Journal of Computer Applications and Information Technology
Journal of Computer Applications and Information Technology (E-ISSN: 3107-6424) is a peer-reviewed quarterly online journal aims to service Researches, Scientists, Academicians, and students all over the world to develop, share and discuss various new issues. This journal is comprehensive, touching on both foundational theory and advanced applications and it serves as a bridge between research and practice, constantly evolving to address new challenges and opportunities across various sectors of society and industry. It also provides innovative research that contribute to technological advancement and societal development. Researchers from academy and industry world are invited to publish their research articles in this journal.
Machine Learning-Based Predictive Modeling for Chemical Reactor Performance Optimization
Chemical reactors are the core operational units in chemical and petrochemical industries, where performance is strongly influenced by nonlinear interactions among operating variables such as temperature, pressure, feed concentration, residence time, and catalyst loading. Conventional kinetic modeling approaches often require detailed mechanistic knowledge and high computational effort, limiting their effectiveness for real-time prediction and optimization. This paper presents a machine learning-based predictive modeling framework for optimizing chemical reactor performance. Multiple regression models, including Artificial Neural Networks, Random Forest, Support Vector Regression, and Extreme Gradient Boosting, are developed to predict key performance indicators such as conversion, yield, and selectivity. The models are trained and validated using experimental or simulation data, and their performance is evaluated using standard statistical metrics. The best-performing model is integrated with an optimization strategy to determine optimal operating conditions under process constraints. Results indicate that the proposed approach achieves high prediction accuracy with reduced computational time. The framework demonstrates strong potential for intelligent reactor operation and advanced process optimization in modern chemical industries.
Copyright (c) 2026 Journal of Computer Applications and Information Technology
Deep Learning–Based Crop Yield Prediction Using Multispectral Satellite Imagery
Reliable estimation of crop yield is essential for effective agricultural management and ensuring food security. Conventional yield prediction approaches are often constrained by limited field data and their inability to represent spatial variability across large agricultural regions. With the increasing availability of multispectral satellite imagery, it has become possible to monitor crop growth and condition continuously throughout the growing season. However, transforming this large volume of remotely sensed data into accurate yield estimates remains a significant challenge. This research presents a deep learning–based approach for predicting crop yield using multispectral satellite imagery. Multiple spectral bands and vegetation-related indicators extracted from satellite data are used to characterize crop development patterns across time and space. A deep learning model is employed to automatically learn representative features from the multispectral inputs and establish a robust relationship between observed crop conditions and final yield values. The proposed framework is validated using multi-year satellite imagery and corresponding ground-based yield records. The results indicate that the proposed deep learning approach achieves higher prediction accuracy compared to conventional machine learning techniques. In addition, the model demonstrates strong potential for early-season yield estimation, enabling timely decision-making for farmers, agricultural planners, and policymakers. The study highlights the effectiveness of deep learning and remote sensing integration as a scalable solution for crop yield prediction across diverse agricultural environments.
Deep Learning–Based Predictive Maintenance of Rotating Machinery Using Vibration and Acoustic Signals
Predictive maintenance has emerged as a key enabler of intelligent manufacturing systems by reducing unplanned downtime and maintenance costs in rotating machinery. Traditional condition monitoring techniques rely heavily on handcrafted features and expert knowledge, which often fail to generalize under complex operating conditions. This paper presents a deep learning–based predictive maintenance framework for rotating machinery using vibration and acoustic signals. Multisensor data are collected from machinery under normal and faulty operating conditions, including bearing defects and imbalance faults. A hybrid deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks is employed to automatically learn spatial and temporal features from raw signals. Experimental results demonstrate that the proposed approach achieves superior fault detection accuracy and remaining useful life prediction performance compared to conventional machine learning methods. The results confirm the effectiveness of deep learning for real-time predictive maintenance in smart manufacturing environments.
Quantum Computing–Based Optimization of Urban Air Pollution Control Strategies
Multiple sources of pollution, nonlinear interactions, and competing environmental and economic goals are characteristics of complicated optimisation problems in urban air pollution control. When dealing with such high-dimensional decision spaces, conventional optimisation strategies frequently encounter scaling issues. This study suggests an optimisation structure for urban air pollution management methods based on quantum computing. In order to minimise pollutant concentrations while meeting operational and regulatory restrictions, the suggested method formulates air pollution reduction as a constrained optimisation problem in which industry rules, traffic management policies, and emission reduction strategies are optimised. To effectively explore the solution space, a hybrid quantum–classical optimisation model is created employing quantum-inspired algorithms. The framework's efficiency is compared with traditional optimisation techniques with respect to of convergence effectiveness and solution quality using simulated municipal air quality & emission data. The results show that for complicated pollution management scenarios, the quantum-based optimisation strategy achieves better optimisation performance and faster convergence. The results show how quantum computing paradigms can be a useful tool for solving large-scale environmental optimisation issues and assisting with data-driven the air quality in cities management.
