International Journal of Communication Networks and Information Security (IJCNIS)
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Technology-Enabled Medical IoT System for Drug Management
Get PDFThis study introduces an innovative framework for the storage and administration of pharmaceuticals, which effectively tackles the pressing requirements of maintaining optimal temperature and humidity conditions, monitoring medicine inventory, and processing real-time data in healthcare establishments. By utilizing a comprehensive network of Internet of Things (IoT) sensors strategically positioned within pharmaceutical storage facilities, our technology effectively guarantees the preservation and security of stored drugs. The study conducted in our research demonstrates that low temperature fluctuation effectively protects medicinal substances, hence reducing potential dangers to patients. The real-time inventory management system effectively optimizes medicine control by following expiry criteria and minimizing wasted spending. Furthermore, our study emphasizes the importance of cloud response latency, as the average data transfer time is a rapid 100 milliseconds. The expeditious integration of crucial data enables prompt notifications and alerts, hence augmenting the quality and safety of pharmaceutical products
A Novel Deep Learning-Based Identification of Credit Card Frauds in Banks for Cyber Security Applications
Get PDFDue to the widespread use of constantly evolving internet technology and the increased frequency of cyber-attacks and crimes, cyber security is crucial for the banking sector. One of the biggest dangers confronting the banking sector globally is credit card (CC) fraud. It is becoming a serious issue and is growing rapidly, especially as the number of financial transactions utilizing CC keeps rising. The prevalence and growth of Internet banking have enhanced CC fraud identification. Finding fraudulent transactions of CC has become a major issue for internet buyers. In this study, an entirely novel deep learning (DL) algorithm is suggested for use in cyber security applications to identify CC thefts in the banking industry. We use a collection of significantly skewed CC fraud data sets to apply the proposed Multi-Gradient Whale Optimized Convolutional Neural Network (MW-CNN). The efficacy of the suggested methodis assessed depending on the performance evaluation criteria and comparing it with traditional techniques
Design of an Integrated Model for Security Establishment in Iot-Enabled Software Defined Networks
Get PDFRobust network designs are provided by software-defined networks (SDNs) for Internet of Things (IoT) applications, both present and future. At the same time, because of their programmability and global network perspective, SDNs are a desirable target for cyber threats. Among its primary drawbacks is the susceptibility of standard SDN architectures to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding assaults often result in a complete failure or service outage by rendering SDN controllers useless with respect to their underlying infrastructure. This study looks at popular machine learning (ML) methods for classifying and detecting DDoS flooding attacks on SDNs. Restricted Boltzmann Machine with Restricted Whales’ Optimizer (RBM-RWO) is the classifier integrated optimizer and other machine learning techniques examined. In this case study, experimental data (jitter, throughput, and reaction time measurements) from a realistic SDN architecture appropriate for typical midsized enterprise-wide networks are used to construct classification models that effectively detect and describe DDoS flooding assaults. Attackers using DDoS floods used low orbit ion cannons (LOIC), user datagram protocol (UDP), transmission control protocol (TCP), and hypertext transfer protocol (HTTP). Despite the high effectiveness of all the ML techniques examined in identifying and categorizing DDoS flooding assaults, When it came to training time is 17.5 ms, prediction speed is 7e-3 observations/s, prediction accuracy of 98%, and overall performance, RBM-RWO performed the best
Application of Audio Communication Technology in Music Production and Remote Music Cooperation
Get PDFMusic has always been regarded as an ancient and pure art. The emergence of audio communication technology changes traditional music storage, playback, and transmission functions and facilitates music production and transmission. After a brief review of audio communication technology's development process and characteristics, this paper thoroughly explains the high portability and versatility of mobile browser HTML5 and then designs and implements a complete audio transmission system on the HTML5 platform. The proposed system provides a reliable basis for audio transmission and significantly improves transmission efficiency. Finally, this paper focuses on the influence of the development of audio communication technology on contemporary music by taking audio communication technology as the starting point for music production and communication
ON k-Semi-Perfect 1-Factorizations of Cartesian Product of Graphs
No full textLet Cn, Kn, and Kmn respectively denote a cycle of length n, a complete graph on n vertices, and a complete bipartite graph on m,n vertices. An r-factor of G is a r-regular spanning subgraph of G. A 1- 1-factorization of a graph G of even order is a partition of the edges of G into disjoint 1-factors. A 1- 1-factorization of a graph G is said to be k-semi-perfect, if Fi U Fj forms a Hamilton cycle for every 1?i?k and every k+1?j?n. In this paper, it is shown that the Cartesian product of some special classes of graphs such as admits a k-Semi-Perfect 1-factorization
Advancing Stress Detection with Machine Learning: A Study on Multimodal Data Integration
No full textStress detection is a critical area in mental health, impacting both individual well-being and productivity. Traditional methods of stress assessment are often subjective and time-consuming. Recent advancements in machine learning have opened new avenues for automatic stress detection using multimodal data fusion techniques, which integrate diverse data sources such as physiological signals, behavioral cues, and contextual information. This paper explores the state-of-the-art multimodal data fusion techniques for automatic stress detection, presenting a comprehensive literature review, detailed methodology, and an analysis of their efficacy. The study concludes by discussing the challenges, potential solutions, and future directions in the field
Designing AI Model for Exploring the Dynamics of Job Satisfaction
No full textThis research focuses on the critical role of job satisfaction in shaping the well-being of individual employees and the overall success of organizations across diverse sectors. It acknowledges the dynamic nature of job satisfaction, influenced by work-related elements, working conditions, career development and interpersonal dynamics.
