundwater reserves (water tables and aquifers). These natural water reservoirs, its distribution in Africa plays a fundamental role in the constraint of its geological evolution and habitability. The aquifers constitute good underground water reservoirs, from fissured or fractured rocks, allowing a water supply, which are less sensitive to climatic variations. On the other hand, surface waters are more sensitive to pollution and drought. The framework for sustaining and preserving these resources is good environmental management of the various watersheds and coastal zone planning. In addition, the control of groundwater pumping, to avoid a drop in piezometric levels.

In Morocco, leishmaniases constitute a real public health problem. Sandflies are the only known vectors of these parasitoses. In order to study the dynamics of sandflies using two different traps and to identify the nature of the soil of potential sandfly breeding sites at Aichoune, monitoring the activity of sandflies was carried out over a period from September 2013 to August 2014. A total of 4471 sandflies were identified. Phlebotomus sergenti, vector of human cutaneous leishmaniasis, is the most abundant followed by Larroussius perniciosus. The maximum species was harvested in the months of June and August. This study shows that the two high-risk Leishmanian months are in June and August, hence the need to strengthen efforts to fight against this disease during these periods. The results allowed us to identify a sandy nature of the larval sites for sandflies. The characterization of the substrate at the level of these larval sites has informed us about the ecological requirements of the larval development of the species, hence the need for a substrate rich in organic matter. These results could help the authorities to prevent from the risk of leishmaniasis. Indeed, medium-term climate forecasts are essential tools for developing a leishmaniasis warning system.

The source Ain Moulay Yacoub Hamma located upstream of Oued Hamma (Outita: pre-rifaine ride) 18 km southeast of the city Sidi Slimane, presents non-permanent flows during the three months February, March and April of the year 2017.The objective of this study is to evaluate the quality of the natural waters of Ain Moulay Yacoub Hamma, the hydro-chemical facies and the origin of their mineralization.Sampling is carried out at the source of these natural waters in order to carry out physico-chemical analyses in our specialised laboratory.As a result, the study showed that the waters of the Ain Moulay Yacoub Hamma spring are meso-thermal waters (42 °C), with a poor quality sulphurous odour loaded with mineral salts (chlorides, sodium and sulphates). In the results presented in the Piper, Schoeller and Stiff diagrams, show that these waters are characterized by a chloride sodium chemical facies.

Human activities generate enormous amounts of wastewater. The hydrogen production from this new resource has gained attention as an emergent technology. Incorporating photovoltaic energy production with different electrolysis systems which can treat wastewaters and produce hydrogen simultaneously will lead to an environmentally-friendly and sustainable hydrogen production.

Among the areas threatened by the floods and which integrate into the sustainable development components, the area of Jebha, which is located on the Mediterranean facade, depends on the commune of M’tioua and falls under the province of Chefchaouen, the center of Jebha is located in a back-country formed by high reliefs with steep rock slopes,There are many talwegs and intermittent rivers around the site, such as Wadi Mesiaba, which has experienced untimely flooding adjacent to the town. Several dwellings too close to the river were invaded by a surge of water that fell violently for more than 24 h on this area. The water level has reached nearly 40 to 50 cm in some places, a large part of the road is flooded with mud. However, the only sustainable stream is the Ouringa Wadi which flows into the Mediterranean.The catchment area of the Jebha area is of the peri-urban type, which manifests itself and exhibits behaviors of natural and urban catchments, this basin has subjected to a strong pressure, linked to the development of the urban part, and It is also subject to natural factors and constraints.

Morocco has experienced unforgettable floods due to their devastating and deadly effects. The Ourika watershed is a water system in the Atlas of the Marrakech plateau. The permeability of the lower layer is very low, the vegetation cover is weak and sparse, the slope is high, and the valley is deep. The basin faces north and northwest and is affected by the disturbance of the Atlantic Ocean, which can produce heavy rains, combined with the cruel and violent pulsation of the river. Wadis are very adaptable to cuts and erosion, and solid loads are always important. Despite the varying intensity, these rapid floods were repeated in the semi-arid mountainous areas. In order to reduce the impact of floods, many improvements have been made and a flood warning system has been installed upstream of the basin. Some of these infrastructures have proven their effectiveness, but others have not withstood the severe flooding that followed.

