ICT: Cyber Security and Applications

Nanoscale molecular dynamics (NAMD) is a widely used scalable scientific software for molecular dynamics applications that simulate the movements of atoms and molecules in a bio-molecular system with millions of atoms. The goal of this paper is to study the scalability of NAMD on a dense GPU-based supercomputer named PARAM Siddhi-AI. Each node of the supercomputer is built with the latest NVIDIA Ampere-based A100 GPUs and AMD 7742 CPUs with 128 CPU cores. The scalability study is classified into two parts, i.e., intra-node GPU scalability and inter-node GPU scalability. The performance is analyzed for two NAMD versions, i.e., 2.13 and 3.0 Alpha for eight different input datasets, however, version 3.0 Alpha only supports single-node GPU runs. The input datasets ApoA1 with 92 thousand atoms whereas STMV with 1.06 billion atoms are used to perform the benchmarking. It is observed that NAMD version 3.0 Alpha performed better than version 2.13 for intra-node GPU scalability. Version 3.0 Alpha is ~ 1.5x to ~ 2x times more efficient on a node with 8 GPUs for the input datasets ApoA1 and STMV respectively. In comparison with CPU performance, the NAMD version 3.0 Alpha shows a speed-up of 22x and 90x whereas version 2.13 shows a speed-up of 19x and 12x for both the input datasets ApoA1 and STMV respectively. In the case of inter-node GPU performance, version 2.13 shows the speed-up of 6.98x and 6x across 4 nodes with 32 GPUs (each node with 8 GPUs) in comparison with CPU performance for input datasets ApoA1 and STMV respectively. The difference in the performance of version 3.0 Alpha and 2.13 is due to the elimination of the CPU bottleneck in NAMD 3.0 Alpha, and this is achieved by offloading almost all (force computations, integrator, and rigid bond constraints) computations to the GPU. The scalability analysis/performance study is also performed for the latest NVIDIA Ampere-based A100 GPUs v/s previous-generation NVIDIA Volta-based V100 GPUs. NAMD is 1.3x and 1.6x times more efficient or faster on NVIDIA A100 GPU in comparison with NVIDIA V100 GPUs for both classes of input datasets ApoA1 and STMV respectively.

In recent years, the massive expansion of actualizing digital platform as the medium to transmit the information from the supplier to receiver has been emerging across the society life circumstance. Such paradigm transformation was widely coming into the conversation sector, where this is called as the concept of ‘muhadathah’ with in its today’s trend moving into the digital orientation. With the newly developed concept as digital muhadathah, the need to critically examine the model application framework should be clearly taken into consideration in assisting the Arabic language process for the learning purpose. This paper aims to examine the potentials of framework model development for digital Arabic language application through engaging the concept of digital muhadathah (conversation). The inquiry process comes into determining both instrument and procedure on developing the framework model of digital Arabic language application. The strategic process on obtaining the inquiry arrangement was made through the critical literature review from the recently peer-reviewed articles related to the topic. The finding of this paper revealed that the digital muhadathah needs to arrange the critically developed framework model on helping the learning process on digital Arabic language application. This paper aims at contributing to provide the essence of developing the framework model on advancing the Arabic language application.

Recently, online education and remote learning have grown in popularity. With this increase comes the necessity for efficient safeguards to guarantee academic integrity during online exams. This research paper includes a thorough investigation into the design of a deep learning-based automated online exam monitoring system. By monitoring and identifying instances of misconduct or cheating during online exams, the suggested method attempts to create a safe and impartial evaluation environment. The research begins by examining the many difficulties and restrictions related to online tests, such as identity verification, content security, and student behavior monitoring. The literature study examines current techniques and tools for proctoring systems, highlighting their advantages and disadvantages. This research paper has suggested a novel method that uses deep learning algorithms for reliable and accurate monitoring based on this research. Face detection, face recognition, face spoofing detection, head-pose estimation, eye tracking, mouth ratio analysis, facial landmark identification, object detection, audio recording, plagiarism checking, and speech-to-text conversion are the main elements of the automated online exam monitoring system. The system offers a multi-modal strategy to monitor and identify probable instances of cheating or unauthorized behavior during exams by merging various components. We have made an effort to suggest a method that is efficient, improves academic integrity in online exams, and has the potential to be included in a number of e-learning platforms and educational institutions.

