International Journal of Communication Networks and Information Security (IJCNIS)
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Application of Wireless Communication Technology in Huizhou Academy Landscape
Get PDFThis paper aims to explore the application strategy and specific plan of wireless communication technology in Huizhou Academy and takes the application of wireless communication technology in the landscape of Huizhou Academy as the research object. This paper proposes a visitor positioni s ng ystem and wireless management system for Huizhou Academy landscape based on ZigBee technology, and analyses the power consumption of the system as well as the transmission distance and other influencing factors; secondly, this paper analyses the effect of the system's application in Huizhou Academy and proposes an improvement strategy. The main contents of this paper are as follows: (1) A wireless management system based on ZigBee is proposed, which adopts the intelligent wireless management system of human-computer interaction, and can effectively solve the problems of insufficient tour guides and loss of tourists in Huizhou Shuyuan tourist attractions. (2) The results of the analysis of system influencing factors show that the system operates reliably and has low power consumption, which achieves the expected goal of system design. (3) 80% of the operators rated the operational stability of the system at 6 points or more; nearly 70% of the operators rated the response speed of the system at 6 points or more, so it can be seen that the operational stability and response speed of the system can meet the requirements. 60% of the tourists rated the operational stability of the system at 6 points or more; nearly 92% of the operators rated the convenience of the system at 6 points or more; so it can be seen that the operational stability and convenience of the system can meet the requirements of the tourists. This shows that the operational stability and convenience of the system can meet the requirements of tourists. (4) Need to increase the publicity of the system; the tourists' operation interface needs to be more concise; need to increase the publicity of the system
Examining the Role of Human Capital Management in Enhancing the Resilience and Agility of the Military Sector on Reserve Personnel of the Indonesian Armed Forces
No full textPurpose - The purpose of this paper is to examine the role of human capital management in enhancing the resilience and agility of the military sector in the Indonesian Reserve Military Personnel.
Design/Methodology/Approach - This study adopts a qualitative research method with a literature review (library research) approach. Data collection involves searching and reconstructing information from various sources such as books, journals, and existing research. The data obtained from observation and documentation techniques are then analyzed using data condensation, data display, and conclusion drawing and verification techniques.
Findings - Firstly, recruitment and selection in the Indonesian Reserve Military involve attracting potential candidates, assessing their qualifications, skills, and physical abilities, and choosing the most suitable individuals for reserve duties in national defense and security. Secondly, training and development focus on enhancing military skills such as marksmanship, combat tactics, and physical fitness. Thirdly, performance management involves goal setting, performance planning, regular feedback, performance monitoring, training and development, recognition and rewards, and performance improvement. Fourthly, succession planning aims to identify individuals with potential to take on key leadership roles in the reserve and develop their skills and abilities accordingly. Fifthly, talent management involves talent identification, talent development, succession planning, performance management, and retention strategies. To enhance resilience, the military reserve focuses on building the mental and physical strength of its personnel.