Deep Learning–Based Prediction of Urban Air Quality Using Multisource Environmental Data
Accurate and fast air quality forecast systems are necessary because urban air pollution is a serious threat to both environmental sustainability and public health. The intricate nonlinear and spatiotemporal interactions included in urban air quality data are frequently difficult for traditional analytical and machine learning algorithms to capture. Using multisource environmental data, this study suggests a deep learning-based methodology for forecasting metropolitan air quality. To improve prediction accuracy, the suggested method incorporates a variety of data sources, such as air pollution concentrations, meteorological variables, mobility indicators, and land-use features. The temporal relationships and spatial correlations between monitoring stations are modelled using a deep neural networks architecture. The suggested neural network model outperforms traditional machine learning techniques in forecasting important air quality indicators, according to experimental results. The model is developed and tested using practical urban datasets, alongside its performance is evaluated using conventional metrics like RMSE, MAE, and R2. The results demonstrate the efficacy of deep learning in conjunction with multisource data fusion for accurate urban air quality estimation, providing insightful information for sustainable urban planning and environmental management.
AI-Integrated Remote Patient Monitoring Framework for Next-Generation Virtual Care Management Systems
The rapid expansion of Virtual Care Management Systems (VCMS) has created an urgent demand for intelligent, autonomous, and scalable Remote Patient Monitoring (RPM) solutions capable of processing continuous streams of physiological data. Traditional RPM architectures, which depend heavily on centralized cloud servers and rule-based analytics, often suffer from latency issues, limited scalability, and reduced effectiveness in providing timely clinical insights. To address these limitations, this study proposes an AI-Enhanced Remote Patient Monitoring Framework that integrates multimodal biomedical sensors, edge-level preprocessing, and cloud-coordinated deep learning workflows for real-time assessment of patient health states. The framework incorporates advanced artificial intelligence components—such as transformer-driven vital-sign forecasting networks, robust anomaly detection models, and federated learning mechanisms—to deliver secure, adaptive, and low-latency monitoring. Experimental results obtained from diverse RPM datasets reveal substantial improvements in event-detection accuracy, signal quality, and temporal prediction performance when compared with conventional machine learning approaches. Findings also show that the hybrid AI architecture reduces overall monitoring latency by up to 35% and increases early clinical alerting precision by 22%. Overall, this work provides a scalable, interoperable, and clinically dependable model for next-generation virtual care systems, supporting proactive and high-quality remote healthcare delivery.
Quantum Computing–Enabled Clinical Decision Support System using Electronic Health Records and Clinical Documentation
The rapid growth of Electronic Health Records (EHRs), along with the need for accurate, real-time clinical documentation, has pushed traditional computing methods to their operational limits. Conventional machine learning techniques often fall short when handling the heterogeneous, high-dimensional nature of healthcare data—especially in situations where clinical decisions must be made quickly and reliably. To overcome these challenges, this study introduces a Quantum Computing–Enabled Clinical Decision Support System (QC-CDSS), which integrates quantum machine learning (QML) with advanced deep learning models to improve diagnostic accuracy and predictive performance. The proposed framework utilizes quantum-driven feature extraction, hybrid variational quantum circuits, and quantum kernel-based classifiers to process both structured EHR records and unstructured clinical narratives. In parallel, sophisticated natural language processing methods, including transformer-based architectures, are employed to capture semantic, contextual, and temporal patterns from physician notes, discharge summaries, radiology interpretations, and related clinical text. The convergence of these techniques—particularly the integration of QML—presents promising opportunities for achieving more effective decision support and faster analysis of high-dimensional medical data. This paper discusses the core concepts, major applications, existing challenges, and future research pathways for incorporating quantum-enhanced machine learning into clinical decision-making systems, emphasizing their potential to address complex healthcare problems more efficiently.
