International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Employee Perspective on Transition to Five Day Work Week in the Banking Sector
No full textPurpose:
It explores factors influencing employee preferences for a condensed work schedule, such as satisfaction with shortened work weeks, willingness to adapt, and socio-economic considerations.
Design/Methodology:
The research utilized purposive convenience sampling to collect relevant data from employees across various banks in the state of Uttar Pradesh. Data was gathered through visits to bank branches and personal interviews with operational employees, resulting in 300 appropriate respondents. The analysis was conducted using the partial least squares structural equation modeling (PLS-SEM) method, which is noted for its effectiveness in estimating complex path models containing latent variables and their relationships. PLS-SEM has garnered considerable attention for its reliability in assessing and refining theoretical concepts, demonstrating robust outcomes in this study.
Findings:
The findings underscore the positive ramifications of transitioning to a five-day work week, including increased job satisfaction and potential productivity gains. The study recommends banking institutions to recognize the importance of this transition in their strategic planning to enhance employee retention and organizational performance.
Research Implications:
Future research avenues include exploring long-term impacts and demographic variables on employee well-being and organizational dynamics. Overall, the shift to a five- day work week presents promising prospects for increasing job satisfaction and operational efficiency in the banking sector.
Originality/Value:
This study adds to the existing literature on the five-day work week in banks by exploring the relationship between employee willingness, the social and economic factors and implementation of five day work week. It aims to illuminate how these elements interact and influence the adoption process. The research examines employees' willingness to embrace a five-day work week, the effects of such an implementation on productivity and customer service, and the broader social and economic consequences. By addressing these variables, which have not been extensively covered in previous studies, this research provides a comprehensive and original analysis of the five-day work week in the banking sector
Identification of Key Factors Influencing Accidents on Construction Sites by Using SPSS
No full textIn response to increased public demand and development activities, the building sector has made amazing progress over the last 20 years. Organizations in the construction industry are becoming more concerned with workplace safety. Inadequate safety management in the construction industry has resulted in human and financial losses for both global society and the economy. However, the impact is larger in poorer countries. This study thoroughly examined major safety standards and construction site safety management systems. This study also looks at other construction site disasters and how to avoid them. Site inspections, literature evaluations, and data gathered from various construction safety standards, such as BIS and OSHA, have all contributed to the present body of knowledge. After discussing a variety of methods that construction firms may take to make their sites safer for workers, the article concludes. An investigation using SPSS reveals several significant factors that contribute to workplace incidents on construction sites. Common causes of workplace accidents include inadequate personal protective equipment (PPE), poor communication, inappropriate use of machines, manual handling injuries, faulty or malfunctioning equipment, and so on. Safety managers may conduct SPSS statistical analysis to identify the factors most closely linked to accidents, thereby prioritizing projects accordingly. The findings show that these factors are strongly associated with accident rates, providing a solid statistical basis for interventions aimed at improving safety. Based on this data, mitigation measures may help to reduce accident incidence and improve overall safety on construction sites. These strategies include expanded training programs, more PPE enforcement, and better site management
Real time 4G band data Acquisition using NI USRP hardware for Performance Evaluation of Spectrum Sensing Techniques in Cognitive Radio Systems using ML-based Algorithms
No full textThe ongoing demand for wireless applications has placed significant role on the available frequency spectrum, leading to substantial underutilization of resources. Traditional spectrum allocation methods often result in inefficient utilization. Cognitive radio technology has emerged as a solution to this problem, by dynamically sensing available real time 4G band RF channels and adjusting transmission boundaries to enable concurrent wireless communications. This research paper evaluates the performance of spectrum sensing techniques with a particular focus on machine learning (ML) based supervised algorithms offer superior performance in spectrum sensing compared to traditional methods. The study encompasses mainly on how real-time 4G band datas are acquired from NIUSRP 2944R Lab-View based hardware setup and how it is helping to improve the spectrum sensing results compaed with the results obtained for simulation set of data. The results offer insights into the accuracy of different ML based techniques in real-world scenarios for enhancing spectrum utilization and enabling efficient wireless communication systems
A Review on Disaster Prediction Using Machine Learning
No full text Climate changes are increasing, with it the natural disasters such as earthquakes, hurricanes forest fire, and floods occurrence rate are also on the rise. These devastating incidents result in human losses, significant impacts on infrastructure and properties and often catastrophic socioeconomic impacts. A lot of approaches have been taken to address issues related to natural disasters i.e. the development of early warning systems, risk assessment and management, disaster response and recovery, and the modelling of the natural disasters for the purposes of prediction and forecasting. The recent development in artificial intelligence (AI), deep learning (DL) and machine learning (ML) can help in better cope with the disaster prediction, detection, mapping, evacuation, and relief activities using sources of big data such as satellite imagery, social media, and geographical information systems (GIS). This paper aims to review research studies that utilize big and complex datasets to develop ML system that can predict and assist before, during and after disasters. Finally, the paper discusses the limitations and future directions of using machine learning for disaster prediction, classification, and highlights the need for further research in this area. Overall, this paper provides a comprehensive overview of the current state of the art in using machine learning for disaster prediction, classification and identifies opportunities for future research
EFFICIENT BUS ROUTE DETECTION USING YOLOV5 AND IOT
No full textBuilding on our prior research, currently under review for publication, which developed a robust system integrating YOLOv5 and PaddleOCR for efficient detection and recognition of bus routes, this chapter extends the system’s capabilities with innovative enhancements. We introduce a user-friendly interface that allows users to upload images or videos using a camera module sensor based on IoT from which the system can extract relevant details, a feature not present in the initial version. This enhancement significantly improves accessibility and usability, enabling seamless interaction with the system in real-world environments. Additionally, we integrate a spell check algorithm based on the Levenshtein distance to refine the textual outputs obtained from PaddleOCR, effectively correcting recognition errors and ensuring higher accuracy in identifying route numbers and destination names.
