International Journal on Recent and Innovation Trends in Computing and Communication
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Enhanced Isolation and Performance in MIMO Antenna for 5G Applications
Get PDFThis paper introduces a compact multiple-input multiple-output (MIMO) antenna design tailored for 5G cellular applications. The proposed array comprises four identical, compact antenna elements designed to deliver extensive radiation coverage and support diverse functionalities. Each antenna element is based on a dual-polarized square-ring resonator, which is fed by two T-shaped feed lines and operates at a frequency of 3.7 GHz, making it suitable for the 5G frequency band. The paper highlights the significance of achieving optimal isolation between adjacent elements and presents straightforward yet effective decoupling techniques to address this. These techniques include the incorporation of sixteen zigzag slots between the radiator and feeder, a T-shaped feed line, rectangular slots within the feeders, a defected ground plane, and the strategic arrangement of radiating elements at varying orientations. The designed antenna achieves a bandwidth of 550 MHz (ranging from 3.45 to 4 GHz) and exhibits high-gain radiation patterns with minimal envelope correlation coefficient (ECC) and total active reflection coefficient (TARC). Experimental measurements validate that the antenna's performance aligns closely with the anticipated characteristics, demonstrating its reliability for practical 5G applications
Navigating the Ethical, Legal, and Moral Landscape of Artificial Intelligence: Socio-Legal Challenges in the Age of AI
Get PDFThis article offers an in-depth examination and analysis of the socio-legal, ethical, and moral issues associated with ‘Artificial Intelligence (AI)’. Advancements in AI technologies continue to evolve and seamlessly integrate into various domains of society, and profound challenges emerge at the intersections of legality, ethics, and morality. This article explores the complex web of AI issues, delving into the socio-legal implications of data privacy, bias, and the potential impacts on employment and socioeconomic structures. Ethical considerations encompass the fairness and accountability of AI algorithms, addressing questions of transparency, bias mitigation, and the responsible deployment of intelligent systems in key areas like healthcare and criminal justice. Moreover, the article examines the moral dimensions of AI, questioning the ethical boundaries of autonomous decision-making and the implications for human agency and dignity. As society grapples with the transformative power of AI, a comprehensive understanding of these socio-legal, ethical, and moral dimensions is essential to steer responsible development, deployment, and regulation of ‘Artificial intelligence’ technologies
Analyzing Surveillance Videos in Real-Time using AI-Powered Deep Learning Techniques
Get PDFModern surveillance systems are going to revolutionize the whole world as they make it possible to analyze, track and judge the activities in a specific area or context. And all this becomes possible just with the help of Real-time video processing and by advanced machine learning. This article will lead you to the developmental approaches and the previous studies of surveillance systems how they work and what are the bases of it? We follow the stream from the analog system to the advanced artificial intelligence systems and try to cover every instance in the progress of AI. Video capture, pre-processing, feature extraction, object recognition, tracking, and behavior analysis are the most important factors which we mostly cover in this article. Recently achieved advancements in artificial intelligence are contributing to the precision of surveillance systems, including deep learning models, edge computing, and hardware acceleration. In this article we discuss the surveillance system installed in a public park for security purposes based on neural networks (RNNs) for behavior analysis and convolutional neural networks (CNNs) for object recognition, to illustrate real-world situations. This system gained 95% accuracy which enhances the working that this system can precisely predict suspicious activities under the area it covers
Performance Evaluation of Nature-Inspired Metaheuristic Approaches for Single Document Text Summarization
Get PDFIn today era, day by day huge amount of data is collected on internet. The reading of text document or retrieving important information are time consuming process, so there is need for introducing effective text summarization technique. Text summarization, is the process of retrieving key information from lengthy document, its plays an essential role in information retrieval and content extraction. The paper we presented a comprehensive examination of nature-inspired metaheuristic algorithms, such as firefly, Cuckoo Search(CS) and Particle Swarm Optimization (PSO) to improve text summarization with an emphasis on single document datasets such as DUC-2001 and DUC-2002. The measurement of generated text summaries quality, generated summaries of datasets are compared with existing golden summaries and evaluated using ROUGE score. Our results show that nature-inspired metaheuristic-based approaches show potential for enhancing text summary of individual documents, metaheuristics methods improve summarizing effectiveness while offering a fresh viewpoint on how to handle the process within the confines of a single document dataset
Fuzzy Lattice Ks-Operator Normal Group
Get PDFIn this paper an algebraic structure fuzzy lattice KS- operator group is defined and derived its properties. We all know that all subgroups of an abelian group are normal. Here the same condition of commutativity is used to define this structure but it is in form of a function as fuzzy set is a function. The basic set on which this structure is defined is a KS operator fuzzy group and also a lattice hence the name. 