A Hybrid Deep Learning and Edge Computing Framework for Real-Time IoT Data Processing
Massive amounts of continuous, real-time data have been produced by the quick growth of Internet of Things (IoT) ecosystems, requiring sophisticated, low-latency, and extremely efficient processing solutions. Due to bandwidth limitations, latency overheads, and escalating privacy issues, traditional cloud-centric architectures are becoming less and less suitable for time-sensitive IoT applications. In response, this study presents a Hybrid Deep Learning–Edge Computing Framework (HDL-ECF) that combines cloud-supported deep learning with on-device intelligence to enable quick and dependable IoT data processing. By strategically allocating computational jobs across edge and cloud resources, the suggested methodology investigates how hybrid AI systems increase efficiency and scalability while lowering response times and preserving energy. By dynamically adapting to changing network conditions, hybrid AI models greatly outperform standalone AI approaches, improving system scalability and overall operational performance, according to a thorough analysis of system design and performance metrics. The results of this research open the door to the deployment of intelligent, autonomous edge infrastructures that can handle the growing computational needs of contemporary IoT networks.
Quantum-Enhanced Models for Predicting Atmospheric Dynamics in Weather Forecasting
Weather forecasting plays a critical role across various sectors, supporting strategic planning and reducing the impact of hazardous climatic conditions. However, the inherently chaotic and nonlinear nature of atmospheric systems limits the performance of conventional forecasting approaches, often resulting in prediction inaccuracies and heightened risk. This study proposes a quantum-driven predictive framework that exploits the computational strengths of Quantum Machine Learning (QML) and hybrid quantum–classical optimization to enhance the accuracy and efficiency of modern forecasting models. The framework employs Quantum Neural Networks (QNNs) combined with Variational Quantum Circuits (VQCs) to learn complex spatial–temporal dynamics embedded within large meteorological datasets. Through quantum feature encoding, key atmospheric indicators—including temperature, humidity, wind velocity, and barometric pressure—are projected into high-dimensional Hilbert spaces, enabling more expressive pattern extraction and robust predictive behavior. Simulation results reveal that the quantum-augmented approach surpasses traditional deep learning architectures in both training convergence and forecast precision when applied to extensive weather records. The hybrid design further supports scalability by intelligently distributing computational workloads between classical processors and quantum hardware. Overall, this research demonstrates the transformative potential of quantum computing in atmospheric modeling, offering a foundation for future real-time forecasting systems capable of managing the increasing complexity of global climate patterns.
Bakialakshmi Varadaradjaperoumal Bhuvana
A Full-Stack Blockchain Framework for DAPP Developers: Architecture, Design, and Implementation
Blockchain is a distributed database used to store an unchangeable, permanent record of all transactions. It is operated by processors that are a member of a peer-to-peer (P2P) network and functions as a decentralized database. Demand for decentralized applications (DApps), which provide accountability, safety, and independence beyond conventional centralized systems, is rising as a result of the quick development of blockchain technology. However, combining frontend, back end, and blockchain components into a unified and effective framework might be difficult for DApp designers. In order to simplify the creation of decentralized applications, this study suggests a full-stack blockchain framework that connects various levels. The framework creates an end-to-end development environment designed for compatibility and scalability by utilizing contemporary technologies, such as Solidity, with Web3.js for smart contract integration, React.js for the front-end, and Node.js/Express.js for the backend. Using cryptographic methods and decentralized storage (like IPFS), a layered architecture is intended to provide modularity, effective data flow, and increased security. The suggested framework streamlines DApp development processes, lowers latency in blockchain interactions, and boosts developer efficiency, according to implementation data. By offering a thorough architectural blueprint and execution method for full-stack DApp creation, this study advances the area of blockchain engineering and opens the door for safe, effective, and user-focused decentralized ecosystems.
Optimized Deep Learning Framework for Intelligent Data Analytics in Information Systems
The exponential expansion of data in contemporary information systems poses a significant barrier to the efficient, accurate, and scalable extraction of useful insights. High-dimensional and heterogeneous data are frequently difficult for conventional machine learning models to handle, which results in poor analytical performance and more computing overhead. The present investigation proposes an Optimized Deep Learning Framework for Intelligent Data Analytics in Information Systems in order to overcome these constraints. The model developed in this study achieved a success rate exceeding 98% across two test sets, indicating a clear advantage in processing accounting information. In the response time evaluation of the financial module, conducted over 60 trials, the system demonstrated an average response time of 0.5 seconds and maintained a 100% success rate, highlighting both its efficiency and reliability. The system can function normally if it can achieve 0.8 seconds and maintain a success rate above 98%. There are 70 test cases created specifically for the finance module in the system operational stability test, 70 of which are executed, with an execution rate of up to 100%. This indicates that the system can function effectively and won't malfunction while in use.