Automated object detection from a video or image is a tedious computer vision task since it involves localization and object recognition. Most of the advanced driver assistance systems utilize deep learning approaches for detecting the objects. Recognizing objects in rainy environment is a challenging task due to the heavy degradations caused by rain streaks of different sizes, direction, and densities. This leads to the blurring of background scenes and limits the accuracy of object detection models. In this proposed work, a deep learning approach for automated object recognition under rainy scenarios is discussed. This system consists of two modules, deraining module and object detection module. Deraining is performed using a multi-stage architecture based on supervised attention module, and object detection is performed using YOLOv7 algorithm. Single-image deraining is the task of restoring rain-free background image from rain corrupted images. To address the degradations caused by rain, deraining module uses multi-stage architecture which progressively restores the image using supervisory attention signals from ground truth images. This work addresses the challenging image deraining task into multiple subtasks by introducing three different stages along with object detection module. Further this method analyzes the effect of rain in object detection and computes the detection performance in rainy and derainy image situations. Comparative analysis for object detection is done with YOLOv5 and YOLOv7, and the results are quantified. Experimental results with qualitative and quantitative analysis demonstrate that proposed method obtained good performance compared with existing methods.

Art and its reach is unattainable; thus, it is frequently concentrated in the hands of a select few people or organizations. To provide fair access to artworks, we need improved trade practices and technical advancement. Use of NFT technology in the art trade can provide a solution to the issue. By enabling artists to sell their creations to eager collectors and to receive the proper royalties from subsequent resales of their works, NFTs help the creators’ economy in the long run. The development of a decentralized marketplace application that makes it easier to buy and sell digital artworks in the form of NFTs is the focus of this research study. The Alchemy Web 3.0 model’s use, which gives the market a seamless connection to the blockchain infrastructure, is the key innovation. The application guarantees a smooth user experience while securely carrying out NFT transactions by utilizing Alchemy’s features. The study also investigates the integration of Pinata API, a provider of scalable and dependable InterPlanetary File System (IPFS) infrastructure. Alchemy Web 3.0 and Pinata API work together and give users of the marketplace access to a solid and trustworthy platform, assuring the safe storage and retrieval of digital goods, and improving the user experience all around.

The rapid advancement of the Internet of Things (IoT) is poised to revolutionize business landscapes, particularly in wireless communication standards that seamlessly connect devices integral to our daily lives. Home automation has made significant strides, digitizing and automating various aspects of our existence for increased convenience. IoT’s potential lies in democratizing home automation, simplifying its creation and making it more widespread. The allure of smart home solutions stems from their ability to enhance comfort and overall quality of life, driving their popularity over recent decades. Today, smartphones and microcontrollers serve as control hubs for intelligent household appliances, with Bluetooth technology playing a pivotal role in device management. Diverse wireless communication technologies, including ZigBee, Wi-Fi, Bluetooth, and GSM, offer versatile options for comprehensive home automation. At the heart of this IoT-driven transformation is a home automation project centered on the efficient control of essentials like fans and lights. This project underscores IoT’s growing role in reshaping domestic environments, showcasing its potential to redefine how we interact with and manage our homes. As IoT continues to advance, its impact on smart homes will likely become even more pronounced, offering innovative ways to make our lives more efficient and comfortable.

One of the machine learning algorithms’ most popular implementations nowadays is activity recognition. It is used, among other things, in biomedical engineering, the creation of games, and the creation of more precise statistics for athletic training. Supervised machine learning methods can anticipate a person’s tasks using data from connected sensors. In this work, UCI Machine Learning Repository is used for input data. To provide supervised models of prediction, it combines machine learning techniques such decision trees, random forests (RF), GNB, and KNN. The phone’s accelerometer, gyroscope, and other sensors produce it. With this knowledge, it is feasible to predict each movement a person makes, which may be divided into six groups: walking, stair-walking, sitting, standing, and lying. To assess the accuracy of several models, we will utilize a confusion matrix.

In this study, machine learning (ML) and computer vision (CV) techniques are used for facial recognition and sign language detection and also to classify the type of flowers given in the input dataset. The proposed work shows the advantages of ML models in various applications. Computer vision involves classifying images of flowers into different species based on their visual appearance. The flower recognition model classifies an image of a flower into five distinct categories such as dandelion, rose, daisy, sunflower, and tulip. Artificial intelligence models such as convolutional neural networks (CNNs) are commonly used for this task. These models learn to recognize the unique features and patterns in flower images through training the large dataset which consists of labeled images. Artificial intelligence algorithm is used for facial recognition to locate and match human faces in digital photographs. This project aims to develop a facial recognition system that is capable of extracting real-time facial features, extraction accuracy, and matching. The system will conduct face identification on fresh, untested photographs after using deep learning techniques to train on a vast dataset of face images. The system will also be capable of overcoming difficulties such as changes in lighting, expressions, and angles, and it will deliver a high level of accuracy and dependability. The model also focuses on sign language detection using gestures. This will help deaf people to communicate easily. The model shows 90% accuracy in recognition.