Research in university pedagogy is constantly changing. The teaching–learning process in universities around the world is evolving in response to societal changes and new constraints. Teaching practices are subsequently changing under the influence of several factors: the integration of information and communication technologies, the diversification of teaching methods and approaches, the change in the relationship to knowledge, and the relationship between teacher and learner. De facto, new forms of teaching–learning have appeared, namely: the flipped classroom, distance learning such as MOOC and SPOC, blended-learning and currently co-modal teaching.The Moroccan university participates in this pedagogical development by the creation of centers of pedagogical innovation, centers of E-learning and other practices resulting from the reforms generated by the strategic vision 2015–2030.In this contribution, we will focus on the pedagogy used in a SPOC training (Small private online classes) within the Moroccan university, secondary education branch of life and earth sciences in order to answer our research question: How are the pedagogical methods and approaches used in a SPOC training device?We proceeded by analyzing a SPOC device in the Higher School of Education and Training of Agadir and by a survey put online and filled in by the students in order to reinforce our research. The results show that the pedagogy used in the SPOC training, favors learning by problem solving, by project, by collaboration etc. This will allow learners to develop a strong sense of responsibility and a sense of belonging. This will allow learners to develop a set of skills that we will focus on through description and study.

This paper essentially grants an in-depth summary and analysis of the existing research studies dealing with authoring systems in computer-based education. It primarily endeavors to underline the momentous role of computerized learning environments in education. Likewise, it provides a framework for teachers to think more deeply and creatively about how they design and structure activities for different learners and learning styles. While the benefits of engaging in the learning design process exist regardless of the delivery mode (electronic or face-to-face), they are particularly relevant to distance learning or oftentimes used in blended learning. Nevertheless the teacher or the designer of the learning environment should not lean into object construction and provision to the detriment of a variety of pedagogical models which are built around collaborative activity on the part of the learners. Identifying the tools needed to create these types of environments requires knowledge of how these tools work and how they are categorized.

The Cloud computing is one of the trendiest buzzwords utilized these days. It is the upcoming technology provisioning resources to the customers in the form of different services like software, infrastructure and platform. Services are made available via the Internet to users on demand. Cloud Computing services are aimed to furnish easy, scalable access to resources, applications, and services and are wholly handled by a cloud service provider. A Cloud Computing service can automatically scale to satisfy the demands of its customers. The service provider delivers the hardware and software needed for the service therefore there is no essential for a company to supply or deploy its own resources or assign information technology (IT) staff to control the service. Universities take benefit of available cloud services delivered by service providers and allow their own users/learners to accomplish business and academic works. In this paper, we will review what the cloud computing services will furnish in the educational field, specially at universities in developing countries where the usage of computers are more concentrated and determine the opportunities of common applications for students and teachers, and particularly considering the Covid-19 crisis. The teachers and students can use the Cloud applications anywhere at any time without going through the installation step using a device connected to Internet. This work presents a guideline for successful Cloud Computing usage.

Mathematics ability has for a very long time been reported to positively affect students’ academic performance in computing courses in general and computer programming in particular. This is justified by the fact that computer programming is usually judged to be similar to mathematics in terms of students’ perceptions of the level of difficulty and complexity of the two subjects. In this study, we aim to examine the impact of students’ mathematical skills on their academic performance in introductory computer programming. This study employed two main methods including a questionnaire survey of some academic staff as well as a standardized introductory programming test and secondary data analysis of students’ national high school exit examination transcripts. The results obtained confirm that there is a very strong positive correlation between students’ mathematical abilities and background, and their academic performance in introductory computer programming.

In recent years, it has been observed that information technologies have been used quite actively in the field of education, especially in developed countries. It is seen that with some needs arising in the field of education in Turkey, today’s conditions and the development of technology, distance education has become increasingly widespread in the field of instrument training, a branch of music education, as well as in all fields of education. For this reason, it is thought that the availability of online platforms directly affecting productivity in distance learning and distance instrument training should be examined. From this point of view, the general purpose of the study is to examine the availability of Zoom, Google Meet, Microsoft Teams, Skype and WhatsApp online platforms in distance instrument training according to variables and present differences among them in accordance with the relevant literature. According to the findings from the research, it is seen that usage availability of Skype for distance instrument training and Microsoft Teams for events such as masterclass, workshop, and seminar of which the number of participants is high, is more advantageous in terms of features is observed compared to the other platforms. In addition, it has been concluded that WhatsApp, which is used for distance instrument training, is disadvantageous compared to other online platforms in terms of the features it contains, although it is preferred quite often.