Cybersecurity Measures for Financial Success: An In-Depth Study for IT Leaders in Banking with .NET/AWS/Azure"
Get PDFThis in-depth research looks at tailored cybersecurity strategies for banking industry IT workers, with a focus on building financial performance guarantees using.NET, AWS, and Azure technologies. The paper provides an in-depth analysis of the cybersecurity features of the.NET, AWS, and Azure platforms, including the evolving nature of threats and the requirements set by regulators. Cryptography, threat intelligence, identity management, and network security are all emphasised as crucial layers of defence. The crucial necessity of AI and ML in strengthening security measures is highlighted by real-world case studies that reveal practical implementations. Proactive incident response planning, frequent audits, and continuous monitoring are some of the steps that the research suggests IT executives take to guarantee the banking industry's long-term financial sustainability. The audit analyser used to determine between fraud and normal payment gateways and also false positive rate ratio using networking theory in order to determine cybersecurity phenomenon perfectly
Applying PMBOK Principles, BMI, and Planning for Ensuring Residential Building Quality in High-Rise Construction
Get PDFThis study explores the integration of quality management principles from the Project Management Body of Knowledge (PMBOK) framework, utilizing the Building Measurement Index (BMI) alongside advanced planning and control strategies for enhancing the quality program of high-rise residential buildings. By leveraging the systematic approach of PMBOK, incorporating BMI metrics, and employing effective planning and control methodologies, this study aims to establish a comprehensive framework for optimizing the quality assurance processes in the construction of high-rise residential structures. Through a thorough analysis of case studies and industry practices, this research contributes to a deeper understanding of how the synergy between PMBOK, BMI, and innovative planning and control techniques can elevate the overall quality standards in the dynamic context of high-rise building projects
Bit Error Reduction in MIMO-OFDM with trellis Codes Using KALMN Filtering
Get PDFThe orthogonal frequency division multiplexing (OFDM) is used to transport data at a rapid rate. Due to the dynamic nature of the network, the bit error rate is the main problem with OFDM. The extra codes used in space-time trellis coding help to lower bit rate error on multipath fading channels. This study uses space-time trellis coding on a wireless channel to improve the bit error rates. In this work, space-time trellis codes with KALMAN filters are used to improve the bit error rate over wireless channels. The proposed modal is simulated in MATLAB software, and the results exhibit that the figure of bit error rate has decreased in network
Applying Artificial Intelligence Techniques on Cyber Security Datasets: Detecting Cyber Attacks.
Get PDFThe rapid expansion of government and corporate services to the online sphere has spurred a notable surge in internet usage among individuals. However, this increased connectivity also amplifies the risks posed by cyber threats, as hackers exploit external networking avenues and corporate networks for personal activities. Consequently, proactive measures must be taken to mitigate potential financial losses and resource drain from cyber attacks. To this end, numerous machine-learning techniques have been developed for cybercrime detection and threat mitigation. This study evaluates several prominent machine learning methods to identify and address significant cyber threats. The research scrutinizes the effectiveness of five techniques: Random Forest, Decision Tree, Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and Naive Bayes. Among these, Random Forest demonstrates superior performance with an accuracy rate of 99.69%, outperforming ensemble models such as Decision Tree, CNN, KNN, and Naive Bayes
Decision Tree Algorithm for Breast Cancer Detection
Get PDFA major form of cancer affecting women around the world is breast cancer. This underscores the importance of early detection for optimal treatment outcomes. This paper addresses the challenge of correctly classifying tumors as malignant or benign in light of the fact that breast cancer is a significant component of cancer cases around the world. As a breast cancer detection algorithm, there are several advantages to using this decision tree algorithm. Decision trees provide insight into the importance of features, which in turn allows for the identification of key factors that contribute to the classification of breast cancer. In addition to that, decision trees are able to deal with both numerical and categorical features, so they are suitable for a variety of breast cancer data sets. It is also important to note that decision trees are less sensitive than other algorithms when it comes to outliers and missing data. To begin with, decision trees provide insight into the importance of features, which allows for the identification of key factors that contribute to the classification of breast cancers. A decision tree can also be used to analyze both numerical and categorical features, making it more versatile for the analysis of breast cancer data in general. The decision tree algorithm, on the other hand, has a lower sensitivity to outliers and missing data than some other algorithms. As a result of utilizing performance metrics to assess the effectiveness of algorithms, it was found that the Decision Tree Algorithm was more effective at detecting breast cancer than other algorithms