This paper presents a project that aims at improving the way in which children between the ages of 2–5 learn alphabets. The proposed solution is a program with a simple and user-friendly UI with an accurate scoring system. The project appeals to children and helps them learn at the same time using gestures. Using image detection technologies and gamification concepts, this project engages young learners in the alphabet learning process. Using the pre-existing technologies available to everyone, this project describes an innovative and powerful approach for learning alphabets. The gestures mimic how humans draw, and hence this project can prove to be a valuable resource in educating children. The program also uses audio cues to make this tedious process more interesting. The project gives an accuracy of 81.53% which is directly correlated to the accuracy of the PyTesseract library.

Sign language detection is the process of identifying hand gestures. Aphonic and auditorily impaired people use gestures to communicate with one another, which becomes a hindrance for normal people to understand. The main objective of this paper is to analyze and accurately predict the hand signs and respond in sign, using technology. It deals with the recognition and classification of 26 American sign alphabets and generating sentences from them. Received sentences are analyzed, and a response to the query is generated. MediaPipe was used for the collection of the dataset, the model was trained using a random forest classifier, and the automated response was generated using the RoBERTa model. Broadening this study to include expressions and words may help individuals with special needs communicate with the outside world more quickly and easily while also advancing the development of automated systems that can interpret and use them.

The CAN bus is the backbone for communication between various electronic control units (ECUs) in modern connected vehicles. However, the increasing connectivity and complexity of automotive systems have also introduced new security challenges, making the CAN bus vulnerable to intrusions and attacks. This paper proposes a method for detecting intrusions in the CAN bus using bidirected graphs. This study’s primary focus is building a mathematical model to identify anomalies using novel graph-based parameters like degree variance. We have chosen the dataset Car Hacking: Attack & Defense Challenge 2020 to test our proposed approach. We have achieved better accuracy in detecting attacks like DoS, fuzzy, spoofing, and replay. The proposed method performs well compared to existing techniques, notably when dealing with replay attacks, with the most remarkable accuracy of 98.38%. This method can detect and mitigate potential intrusions, ensuring connected vehicles’ safe and secure operation.

Sentiment analysis is a tool to analyze textual data and categorize the emotional tone of the world's thought process regarding any subject of interest. ‘Twitter’ a social media app enables users to reverberate their opinions within the constraints of 280 characters, known as tweets. This study focuses on the examination of Twitter sentiments on the currency exchange rate between India and the USA using natural language processing techniques. The procedure involved extrication of tweets, data preprocessing, data tokenization, and then tweet polarization. Based on the polarity and sentiment score, the categorization of the sentiments results in either positive, negative, or neutral sentiment. The paper aims to understand the existence of correlation between the sentiments of twitter users and the change in the currency value of the respective country considering the parameters such as the frequency of tweets, their like count, and the time period. A model can be designed based on the relation based on the correlation hypothesis and can be used to predict the sentimental value on the postulated currency.

In our research, we’ve developed an innovative agent-based model (ABM) to enhance farming yield. This dynamic virtual farming ecosystem simulates interactions between various agents, including plants, insects, and a virtual farmer, within a realistic agricultural environment. The model factors in plant growth, disease spread, insect behaviour, and farmer activities are replicating the complexities of real-world farming. Agents interact with environmental attributes such as soil fertility, water availability, and may possess attributes like disease resistance. Daily operations such as ploughing, irrigation, and health monitoring are simulated. Virtual insects with life cycles affecting crop consumption and yield are introduced. This ABM tool serves as a versatile means to study agricultural systems and devise sustainable productivity improvement strategies, bridging theory, and computational modelling.

By making personalized restaurant recommendations based on visitors interests and needs, the Restaurant Recommender for Food Tourism project seeks to improve tourists gastronomic experiences. A trustworthy and effective system is required to help travelers choose the best restaurants that suit their tastes and dietary requirements due to the rising popularity of culinary tourism, which encourages visitors to sample local cuisines and dining venues. In order to build a strong recommendation system, this project uses machine learning methods and user data analysis. The recommender system creates personalized recommendations that match the person’s specific interests by considering variables like cuisine type, price range, location, review counts, and user reviews. The system also has a user-friendly interface that enables users to enter preferences with ease.