Science and Art Centers (SAC-BİLSEM) are educational institutions opened to make gifted students, educated under the Ministry of National Education, discover their individual talents and develop their capacities to use at the highest level after they are selected in art, music, and general mental ability. Different applications were made before 2018 to identify students with musical talent as one of the talents in BİLSEM institutions. These applications, which have changed over time, have caused various problems to arise in different dimensions. In the course of the workshops held under the Ministry, these problems were discussed with the participation of the experts and a new diagnostic approach was tried to be found with the solution recommended. This search resulted in a new BİLSEM Music Diagnostic Exam that combined dynamics such as contemporary special talent theories, objective measurement and evaluation principles, equal opportunities with the opportunities offered by music technology and was applied for the first time in Turkey with its original content in 2018. This exam will also make possible new opportunities with the use of smart technologies in the future. This study aims to introduce the technological transformation-based development process, content and applications of the BİLSEM Music Diagnostic Exam after 2018.

In the last few years, databases have become very important and very large because they play a strategic and important role in most organizations and because they receive a huge flow of information from multiple sources every moment in building BigData. This situation identified several limitations and weaknesses in relational database management systems (RDBMS), such as availability, real-time response, horizontal scalability, decision support, advanced data analysis, and especially the management of Bigdata which can reach zeta bytes in storage. This requires the storage and organization of this data in a new management system database not fixed by a rigid structure and resolves all problems associated with the storage of database in a relational system. In this view, organizations need a new NoSQL (not only SQL) system that overcomes the limitations of the relational system. The change of the database management system requires the migration of the databases from the relational system to another NoSQL, taking into consideration all stored data and keeping the majority of the possibilities and functionalities of the old system, with all the advantages of the new system. In this paper, we will identify the elements of relational databases that belong to nature: data, structure, and semantics, which we must migrate to a NoSQL system, such as a document-oriented system. Also in this paper, we will present the rules for transforming the structure of the relational system to another document-oriented NoSQL, according to the principal’s basics of a new approach to migration.

Currently, the rapid progression of technology has driven systems to manage a huge amount of data emanating from various sources such as social networks, event logs, websites surfing, and many other sources. Since 2008, many companies have helped develop a set of technologies to replace the classic systems called RDBMS (Relational Database Management System). A new need has just been created, namely, replacing the old databases mainly relational to their NoSQL (Not Only SQL) counterpart. This work aims to present a new methodology for preparing NoSQL oriented-column databases from relational databases. To illustrate this methodology, we developed software using PostgreSQL as an RDBMS and Cassandra as a NoSQL oriented-column database. Despite it being hard engineering work, the obtained results show that our proposal can effectively solve the problem posed by this article.

For several years, Big Data has been considered the next evolution for processing various data in Data Lake. However, one of the major difficulties for Big Data development is its need for structured knowledge. This is why, a large part of research focuses on ontologies formalization that has, among other things, given rise to ad hoc languages as Web Ontology Language (OWL). In this paper, ontologies were embedded into Data Ponds to operate and cooperate with agents. Indeed, a new architecture has been built based on traditional multi-agent system in a Big Data context. This architecture enables end users to access to information from Data Lake in real time with the slightest effort. This issue has been the subject of research for nearly ten years, but it remains one of the major obstacles to Big Data development.

In the last decades, the amount of data produced every day is absolutely horrible. So-called big data, that refers to the exponential growth of massive data and the difficulties that appear with it. In this context, data lakes have been proposed to address this issue. It allows storing heterogeneous data in a central repository without any predefined schema. However, existing literature on data lakes is fairly fuzzy and obscure, and the different contributions that have been proposed do not incorporate all aspects of data lakes, still inconsistent. Thereby, we present in this paper a clear and comprehensible overview of data lake definitions, architectures, and technologies. Indeed, we will classify the different scientific contributions on the data lake according to each layer of their architecture. This allows the different young researchers to identify the research problem on which we want to work, which forms the key point to the publication engineering. Also, we discuss the capital importance of some major functionality that promotes the creation of data lakes so that they don’t turn into a data swamp.