By unravelling these factors, the researchers seeks to provide actionable insights for both employees and employers. For employees, understanding the determinants of job satisfaction, empowers them to make informed decisions about their careers and work environments. Employers, in turn, can benefit from valuable insights to optimize workplace conditions and enhance employee satisfaction.
Ultimately, the present study was undertaken to examine the job satisfaction across different demographic groups, to identify important factors contributing job satisfaction and to develop AI model for exploring the dynamics of job satisfaction. Convenience sampling method was used to collect data through Google Forms. Statistical tools like t-test, Chi-square, Regression were used to analyze the data. A satisfied workforce is more likely to be productive, innovative, and committed, contributing to organizational success. The findings hold the potential to suggest that organizations should focus on improving specific aspects of the work environment, such as training programs and working conditions, to enhance overall job satisfaction among employees that benefit both individual well-being and broader organizational goals
The Scenarios of Educational Administration Curriculum thatFocuses on Outcomes-Based Education Lampang Inter-tech College
No full textThe purpose of this article is to present a perspective on the need for a learningoutcome-based educational administration curriculum. The respondents were 17 experts in educational administration who are stakeholders as users of theMaster of Education program from 4educational organizations in Lamp angprovince, namely Lampang Municipal Education Bureau, Lampang Primary Education Area Office Districts1, 2 and 3, and Lampang Lamphun Secondary Education Area Office. Higher Education B.E. 2565 (according to the concept of OBE: Outcome-Based Education) 4 core competencies; 23 knowledge of school administration, 28 school management skills, 22 morality and ethics of school administrators, and 6 personal characteristics of school administrators arising from learning behaviors according to the concept of Bloom's Taxonomy of Learning. Attitudes, values (Affective Domain), and physical changes (Psychomotor Domain). The research results provided the useful guideline for the preparation of the Master of Education Program in Educational Administration at Lampang Inter-tech College
An Approach to Congestion Monitoring and Network Classification using Machine Learning
No full textWith the rise in smart devices, there has been a significant increase indata generation anddiversity. This calls for innovative network solutions that can efficiently analyze and understand flow of traffic. For these solutions to manage the enormous volume of data automatically, they must be scalable and intelligent. Thanks to advances in high-performance computing, machine learning (ML) hasbecome a viable option for solving complex problems. It has proven effective in various domains such as healthcare and computer vision. Both industry and academia are paying more attention to network slicing (NS) as a way to address the many service needs of contemporary networks.This research focuses on analyzing network data to identify network segments based on traffic flow performance. To tackle the challenge of high-dimensionaldata, we use feature selection techniques to identify the most relevant features. Furthermore, we employthe K Means algorithm to gain deeper insights and differentiate betweendifferent traffic behaviors.The results demonstrate a strong correlation between occurrences within thesame cluster, highlighting the effectiveness of unsupervised learning in capturingunderlying patterns in the data.To enhance integration into real-world settings, network function virtualization isutilized to seamlessly incorporate these insights into operational networks
Facial Expression Control System for VLC Media Player
No full textComputer vision research increasingly focuses on face detection recognition in the context of sign language interpretation and human-computer interaction. The present proposed system is an illustration of a vision-based HCI, which facilitates efficient, and natural communication between people and computers through the use of cameras and image processing algorithms. This project presents a novel approach to interface design, leveraging mouth gestures for controlling the VLC Media Player, a widely used multimedia application. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), two deep learning methods, are used to teach the system to recognize and categorize a variety of predefined mouth motions with accuracy. Through the utilization of computer vision techniques, specifically facial landmark detection and gesture recognition, the system interprets a user's oral movements to execute corresponding commands within the VLC Media Player. It’s particularly aimed at assisting individuals who are severely disabled or paralyzed, offering them a new level of independence when interacting with digital media. This proposed system's main objective is to establish a system, which able to recognize facial expressions and for directing the media player. This project advances human-computer interaction by introducing a novel interface paradigm that utilizes facial gestures' expressive capabilities