An optical character recognition engine is the technological solution for preserving books and manuscripts that may soon be lost due to deterioration. In digital form, documents and/or text files are editable, searchable, and shareable. To save them from getting destroyed, documents and/or text files need to be scanned/converted into digital form and passed onto the optical character recognition engine to generate the digital text file. For a large amount of data, manual typing and conversion is nearly impossible. In this paper, the authors have tried to analyze the working of the Tesseract OCR engine for the images that contain Gujarati text.

In the realm of orphan record management, conventional centralized systems, such as Management Information Systems (MIS) and government portals, have long been relied upon for their organizational benefits. However, they also come with notable drawbacks, including concerns about data security, privacy, and the extent of control over these critical records. The proposed solution, known as OrphChain, represents a fundamental shift in how we manage and track orphan records. This innovative system leverages blockchain technology, where the blockchain ledger serves as an immutable and highly secure repository. The governance and updating of orphan records are handled through smart contracts, providing a transparent and trustworthy framework. One of the key highlights of OrphChain is the empowerment it provides to orphans themselves. Moreover, authorized entities or sectors that require access to orphan records can do so seamlessly and securely via OrphChain, using unique identification (ID) and QR code mechanisms for verification. By harnessing the capabilities of blockchain technology, involving a wide range of stakeholders, and effectively utilizing MongoDB for off-chain data storage, the system not only brings orphan care processes into the digital age but also lays the foundation for a future that is more efficient, transparent, and secure for all parties involved in the orphan care ecosystem. To facilitate seamless interactions between the blockchain network and external applications, a back-end API has been implemented. This ensures that the benefits of OrphChain extend beyond the blockchain itself, making it a practical and versatile solution for the management of orphan records.

When images are transmitted over networks, there is a risk of interception by malicious users. Encrypting images before transmission ensure that even if intercepted, the images remain unreadable without the decryption key. Integration of image encryption and watermarking involves combining techniques to protect the confidentiality and authenticity of digital images. This paper proposes the combination of image encryption and watermarking; firstly, the Rubik’s cube approach is used to scramble the secret image, followed by diffusion, which is implemented using Fourier random phase encoding. Concurrent to the image encryption scheme, wavelet sub-band coding is applied for the cover image, which results in frequency sub-bands, and then the encrypted image is embedded on the LL band of the cover image, and embedding is progressed with random location selection with the reference of kuberakolam as a randomization index. The novelty of the proposed work is that diffusion is implemented in the frequency domain with Fourier random phase encoding and embedding using random location selection with the reference of kuberakolam and well in the LL sub-band of the wavelet transform. The proposed work is done in hybrid mode spatial and transform domain, and this integration is particularly important in scenarios where sensitive or valuable images need to be securely transmitted and shared. The proposed encryption work is validated and proved using statistical attack and differential attack, and the watermarking progress is validated using imperceptibility and robustness.

Animal detection is one of the wildlife conservation techniques that can help with the issue of rapid decline in global wildlife population. The main goal is to create an extremely precise and effective object detection system that can help with species monitoring, anti-poaching operations, conflict reduction between people and wildlife conservation planning. In order to eliminate the need for region proposal creation which is required in usual CNN-based techniques, a single stage object detection technique is utilized in the proposed work. The YOLO algorithm has been implemented in this paper for detecting accurately and identifying the class of the animal in a changing weather condition. The bounding boxes are clearly annotated to form the ground truth labels for object detection model training. Performance comparisons are made with the existing system in the field of wildlife conservation and the mAP of 93.8% was achieved. From the obtained results the proposed method seeks to support evidence-based decision-making and effective conservation policies by enabling precise and efficient identification and monitoring of animal population, thereby promoting the cohabitation of humans and wildlife in a sustainable manner.

Groundwater supply and sustainability are crucial for various sectors such as agriculture, drinking water, and industry. However, manual groundwater level monitoring is labor-intensive, time-consuming, and prone to inaccuracies. To overcome these challenges, this research proposes the implementation of real-time groundwater monitoring using the Internet of Things (IoT). The proposed system involves a network of IoT devices equipped with sensors that continuously monitor and transmit groundwater level data to a central platform. These IoT devices are strategically positioned to cover the groundwater based in the Tirupur area and its surrounding areas within a 10 km radius. The wireless communication technology enables the seamless transfer of groundwater level data to a cloud platform, where it is processed, stored, and visualized in real time. The implementation of IoT-powered groundwater level monitoring has the potential to revolutionize water resource management. By providing accurate, reliable, and timely data, it helps mitigate over-extraction, optimize groundwater usage, and support sustainable water management.

