International Journal of Communication Networks and Information Security (IJCNIS)
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Design and Implementation of an Any Time Electricity Bill Payment System
No full textThis work presents the design and implementation of an any time electricity bill payment (ATP), which is an unmanned system designed to collect payments from consumers by various modes such as cash, cheque, or demand draft (DD). The ATP operates 24/7 and provides a touch screen and multimedia-based interface to facilitate easy and convenient payment transactions. This paper discusses the objectives, simulation, output state diagram, area report, timing report, and algorithm for the Mealy Machine implementation of the ATP
HYGIENIC ANALYSIS OF THE NUTRITIONAL CONDITION OF THE EMPLOYEES OF TEXTILE ENTERPRISE
No full textFlattened enterprise of workers real eating status hygienic analysis of the year cold ( winter-spring ) and in hot (summer-autumn) seasons done increased. Daily ration contained high risk to the group belongs to There are 10 types food of products comparative analysis take went being this? analysis to the results than the flour is cold 46,7-88,0% in the season and hot bakery products by 33,3-44,0% in the season desired analogous 46.4-71.8% and 35,2-54,5% respectively, pasta 16,4-20,0% and 9,1-24,0 %, confectionery products 17-20 grams and 2-7 grams , sugar 1,43-2,4 and 1,14-1,9 times, margarine 17-15 grams and 13-11 grams of salt and 7-6 grams and 4-3 grams excess consumption done was determined
Comparative Performance Evaluation of Capsule Networks for Banana quality detection
No full textInnumerable communities across the world rely on bananafarming for both nutritional support and economic security.Diseases such as Panama Disease, which is especially harmful tobanana crops, are one of the major obstacles that the businessmust overcome. Our method guarantees the real-time capture ofdata required for determining crop health by continuallymonitoring important parameters in banana growing. Thepurpose of this investigation is to identify the most effective deeplearning algorithms for predicting the quality and maturation ofproduce in order to extend its shelf life. In this investigation, weemploy two datasets of banana fruit: the first dataset is generatedby us, while the second dataset, Fruit 360, is obtained fromKaggle. Our dataset comprises 2100 images, each of whichcomprises 700 images, under three categories: mature,underdeveloped, and over-ripe. In order to optimize the datasetsize to 18,900, an image augmentation method is implemented.The proposed model achieved an accuracy of 97.45% with theoriginal dataset, while the CNN model obtained an accuracy of99.56% with the augmented dataset and 99.42% with theproposed model
Optimizing Human Resource Management Strategies for Cybersecurity Workforce Development in the Era of Digital Transformation
No full textThe rapid digital transformation has created an unprecedented demand for skilled cybersecurity professionals. Human Resource Management (HRM) strategies must evolve to address this workforce shortage, especially as cyber threats become more sophisticated. This paper aims to explore the optimization of HRM strategies to effectively develop the cybersecurity workforce. By conducting an extensive literature review, we identify key HRM practices that influence cybersecurity talent acquisition, retention, and skill development. The findings highlight the importance of adaptive training programs, strategic recruitment, and fostering a culture of continuous learning. Furthermore, the research examines the challenges organizations face in aligning their HRM strategies with the dynamic nature of cybersecurity demands. The implications for HR managers and organizational leaders are discussed, providing insights into how to build a resilient cybersecurity workforce. This study contributes to the ongoing conversation on workforce development in cybersecurity, emphasizing the need for innovative HRM approaches
"The Role of Political Connections on the Relationship between Corporate Governance and Management Accounting in Companies Listed on the Iranian Stock Exchange: A Machine Learning and Neural Network Approach"
No full textThe main objective of this research is to examine the impact of political connections on the relationship between corporate governance and management accounting in companies listed on the Tehran Stock Exchange. The statistical population includes listed companies during the period from 2008 to 2023. This study is descriptive-correlational and utilizes both parametric and non-parametric statistical models. Various machine learning methods, including random forests, decision trees, SVM, and neural networks, were used for data analysis.