Our enhanced system not only retains the high detection accuracy and efficiency of the original model but also significantly improves the overall user experience through better interaction and more reliable textual information. The results demonstrate that the incorporation of the UI and spell check algorithm markedly enhances the system’s practical application, making public transportation navigation even more accessible and user-friendly. This chapter contributes to the advancement of intelligent transportation systems, highlighting the importance of user-centric design and error correction in real-world applications, and provides valuable insights for researchers and practitioners in the field
Global Research Trends and Citation Impact in Ceramic Decorative Pattern Art: A Comprehensive Bibliometric Analysis (1978–2024)
No full textCeramic decorative patterns hold cultural and aesthetic value, blending craftsmanship with symbolism. Recent research integrates traditional art history with technologies like computer-aided design and pattern recognition, emphasizing the need to understand global research trends and citation impact. A bibliometric analysis of 395 publications (1978-2024) from the Web of Science explored citation impact, publication trends, and keyword co-occurrence, mapping themes such as cross-cultural exchange, craftsmanship, and technological innovation. Research output surged after 2008, peaking in 2014-2020 with 9.28 citations per article. Studies combining traditional and modern design, especially in cross-cultural contexts, had higher citations. Recently, emerging technologies like deep learning have gained attention. While traditional elements remain central, modern technologies are driving newresearch directions. Future studies should leverage these innovations to reinterpret ceramic art, with interdisciplinary approaches being crucial
DEVELOPMENT OF TANKER MANAGEMENT SELF ASSESSMENT SYSTEM IN TANKER SHIP OPERATIONS
No full textOne example of a ship safety management system failure is a casualty that occurs in the ship system. This study is critical because it will study how the Tanker Management Self Assessment (TMSA) system works to ensure the efficiency and safety of tankers. The study shows that the tanker's safety culture, communication, safety performance, compliance, and Port State Control (PSC) Inspection Pass have improved due to the implementation of the TMSA system, which has reduced the accident rate. In addition, the system has increased safety awareness and culture among the ship's crew, increasing crew involvement in safety management. Limited resources, complexity, and limited training are some obstacles and limitations when developing the TMSA system. It is concluded that improving the development of the TMSA system for tanker operations is essential for crew safety
Review on Low Power Alu Design Using Various Techniques
No full textVLSI technology has advanced significantly, and there arenumerous effective methods for creating VLSI circuits. PTL, GDI(Gate Diffusion Input) methods, and CMOS are a few of the styles.The weaknesses of CMOS and PTL approaches can be eliminatedby using the GDI technique to create low-power digitalcombinatorial circuits. This method maintains a low level of logicdesign complexity while lowering the amount of power consumed,propagation latency, and size of digital circuits. This paper discussesthe benefits and limitations of GDI compared to CMOS design bycomparing the various approaches with respect to layout area,transistor count, latency, and power dissipation
Evaluating the Effectiveness of the Proposed System Using F1 Score, Recall, Accuracy, Precision and Loss Metrics Compared to Prior Techniques
No full textThis paper evaluates the effectiveness of a proposed system using key performance metrics such as F1 Score, Recall, Accuracy, Precision, and Loss. The system is benchmarked against prior techniques to demonstrate improvements in classification performance and model robustness. The F1 Score and Precision metrics highlight the system's ability to handle imbalanced data, while Recall indicates its sensitivity to detecting relevant instances. Accuracy is used to assess overall performance, and Loss metrics provide insights into model optimization and convergence. The comparative analysis reveals that the proposed system achieves superior results across all metrics, indicating its potential for enhanced performance in various applications
DESIGN OFMAC USING FEED FORWARD NEURAL NETWORKS
No full textThis research investigates the usage of feed forward neural networks,traditional Indian Vedic multiplier,carry skip adder,parallel in parallel out register and MAC. An Artificial Neural Network (ANN) operates in a parallel information processing structure, comprising processing units. The efficiency of the network is determined by the processing unit. Hence, there is a need to design a processing unit that is both efficient and capable of delivering superior performance. This processing unit encompasses a MAC unit (Multiplication and Accumulation) and an Activation unit. The main focus of high-speed processors is to minimize power consumption and processing times. The
implementation of the design utilizes Verilog, a hardware description language, and the testing phase is carried out using the Modelism simulator.The study performance the 4 bit mac using feed forward network with 4 bit mac using logic gates