Natural Language Processing is very challenging because of the smart nature of natural language, which has multiple forms and beyond linguistics is very region-dependent. Sarcasm is one of the most difficult problems for sentiment analysis algorithms. Its complexity comes from the use of implicit indirect expressions to convey opinions. Sarcasm is highly context-dependent, a sentence is only inferred to be sarcastic by mastering the dialectal form of language in which it is written and by knowing well the context as well as the idioms used in this language. In this research, we present a novel framework based on the combination of the BERT model and the TF-IDF words representation. The model we propose includes a feature engineering module based on BERT transform and a weighted TF-IDF to extract features that are fed up to an SVM classifier. We validate the model on two datasets representing 5 Arabic dialectal forms and manage to improve F1-score by 0.13 for the ArSarcasm dataset.

The proliferation of social media has allowed Internet users to post their views and opinions online. This generated a vast amount of raw data in informal ways. For many organizations and individuals, this data is vital for providing insight into future decisions. Preprocessed corpora are considered as the basic requirement for the development and evaluation of opinion mining (OM) systems. However, the vast majority of corpora intended for OM research are not large and free for the researchers’ community. This lack of free and large OM corpora represents a major obstacle for promoting research on sentiment analysis systems, especially for rich and complex languages as the Moroccan Arabic (MA) one. To overcome this gap, this paper presents a new contribution to the MA resources. A free and large Moroccan Arabic corpus consisting of 18000 manually labeled tweets resulting in a lexicon-dictionary of 30000 words labeled as positive, negative and neutral. To the best of our knowledge, MAC (Moroccan Arabic Corpus) is the first open and largest MA corpus for sentiment analysis. It is pioneer by its size, its quality given by the consistency of the native annotators (IAA = 0.9), and its accessibility to the research community. The MAC is benchmarked for forthcoming works through an exploratory data analysis carried out using the two-sentiment analysis approaches for polarity classification as well as language identification. In addition, the MAC corpus along with the necessary code to explore it have been released.

The Coronavirus, also known as COVID-19, initially surfaced in Wuhan, China, in December of 2019. The virus was one of the most widely discussed subjects on social media. As a result, these social media sources are exposed to and present a variety of viewpoints, beliefs, and feelings. Big data is a significant resource for computer scientists and scholars who want to understand how people feel about current events. We present a real-time implementation of a system that can identify Twitter opinions about the COVID-19 Vaccine using Hadoop in this work. All tweets are divided into three categories (Positive, Neutral, and Negative). Sentiment analysis was conducted by Logistic Regression, Random Forest, Deep Neural Network, and Convolutional Neural Network.

The development in the remote sensing and geographic information systems facilitated the monitoring processes of changes in land cover and use. This article aimed to evaluate the classification accuracy of five supervised classification methods: Neural Network, Naive Bayes, K-nearest neighbors, discriminant analysis and Decision Tree using the Turkish RASAT satellite images. The Bursa area in Turkey was taken as a study area to examine the RASAT satellite images. MATLAB and Python programming languages were employed to develop the training dataset and generated the five classifiers. According to the performance analysis using confusion matrix metric, the best overall accuracy was achieved by K-nearest neighbors. the K-nearest neighbors method produced 100% performance accuracy using RASAT satellite image. This comparative analysis showed that the K-nearest neighbors can be used as a trusted method for satellite image classification.

Recently a lot of smart applications based on using a data set, in the most of the cases, the data set is images, like in smart systems based on detection, recognition and auto decision, also in the systems based on data transmission and smart networks, according to those applications the most critical problem is our ability to save this data from the noise effect, which really could create wrong message or makes our data unclear for proving and analysis, however using data in its original format could take long time, which will consume our storage capacity, the bandwidth usage, processing resources and the energy used for the operation, this will lead us to use a kind of compression that gives us the best solution for all the drawbacks mentioned before. The JPEG compression gets a lot of attention in this term, since its produce a high-compression ratio with reconstructed image close to the original one, due to using DCT transform, which give us a good representation of the image in the frequency domain, however with all this benefits the JPEG standard is so sensitive to the noise effect, since the encoded data related to each other, its look like a related chain, so the smallest perturbation causes a tremendous collapse in terms of decoding (reconstruction of image), in this paper we are going to test and study the data sensitivity to the channel noise based on transmitted using JPEG compression, which allows us to offer efficient techniques in terms of restoration or data correction.