Research Limitations/Implications - Increased investment in human capital development, Expansion of military sector training and development programs, Effective measurement methods should be considered for future research
Enhancing Pensioner Authentication Through Biometric Verification: The Integration of Fingerprint and Vein Recognition Technologies
No full textThe article examines the implementation of a biometric verification system for pensioners that features fingerprint along with vein recognition technology. The purpose of the system is to increase the precision, security, and effectiveness of pension payments by providing a secure and convenient method for retiree verification. The system employs the integration of biometric data to overcome challenges associated with traditional verification systems, thereby cutting down on fraud and lessening administrative costs. The results imply that biometric technologies markedly improve verification reliability and can be adapted for use in many geographical areas. The study brings attention to the essential need for policymakers to carry out pilot projects and fully adopt these technologies in the context of managing pension systemsThis document analyzes the application of integrative fingerprint and palm vein pattern recognition methods in pension disbursement aimed at enhancing the security and precision in making announced payments By performing a thorough assessment of some typical use cases, the research explains how the dual-biometric verification can minimize the fraudulent claims and administrative mistakes in the pension systems. The studies recommend that these technologies should be implemented saying that they seem to work from the field enhancing the processes of distributing justice and efficiency in the pension processes. The research highlights the importance of effective diffusion and planning additional investigation to fine-tune these systems for specific populations and regions
AI and Privacy: Securing Personal Data in Intelligent Networks
No full textThis article explores the dynamic intersection of artificial intelligence (AI) technologies and privacy issues, emphasizing the importance of safeguarding personal data amidst expanding AI capabilities. This article systematically examines a wide array of literature, including peer-reviewed journals, industry reports, and case studies, to provide a nuanced understanding of the privacy challenges and technological solutions currently at play. Key technologies such as machine learning and deep learning are discussed, along with their implications for privacy, highlighting specific concerns like data misuse and surveillance. The review also covers a broad spectrum of global privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) and delves into ethical frameworks proposed to guide the development and implementation of AI with respect to privacy. By presenting case studies, the article illustrates both successful implementations of privacy-preserving AI technologies and significant failures, providing critical lessons learned. Furthermore, it explores innovative privacy-enhancing technologies like homomorphic encryption and AI-driven approaches to privacy management. The review identifies ongoing challenges and emergent opportunities in the field, urging continued research and proactive policy-making to foster an environment where AI enhances rather than compromises personal privacy. This comprehensive overview aims to inform researchers, practitioners, and policymakers about the current landscape and future directions in AI and privacy
A Study on the Ethical Marketing Role in the Behavior of Banking Services Customers
No full textMarketing has always been accused of lying, fraud, invasion of privacy, environmental pollutions, promoting consumerism, and disregarding vulnerable groups in society; while it has been formed with the aim of helping countries economy and providing consumer demands. So the present paper seeks to study the effect of ethical marketing on the behavior of banking services customers. Ethical marketing was measured in three dimensions, i.e. descriptive, normative, and analytic ethical marketing, and customer behavior was measured in two satisfaction and loyalty dimensions. The survey research method was applied in this paper. To achieve the research objectives, a researcher- made questionnaire was distributed among 360 customers of Mellat Bank in Tehran. Simple random sampling method was used. The results revealed that there is a significant relation among all three ethical marketing dimensions and two customer behavior dimensions and all research hypotheses were approved
Handover of Mobile Network Services Based on Mobility Direction by Detecting the Position of User Equipment
No full textOptimizing the handover process enhances user experience and network efficiency, which is crucial for stabilizing cellular service fluctuations. Continuous mobile scanning exacerbates the situation by increasing quality of service issues and uneven bandwidth allocation across multiple cells. Additionally, service disconnection and reconnection can increase latency and reduce network performance. Therefore, this research introduced a handover model that focused on cells based on the direction of mobility of user equipment (UE) using reference signal received power (RSRP) to identify preferable cells relative to user directionality and velocity. The use of trilateration provides accurate UE positioning for better management of the handover process, reduces the possibility of ping-pong effects, and increases connection stability. This simulation shows an increase in handover performance by considering the direction of the UE. As a result, this process can shorten the handover time and frequency, and reduce the occurrence of ping-pong effects, thereby improving network efficiency and user satisfaction. This scenario approach offers an adaptive responsive mechanism for 5G networks to dynamic conditions, providing reliable connections and better quality of service
