International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Enhancing Braille Literacy: A Plasticine-Based Intervention Study for Blind Students
No full textEnhancing Braille literacy among blind students is crucial for their academic success and integration into society. This study investigates the effectiveness of a novel intervention using plasticine-based Braille notation to improve reading competency. Adopting a mixed-methods research design, the study involved 106 blind students from diverse educational settings. Quantitative measures included standardized Braille literacy tests assessing reading speed, accuracy, and comprehension, while qualitative insights were gathered through surveys and interviews. Results indicate a significant improvement in all literacy measures post-intervention, with reading speed increasing from 25.6 to 34.8 words per minute, accuracy improving from 78.2% to 86.5%, and comprehension scores rising from 65.4 to 73.9%. Participant satisfaction with the plasticine-based approach was high, with 92.5% reporting engagement with learning material and 94.3% expressing overall satisfaction. Correlation analysis revealed a positive relationship between age and improvement in reading competency, suggesting older participants showed greater gains. Qualitative analysis unveiled themes of enhanced tactile experience, increased motivation, and improved retention of Braille characters. Furthermore, a comparison of literacy scores by educational level indicated that participants at higher levels demonstrated greater improvements, with tertiary-level learners showing the most significant increase. These findings underscore the effectiveness of the plasticine-based intervention in enhancing Braille literacy among blind students, particularly for advanced learners. This study contributes valuable insights into innovative approaches for promoting Braille literacy and improving educational outcomes for visually impaired individuals
The Concept of Scientific Articles and Implications for the Visibility of Academicians
No full textAcademic articles or scholarly articles are a type of writing that is produced with the use of knowledge and a summary of some specific subject matters. This scientific article is the result of an empirical analysis, the discovery of certain ideas and a precise analysis of the field of study. The results of scientific articles are usually published in various types and in various platforms. Scientific publication is a process of disseminating research results, ideas, or new discoveries to the public scientifically through various types of media, such as scientific journals, conferences, monograph papers, policy papers, books, and others. The main purpose of this scientific publication is to share findings and new knowledge with experts in the same field or the community in general. The process of publishing academic materials like this requires some basic things such as reliability analysis, refereeing and even processing before a paper is published for general reading. All the processes involved in the publication of academic materials are very important to determine the quality of the paper and ensure that the knowledge presented is not deviated from the track
A Novelty-based Network Slicing Classification and Potential Approaches for Next Generation Wireless Networks
No full textWith the increasing demand for specialized and diverse services in this era of digital transformation, network slicing has been suggested as a practical solution to meet the unique requirements of various applications and users. When it comes to service quality, different services have different needs, and network slicing allows for the virtual division of a physical network into multiple logical networks that may be set up to match those needs (QoS). Examining the architecture, pros, cons, and potential impacts on future network installations, this research study examines network slicing. Network slicing frameworks could be made more efficient at resource allocation and service customization by utilizing the important concepts and technologies discussed in this research. Since the performance of the new wireless communication standard 5G is still mostly unknown, more research was required to address the issues brought about by it. 5G is a multi-system support since it can make vertical enterprises more advantageous and allow for a huge interconnection of gadgets. We need to establish the framework for the interaction of all these contemporary devices and apps. The communications infrastructure is increasingly relying on network slicing as a technique to meet its needs. The article delves into the topic of Next Generation Mobile Network (NGMN) and its many segmentation methods, specifically focusing on two technologies: "Network Function Virtualization (NFV)" and "Software Defined Networking (SDN). Along with discussing how to include ML methods into network slicing for networks that will exist after 5G, we also cover the benefits of ML approaches for mobility prediction and resource management. Our research also includes investigating ML methods
A Smart Healthcare System for IOMT using an Ant-Colony Optimized Centroid-Based Hybrid Protocol with Cluster-Centric Energy Efficient Routing in WSN-Assisted IOT
No full textTo enhance energy efficiency in Internet of Things (IoT) environments, this study explores the development of a Smart Healthcare System utilizing Wireless Sensor Networks (WSN) and the Internet of Medical Things (IoMT). The proposed approach integrates Cluster-Centric Energy Efficient Routing (CEER) with an Ant-Colony Optimized Centroid-Based Hybrid Protocol (ACOCHP) to maximize data transmission and extend the lifespan of WSN nodes. The centroid-based strategy ensures balanced energy consumption across system clusters, while the combined protocols leverage Ant-Colony Optimization (ACO) to determine optimal routing paths. By merging these methods, the study addresses key challenges in WSN-assisted IoT, particularly in medical devices where energy-efficient and reliable data transmission is critical. Simulation results indicate a substantial improvement in network performance compared to existing standards, with a 25% increase in energy efficiency and a 30% enhancement in network longevity. The proposed approach enhances data availability and accuracy, making it a robust choice for smart medical devices requiring real-time data processing and continuous monitoring
Factors Influencing Sustainable Tourism Development in Da Nang City
No full textThe objective of this study is to identify the factors influencing the sustainable tourism development in Da Nang City. The research conducted a survey of 379 local residents using questionnaires and processed the data using descriptive statistics and exploratory factor analysis. The results of the study indicate that there are 10 groups of factors affecting sustainable tourism development in Da Nang City: "social security and traffic accidents," "economic development," "waste management," "local government management," "tourism development planning and benefit distribution," "cultural values," "returning tourists and tourism activity duration," "satisfaction," "prices of goods and services," and "warning and rescue systems.