Today, renewable energies constitute a real opportunity to meet part of our energy needs while respecting the environment. They are very advantageous because of their abounded availability all over the planet. One of the honorable challenges of the scientific community is to reduce the cost of the solar cells. An interesting solution to do so is to miniaturize the absorber thickness without forfeiting the cell performances. In this work we report on the effect of the period of nanoparticle gratings and the angle of the lateral angle of Trapeze-Like nanoparticles on the optical absorption. A suitable optimization of the aforementioned parameters enables a significant improvement of plasmonic nanostructures’ absorption and to supplemental tuning of the plasmonic resonance over a large optical band.

Today, research in the field of artificial intelligence is entirely focused on deep learning, which has led to major advances in image and text processing. Potential application fields and use of deep learning are increasingly diverse since it exceeds various machine learning approaches in terms of performance. The implementation of deep learning in the cloud is the most common and typical method. Data collected by IoT devices is usually sent to the cloud for analysis and processing. However, storage and processing resources are limited due to the constraints on the size, energy, power and computing capacity of IoT objects. In addition, ensuring a reliable connection between the different IoT objects is difficult since the communication between these devices is mainly wireless. In this context, compression of collected data before transmission is crucial for saving bandwidth and energy. In this paper, we studied the impact of standard image compression on the classification performance of convolutional neural networks using a pre-trained model in the cloud. The obtained results showed that, on average, an image can be compressed by a quality factor of 10 and 20 for a JPEG and MozJPEG encoder, respectively, while maintaining a correct classification. Furthermore, the results showed that SSIM is more suitable than PSNR for evaluating the classification performance of convolutional neural networks with regard to the image degradation induced by lossy compression.

With the advances in remote sensing domain, images with higher spatial and spectral resolution are obtained from increasing number of sensors, and they have been employed in more research fields, including object detection and tracking. In particular, the detection of marine vehicles has a significant role in civil and military applications. However, due to the varying type, size, posture, and complex background of the ships to be detected, ship target detection is still considered as a challenging task. Deep learning techniques with their wide-spread use in computer vision applications have been successfully applied to object detection problems that is important to monitor marine traffic and ensure maritime safety. In this study, a freely available aerial image dataset is utilized to train and test the two popular single-stage object detection models, namely YOLOv4 and Tiny YOLOv4, based on the “You Only Look Once” approach. Produced results were analyzed using conventional accuracy metrics, and average prediction times were also compared. The trained models were evaluated on different ship images and detections were performed. As a result of the study, mean average precision (mAP) values of 80.82% and 62.30% were obtained using YOLOv4 and Tiny YOLOv4 architectures, respectively. This indicates major performance difference between YOLOv4 and Tiny YOLOv4 models for ship detection studies.

Digital image processing has been widely used in many applications in various science domains. Detection, recognition, extraction and tracking of an object can be given as examples of such applications on 2D images. In this study, we present an approach to predict the size of a wall in terms of width and height, on 2D images by using a reference object. The size of the reference object is already known beforehand and its size gives us hints to predict the size of the wall, which is called target object. The proposed approach has been tested and evaluated on a real world images and the performance results are presented at the end of the paper.

This study presents an application for road data users to make it easier to identify the centerline of roads. Images obtained from high-resolution unmanned aerial vehicles (UAV) provide a reliable database for fundamental applications such as road safety, road maintenance, traffic network, city planning, and vehicle navigation systems, thanks to accurate road extraction and centerline. Road extraction methods are based on algorithms that usually classify roads from 2D images. However, such data are difficult to provide high accuracy spatial information. Moreover, there are limitations for spatial information extraction problems for the road centerline. To overcome these limitations, we present a method to extract road centerline with 3D data based on point clouds that provide reliable information from UAV images. Commonly used three measures, namely Completeness, Correctness and Quality, for the road centerline extraction are 0.905, 0.999 and 0.905, respectively.

Real estate values are needed and used in many finance, engineering, and construction operations. It is significant to assess property values in a standard-based, objective manner. Utilizing Geographic Information Systems (GIS) and Building Information Modelling (BIM) technologies, real estate values can be assessed with three-dimensional (3D) geospatial and built environment analysis. In this study, criteria that affect real estate value are grouped as environmental, physical, legal, and socio-economic factors. Then, the Industry Foundation Classes (IFC)-based 3D property valuation model is designed. By proposing new property sets and properties, features and their attributes are mapped with entities and data types in the IFC schema to demonstrate the likely use of IFC data for property valuation. Conducting BIM&GIS analysis for both external and internal criteria, property values can be assessed by using the created holistic model. It is thus aimed to develop a valuation framework that can be used as a reference in several value-based applications such as property taxation, urban renewal, and land share calculation.