An Efficient Optimization of Multimodal Web Page Genre Classification Based on Objects Using LR-YoloV4 and (BM)2- CWRNN Deep Learning Techniques
No full textWeb data mining has emerged as a convenient and crucial platform for extracting valuable data. In order to upload and download data, users prefer to use the World Wide Web. Therefore, an alternative way is offered by the web classification for supporting effective information retrieval on the Web multimedia data. In this study introduces a video analysis that involves selecting representative frames from a video sequence. Manhattan distance, also known as taxicab distance, is one of the distance metrics used in keyframe extraction. The video quality measure involves comparing the content of the video ad to a reference, such as a non-advertisement video or an ideal ad. SSIM quantifies the structural similarity between the reference and the ad in terms of luminance, contrast, and structure. To identify and categorize objects in video or image, often bounding boxes are drawn around the detected objects. The purpose of YOLOv4 is to design an object detector that algorithm is a novel optimization metaheuristic algorithm that is inspired by the efficient performance of blue monkey swarms in nature to enhance video quality. The various machine learning classifiers were chosen for classification, named BM2-CWRNN. The extracted features from the video, the web pages are considerably categorized by the classifier as per their corresponding domain. The publicly accessible Web classification URL datasets are utilized. The results attained the proposed CWRNN are contrasted with the Brownian motion algorithms. The experimental results indicated that the classification accuracy is higher. The accuracy rates are attained via the proposed BM2-CWRNN and the web pages are effectively classified consistent with their classes
Development of Solar Energy Harvesting (SEH) for Internet of Things (IoT) to Enable Continuously Replenishing Energy Resources in Mobile Wireless Sensor Networks (WSN)
No full textThis research article introduces an intelligently based solar energy developed harvested systems designed to provide longer term & stable powers to a Wireless Sensor Network (WSN) using IoT devices. The system includes a solar panel, a li batteries & a controller circuitry, utilizing hardware for lithium battery charge management to enhance reliability and stability. It prioritizes solar energy utilization under adequate sunlight, with the lithium battery serving as a backup during unfavourable conditions. Integration of a maxim. power point tracking (MPPTs) circuit optimizes solar based energy utilization and prolongs the lithium battery's lifespan by reducing charge- discharge cycles. This approach supports the use of small power equipment, making it suitable for outdoor-based IoT applications. The system offers a reliable, efficient, and sustainable power solution for various IoT applications, ensuring uninterrupted operation in dynamic environments
Wind Speed Forecasting Based on Secondary Decomposition and LSTM
No full textImproving the reliability of wind speed forecasting is critical for optimizing wind power generation efficiency and grid stability. Accurate predictions enhance operational planning and decision- making, thereby supporting the sustainability and economic viability of wind energy. Given the inherently stochastic and noisy nature of wind, implementing a preprocessing step is essential to obtain more accurate wind speed data. Decomposition techniques are recognized as essential preprocessing, which effectively transform unstable wind speed data into multiple regular components. This study introduced a hybrid wind speed forecasting model that integrates a secondary decomposition algorithm with a Long Short-Term Memory (LSTM) algorithm. For the decomposition part, Wavelet Decomposition (WA) was first used to extract the low-frequency part from the original wind data. Then, the Symplectic Geometry Mode Decomposition (SGMD) decomposes the rest of the high-frequency components. The predictive phase of the model utilizes the LSTM algorithm. Experimental results demonstrate that the proposed secondary decomposition method significantly outperforms single decomposition models. Additionally, the superiority of the proposed hybrid model is evident when compared with other hybrid models. The proposed model demonstrates substantial improvements in prediction accuracy of a utilized dataset by 37%, 13%, and 17% reduction in terms of MAPE, RMSE, and MAE respectively for 1-3 steps of the forecast. Overall, the proposed model provides more accurate and reliable wind speed forecasts compared to other benchmark models
Automatic Detection of Corneal ulcer by Machine Learning Algorithms
No full textA corneal ulcer is a common ulcer in eyes which raised due to bacteria, over heat, pressure, viruses. Most of the time its not easily identified and leads to loss of vision. Medical practitioner needs more assistance from various test, laser treatments to sustenance corneal ulcer. Early detection may save the vision of corneal ulcer patients. This study utilized image processing with machine learning algorithms for corneal ulcer classification. This process starts from data augmentation, Feature extraction by GLCM and classification by Support Vector Machine (SVM), Random Forest(RF) and Decision Tree (DT). These algorithms experimented for corneal ulcer classification. Accuracy of all algorithms were compared with accuracy, F1 Score, Recall, Precision and found that SVM reached 95.67% of accuracy for corneal ulcer classification