International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Android Malware Detection and Classification Using Machine Learning Algorithm
No full textThe cyber security approach that is being offered in this project addresses the growing dangers that rogue applications that target mobile devices are posting. With several forms of malware, such adware, spyware, and ransomware, multiplying quickly, these threats to Android users throughout the world are getting worse. This paper aims to create a thorough classification framework that uses both static and dynamic information to detect Android malware effectively in response. Our method seeks to provide a comprehensive understanding of malware traits and actions by fusing dynamic runtime behavior monitoring with static analysis of APK files. To develop a powerful classification model that can reliably classify various kinds of Android malware by utilizing machine learning algorithms such as Gradient Boosted Trees (GBT) and Ridge Classifier. APK files' metadata, permissions, and code structure are extracted using static analysis, but runtime behaviors including API calls, network traffic, and system interactions are captured using dynamic analysis. Our suggested methodology shows promising results in terms of categorization accuracy, precision, recall, and F1-score after comprehensive testing and evaluation on real-world Android malware datasets. A thorough understanding of malware behavior is made possible by the combination of static and dynamic features, which makes proactive threat detection and mitigation techniques in mobile security easier to implement. Persistently exploiting gullible people with false links, URL phishing is a cyber threat that can result in financial loss, theft of identities, and data breaches. The objective of this work is to create and deploy a strong defense against URL phishing assaults and mobile security procedures against new and emerging Android malware vulnerabilities
SMO-Optimized Shunt Active Filter to Mitigate Harmonics in DN
No full textSemiconductor devices play a vital role in the efficient and smart interconnection of
devices in industrial and home automation. This voluminous implementation of semiconductor devices inadvertently introduces harmonics into the power distribution network. These unwanted signals result in various issues like equipment malfunctioning, power quality issues, and increasing energy losses. Various mitigation techniques are employed to reduce harmonics in the network. By implementing these mitigation techniques, the adverse effects of harmonics caused by semiconductor devices can be minimized, ensuring a more reliable and efficient operation of power systems.
In this research work, a shunt active filter optimized by an SMO is used to reduce the procreation of harmonics in the 33- bus radial network. The FFT analysis is carried out using the MATLAB / Simulink model. From the results, it is clear that the
%THD of SMC controlled active filter is measured as 2.79 and SMO optimized filter is measured as 2.29. Also, the time domain parameters for the 33-bus radial network are analyzed. The steady-state error of the network is outlined as 0.94
Conceptual Framework for Sustainable Employability Skills for TVET Graduates in Malaysia
No full textMalaysia has expressed a vision to achieve a High-Income Nation since 1991 and one of the required criteria is the development of human capital. Since then, Malaysia is actively focusing on the initiative and set out in national policies such as the Eleventh Malaysia Plan (11MP), Industry 4.0 Policy and TVET 4.0 Framework. In a report by the World Bank in 2014, it shows that highly skilled workers can be produced through education especially in the field of Technical and Vocational Education & Training (TVET). Therefore, one of the important measures is to ensure that graduates in Malaysia are equipped with competent skills. This problem may be observed in the fact that the country's unemployment rate is rising from 3% to 4.7 percent in August 2020. Graduates' employability skills are one of the most important criteria in addressing manpower need. TVET institutions must guarantee that its graduates are prepared to enter the workforce and satisfy the demands of the industry. Thus, the purpose of this concept paper is to discuss and propose a conceptual framework of sustainable employability skills. This study uses the literature review method from previous studies. This study is intended to contribute to the development of the notion of sustainable employability skills in TVET education, which will aid in the development of positive human capital
Facial Recognition Using Local Neutrosophic Rough Sets and Machine Learning: A Novel Feature Reduction Technique