Enhancing Predictive Accuracy Using Ensemble Machine Learning Models: A Data-Centric Approach
Due to the abundance of data and processing capacity, artificial intelligence (AI) has developed and is now linked to deep learning. In order to optimize the efficiency without changing the underlying data, academics have traditionally taken a Data-centric approach, concentrating on creating innovative models and algorithms. The Data Centric Approach, also called the Data Oriented model, was created as a result of notable AI expert Andrew Ng's current emphasis on improved (quality) data rather than better methods. In the field of ML, the shift from a model-oriented to a data-oriented model has accelerated. Notwithstanding its potential, the Data-Centric Approach has a number of obstacles to overcome, such as (a) producing high-quality data, (b) protecting data privacy, and (c) resolving biases to make datasets equitable. There hasn't been much work done lately to prepare high-quality data. By concentrating on producing high-quality data using techniques like data augmentation, multistage hashing to remove duplicate instances, detecting and correcting noisy labels, and confident learning, our study seeks to close this gap. Our study presents an Enhancing Predictive Accuracy Using Ensemble Machine Learning Models: A Data-Centric Approach (EPAE-MLDCA). The EPAE-MLDCA continuously beat the data model centric model according to a comparative performance study. This research shows how the EPAE-MLDCA approach may be further investigated and used in a variety of fields, including entertainment, finance, healthcare, and education, where high-quality data could greatly improve performance. We discovered that compared to data-centric techniques, the EPAE-MLDCA approach provided a greater accuracy.
Copyright (c) 2025 Journal of Computer Applications and Information Technology
Scalable and Secure Framework for Cloud-Based IT Infrastructure Using Software-Defined Networking and Distributed Storage
Due to its scalable and affordable processing, cloud computing (CC) has become the real trend in enterprise IT service models. The challenge of maintaining and safeguarding hybrid cloud infrastructures has increased as more businesses use them. Software-defined networking (SDN) is being adopted by enterprise networks in order to reduce operational costs and provide flexibility in network management. Both technologies are frequently used in enterprise information technology (IT) services. However, it's uncertain how software-defined networking and CC will affect the security of commercial networks. Advanced computing paradigms like serverless computing, containerization, and edge computing are supported by the smooth administration of compute, storage, and networking resources made possible by SDI's interaction with cloud platforms. The study presents a Scalable and Secure Framework for Cloud-Based IT Infrastructure Using Software-Defined Networking and Distributed Storage (SFCIT-SDNDS). The SFCIT-SDNDS technique involves data collection, and module development processes. SDN will identify any threats in the various domains that make up the network and send the encrypted data to the blockchain. Our SFCIT-SDNDS system can use real-world network data to indicate a wide range of applications, as can our new network paradigm. A set of experiment was performed to analyze the scalability of the SFCIT-SDNDS model. According to our comprehensive experimental examination of performance evaluation, the suggested SFCIT-SDNDS framework outperforms other existing approaches by 97.75%.
The Enterprise Service Bus (ESB) Performance Metric in SOA: An Essential Enabler for Upcoming IT Infrastructure
The need for scalability, interoperability, and agility—all fundamental components of Service-Oriented Architecture (SOA)—is driving the development of enterprise IT systems. The Enterprise Service Bus (ESB), which serves as the communication backbone of this framework, is essential for facilitating smooth integration between various platforms, services, and applications. However, ESB performance metrics are a crucial enabler for the future of IT infrastructure since an ESB's efficacy is heavily reliant on its performance. Throughput, latency, dependability, fault tolerance, and scalability are among the key performance indicators of ESBs that are examined in this study along with their effect on the overall effectiveness of SOA-based systems. Organizations can evaluate integration bottlenecks, maximize resource use, and guarantee the smooth coordination of business activities by measuring these parameters. Furthermore, the ability to track and improve ESB performance becomes even more important for maintaining operational excellence as businesses move toward next-generation infrastructures that incorporate cloud computing, IoT, and edge services. The results demonstrate that ESB performance measurements are strategic instruments that support resilient, adaptable, and future-ready IT ecosystems rather than just technical indications. In order to construct agile and resilient enterprise infrastructures, the study highlights the need for standardized standards for assessing ESB performance.