This paper examines the potential of cloud computing to revolutionize the Indian agricultural sector, government operations, and rural connectivity. It highlights the benefits and challenges associated with cloud computing in agriculture and proposes a structured model to implement it effectively. Cloud computing allows farmers to access real-time information, make informed decisions, and improve access to markets. The paper examines the difficulties and advantages of cloud computing for the government in transitioning to a cloud-based version of itself for its operations. Additionally, it draws attention to specific areas related to the agricultural sector in India and certain applications offered by the government to enhance the consumer experience for stakeholders. The Government of India has demonstrated its commitment to developing technology-driven agriculture through e-NAM, Kisan Suvidha, and Agri-market initiatives. However, some challenges must be addressed to ensure the successful adoption of cloud computing in the agricultural sector. The proposed implementation model outlines the essential stages of the process, including the needs assessment, the selection of cloud providers, the automation of workflow, the modernization of applications, the implementation of security measures, and the implementation of continuous improvement. The model emphasizes the importance of training, feedback mechanisms, and collaboration. Furthermore, the paper underscores the need for a specific feedback system and grievance redress for agricultural cloud applications to enhance user experiences. To reap the full benefits of cloud computing in the Indian agricultural sector, a comprehensive strategy is necessary. This strategy necessitates technology adoption, awareness-raising, education, and stakeholder engagement. Utilizing cloud technologies, the Indian agricultural sector can realize sustainable growth, increased efficiency, and equitable development. This paper emphasizes the importance of cloud computing in transforming the Indian agrarian landscape.

Purpose: The aim of the study is to determine the effectiveness of e-business impacted through the use of ICT-based promotional initiatives in the retail and service sectors. The conceptual model has been designed from the previous literatures that explained ICT-based promotional activities, viz. advertising, direct marketing, personal selling, resulted benefits in terms of branding, cost-effectiveness, positioning, and customer satisfaction. These in turn enhance the effectiveness of e-business. Research Question: (a) Whether ICT-based promotional activities influence business effectiveness? (b) How are the above said promotional activities influencing business effectiveness? Methodology: Data were collected from 400 respondents for hypothesis testing through a structured questionnaire in 5-point Likert scale. Exploratory factor analysis and structural equation modeling were used for establishing hypotheses used in the research model through SPSS 28.0 and Amos 28.0. Findings of the study: The result ascertains the positive significant influence of all the factors on the effectiveness of e-business. Originality: This paper contributes to test the effectiveness of e-business influenced by ICT-based promotional activities, and no such existing literature of previous research conducted has been found to provide any secondary data. Hence the analysis is based on primary data collected, which justifies originality of the study.

This research investigates the adoption of AI conversational agents within the Indian banking sector. Drawing upon the UTAUT-2 model, the study extends the framework to encompass trust, anthropomorphism, and perceived privacy risk. A cross-sectional design was employed collecting data from 384 actively engaged mobile banking customers in India. The study's findings reveal that performance expectancy, effort expectancy, hedonic gratification, trust, and human-like traits positively influence the intention to adopt AI conversational agents. In contrast, privacy and security concerns exert a negative impact on adoption intent. This research contributes a holistic understanding of AI agent adoption dynamics, addressing the multi-faceted factors that influence user behavior within the Indian banking context.

In the era of digital finance, the convergence of cryptocurrencies and traditional stock markets has become an area of profound interest and meticulous scrutiny. This systematic literature review embarks on a comprehensive exploration of the evolving relationship between these two intricate financial domains. Through a rigorous process involving the synthesis and in-depth analysis of a substantial body of relevant research, the primary objective of this study is to illuminate the multifaceted dynamics, temporal variations, and influential factors that intricately shape the correlation between cryptocurrencies and stock markets. The methodology employed for this review involves a meticulous examination of 34 scholarly articles spanning from the inception of cryptocurrencies in 2009 to August 2023, with a particular focus on the association between cryptocurrencies and the stock market. Within this extensive body of literature, four major research streams were discerned and are thoroughly discussed in this review. These streams encompass investigations into the correlation between cryptocurrencies and stock market. In the course of this systematic review, seven compelling research questions emerge, offering tantalizing avenues for future exploration. The findings have several implications for the current state of the literature on cryptocurrency and the stock market, including identifying study gaps and potential future research initiatives.

