International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Robotic Facial Profile Identification using Neuro-Fuzzy System
No full textFacial recognition technology can verify or identify a person's identity. People can be recognized using facial recognition software in real-time or in images and videos. To identify the face, neural networks are trained to correctly classify the coefficients determined by the eigenface technique. The network is used to recognize the face photographs that are supplied to it after being trained on images from the face database. The basic difference between neural networks and fuzzy logic is that neural networks are mainly based on learning, help to perform predictions, and are difficult to extract knowledge from, while fuzzy logic isn't based on learning, helps to perform pattern recognition, and knowledge can easily be extracted. An artificial intelligence procedure called a neural network trains computers to examine evidence in a way similar to the human brain. Deep learning is a kind of machine learning method that uses networked nodes, or neurons, organized in a coated design to copy the associations of the human brain. A neuro-fuzzy system is an ambiguous system that applies data trials to govern its parameters (fuzzy rules and fuzzy sets) using a knowledge scheme inspired by or developed from neural artificial intelligence that merges fuzzy logic components with neural networks. One class of machine learning techniques used to model complicated patterns in data is neural networks. One kind of logic that permits approximate reasoning is fuzzy logic. Neuro-fuzzy systems are effective instruments for handling intricate, ambiguous, and ever-changing jobs. They combine the advantages of neural networks and fuzzy logic to attain exceptional outcomes
Design and Implementation of a Cascaded H-Bridge Multi-Level Inverter for Renewable Energy Applications
No full textThis paper presents the design and implementation of a Cascaded H-Bridge Multi-Level Inverter (CHB-MLI) tailored for renewable energy applications. The CHB-MLI topology is selected due to its modular structure, ability to produce high-quality output waveforms with reduced Total Harmonic Distortion (THD), and increased efficiency, making it particularly well-suited for integrating renewable energy sources such as photovoltaic (PV) systems and wind turbines into the power grid.The design process involves detailed consideration of the inverter's power circuit, control strategies, and the choice of components to ensure optimal performance. Simulation studies are conducted using MATLAB/Simulink to evaluate the inverter's performance under various operating conditions, demonstrating its capability to maintain a stable output with low THD and high efficiency. Additionally, a prototype is developed to experimentally validate the simulation results, with measurements confirming the inverter's effectiveness in real-world applications.The results highlight the CHB-MLI's potential to enhance the integration of renewable energy into the grid by improving power quality and reducing losses, thereby contributing to the broader adoption of renewable energy technologies. This research provides valuable insights into the practical implementation of CHB-MLI and lays the groundwork for future advancements in inverter technology for sustainable energy systems
Exploring Current Encryption Technologies in Iot and Their Impact on Data Security
No full textThis study systematically assesses encryption technologies in the context of the Internet of Things (IoT), with a specific emphasis on their ability to protect device data and reduce privacy vulnerabilities. This research utilizes comparative analysis and case studies to examine the strengths and limits of symmetric and asymmetric encryption algorithms, as well as hash functions, in various IoT applications. The findings highlight significant challenges associated with scalability and limitations in resources. Given these observations, the paper suggests future avenues for research that aim to enhance encryption technology in order to effectively address the changing requirements of IoT security
Investigating MOOCs influence on UTAUT Developed model Applying in teaching at universities of Palestine
No full textThe increasing prevalence of MOOCs offered by leading colleges,institutions, and universities in massive open Internet courses haschanged the typical education experience. It has allowed professors,education providers, policy-makers, and scientists to examine howthese courses can best be taken and used. MOOCs give completeaccess to recorded lectures, examinations, readings, forums, andinteractive events or laboratories to imitate the intellectual andcommunity experience a student would receive in person in a liveclassroom. The main objective of the study is to investigate the mainfactors affecting the acceptance of MOOCs among students inPalestinian universities. The purpose of this research was todetermine the extent to which Palestine University students werepersuaded by the expanded UTAUT model to utilize Massive OpenOnline (MOOC) courses using questionnaires. To determine theelements determining the acceptability of MOOCs, a quantitativeapproach (survey approach) was adopted
DEFENDING WIRELESS SENSOR NETWORKS FROM WORMHOLE ATTACK: A ROBUST APPROACH USING AODV
No full textWormhole attacks pose a substantial threat to the security of Mobile Ad-hoc Networks (MANETs), potentially causing data theft, wormhole attack, and routing disruptions. This paper introduces a robust Hybrid Intrusion Detection System (HIDS) aimed at defending against wormhole attacks by strengthening the Ad-hoc On-Demand Distance Vector (AODV) routing protocol.Our approach integrates hop count analysis, dynamic trust management, behavioral analysis, cross-layer collaboration, and real-time response mechanisms to detect and mitigate such attacks effectively. The system model operates within a dynamic network topology, utilizing the Random Waypoint Mobility model and IEEE 802.11 standards for communication, with traffic generated using Constant Bit Rate (CBR) UDP traffic.
The HIDS continuously monitors hop counts, trust metrics, and traffic patterns. Hop count analysis detects anomalies by comparing actual hop counts with expected values,while the dynamic trust management framework evaluates node behavior based on packet forwarding, receiving, dropping, and malicious activity reports. Behavioral analysis monitors traffic volume and packet travel time to identify patterns indicative of wormhole attacks. Cross-layer collaboration aggregates data from multiple protocol layers, enhancing detection accuracy.