A New Scheduling Challenge for Real-World Human-Robot Collaboration in Internet of Things Applications
Cloud computing, robotics, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) are some of the technologies that make up the Internet of Robotic Things (IoRT). Transportation, industry, healthcare, and security all heavily rely on IoRT. IoRT has the potential to significantly accelerate human growth. Robots can send and receive data to and from other people and devices thanks to IoRT. This study reviews IoRT in terms of comparable architectures, methodologies, and capabilities. The associated research difficulties are so outlined. When creating robotic systems and objects, IoRT architectures are crucial. Human-robot cooperation, or HRC, is quickly emerging as a crucial element of smart manufacturing, healthcare, and service sectors due to the rapid integration of Internet of Things technology. However, real-world implementation of such systems poses challenging scheduling challenges due to the dynamic, diverse, and resource-constrained nature of IoT contexts. This paper presents a novel scheduling problem for real-world human–robot collaboration in Internet of Things applications, which aims to balance communication disruptions, computation demands, and safety-critical interactions. In contrast to traditional scheduling methods, which primarily deal with deterministic scenarios, the proposed perspective emphasizes flexibility in response to erratic human behavior, changing network conditions, and diverse device capabilities. The challenge is increased by the need to guarantee energy efficiency, real-time responsiveness, and secure data exchange across scattered IoT nodes. This scheduling problem needs to be fixed to improve system productivity and human safety, reduce job execution latency, and accomplish seamless coordination. This work lays the foundation for exploring new scheduling models that bridge the gap between theoretical frameworks and the practical requirements of IoT-enabled human–robot collaboration.
Energy-Efficient Computer Systems: RISC-V Extensions for Machine Learning Inference at IoT's Edge Computing
Over the past few decades, there have been numerous turning points in the massive transformation of computing systems. The limits of instruction-level parallelism (ILP) and the end of Dennard's scaling pushed the semiconductor sector toward multi-core devices, notwithstanding Moore's law, which directed the industry to pack more and more transistors and logic into the exact same volume. The era of domain-specific architectures (DSA) and processors for novel workloads like machine learning (ML) and artificial intelligence (AI) has recently begun. In addition to the difficulties brought on by tighter integration, extreme form factors, and increasingly varied workloads, these trends—possibly with additional limitations—further complicate the architecture, design, implementation, and power consumption optimization of systems. Nowadays, across the board, energy efficiency is a first-order design constraint and parameter for computing equipment. The creation of energy-efficient computer systems using RISC-V architecture modifications specifically suited for machine learning inference is investigated in this study. While preserving inference speed and accuracy, the suggested extensions seek to decrease energy consumption, minimize instruction overhead, and maximize hardware utilization. Through the integration of domain-specific accelerators, memory-access optimizations, and lightweight vector operations, the extended RISC-V platform exhibits notable gains in performance per watt when compared to traditional architectures. Results from experiments and benchmark assessments demonstrate how well-suited the method is for real-time Internet of Things applications including industrial automation, smart healthcare, and environmental monitoring. AI-enabled IoT systems that are low-power, scalable, and sustainable are being advanced by this work.
Edge-Driven Machine Learning for Real-Time Health Anomaly Detection in IoT-Based Healthcare: A Modular Framework
The rapidly expanding field of healthcare IoT has the potential to transform patient monitoring and intervention. Real-time anomaly detection is necessary to safeguard patient information and device integration in light of these new security threats brought about by this interconnectedness. Integrating the Internet of Things (IoT) into healthcare enhances patient care while also improving the performance and reliability of healthcare delivery system. Ethical concerns, interoperability, and data security must be resolved if the benefits of IoT in healthcare are to be fully realized. Patients' health data is measured by IoT devices on a regular basis and shared with a server for additional analysis. Various machine learning (ML) approaches are employed on the server to help with early disease identification and to send out alerts when vital signs deviate from normal. IoT devices regularly measure patient health data and send it to a server for further analysis. The server employs a variety of ML techniques to assist in the early detection of diseases and to notify users when vital signs diverge from normal. Patient health data is routinely measured by IoT devices and sent to a server for additional analysis. The server uses a range of edge-driven ML models to alert users when vital indicators deviate from normal to anomaly identification. In this study, we introduce an edge-driven ML for real-time health anomaly detection in IoT-based healthcare: a modular framework (EDML-RHADIOTF). The EDML-RHADIOTF involves data preprocessing, feature extraction and anomaly classification. In comparison to previous solutions, the suggested EDML-RHADIOTF model shown an outstanding enhancement in anomaly detection based on an analysis of its performance outcomes. With a 96.55% accuracy rate in both reduced and full feature space, SVM performed better across anomaly classification of IoT attacks. A reduction in computing response time, which is necessary for quick response and real-time attack detection, complemented this improvement.