Results indicate that variables such as the percentage of institutional shareholders (IO) and the percentage of government ownership (GO), as indicators of political connections and corporate governance, have a significant impact on the application of management accounting. Additionally, the interaction of these two variables (IO*GO) shows high importance in the model, demonstrating the influence of political connections on the relationship between corporate governance and management accounting. Moreover, profitability (PROF) and company size (SIZE) were identified as important factors affecting the implementation of management accounting.
The neural network analysis results show that political connections play a significant role in shaping the relationship between corporate governance and the application of management accounting. The composite variable IO*GO (interaction between institutional shareholders and government ownership) has shown the most significant impact on this relationship. These findings indicate the profound influence of political connections on governance structures and management decisions in Iranian companies.
This research emphasizes the complexities arising from the intersection of political and economic interests, suggesting the need for a review of macroeconomic policies and the establishment of more effective regulatory mechanisms. This study can assist policymakers and regulatory bodies in improving transparency, increasing efficiency, and enhancing Iran's position in international business indices. In other words, the present research emphasizes the importance of reforming existing structures and creating effective control mechanisms in the country's macroeconomic policies. It also highlights the necessity of creating a healthy and fair competitive environment in Iran's economy and strengthening anti-monopoly and conflict of interest laws. This study can help policymakers and regulatory bodies improve the business environment, increase transparency, and enhance Iran's position in international indices
Harnessing Ensemble Learning Approaches for Strong Mobile App Success Prediction Model
No full textWith the creation of mobile applications across diverse domains, the ability to predict the success of these apps has become crucial for developers, investors, and marketers. This paper explores the efficacy of ensemble learning techniques in constructing robust prediction models for mobile app success. Ensemble learning combines multiple base learners to enhance prediction accuracy and generalization. We investigate various ensemble methods such as bagging, boosting, and stacking, employing diverse base learners including decision trees, neural networks, and support vector machines. The study utilizes a comprehensive dataset comprising various features including app characteristics, user reviews, download statistics, and market trends. Through rigorous experimentation and evaluation, we demonstrate the effectiveness of ensemble learning in improving prediction accuracy compared to traditional single-model approaches. Furthermore, we analyze the contribution of individual base learners within ensembles, highlighting their complementary strengths in capturing different aspects of app success. Google play store contains numerous apps, with new ones being added daily. It is challenging for a developer to determine if they are on the right path to creating a successful app due to the intense competition. Factors such as ratings, number of installs, and reviews can dictate the success of an app. In this study, we used Exploratory Data Analysis to identify connections between different aspects of an application in order to forecast its success. Information from the Google play store was utilized for training three distinct models - Random Forest, Support Vector Machine, and Linear Regression - to forecast the app's success. Our findings emphasize the importance of ensemble learning in predicting mobile app performance, offering insights that can help inform decisions on app development, marketing, and funding. In the growingly competitive mobile app sector, stakeholders can utilize powerful predictions from suggested ensemble models to recognize promising app opportunities, optimize resource distribution, and manage risks efficiently. Results of the study reveals that Successful Google Play Store apps often feature terms like "photo" and "share" in descriptions, and user ratings significantly influence app success, with sentiment analysis providing deeper insights. PCA revealed critical relationships among features, and models like Random Forest, SVM, and XGBoost showed high accuracy in predicting success. The research highlighted the need for aligning reviews with ratings, version-aware rating systems, and eliminating noisy data to enhance predictive accuracy
Review on Implementing Secure IIoT Systems in Manufacturing