The world’s population is anticipated around 9 billion by 2050, creating a challenge for traditional farming methods to meet the increasing food demand. Therefore, alternative farming solutions that incorporate modern technologies are necessary. Hydroponics has gained popularity in recent years as it offers a sustainable approach to agriculture by producing high-quality crops with minimal resource usage. Hydroponics involves soilless farming and providing nutrients through a water-based solution. This paper explores the technologies employed in IoT-based hydroponic farming. Different types of plants and their nutrient requirements, including leafy greens, tubers, and fruit-bearing plants, are considered in review to ensure optimal growth. This work focuses on parameters such as pH levels, temperature, and other environmental factors for spinach plant. To monitor and achieve better crop yields, Internet of Things (IoT) and sensor network are established. Wireless sensor networks play a vital role in collecting data on temperature, humidity, and other variables from the field, creating a valuable repository of information. This data is then processed and transmitted through networks. This is discussed in results’ section. Also, this paper showcases the research conducted by scientists and researchers worldwide. It presents a survey of various other technologies like image processing, to monitor plant growth, track diseases, and identify potential issues. It also highlights the existing challenges and identifies open issues that require further exploration in the field of hydroponics.

Spelling errors, a commonplace phenomenon among students dominated by those with learning disabilities, call for effective systems that automatically and accurately identify and classify spelling mistakes. This research focused on developing an automatic spelling error classification tool to identify the Malayalam writing system’s phonological and orthographic spelling error categories. For the analysis of spelling errors in Malayalam, the study considered the input pair of real words and spelled words for extraction of six linguistic features: the difference in word length, edit distance, character overlapping percentage, the number of common phonemes, the number of common bigrams, and the number of common trigrams. After analysing spelling errors, machine learning techniques classify them. The research appraised the effectiveness of feature engineering apropos automatic spelling error classification in Malayalam. Results indicate that the fine-tuned Random Forest classifier achieved the highest classification accuracy of 71%, effectively distinguishing between the two error categories. The findings pinpoint the impact of linguistic features on accuracy and afford insights into the imperative of language technology tools for educational purposes.

Tour planner aims to enhance travel experiences by providing optimized and personalized itineraries for tourists. The planner utilizes various algorithms and data analysis techniques to consider factors such as user preferences, time constraints, geographical distances, and historical data to generate the most suitable tour plans. The planner incorporates several key features to achieve its objective. First, it employs advanced algorithms for calculating distances between different points of interest. The distance calculation algorithm, such as the Haversine formula used in this implementation, enables the planner to estimate travel times accurately and efficiently, considering the Earth's curvature. The implementation showcases the planner's functionality by generating day-wise tour plans based on the calculated distances and place types. The generated plans are displayed to the user, providing insights into the recommended attractions, their ratings, coordinates, and place types. This interactive presentation of the tour plans allows users to visualize and evaluate the proposed itineraries.

The concept of deploying IoT networks for healthcare application is one that has the potential to improve patient care. This idea is exciting and has the capacity to lower redundancies in the contemporary infrastructure. Because there's a superb amount of statistics that desires to be processed in a sensible manner, an approach that is based totally on gadget studying is essential to the powerful implementation of IoT networks for this reason. Throughout the entirety of this article, we will go into the specifics of how AI-enabled IoT is being utilized in the field of health care. For the purposes of future research activities, this investigation would act as a baseline study in order to better understand the role that the Internet of Things plays in intelligent medical care.

It is a challenge to identify paddy diseases and insects because the structure of paddy diseases and pests is intricate, and the aspects of various species of paddy diseases and insects are quite similar. In order to stop the further spread of diseases and insects, it is essential that these pests, which include a wide variety of insects and diseases, are detected and categorized as quickly as possible. Deep complex neural networks, also known as CNNs, are widely acknowledged to be the most cutting-edge approach to image identification at the moment. This paper will discuss several deep neural network and machine learning techniques that are utilized for the identification of paddy diseases on the basis of images of paddy leaf that are diseased with certain diseases. This paper carried out an in-depth investigation into the amount of published articles that discussed a variety of diseases that can affect paddy as well as other types of plants and fruits, and evaluated these publications using significant criteria. These requirements are the image data collection size, the number of diseases, the pretreatment, the segmentation approach, the classification type, the classification accuracy, and other similar factors.

