International Journal of Communication Networks and Information Security (IJCNIS)
Not a member yet
1021 research outputs found
Sort by
Optimized Feature Selection and classification for Non-Portable Executable Malware
No full textMalware is a program that executes harmful acts and steals information. nowadays it is widely recognized as oneof the largest hazards. In this research work machine learning is used to identify and detect Non-PE file features. The variousdistinct aspects of the Non-PE files features can correlate with one another, being clean or affected, led to the identification ofsuch features. by using machine learning algorithms such as Ada Boost Classifier,Gaussian NB, KNClassifier,RF Classifier, SGD classifier, and feature selection produced the best detection rate also Prediction accuracy of thealgorithms is used to compare the efficacy and efficiency
PERCEPTIONS OF PROJECT MANAGEMENT SKILLS AMONG IT PROFESSIONALS ACROSS VARIED WORK MODELS
No full textProject management skills are a cornerstone for IT employees engaged in delivering successful projects. These skills not only ensure the efficient execution of projects but also contribute to the overall success and competitiveness of IT organizations in a dynamic and competitive landscape.In essence, the importance of project management skills for IT employees cannot be overstated. These skills contribute to the efficiency, effectiveness, and overall success of IT projects, thereby shaping the competitiveness and growth of IT organizations in a dynamic and competitive industry. This article has dealt with project management skills of IT employees in different work models
An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy
No full textWith the rapid expansion of the Internet of Things (IoT), ensuring robust security and privacy for the vast network of interconnected devices has become paramount. Traditional security mechanisms often fall short in addressing the unique challenges posed by IoT environments, such as limited computational resources, energy constraints, and the need for scalability. This paper introduces the Efficient Lightweight Integrated Blockchain (ELIB) model, a novel framework designed to bolster IoT security and privacy while accounting for the inherent limitations of IoT devices.
The ELIB model leverages the decentralized nature of blockchain technology to create a secure and transparent mechanism for IoT communications and transactions. By integrating lightweight cryptographic algorithms and optimized consensus protocols, ELIB significantly reduces the computational and energy demands typically associated with blockchain operations, making it suitable for resource-constrained IoT devices. Furthermore, the model incorporates privacy-preserving techniques to protect user data and ensure confidentiality in IoT networks. Through extensive simulations and real-world deployments, we evaluate the performance of the ELIB model in terms of security, privacy, scalability, and resource efficiency. The results demonstrate that ELIB outperforms existing IoT security solutions, providing enhanced security and privacy without compromising the performance or usability of IoT devices. Our model also facilitates seamless integration with existing IoT architectures, offering a practical and effective solution for securing the IoT ecosystem. This study contributes to the ongoing efforts to secure IoT environments, presenting a comprehensive model that addresses both security and privacy challenges. The ELIB model not only strengthens the security posture of IoT networks but also paves the way for the development of more resilient and trustworthy IoT applications and services.Internet of Things (IoT) is the most emerging tech- nology in the last decade since the number of smart devices, and its associated technologies are rapidly grown in both industrial and research prospective. The applications are developed using IoT techniques for real-time monitoring. Due to Low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography technique are not suitable. In this study, we initially reviewed and identified the security and privacy issue exists in IoT system. Secondly, as per Blockchain technology provides some security solutions. The details analysis, including enabling technology and integration of IoT technologies, are explained. Lastly, a case study is implemented using the Ethererum based Blockchain system in a smart IoT system and the results are discussed
Integration of Artificial Intelligence in e-Health: Analyzing AI’s Role in Diagnostics and Patient Management
No full textObjective: This review systematically examines the integration of Artificial Intelligence (AI) in e-Health, focusing on its role in diagnostics and patient management. The aim is to provide a comprehensive analysis of AI technologies, their applications, and their impact on healthcare delivery.
Methods: A structured literature search was conducted across multiple databases, including PubMed, IEEE Xplore, and Google Scholar, using keywords such as "Artificial Intelligence,""e-Health,""diagnostics,""patient management," and "machine learning." Studies published from 2010 to 2024 were included, with a focus on peer-reviewed articles, systematic reviews, and relevant case studies.
Results: The integration of AI in e-Health has significantly advanced diagnostic capabilities through machine learning algorithms and data analytics, enhancing disease detection accuracy and reducing diagnostic errors. AI applications in diagnostic imaging and pathology are highlighted, demonstrating improvements over traditional methods. In patient management, AI has enabled personalized care through advanced data analytics, integration with electronic health records (EHR), and tailored treatment plans. However, challenges such as data privacy, ethical considerations, and technological barriers persist.
Conclusions: AI holds transformative potential in e-Health, with notable improvements in diagnostics and patient management. Future research should address existing challenges and explore innovative AI technologies to further enhance healthcare delivery. The integration of AI in e-Health represents a significant advancement with the promise of better patient outcomes and more efficient healthcare systems
Diversified Task Assignment Paradigm to mobile crowd sensing using Particle Swarm Optimization based Graph Attention Network for Smart Farm management in agriculture
No full textAdvancement of technologies like Internet of Things and Artificial Intelligence has enabled agriculture sector towards smart farming and precision agriculture. On proliferation of portable mobile devices, it is possible to achieve a promising solution which is considered as mobile crowd sensing. Mobile crowd sensing is employed for massive data collection on empowering worker or farmer to perform sensing of the environment with their smart device. Itis easy to sense and collect the soil condition , crop condition and its environment condition for smart farm management. Despite of appealing advantages, there exist various challenges in time sensitive sensing and transmission of the sensed information to the base station for effective data management and data integrity through mobile crowd sensingapproaches.In order to perform to effective sensing assignment to the smart devices in the crowd, deep learning architecture has to be used to organize the incoming and outgoing smart devices for sensing task. In this article, a graph attention network is designed for effective management of the mobile crowd sensing smart devices on basis of temporal and spatial analysis.Further suitable worker selection on basis of the task has to be selected using metaheuristic approaches. Graph attention network process the each smart devices in the crowd in graph structure. Next, attention coefficient is incorporated to gather the changes of the smart devices on basis of its sensing coverage and residual energy in specified time. Those information helps to compute the Attention score. Particular attention score is processed in the softmax function which uses Multiclass Support Vector Machine classifier to categories the smart devices. Finally categorized smart device to particle swarm optimization approaches. PSO is as metaheuristic architecture for smart device ( worker) selection for sensing, collecting , Transmitting the acquiring the sensed data of the farm to the base station or server for future processing.Simulation analysis of the model using Matlab tool proves that it is high capable in assigning appropriate task to quality smart device on maintaining the consistency in farm sensing and strong data integrity in managing the sensed data effectively. Performance analysis of the model proves high throughput and packet delivery ratio in assigning the smart device towards data sensing and acquisition for smart farm management compared to state of art approaches. 