This study presents the supervised classification of point clouds collected through a vehicle-based Mobile Laser Scanning (MLS) system in an urban environment. A benchmark dataset representing the Technical University of Munich (TUM) City Campus was utilized. The main contribution of this paper is to reveal the performance difference between the 2D and 3D k-nearest neighbor (kNN) information on the point-based classification of an MLS system using local geometric and eigen-based features. Random Forest (RF) classifier is utilized for the supervised classification of 8 classes predefined in the benchmark set: artificial terrain, natural terrain, high vegetation, low vegetation, building, hardscape, artifact, and vehicle. As a result, we achieve an impressive classification performance using the 2D kNN information. We reveal that 2D kNN with 12 features not only improves the processing speed but also provides an improvement of 7.3% compared to the 3D kNN, and at least 2.8% overall accuracy improvement compared to the state-of-the-art approaches previously developed and tested using the benchmark.

Nowadays, research in the field of artificial intelligence is completely oriented towards deep learning, which has led to great achievements in image and text processing. The potential fields of application and use of deep learning are increasingly diversified, as it outperforms the various machine learning approaches. However, training a deep learning model on a large dataset is a difficult and expensive task that consumes a lot of time and computational resources. As a result, transfer learning techniques are increasingly employed for building very efficient models with small amounts of data and within a short time. In this paper, we evaluate the performance of transfer learning methods for the classification of different surface defect types. Five kinds of transfer learning approaches: VGG, MobileNet, Inception, Xception and DenseNet are experimentally investigated to construct surface defect classification models. The experimental results showed that MobileNet and DenseNet achieved perfect scores (100% precision and recall) for the classification of different kinds of surface defects, outperforming the other models that also reached very high scores but had some shortcomings for the classification of some defect types.

Many deep learning tasks, such as image classification, natural language processing, video analysis, and speech recognition, have been accomplished using DNNs (Deep Neural Networks). However, high-performance deep neural networks’ success comes with an increase in computational and memory requirements. Field-Programmable Gate Arrays (FPGA) devices are ideal for deploying DNNs, and they have the appropriate qualifications due to their flexibility, power efficiency, and computing performance. However, DNNs are generally deployed on FPGA using a high-level language such as Python then, manually transformed to Hardware Description Language (HDL) and synthesized using a commercial tool. This method is time-consuming and requires HDL skills, which reduces the use of FPGAs. The paper proposes “DNN2FPGA,” a generic design flow to implement the DNN models automatically on the FPG, which can overcome the implementation problem. The article reviews many related works and shows the proposed design flow and hardware implementation. Also, it compares our solution and other recent similar tools. We validate the proposed solution using two case study results: A Multi-Layer Perceptron (MLP) used to solve the classical XOR problem and DNN for MNIST dataset classification. Finally, we present the conclusion and future works. This paper presents a new generic design flow of implementing DNN models automatically from the high-level language to FPGA devices, which takes the model in graph presentation as input and automatically generates the FPGA's hardware implementations.

Medical image classification plays an important role in preventing tumors and other diseases. It helps radiologists to analyze medical images with high efficiency by categorizing the images to the appropriate pathological stage. The Deep Convolutional Neural Networks (DCNNs) are an extension of Machine Learning (ML) methods that has proven its potential and capability for classification tasks. It dominates with best results on different image types. Medical images Dataset are hard to collect because it needs professional radiologists to label them. This paper presents how to apply CNNs based algorithms on Female Pelvis Dataset to classify Cervical Cancer (CC). Two approaches are proposed based on Transfer Learning (TL), the first adopt the Convolutional Neural Networks as Feature Extractors then two classifiers are used to classify the images: SVM (Support Vector Machine) and XGB (Gradient Boosted Tree), the other one process to classification by fine tuning the models with hyperparameters adjustment. The CNNs adopted in this work are: DenseNet121, MobileNetV2 and InceptionResNetV2. Data Augmentation is a necessary preprocessing step to enhance our Female Pelvis Dataset. The experimental results demonstrate that the classification performance achieve an accuracy of 96% for the best two classification models to differentiate Malign cases from Benign ones.