No full textThe Industrial Internet of Things (IIoT) is a manufacturing revolution that combines IT and OT with intelligent networked devices to enable real-time data collecting and analysis for improved product quality and worldwide production. However, stricter security standards are required to protect industrial communication. The Internet of Things links equipment to the internet, facilitating data gathering and analysis, while edge computing reduces latency, speeds up data processing, and improves security. Sensors, actuators, GPS devices, and mobile devices are used to incorporate IoT technology into many systems. IIoT and IoT research is critical for understanding and addressing field difficulties. Fog computing, a large-scale distributed system, supports fog-layered IoT devices, enabling businesses to adapt to changing data storage and processing requirements. Cloud computing visualizes and organizes massive volumes of data, making it an invaluable resource for businesses. Technology has revolutionized IoT and IIoT architecture, mandating the design, standardization, and collaboration of microservices. In IoT and IIoT, trust management and reputation evaluation are critical for accurate data collection, context awareness, and user security. The selection and implementation of an architecture are crucial. Data security, effective data storage, cloud service utilization, and real-time analytics tools are all challenges. There needs to be more process standardization in the IoT area to ensure implementation. The proliferation of protocols complicates IoT deployment even more. Investigate various cyber-physical systems to address IIoT deployment issues. More research is needed on cross-layer or heterogeneous integration architectural systems. In the IIoT environment, stable and robot architecture systems can increase resource utilization, service quality, and network optimization
Revolutionizing IT Governance with Artificial Intelligence: Advanced Automation Strategies for Compliance and Risk Management
No full textArtificial Intelligence (AI) has revolutionized various business domains, including IT Governance, by offering significant potential for automating compliance and risk management processes. This paper explores the use of AI technologies—such as Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA)—to enhance IT Governance frameworks. It examines how AI can overcome the limitations of traditional IT Governance models, which are often burdened by manual processes, slow response times, and high operational costs. By leveraging AI, organizations can establish more efficient, accurate, and proactive governance mechanisms that ensure continuous compliance with regulatory requirements and enhance risk management. The study includes a review of existing literature, case study analysis, and an evaluation of AI-based tools currently used in IT Governance. It highlights AI's capabilities in real-time monitoring, predictive analytics, and automated decision-making, contributing to more resilient governance structures. Findings indicate that AI-driven IT Governance reduces human error and operational costs, while also improving the identification and mitigation of risks. This research offers a roadmap for integrating AI into governance frameworks, transforming the management of compliance and risk in the digital age. The paper also addresses challenges such as ethical concerns, data privacy, and the need for regulatory guidance on AI in IT Governance
Hybrid Compressed Sensing and Secure Fault Tolerant Data Aggregation in Wireless Sensor Networks
Get PDFWireless Sensor Networks (WSNs) commonly comprise numerous low-cost sensor nodes that possess limited sensing, computation, and communication capabilities. Given the constrained resources of these sensor nodes, it becomes crucial to minimize data transmission to enhance both the average sensor lifetime and overall bandwidth utilization. Data aggregation serves as a process of summarizing and merging sensor data to reduce the volume of data transmitted within the network. Since wireless sensor networks are typically deployed in remote and challenging environments for transmitting sensitive information, sensor nodes are vulnerable to node compromise attacks. Consequently, security issues such as data confidentiality and integrity assume paramount importance. Therefore, when designing wireless sensor network protocols, such as data aggregation protocols, it is imperative to prioritize security and energy efficiency. In this work, we focus on these issues and develop a novel data aggregation approach by using a compressed sensing mechanism. The proposed approach is Hybrid Compressed sensing Secure Fault Tolerant Data Aggregation (HCSFTDA). Moreover, we focus on incorporating security therefore we present a novel mechanism for key distribution and data integrity verification. The performance of the HCSFTDA approach is measured in terms of packet delivery rate, average energy consumption and overhead and compared with existing approaches. The comparative analysis shows that the HCSFTDA achieved better performance. The experimental analysis shows that the proposed model reported average energy consumption as 0.0667, packet delivery as 98% and reduced communication overhead as 400 Kbps
Mobile Ad-Hoc Networks: A Classification System for Routing Protocols
Get PDFIn a Mobile Ad-hoc Network (MANET), all nodes are mobile, interconnected in varying patterns, and each node acts as a router, actively participating in route discovery and maintenance for communication with other nodes in the network, with the network topology constantly changing due to node mobility. Routing and broadcasting have been primary areas of research interest since the inception of commercial MANETs. Routing ensures the successful delivery of data packets from source to target nodes, while broadcasting is vital for addressing a range of network issues, including routing problems. This paper introduces a classification system for routing protocols that expands beyond the traditional categorisation of proactive, reactive, and hybrid methods. It identifies eight distinct groups to encompass a broader range of routing methodologies, ensuring the inclusion of significant approaches that may have been overlooked in the conventional classification. Further, the paper classifies power-aware routing protocols and highlights various broadcasting schemes, providing a comprehensive overview of both topics. Finally, the paper explores mobility models, categorising them and highlighting simulation platforms ns-2 and ns-3