Advanced Hybrid Feature Selection Using Harvest Algorithm, Convolutional Neural Networks and Cfs
No full textPredictive modeling and data analysis are severely hampered by the enormous dimensionality and complexity of medical datasets. Enhancing model performance, interpretability, and computing efficiency all depend on careful feature selection. In order to extract the most pertinent characteristics from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, this paper offers a novel hybrid feature selection method that combines the Harvest Algorithm, Convolutional Neural Networks (CNN), and Correlation-Based Feature Selection (CFS). De-identified medical records, including diagnostic codes, vital signs, prescriptions, and other clinical observations, are all included in the MIMIC-III dataset. Our suggested approach makes use of CNN for deep feature extraction, the Harvest Algorithm for the first feature subset creation, and CFS for the final feature selection based on correlation measures. Test findings show that when compared to conventional feature selection techniques Random Forest with Information Gain Method (RF-IG) and SVM with Recursive Feature Elimination (SVM-RFE), our hybrid strategy greatly increases the accuracy and efficiency of prediction models. The chosen characteristics demonstrate the potential of our approach in clinical decision support and medical data analysis by offering significant insights into important variables influencing patient outcomes
Blockchain Technology in Wireless Networks: Securing IoT and Next-Generation Communication Systems
No full textThe union of blockchain innovation with remote networks, especially with regards to IoT and 5G, addresses a huge change by they way we approach the security of correspondence frameworks. This paper investigates the most recent headways and patterns in coordinating blockchain with cutting edge remote networks to address security, privacy, and scalability challenges. A far-reaching examination of late information, models, and contextual investigations is introduced, alongside creative structures for blockchain execution. Also, this paper incorporates information tables, graphs, and models that delineate the capability of blockchain to upset remote organization security
An AI-Driven Deep Reinforcement Learning Using Information Security Approach For Green Computation with Quality of Service Optimization in 6G Network
No full textAn escalating energy demand is a critical challenge for future 6G networks due to the increase in data traffic caused by accumulation of connected devices and more complex (data-intensive) applications. Algorithmically, green computation has been desired for 6G deployment because this model is specially focused on the energy footprint of ICT (information & communication technologies) which makes it environmentally sustainable. Hence, in this paper we explore the capability of DRL, a state-of-the-art AI technique, to fine-tune resource allocation and computation, an offloading strategy for green computing within 6G networks that guarantees satisfactory Quality of Service (QoS) for users and also enhance the information security of model. These sources highlight the drawbacks of Classical optimization techniques, which require simple mathematical models that may not be suitable for modelling the dynamic complexities and heterogeneous requirements of 6G network, whereas DRL represents an alternative solution by enabling agents to learn optimal policies when interacting with environment. Specifically, in this paper, we investigate how DRL agents can be used to learn when and where computing offloading processes should happen or not on both the MEC servers (computation) as well as network configurations dynamically under stochastic traffic patterns and wireless channel conditions for energy management purpose by considering QoS constraints such that latency time bound of 10 m/s is satisfied with a probability higher than 95%. The paper will also leverage examples from the sources DRL applications in systems such as AI enabled Base station (BS) management where intelligent BS activation/deactivation strategy can be done which shall keep energy consumption on low during low-traffic periods. It will also investigate the application of DRL to flexibly optimize energy harvesting, security of model and trading for machine-type communications (MTC), a major power-limited scenario in 6G. The paper will also discuss the issues in practicing DRL, including resource-intensive experiences of training elaborate deep reinforcement learning models and reward model efficiency optimization that balances both energy conservation and QoS satisfaction. We further discuss how to address these challenges in order for DRL and other AI models to truly realize their potential as green computation strategies, when it comes down as a final solution at operating 6G network scales
Blended Learning in Football Education Using Quadruple Quadrants and Flip Learning
No full textFootball Education Using Fusion Learning ,this unique hybrid learning approach, which combines the advantages of traditional face-to-face instruction with ICT-enhanced learning—including both offline and web-based online learning—utilizes the quadruple quadrants and flip learning. It does this without sacrificing control over time, location, path, or speed. Since commuting from one place to another takes a lot of time and transportation costs are high, emerging nations like India need this learning and teaching medium. We ought to make the most of the technological tools that the modern digital world offers in order to improve the standard of sports instruction. Utilizing this easily accessible innovation is crucial in order to satisfy the highest instructional standards and promote sports mindfulness through a combination of game-based learning and training methodologies. In addition to being more effective than the traditional training method, the mixed strategy for instruction creates a favorable environment that attracts the attention of rivals. This idea makes it easier for rivals or understudies to accurately understand the strategy. ICT-based learning creates the foundation for long-term athlete collaboration while providing mentors and competitors with the benefits of creative activity in a study hall setting. Flipped Classroom,this type of mixed discovery also challenges the notion of traditional learning by providing online educational resources outside of the study hall. In any case, with the help of this learning module, it is possible to use the online resource to employ the essential strategic knowledge about football more flexibly without sacrificing the educational benefits
Tax Transparency Moderates the Effect of Green Supply Chain and Green Accounting on Corporate Reputation and its Impact on Financial Performance Risk
No full textA company's reputation is influenced by its adherence to good or bad business ethics. Tax avoidance is a decision that reflects poor business ethics. Management's efforts to enhance tax transparency signal to investors that the company upholds strong ethical standards by being transparent about its taxes, which helps reduce tax avoidance. This study examines the impact of green supply chains and green accounting on corporate reputation and how these factors influence financial performance risk, with tax transparency serving as a moderating factor. The study uses a sample of 658 companies over a two-year period, selected through purposive sampling, and applies moderation regression analysis. The findings support three hypotheses and reject two, indicating that the green supply chain positively affects corporate reputation, and tax transparency strengthens the relationship between the green supply chain and green accounting with corporate reputation. The practical implication is that green supply chains can serve as a strategy to enhance corporate reputation, and tax transparency can reinforce corporate environmental policies, further improving corporate reputation