T-Tracking: An Energy-Aware Distributed Approach for Face-Based Target Monitoring
In monitoring applications, detecting and tracking targets within mobile Wireless Sensor Networks (WSNs) is a critical and challenging task. This paper introduces a novel distributed tracking framework, known as Target Tracking (T-Tracking), designed to enhance monitoring accuracy while optimizing energy efficiency. The framework focuses on a specific concept termed the polygon face, where nodes are grouped based on their ability to communicate within defined boundaries. Within each polygon face, the brink detection algorithm is employed to identify edges formed by node pairs that offer the highest coverage. To ensure energy-efficient operation, nodes are dynamically selected based on their energy levels and sensing capabilities using a decentralized decision-making process. The proposed system effectively addresses common challenges such as coverage gaps, node failures, and energy depletion. When a target moves beyond the current coverage area or when monitoring nodes approach low energy levels, the tracking system activates a query mechanism to identify the optimal replacement node. This selection is based on a comparative evaluation of energy reserves and sensing range, ensuring continuous and reliable monitoring. By selecting the most suitable node in real-time, t-Tracking maintains high tracking quality (QoT), minimizes communication overhead, and extends the overall network lifetime. The framework is particularly beneficial for applications in surveillance, security, and mobile object tracking in complex environments.
Modeling and Analysis of Key Management Security Factors for Organizational Data Protection: A Multi-Source Approach
Despite a wealth of technically advanced outcomes and strategic efforts, managing information security continues to be a persistent and complex challenge for organizations. The dynamic nature of hierarchical data security is heavily dependent on a range of interrelated factors that must be aligned effectively. For data security administrators, it is crucial not only to identify relevant data but also to understand and manage the interdependencies among various security factors. The primary objective of this study is to develop a comprehensive model of key Management Security Factors (MSFs) essential for ensuring hierarchical data safety. Initially, an extensive literature review was conducted, involving a systematic and open-coding analysis of 136 academic and industry papers. This process led to the identification of critical variables influencing data security. These variables were organized into 12 major categories: physical security, system vulnerabilities, technical infrastructure, awareness, access control, risk management, organizational resources, internal organizational dynamics, confidentiality-integrity-availability (CIA), business continuity, security governance, compliance, and policy frameworks. Subsequently, interviews with 19 industry experts were conducted to validate the practical significance of these components and to explore how they interact with one another in real-world environments. The result was a detailed visual representation that highlights both direct and indirect key-security indicators. Direct indicators have a measurable impact on the security posture of an organization, while indirect indicators influence security through interconnected pathways. This model serves as a valuable decision-making tool, enabling data security leaders to address core and peripheral factors more effectively and ensure a robust and adaptive security management strategy.
Confidentiality-integrity-availability (CIA)
Mobility Aware Vehicular Cloud Systems based on Edge Computing
Remote sensing from a single integrated system in autonomous vehicles often leads to false alerts and deadlock conditions, posing major challenges to safety and functionality. To address this, cloud-based vehicle control systems have been explored, as they can synchronize data from multiple vehicles using distributed sensors. However, cloud systems face inherent limitations such as long-haul connectivity issues, increased latency, and packet loss due to interference. As a result, Mobile Edge Computing (MEC) has emerged as a promising solution in next-generation wireless networks, particularly in 5G environments. This paper proposes an edge computing (EC) based method for vehicle charging, aligned with a broad, data-driven development model. In this framework, mobility-aware edge servers interact with nearby vehicles, providing real-time information on the availability of charging stations (CSs), collecting dynamic data from moving vehicles, and implementing decentralized big data processing. This method reduces dependency on the cloud, enabling faster response times and more efficient data handling. If the access schedule between edge servers and the cloud is optimized, with cloud reliance reduced by 50%, the system maintains the same reliability while benefiting from continuous edge-based monitoring. This innovative model improves scalability, enhances energy efficiency, and supports the seamless operation of future intelligent transportation ecosystems.