Automatic Modulation Classification (AMC) is a fundamental task to blindly identify modulation schemes within Radio Frequency (RF) signals. This process holds paramount importance within the realm of Cognitive Radio (CR) applications, enabling critical tasks such as interference detection and link adaptation. Deep learning (DL) has shown remarkable effectiveness in addressing the AMC challenge; however, most DL-based AMC schemes have high processing and storage requirements, rendering them unsuitable for resource-constrained devices. To tackle this issue, this paper introduces a lightweight neural network (NN) constructed by fusing Gated Recurrent Units (GRUs) and multiple convolutional blocks. These convolutional blocks are meticulously designed using asymmetric kernels to reduce computational complexity, and Squeeze and Excitation (SE) blocks to enhance channel interdependencies with minimal computational cost. Additionally, the convolutional blocks incorporate skip connections to improve classification accuracy and mitigate the vanishing gradient problem. Experimental results on the RadioML 2016.10A dataset demonstrate that our model outperforms benchmark DL-based AMC models in terms of accuracy while utilizing the fewest trainable parameters and exhibiting moderate inference time.

A voice-controlled tea stand is a concept that involves using voice recognition technology to control various aspects of a tea stand or tea-making process. This innovative idea combines traditional tea preparation with modern voice-activated technology, creating a convenient and interactive experience for customers.

Organizational innovation metrics are measurements used to assess and quantify the effectiveness of innovation initiatives within a company or organization. These metrics provide insights into the success, progress, and impact of innovation efforts, helping companies track their innovation performance and make informed decisions.

Third parties typically lack openness and trustworthiness when it comes to financing a project or research in many firms or organizations. The scope of the problem is not just restricted to companies/organizations but also government grants, college/university grants, or any entity where funds are involved. Blockchain technology can help to solve this problem by providing a secure and transparent platform for project financing. Our implementation makes use of an existing blockchain called Ethereum, and self-executing contract also known as smart contracts on which a semi-decentralized application relating to our subject is constructed. On many occasions, funds allocated for projects/research are not spent appropriately. So, we set out to create a project model that addresses such critical issues. The solution considered is to develop a semi-decentralized application that considers security features of decentralization and convenience of centralization on an existing blockchain that suits well which supports our cause. The result obtained can track the flow of funds in and out of any organization/company which can prevent many queries associated with the origin and destination of the fund.

A novel approach to perform sentiment analysis on real-time Twitter data using long short-term memory (LSTM) neural networks with mutually inclusive classifiers. The work leverages the vast amount of publicly available Twitter data, enabling us to analyze the sentiments expressed by users in real-time. The Twitter data by cleaning, tokenizing, and removing stop words to create a high-quality dataset is being preprocessed. The preprocessed data is then fed into the LSTM neural network, which is known for its ability to model long-term dependencies in sequential data. To enhance the sentiment analysis performance, a mutually inclusive classifier framework is proposed. Traditional sentiment analysis models often categorize sentiments into distinct classes, such as positive and negative. In contrast, our mutually inclusive classifiers allow for multiple sentiments to be assigned to a single tweet. This approach is better suited to capture the nuances and complexities of human emotions often expressed on social media platforms like Twitter. The LSTM model with mutually inclusive classifiers using Python and popular deep learning libraries like TensorFlow and Keras is implemented. The model is trained on a labeled dataset, and hyperparameters are tuned to optimize its performance. To evaluate the effectiveness of the approach it uses various metrics, such as accuracy, precision, recall, and F1-score. In the experimental analysis, demonstrated the LSTM-based sentiment analysis model with mutually inclusive classifiers outperforms traditional sentiment analysis models. The findings contribute to the advancement of sentiment analysis techniques and offer valuable applications in understanding and analyzing user sentiments in the dynamic world of social media.

This study aims to examine social media adoption for digital learning innovation as this might be the element to enhance in building teaching and learning support. This study was involved among 146 learners from five different higher education institutions in Malaysia. This quantitative study was conducted using a questionnaire. The results obtained were analyzed using SPSS for Windows software (version 23.0) through descriptive analysis and inference analysis. Descriptive analysis was used to obtain frequency, percentage, mean and expert's percentage. While the inference test involves the Pearson correlation test to explain the relationship between variables. The results of the study showed that the use of social media among students is very high (min = 4.33, sp = 0.50). However, the use of social media for building the support for teaching and learning materials is only at a modest level (min = 3.34, sp = 0.67). Pearson correlation analysis showed a high simple relationship (r = 0.585, p < 0.01) between the purpose of using social media and students’ perception of social media as a medium for teaching and learning process in higher education context.