Artificial neural network (ANN) can be utilized as a tool for modeling product quality prediction of Crude Distillation Unit (CDU) that is the heart of petroleum refineries. In CDU, regardless of its origin crude oil separates into economically and commercially valuable fractions. Prediction of product quality can reduce the dependency on on-line sample analyzers and also provide early detection of malfunction of CDU operations.In an attempt for this, 7–20-1 back-propagation ANN architecture was implemented to estimate 95% naphtha cut point properties using seven CDU process parameter as 120 input data sets. The best model architecture consists of 20 neurons in one hidden layer. Proposed model obtained 1.12˚C error value for training sets while acceptable error level was 1.7 ˚C. When compared with the actual data, ANN model predicted 95% naphtha cut point product quality 96% accuracy.

While technology facilitates our lives, it also allows us to lead a quality, efficient energy and to do more productive work in less time. One of our basic needs that takes our time is to eat. One of the problems people face is, ‘‘What can I cook today?’’ It is the question. The Smart Recipe Suggestion System (SRSS) is a system that can make food recommendations to the person with the materials available. This system is designed as a mobile application, including the deep learning method, to answer people’s questions during the day. This paper proposes an approach that recognizes the person’s materials with image processing technology and presents the most suitable meal suggestions to be made with those materials. Recipes are collected from web sites through RabbitMQ. Food items are captured from images by using object detection processes through deep learning. Available food items are modeled as NoSQL and stored to MongoDB document based databases. For the real-world application Firebase mobile cloud platform is used.

Universities in Morocco are forced to change the way they operate. It is important that modern Moroccan universities are not only teaching centers, but above all successful knowledge-based organizations. Such an approach will improve the competitiveness of a particular institution and make its operation more useful for the economy of the region. The implementation of a comprehensive and intelligent IT solution within a university and the provision of educational services, customized to the needs of the market, will enable universities to achieve a smart type of institution [1].In this paper we will take Ibn Tofail University as a case study.

In this study, a LoRa mesh network is designed using Dijkstra’s sh ortest path algorithm. Although LoRa devices are capable of exchanging data over very long distances in the open area due to their technology, this communication distance is significantly reduced in closed areas. Data communication is performed in between LoRa devices installed at fixed positions. In particular, this study targets data exchange between LoRa devices that are not directly connected to each other. In data communication, the devices on which data is transmitted to the destination device are determined by Dijkstra’s shortest path algorithm. The tests were performed using the Semtech SX1276 LoRa module. The module was operated on the TTGO LORA 32 OLED development board from the LilyGO Company. The test results show that communication between devices that are not directly connected to each other is performed using Dijkstra’s shortest path algorithm with a low data transmission time.

Recently, tracking a User Equipment (or UE) in real-time is based essentially on the development of applications on smartphones, it has become an excellent means. As such, this paper addresses the problem of Global Positioning Coordinate (or GPC) of an UE when Global Navigation Satellite System (or GNSS) is not available. In this paper, an android application named GSM Locator is proposed, able to exploit the almost total presence of the Cellular Network (or CN), and its abundant signals in a crowded environment, which is based on Receive Signal Strength (or RSS) measures in order to deduce the distance between UE and Base Station (or BS). In addition to that, knowing the GPC of BS around the UE, and using the triangularization method, the GPC of the UE is determined. These coordinates are projected in a map of an android smartphone. The effectiveness of the proposed algorithm was assessed by comparing obtained results with the real measures using TEst Mobile System (or TEMS) equipment of cellular operator.

Nowadays, the smart city represents the future trends in urban areas, it allows the monitoring and control of all the parameters needed to manage the city, including the environment, life, economy, people, governance and mobility. This concept will raise awareness among citizens and improve the quality of life through better use of public resources. Nevertheless, the rapid growth of smart city and Internet of Things applications is associated with many challenges requiring massive research efforts.In this work, we focus on designing and developing a smart city architecture based on the centralization of smart city services, self-control and adaptation, and future system behavior prediction. Indeed, our proposal integrates a central management application to visualize the different events of the various smart applications and even the dependencies between them. This will allow monitoring and controlling all aspects of smart city applications and system health. In addition, this central application helps gain users’ trust by explaining the different decisions and actions taken. Moreover, our architecture supports not only the generation of intelligent decisions in real time, but also short, medium- and long-term decisions based on prediction mechanism.