Real-time response mechanisms dynamically adjust routing paths to avoid compromised nodes and quarantine suspicious nodes, maintaining network integrity and performance. Periodic evaluations assess detection accuracy, false positive rates, and resource consumption, ensuring the system's effectiveness and efficiency. The proposed HIDS presents a scalable and robust solution for augmenting the security and reliability of MANETs in the face of wormhole attacks
A Novel Approach to Secure Communication Protocols in IoT-Enabled Power Systems
No full textThe integration of Internet of Things (IoT) devices in power systems has revolutionized grid management and efficiency. However, this increased connectivity also introduces new cybersecurity vulnerabilities. This paper proposes a novel multi-layered encryption and authentication protocol for securing communications in IoT-enabled power systems. The proposed approach combines lightweight cryptography, blockchain-based key management, and anomaly detection using machine learning. Simulations demonstrate that the protocol achieves high security with low computational and communication overhead. Results show a 99.8% attack detection rate and 40% reduction in latency compared to existing methods. The findings suggest that the proposed approach can significantly enhance the security and reliability of smart grid communications
Ransomware Detection: Techniques, Challenges, and Future Directions
No full textThe ongoing development of ransomware threats calls for sophisticated detection methods that can lessen these widespread and damaging assaults. This survey report offers a thorough analysis of current ransomware detection methods, assesses their efficacy, points out problems, and considers potential directions for future investigation. We address the use of hybrid strategies, anomaly detection system integration, and machine learning algorithms in thwarting ransomware attacks by examining more than forty cutting-edge academic publications. Our goal is to offer a thorough resource that will direct future advancements in cybersecurity defenses against ransomware
IMPACT OF COUNTRY OF ORIGIN IMAGE ON BRAND EQUITY DEVELOPMENT IN THE MOBILE PHONE SECTOR
No full textIn today's highly competitive global market, where brands havebecome vital assets for companies and institutions across variousindustries, playing a crucial role in shaping consumer behaviorand perceptions, this study aims to investigate the contribution ofconsumers' perceptions of a brand's country of origin to theformation of brand equity through the sources and dimensions ofcustomer-based brand equity (CBBE) model developed by Aaker.These dimensions include brand awareness, perceived quality,brand associations, and brand loyalty.To achieve the research objectives, a research model wasconstructed, incorporating these variables to examine theirrelationships. Data for the study were collected using a surveymethod, with a sample size of 1,076 Algerian consumers ofmobile phone brands. The collected data were statisticallyanalyzed using the SPSS software (version 26) to provide adescriptive overview of the sample.To analyze the relationships between the study variablesand test the hypotheses, the partial least squares structuralequation modeling (PLS-SEM) approach was employed. Thisanalysis was conducted using the SmartPLS4 software. Varioustests were performed to assess the reliability of the measurementinstrument and the validity of the structural model. The resultsrevealed that the country-of-origin image had a positive impacton brand equity, particularly through its dimensions of brandassociations and brand loyalty
Effectiveness of a Self-Instructional Module on Prevention of Birth Injuries among Staff Nurses Working In Selected Maternity Hospitals at Bangalore
No full textBackground and Purpose of the Study:
Introduction: The infant faces several difficulties as they shift from intrauterine to extrauterine life. The fetus must experience painful uterine contractions in order to exit the mother's womb and enter the waiting outside world through the birth canal. The majority of newborns progress without incident, however others may experience problems that lead to delivery trauma or injury. The current study's objective is to evaluate staff nurses' expertise in birth injury prevention.
Methodology: For the study, a one-group, pretest-posttest experimental design with a quantitative research methodology was chosen. The study sample was chosen by the researcher from among 60 staff nurses working in a particular OBG setup. Participants' responses to a structured knowledge questionnaire were utilized to gather data.
Result: When compared to the posttest, where 40 (66.7%) of the subjects had obtained appropriate knowledge and 20 (33.3%) had received moderate knowledge, 30 (50.0%) of the individuals had inadequate knowledge, 30 (50.0%) had moderate knowledge, and none had adequate knowledge. At the 0.05 level of significance, a significant correlation was discovered between the number of children, the family type, the total number of years of experience, and the experience in the OBG department.
Conclusion: With a mean knowledge score of 20.83 (52.1%) and a standard deviation of 13.4 in the pre-test, 30 (50.0%) of the individuals had insufficient knowledge, while 20 (50.0%) had moderate knowledge. In the post-test, however, there was a considerable increase in mean knowledge score of roughly 32.23 (80.6%) with a standard deviation of 13.2. There has been an average improvement of 11.4 (28.5%). The results of this study show that the self-instructional module was successful in improving staff nurses' understanding of birth prevention
Aspect-Based Sentiment Analysis of student Sentiment Towards E learning Using VADER Analyzer
No full textWith the forced shutdown of educational institutions due to the COVID-19 pandemic, eLearning has become a vital tool for continuing education and training for students of all ages and levels. Sentiment analysis (SA) can be a useful tool for evaluating student satisfaction and understanding their perceptions of their learning experiences. However, it seems that there has been limited research on the implementation of this technique in the Malaysian educational sector. Thus, the primary objective of this research is to discover how graduate students at a Malaysian university perceive the eLearning system by the university. Aspect-based sentiment analysis was performed using a rule-based model, VADER, and several Python modules, including the NLTK library, Seaborn, Matplotlib, SpaCy, and TextBlob, were applied on the collected data. Findings showed that the majority of sentiments towards eLearning is positive (84.6%), while 7.7% were negative, and 7.7% as neutral. Moreover, the most frequent aspects used are “system,”“information,”“assignment,”“design,” and “course”. The findings are consistent with earlier research highlighting the importance of excellent communication and feedback for student engagement and satisfaction with the eLearning system. The study also advises that technical concerns and communication obstacles should be addressed in order to improve students' eLearning experiences. The findings imply that universities may improve the eLearning experience by focusing on system clarity, communication, and feedback systems. This can inform policy decisions and improve student learning outcomes in online contexts