Brain Tumor Segmentation and Classification Based on Deep Learning Using a Dense-Net Recurrent Neural Network
No full textBrain tumors are malignant cellular growths that spread uncontrollably throughout the brain. The subsequent prognosis and treatment planning depend heavily on the accuracy of tumor segmentation. For the purpose of segregating tumors, Deep learning brings into consideration of unidentified location of malignancies inside certain regions when analyzing Magnetic Resonance Imaging (MRI) data. To achieve adaptable and efficient brain tumor segmentation, it first presents a pre-processing technique that focuses on a restricted region of the image rather than the complete image. This technique is simple and efficient since it analyses only a small portion of the brain image in each slice during the second phase, thus reducing computing time and avoiding the over fitting problem that plagued previous deep learning models. For feature extraction, a Recurrent Softmax Convolutional Neural Network (RS-CNN) based on the Dense Net Recurrent Neuron Network (DNRNN) is proposed. In addition, the Fuzzy Clustering Scaled Network (FCSN) mechanism is introduced to enhance the segmentation accuracy of brain tumors over existing models. To measure the performance, a Dense Net Recurrent Neural Network (DNRNN) is utilised to construct feature maps that modify the core network and classify the ensuing feature maps. These feature maps are then used to generate tumor area charts with prediction accuracy. The suggested method was evaluated on MRI brain images with malignancies using the Unique Client identification (UCI) data set. The results showed that the test time improvement enhanced the tumor segmentation accuracy
Advanced Foot Step Power Generation System
No full text This project will radically change the way we think about energy and mobility by advancing sustainable power generation and improving the convenience of on-the-go gadget charging. Through establishing a connection between technology and everyday life, this project represents a major advancement towards a future in energy that is more user-focused and sustainable. The goal of this project is to create a shoe that uses the energy of your steps to create power. The shoe's battery stores the electricity, which you can use to charge your gadgets while you're out and about. This is a practical and sustainable method of producing energy, and it may significantly alter how we power our lives going forward. Concerns over environmental sustainability are growing, and there is a growing need for AFPG provides a viable alternative by harnessing a readily available and renewable resource: human movement. sustainable energy sources. An outline of AFPG technologies—such as piezoelectric, electromagnetic, and triboelectric systems—that transform mechanical energy from foot pressure into electrical power is provided in this study
Intuitionistic Fuzzy Semihypergraphs
No full textA Semihypergraph is a connected hypergraph in which each hyperedge must have atleast three vertices and each pair of hyperedges has atleast one vertex in common. In this article, the intuitionistic fuzzy semihypergraphs, semi µ- strong, semi ?- strong, strong IFSHGs and effective IFSHGs are introduced and some kinds of IFSHG such as simple, support simple, elementary, sectionally elementary IFSHGs are discussed
Journey of Software Quality Models: A Comprehensive Study from Inception to AI
No full textThis study embarks on a comprehensive exploration of the evolution of software quality models, tracing their journey from inception to the era of Artificial Intelligence (AI). Spanning decades of research and development, the investigation delves into the foundational principles, key milestones, and emerging trends shaping the landscape of software quality assessment. Through a meticulous review of seminal works, standards, methodologies, and recent advancements, this study offers a thorough understanding of the progression of software quality models. By bridging the historical foundations with contemporary AI-driven approaches, it illuminates the trajectory of software quality assurance, providing valuable insights for researchers, practitioners, and stakeholders in the software engineering community
Analysis of the Influence of Servant Leadership, Job Crafting and Quality of Work Life on Organizational Citizenship Behavior and High School Teacher Performance in Pematangsiantar City
No full textThis study aimed to determine how servant leadership, job crafting, and quality of work life impact organizational citizenship behavior and teacher performance based on job satisfaction. This study involved 278 high school teachers in Pematangsiantar City. This study aimed to determine how servant leadership, job crafting, and quality of work life affect organizational citizenship behavior and teacher performance through job satisfaction. This study used a quantitative approach. This study used a sample of 278 high school teachers in Pematangsiantar City. This study obtained data from the results of filling out the questionnaire and then analyzed using the Structural Equation Modeling analysis technique with the help of the AMOS version 22 program. The results showed that satisfaction could positively and significantly mediate the influence of servant leadership, job crafting, and quality of work life on organizational citizenship behavior and teacher performance. Servant leadership partially did not affect OCB and teacher performance, and job crafting partially did not affect OCB and teacher performance. The results of this study indicate that satisfaction was able to positively and significantly mediate the influence of servant leadership, job crafting, and quality of work life on organizational citizenship behavior and teacher performance. Servant leadership partially does not considerably influence OCB and teacher performance. Job crafting hurts teacher OCB and is not significant on teacher performance