COVID-19 scourge has made it challenging to combat digital crimes due to the complexity of attributing potential security incidents to perpetrators. Existing literature does not accurately pinpoint relevant models/frameworks that can be leveraged for crowd-sourcing digital forensic evidence. This paper suggests using feature engineering approaches for crowd-sourcing digital evidence to profile potential security incidents, for example, in a COVID-19 scenario. The authors have proposed a conceptual Crowd-sourcing (CRWD) model with three main components: Forensic data collection, feature engineering and the application of machine learning approaches, and also assessment with standardized reporting. This contribution is significantly poised to solve future investigative capabilities for forensic practitioners and computer security researchers.

The objective of this work is the use of artificial intelligence to improve the safety of connected autonomous vehicles, in this project, we will propose the architecture and validation of a self-defense system for the detection, classification, prediction, and decision-making at the level of control system on the connected autonomous vehicle in the presence of security attacks or risk of attacks. A system that can be integrated into all connected autonomous vehicles and admits improvements to follow the evolution of attacks and transport systems. In this project we will propose a self-defense system consists three treatment blocks; the first block has the role of the permanent scanning of the various connected interfaces to detect any intrusions or security attacks. The second block will collect the information from the first block; it will analyze them and classify the attack according to its impact on the operation of the vehicle or on its connected environment. The result will be communicated to the third block which must predict the impact of the attack for decision-making.

The Vehicular Adhoc Network (VANET) is a prominent application of Intelligent Transportation System (ITS). The high mobility and volatility are the characteristics of VANET that is vulnerable to various internal and external attacks. These attacks attend to disrupting the normal operation in the vehicular network, spoofing on Global Positioning Signals (GPS), tunneling the far away nodes in the network for communication. The authentication mechanism protects the VANET against malicious entities and act as an armament against attacks. The existing authentication protocol concedes higher computation time along with higher storage space. In this paper, a Chaotic Light Weight Authentication (CLWA) protocol is proposed for secure communication in the vehicular network. The performance analysis of CLWA scheme depicts that it performs well by furnishing 57.20% lower in storage cost & 58.97% lower in communication cost and 67.16% lower in operation time than the existing algorithms.

Today, cellular wireless communication has been widely used in many intelligent automation systems, embedded technologies, robotic smart building, climate monitoring, e-learning, decision support systems, wearable devices for e-health, image, video, and speech processing. The Long-Term Evolution (LTE) network, which is a cellular wireless network technology, is one of the most important parts of the spread of smart systems. Attacks on IP-based LTE networks cause all smart systems to be affected. Attacks and security issues on the LTE network cause the network to slow down or be completely disabled. It also prevents users from receiving the desired Quality of Service (QoS) service. Thus, it cannot serve all smart systems using the LTE network. In this study, problems such as DoS, DDoS, mobile botnet, signalling amplification attacks, network access issues, and IMS security Issues that can be encountered in the cellular LTE network are classified.

The networks used in many areas such as location-based services, robotics, smart building assessment, smart water management, smart mobile learning, medical image analysis and processing, wearable technologies have to deal with various security problems. Active queue management algorithms are used to manage network resources and solve problems in the network. DDoS attacks prevent the effective use of network resources and cause network services to be disrupted or dropout. In this study, we classify the performance of queue management algorithms such as RED, SRED, BLUE, SFB, REM and CoDel under DDoS attacks according to delay, throughput, jitter, fairness index values. As a result of the comparison, thanks to the flexible structure of the CoDel algorithm, it gives better results in terms of packet loss and fairness index value under DDoS attacks.

The Internet of Things (IoT) is the concept of connecting the electronic objects we use in our daily lives to the Internet and allowing them to communicate with one another. These devices can collect a large amount of data, monitor objects, reduce workload, and boost efficiency by saving resources. All of these scenarios highlight the significance of security. Cryptography is an important aspect of information security in today’s society. Cryptography is the technique of transforming information and using codes to make encrypted conversations so that only the people for whom the information is intended are processed. Cryptographic algorithms can be used in a variety of ways. In an ideal world, a low-cost, high-performance encryption method would be needed. In our study, we applied and analyzed in detail the cost and performance of the widely used cryptographic algorithms 3DES, AES, Blowfish, CAST5, RC4, and RSA on Raspberry Pi 4 an IoT based devices to secure the publish/subscribe communication method used for the communication of these devices.