DNA Computing Inspired Network System by using a Wireless Network Security
Universal use of wireless networks and popularity among consumers to connect to the web generates incentives for attackers and enables them to stepping-stone attack the wireless link to steal information from legitimate customers by exploiting wireless media information packets and using multiple malware and sniffing attack methods. Wireless network resources are more susceptible to intruders using links to gain access to the access point. We take inspiration from DNA bases in this job to encrypt and decrypt user data and use a DNA algorithm for users to access their wireless network and stop intruders from accessing the same wireless access point. This makes the customer more dependent on their information authentication as they travel through the wireless network. In this study, we need to use DNA nucleotide bases for protection against replacement and mutation, which converts user information into DNA sequences designed for users who access the wireless network AP list and also encrypts and decrypts the information flow stream. Users access wireless list sequences using matching or mismatching sequences for a lawful user that helps identify intruders and make it inflexible to stop information, which is to enhance wireless security and management to mitigate the faults of past safety methods, while the present major problems of the absence of a mathematical understanding of encrypted DNA bases.
Wireless Networks based Smart Medical Monitoring System
Wireless Sensor Network (WSN) innovations are rapidly becoming a significant area of research in the healthcare industry due to their potential to support sustainable personal health development. These networks enable continuous monitoring of vital health parameters, offering critical assistance to physicians for accurate diagnosis and enhanced patient care. This paper explores the development and impact of emerging handheld smart medical monitoring systems that leverage WSN-based applications. These systems consist of various physiological sensors, wireless transmission units, and processing modules. Once the data is collected, it is transmitted to the patient’s smartphone, which acts as an intermediary platform. From there, the data is shared with medical professionals through dedicated smartphone apps or stand-alone systems. These specialists can remotely access the patient’s health information and, based on the analysis, recommend appropriate treatments or medications. This innovative architecture minimizes the need for frequent in-person doctor visits, particularly for patients requiring regular health status tracking. By automating data collection and remote reporting, WSN-based health monitoring enhances healthcare efficiency, reduces patient burden, and promotes timely medical intervention. Overall, these systems offer a cost-effective and scalable approach to personal health management, aligning with the global goals of sustainable and technology-driven healthcare solutions.
A Virtualization with Xen Using an Exploration of Computer Network
As universities have security policies in place to safeguard the integrity of network infrastructure, computer science students who take a network programming or system administration class that includes a hands-on lab are usually not granted administrative privileges on lab computers. By implementing Virtual Machines (VM) technology such as VMware and Xen, the research community is starting to tackle this. The objective is to provide a virtualized environment so that learners have complete control over their operating system and can execute the required classroom duties. To date, most of the classroom trial deployments of VMs have been tailored to the classes of network security and system management. There may be beneficial advantages for hands-on laboratories that support networking ideas taught in lectures. A hands-on laboratory also gives students the chance to engage with' real-world' issues that involve creativity and resourcefulness. Students of computer science are introduced to networking either using the seven-layer OSI stack as the model or using a ' top-down ' strategy that begins with the implementation [a reference to Comer, Stevens, Kurose textbooks]. Virtualized designs must be able to provide an atmosphere to enable learners to operate their TCP / UDP programs and assess realistic network performance through WAN metrics such as throughput, drop rates, and packet delays to promote a deeper understanding of the content. This article uses Xen virtualization to accompany network programming courses to investigate three distinct technical designs. This article uses Xen virtualization to accompany network programming courses to investigate three distinct technical designs. Designs include a physical network lab configuration comparable to the job performed by other scientists, a method of installing a laptop and using a Live CD / DVD based on Xen. In two of the models, activities and outcomes based on a network programming class are shown and future research is suggested with Xen Live CD / DVD.
Wireless Technologies for Internet of Things Applications in Smart Cities
Since the term of an intelligent community was invented, IoT (Internet of Things) has been assumed as the main framework in a smart city. The spread of off-the-rack cell phones outfitted with various remote interfaces along with advanced sensors is preparing to original remote Internet of Things (IoT) situations, portrayed by multi-bounce framework fewer remote systems where gadgets conveyed by clients go about as sensors/actuators just as system hubs. Specifically, the WORK shows Real Ad-hoc Multi-bounce Peer-to peer-Wireless IoT Application (RAMP-WIA), a new arrangement that encourages the turn of events, sending, and the executives of utilizations in sparse Smart City conditions, described by clients ready to work together by permitting new features to be sent on their cell phones to distantly screen and manage fixed/cell phones. Therefore, the recent work promotes the importance of IoT technology on the technology roadmap(TRM) and RAMP-WIA of a intelligent community. These results not only explain the probability and reliability of the projected approach.