In the past, the degree of knowledge and skills the students acquired over such time was used to assess the success of higher education. Yet as time went on, the effects of globalization and the significant increase in employment in the IT sector altered the situation, leading to the appearance of a new higher education model that includes campus placement as the capstone of its higher education offerings. In recent years, institutions have been ranked according to the number of successful job placements they have made on campus during a given year and their average pay. This has led to considering institutional obligation and campus placement for successful students. A new higher education model that uses campus placement as the cornerstone of its higher education programs has emerged due to the consequences of globalization and the significant expansion in employment in the IT industry over time. Institutions are now graded based on how many successful job placements they made on campus in a particular year and the average salary they paid. This has caused the idea of institutional responsibility and campus placement for accomplished students to be considered. This study is about the newly suggested model and strategy that students should follow to receive suitable, challenging job offers from reputable international companies, as well as the hard work and intelligent work they must put in for their sustainability and advancement within the organization. The report also discusses the advantages, limitations, and drawbacks of an online placement approach focused on students.

Diabetic neuropathy is a prevalent consequence of diabetes, impacting as many as half of those diagnosed with the condition. This ailment results from nerve damage, manifesting as sensory and motor symptoms such as tingling, numbness, and discomfort in the extremities, especially the hands and feet. Diabetic neuropathy considerably diminishes the patient’s quality of life, prompting both physical and emotional upheaval and consequently altering their behaviors and daily routines. This study aimed to achieve two main goals: first, to understand the role played by psychosocial factors in influencing the actions of those with diabetes and neuropathy, and second, to determine the relationship between a patient’s psychosocial factors and their responses under these circumstances.

The paper investigates a retrospective study of Assets Liability Management research published in English-language journals. We comprehended a wide range of literature reviews showing the relationships between the subfields of ‘Asset liability management’ and the time evolution. Through a network map analysis based on keywords, we create and provide a structure to this literature over the selected time period. We conclude the paper, by highlighting the aspects of future pathways in the concerned field of research. The study aims to explore the intellectual development that happened in the selected field. Besides, a scientific performance analysis is put through, addressing the most influential publications, collaboration patterns among top researchers, and frequently used keywords in the research field over the selected period. The study is conducted, by taking bibliometric citation analysis, an evolving and scientific technique. The study reviews 745 papers published in the Social Science indexed journal of Scopus database from 2005 to 2021. For statistical analysis of data, a bibliometric test is applied using the VOS viewer software. The study ends by concluding the main observations throughout. The findings reveal the leading papers, authors, and keywords in relation to asset liability management. The research themes found there is hardly any study available that has tapped the link between assets liability management and Urban Cooperative Banks.

Education plays a pivotal role in the lives of individuals, as well as in the progress of nations and the global community. As the Information and Communication Technology (ICT) continuously advances and improves with each successive generation, education becomes the bedrock upon which the younger generation builds their future. In recognition of this, Government of India has formulated a new education policy in 2020. In line with this holistic development, we have steered a comprehensive study utilizing a diverse range of Machine Learning (ML) and Deep Learning (DL) algorithms to survey public Sentiment Analysis (SA) and their acceptance towards the National Education Policy (NEP) in India. Additionally, we have undertaken a detailed analysis of recent literature survey pertaining to the NEP, illustrated the research gap and finally conducted a comparative assessment of the findings. The accuracy has been recorded maximum at 94% using LSTM model in comparison of NB, SVM, KNN models.

India is a country with a rich tradition of agriculture, with approximately 70% of the population dependent on it. Farmers have a wide variety of crops to choose from and must carefully select suitable pesticides to protect their plants. Plant diseases can significantly reduce crop quality and quantity. These diseases are typically identified by visually observing patterns on the plants. Monitoring plant health and disease is critical for successful cultivation of crops. Historically, disease monitoring and analysis were performed manually by experts, which was both time-consuming and labor-intensive. However, image processing techniques can be used to detect plant diseases, with symptoms typically appearing on leaves, stems, and fruits. Using a plant leaf to identify disease symptoms, the appropriate medication can be administered to crops through an automated prototype. Proper detection and treatment of plant diseases are crucial for sustaining agriculture in India.

In recent years, cases of gender-based violence in journalism have increased. Therefore, this text will analyze the link between both phenomena. In addition, the ultimate goal is to document existing publications on journalism and gender-based violence. To search for accurate information, the Scopus database was used. Regarding the correct selection of articles, the documents that were related to the topic developed were used, as well as the texts that had the Spanish language were Open Access and were between 2018 and 2022. In addition, the PRISMA method was used. In the course of the research, there were different limitations, so changes had to be made to the text. Finally, the research seeks to increase the dissemination of cases of gender violence that occurs in the journalistic field.