No full textFacial recognition is a fast-growing area used widely in identity verification, monitoring, and access control systems. Local rough set and Neutrosophic set have emerged as effective tools for addressing uncertainty in facial recognition tasks. This paper proposes a novel approach that integrates Local Neutrosophic Rough Sets (LNRS) with Machine Learning (ML) to improve the accuracy of facial recognition. The proposed methodology utilizes Variational Autoencoders (VAEs) combined with Logistic Regression to develop a robust semi-supervised learning model. LNRS are employed to manage the uncertainty and indeterminacy in facial recognition. Additionally, the approach incorporates feature reduction using Support Vector Classifiers (SVC) and Light Gradient Boosting Machine (LightGBM) classifiers to optimize prediction performance. This model predicts four types of emotions that demonstrate the confusion matrix. Experimental results indicate a significant improvement, with the proposed method achieving 92% accuracy compared to the baseline. The integration of semi-supervised learning, LNRS theory, and advanced data reduction techniques demonstrate the proposed approach's efficacy for enhancing facial recognition accuracy
INTEGRATING FLYWEIGHT DESIGN PATTERN AND MVC IN THE DEVELOPMENT OF WEB APPLICATIONS
No full textThis paper focuses on the combination of the Flyweight design pattern with the MVC structure that would boost the performance of web applications. Thus, the given study proves the effectiveness of performance enhancement as well as scalability through the combination of Flyweight for minimizing storage via shared data and MVC for compartmentalizing concerns. It is noted that the implementation focus on the UI components and data models to achieve evident improvement on the resource usage and response time. Nonetheless, some issues in the implementation of Flyweight object and applying the pattern across different contexts are discussed to delineate the future research areas
AI, IoT, and Blockchain in Fashion: Confronting Industry Applications, Challenges with Technological Solutions
No full textThe fashion industry is undergoing a transformation driven by the convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain technology. These cutting-edge technologies offer innovative solutions to a range of challenges that have long impacted the sector, from design inefficiencies to supply chain complexities and lack of transparency. AI enhances design processes, enables better demand forecasting, and delivers personalized customer experiences through advanced data analytics and machine learning algorithms. IoT facilitates smart textiles, connected garments, and real-time inventory management, allowing for improved operational efficiency and new, interactive customer engagement models. Blockchain technology provides robust solutions for transparency, traceability, and security by creating decentralized, immutable records that verify product authenticity and ethical sourcing throughout the supply chain. The integration of these technologies is not without challenges. Issues such as data privacy, cybersecurity threats, scalability, and the lack of industry-wide standardization present significant barriers to widespread adoption. Data collected through IoT devices and AI systems must be securely managed to protect consumer privacy, while Blockchain networks need to overcome scalability concerns to handle the massive amount of data generated in global supply chains effectively. The absence of common standards and protocols hinders seamless interoperability between various technological platforms. This paper explores the current applications of AI, IoT, and Blockchain in the fashion industry, highlighting their potential to enhance efficiency, sustainability, and consumer trust. It also identifies the critical challenges these technologies face and proposes practical solutions to overcome them, such as implementing advanced encryption methods, developing new consensus mechanisms for Blockchain scalability, and fostering industry collaboration to establish standardized frameworks. Ultimately, the successful integration of these technologies could lead to a more transparent, efficient, and customer-centric fashion industry, setting new standards for innovation and sustainability
Effect of Awareness Program on Stress and Anxiety among Parkinson’s Disease Patients
No full textBackground: Parkinson's disease is a common degenerative neurological illness that reduces life expectancy and causes loss of independence. Aim: To evaluate the effect of awareness program on stress and anxiety among Parkinson's disease patients. Study design: A quasi-experimental research design was usedto fulfill this study using a pre-test and post-test one-group design. Setting: The study was conducted in the neurology outpatient setting at Sohag University Hospital. Subjects: the study included a convenient sampling technique of 100 patients with Parkinson's disease. Tools of data collection:Three tools were used for data collection; Tool (I): Structured interview questionnaire: This tool was made up of the following three parts: Part 1:Personal data of the studied patients: It contained information on the age, gender, education level, and place of residence of the patients, Part 2: Structured multiple-choice questionnaire (pre and post) to assess the patients' knowledge regarding Parkinson's disease, and Part (3): Patients' practice questionnaire (pre and post), Tool II: Perceived Stress Scale-10 (PSS-10), and Tool III:The State-Trait Anxiety Inventory. Results:There was a statistically significant difference in the total score of the knowledge and practices after the awareness program application among Parkinson's disease patients. Astatisticallysignificant difference and reduction were found between stress mean scores and anxiety at (P=0.001) pre and post-awareness program application. Conclusion: Theawareness program application has a significant improvement in knowledge and practice with a reduction in mean post-test stress and anxiety among Parkinson's disease patients. Recommendations: It is stronglyadvised to apply continuous training for Parkinson's disease patients about the importance of the awareness program application regarding stress and anxiety management strategies to be able to use them as a part of routine care