Exploration and Implementation of Computer System Data Security Management System
Most system security policies for computers are written relatively loosely. This is designed in many respects to provide easy access to all the features of the computer network. However, without the need to access many of the network features, so much leeway gives users the ability to perform functions that might damage the infrastructure or have access to information they do not necessary to look at. The intrusion prevention mechanisms, firewalls, and other protective equipment ensure the secure functioning of the system, but inside increasingly widespread use of networks, malware, hackers as well as extra safety accidents, these safety measures devices can deliver tens of thousands of alerts in a very small time, rendering it impossible for network management staff to locate valuable information. With this in mind, this paper deals with a look at computer system security. This paper deals with computer misuse and detection, and the exploration and application of a control of safety system. Detection of misuse includes at least two elements. Audit elimination and profiling capacity are these. Huge files can be set up when audit functions are allowed in the operating system. The automatic audit features can easily search audit files by setting staff user profiles, checking for usage that falls below what is determined to be usual, alerting administrators, and removing old audit data.
MAC Protocol for Forest Fire Detection and Decision Making by WSN
This paper portrays numerous MAC convention enhancements that can be actualized to beat the limitations that originate from a genuine program. Observing wood fire needs altogether various intermissions and information sizes, contingent upon whether it is checking ordinary circumstances, transmitting a caution or operating system the executive capacities. To spare force, we use MAC calculations which shut down the radio broadly. We show that by misusing data about the connection quality list and utilizing the channel's leisure time productively just when there is a greater amount of information to be transmitted than typical, we direct to limit latency and the question and enlarge utilization of transfer speed while keeping up the force utilization at a low rate.
Encoding and Decoding of Message Signal using RSA Algorithm for Data Security
Encoding is among the best techniques for retaining and checking information shared in the presence of third-party adversaries, via public news channels. In the encoding process, the email that was sent or saved.is produced into gibberish or unreadable sort. The opposing system in which the destination node is unwilling to confirm the content of This secure interaction is known as decoding. The processes of encoding and decoding are managed using encrypted keys that are communicated between the sender and a client exclusively. This procedure can be linked with any message mode, Such as audio, video, data with multimedia, or text. The modern role entails the well-known rivest, Shamir, Adelman (RSA) encoding and decoding message signals algorithm for time complexity. Experimental implementation has examined the effectiveness of the method proposed, showing that using Matlab simulations, how the time complexity works in RSA algorithm. The results of the proposed methodology indicated it is legal, consistent, powerful, and used in secure audio communications and that the recovered message signal within the time was strongly interpretable. The message signal should be encrypted or decrypt within the time so that the time complexity method is used in the RSA algorithm.
Copyright (c) 2024 Journal of Computer Applications and Information Technology
Cybersecurity in the Context of COVID-19: Analysis of Cyberattacks in the Pandemic
The COVID-19 pandemic was a wonderful, remarkable occasion that adjusted the lives of billions of residents all around the world, bringing about what turned out to be regularly alluded to as the new-ordinary as far as cultural standards and how we live and work. Besides the remarkable sway on society and business overall, the pandemic created a lot of special digital wrongdoing related conditions which additionally influenced society and business. Security in the healthcare sector is essential as it includes patients' data and medical details. Our examination utilized information investigation to evaluate the effect of cybercrime on emergency clinics, distinguish weak medical services, associations, and set up preventive estimates custom-made for the medical services industry. We revealed that emergency clinics altogether lost, in any event, $2 billion during the initial 5 months of COVID-19, medical care suppliers in California are the most powerless against digital assaults.
Educational Statistics of School Students in Computational Intelligence Thinking in Elementary Education
The computational reasoning outfits understudies with basic reasoning which permits them to conceptualize, break down, and take care of increasingly complex issues. These abilities are pertinent to all substance zone as understudies figure out how to utilize techniques, thoughts, and mechanical practices all the more adequately as advanced locals. This examination inspected more than 200 basic understudies' pre-test and post-test modifications in computational speculation from a 10-week coding series utilizing adjusted exercises from code.org's Blockly programming language and CSUnplugged that were conveyed as a component of the ordinary school day. Members profited by the initial approach to software engineering (CS) exercises with increments in computational reasoning and relating coding ideas to this present reality. Meetings from members included instances of CS associations with regular daily existence and between disciplinary examinations at school. In this manner, the examination features the significance of utilizing CS access in different basic study halls to advance youthful understudies' computational intelligence opinion inspiration in CS themes; and the learning of basic delicate aptitudes, for example, joint effort, ingenuity, deliberation, and innovativeness to prevail in the present computerized world.