Development Of A Hospital Selection Model With Service Quality As An Intervening Variable In Medan City
No full textHospitals, as one of the institutions that facilitate the scope of health, are critical. However, at this time, many hospitals that were initially service industries have changed into business industries. So there is competition in providing services for business purposes only. This study will test the hospital selection model with service quality as an intervening variable in Medan. Data collection uses a quantitative method with partial least squares (PLS). This study used a sample of 384 people. The study was conducted in several private hospitals in Medan. The study results showed that Market orientation, customer emotional response, and trust were proven to significantly influence the implementation of marketing strategies and service quality at Medan City Hospital
Analysis of Medical image fusion using Yager's heuristic fuzzy analysis for multiple modes
No full textMulti-scale image fusion is one of the most important fusion techniques in which multi-scale fusionand subtraction tools play very important roles. Quaternion wavelet transform (QWT) is one of themost widely used optimization techniques. Therefore, this paper introduces a new multi-modalimage fusion method using QWT and various features. First, we apply QWT to each image to obtainlow coefficients and high coefficients. Secondly, the weighted average fusion rule based on the phaseand amplitude of the low-frequency sub-bands and the spatial variance is proposed to fuse the lowfrequencysub-bands. Then, the highest fusion rule is selected according to the ratio and the powercoefficient is aimed to be combined with high sub-bands. Finally, the final merged image is createdby inverse QWT. This method consists of multifocal images, medical images, high-resolution imagesand remote sensing images. The results of the experiment show the effectiveness of this method
Sustainability in the Indian Himalayan Region: A Thematic Analysis of Tourism and Hospitality Research Over Four Decades
No full textThis review synthesises and critically evaluates the body of scholarly literature on tourism and hospitality in the Indian Himalayan Region (IHR) from 1977 to 2023. It aims to systematically map out major research themes, identify key areas of academic focus, and assess their alignment with sustainable development principles. Sustainable tourism is a type of tourism that considers its economic, social, and environmental impacts, aiming to minimise negative effects while also enhancing positive outcomes. By analysing a comprehensive range of studies, this review highlights key research areas and provides insights into how they have addressed or diverged from sustainability goals. The review employs a thematic analysis method, with the 'Lens' software/webtool specifically used to supplement other databases by integrating bibliometric data across various sources, thereby enhancing the comprehensiveness of the literature search. Through this thematic analysis, several major themes were identified that reflect the sustainability criteria in the IHR: 1. Environmental and Biodiversity Conservation, 2. Cultural Heritage and Community Engagement, 3. Infrastructure and Accessibility, 4. Tourism Regulations and Policy, 5. Local Livelihoods and Socioeconomic Impact, 6. Overdevelopment and Carrying Capacity, 7. Wellness,
Health Issues, Waste Management, and Sanitation, 8. Tourism and Technology, and 9. Training, Education, and Awareness. The analysis reveals several critical gaps in the existing research that hinder a complete understanding and effective implementation of sustainable tourism practices. These gaps include the need for longitudinal studies to assess long-term impacts, the development of robust methodologies for evaluating sustainability initiatives, the integration of advanced technologies such as GIS and AI, and the importance of interdisciplinary collaboration. Furthermore, the review emphasises the need for community-based research to ensure local perspectives are incorporated into tourism policies. By providing a detailed overview of the evolution of tourism and hospitality research in the IHR, this review contributes to the field by offering a thorough overview and laying a foundational framework for future studies and policy development. The recommendations presented aim to address identified gaps and advance sustainable tourism and hospitality research, ultimately supporting the development of effective policies and practices for this ecologically fragile and culturally rich region