International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
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A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment
Full text linkThe Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology. An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method\u27s application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization
Predicting Financial Distress Within Indian Enterprises: A Comparative Study on the Neuro-Fuzzy Models and the Traditional Models of Bankruptcy Prediction
Full text linkThe financial distresses is of major importance in the financial management system particularly in the case of this competitive environs. There are several traditional methods existing for predicting the financial distress within the country. Major factors influencing the financial distress is the stock market, credit risk and so on. Hence there is a need of models which could make dynamic predictions with the use of dynamic variables. There are several machine learning and artificial intelligence-based bankruptcy prediction models available. The neural network concepts and the computational intelligence-based methods are highly acceptable in the prediction arena. This research presents a comprehensive review of the existing prediction approaches and suggests future research directions and ideas. Some of the existing methods are support vector machines, artificial neural network, multi-layer perceptron, and the linear models such as principal component analysis. Neuro-fuzzy approaches, Deep belief neural networks, Convolution neural networks are also discussed
Evaluating the Perfomance of the Modified Dynamic Hose Model for Virtual Private Networks
Full text linkThis paper is designed to model a Modified Dynamic Hose Algorithm for data traffic management. The Virtual Private Network (VPN) under study was characterized and the data for transmission was modeled. Then Algorithm for Modified Dynamic Hose Model to handle varying traffic rates was developed and simulated using MATLAB. The results obtained from network characterization shows that variation in window size and packet size affects the throughput in a VPN as an increase in window size from 50kb to 100kb improved the throughput generated from 15 for the Conventional Hose Model to 28.3 for the Modified Dynamic Hose Model resulting in 13.3 throughputs, which translate to 47% improvement. Also variation in window size and packet size affects the throughput in a VPN as an increase in window size from 10kb to 50kb resulted to a maximum throughput of 3.01 for the Conventional Model as against 15 for the Modified Dynamic Hose Model resulting to additional 11.99 or improvement of 79.93%. The Modified Dynamic Hose Model algorithm, unlike the Conventional Hose Model, determines whether to drop a particular packet or to queue it thereby improving the bandwidth utilization, minimize latency (delays) and Virtual Private Network Throughput
Analytics in SAP S/4 HANA of SD/MM/LE: A New Technology That is Faster and More Reliable
Full text linkOperational analytics is all about answering business questions while doing business and supporting business users across the organization, from shop floor users to management and executives. Therefore, business transactions and analytics must co-exist together in a single platform to empower business users to drive insights, make decisions, and complete business processes in a single application and using a single source of facts without toggling between multiple applications. Traditionally transactional systems and analytics were maintained separately to improve throughput of the transactional system and that certainly introduced latency in decision making. However, with innovation in the SAP HANA platform, SAP S/4HANA embedded analytics enables business users, business analysts, and management to perform real-time analytics on live transactional data. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics
Implementation of Artificial Intelligence in Traffic Management in the United States
Full text linkThis paper investigates the application and deployment of artificial intelligence (AI) in enhancing traffic management within the U.S., focusing mainly on predicting future traffic demand using machine learning and deep learning models. Utilizing datasets from the Tom-Tom Traffic Index and the Python programming language for data processing, the study aims to mitigate traffic congestion through accurate traffic prediction. The study specifically examines Baltimore, Maryland (used as a proxy for major U.S. cities) to assess the efficiency of AI technologies on traffic levels and provides a comparative analysis of machine learning and deep learning algorithms (decision tree, random forest, logistic regression, and deep learning neural network). The results revealed that decision tree models surpass other algorithms with an 85% accuracy rate in congestion prediction. The study contemplates the technical aspects of traffic management systems and addresses the practical implications for city planning and the overarching goals of reducing congestion and facilitating transportation logistics. The paper offers valuable insights to transportation planners, logistics managers, and academic researchers.
Next Generation AI-Based Firewalls: a Comparative Study
Full text linkCybersecurity is a critical concern in the digital age, demanding innovative approaches to safeguard sensitive information and systems. This paper conducts a thorough examination of next-generation firewalls (NGFWs) that integrate artificial intelligence (AI) technologies, presenting a comparative analysis of their efficacy. As traditional firewalls fall short in addressing modern cyber threats, the incorporation of AI provides a promising avenue for enhanced threat detection and mitigation. The literature review explores existing research on AI-based firewalls, delving into methodologies and technologies proposed by leading experts in the field. A compilation of 20-25 references from reputable sources, including ijcseonline.org, forms the basis for this comparative study. The selected references provide insights into various AI-based firewall architectures, algorithms, and performance metrics, laying the groundwork for a comprehensive analysis. The methodology section outlines the systematic approach employed to compare different AI-based firewall methods. Leveraging machine learning and deep learning approaches, the study assesses key performance metrics such as detection accuracy, false positive rates, and computational efficiency. The goal is to provide a nuanced understanding of the strengths and weaknesses inherent in each approach, facilitating an informed evaluation. The comparative analysis section employs graphical representations to elucidate the findings, offering a visual overview of the performance disparities among selected AI-based firewall methods. Pros and cons are meticulously examined, providing stakeholders with valuable insights for decision-making in cybersecurity strategy. This research aims to contribute to the ongoing discourse on AI-based firewalls, addressing current limitations and paving the way for advancements that fortify the cybersecurity landscape
A Lightweight Way to Secure Automotive Networks Using CAN/CAN-FD
Full text linkIn-vehicle communication uses the CAN/CAN-FD bus, and communication speed and security are important. As current CAN/CAN-FD communication is used without encryption, many cases of vehicle hacking have been reported over time. With the advent of autonomous driving and connected cars, vehicles are no longer independent; they can be infiltrated from the outside and personal information such as vehicle location and driving habits can be accessed through the vehicle, posing a serious threat to personal privacy and life. Therefore, communication data needs to be encrypted to increase the security of communication. In this paper, data frames are encrypted using a shuffling algorithm in the CAN/CAN-FD communication system environment. We also compare and analyse standardised encryption methods, namely AES and ARIA, and shuffling algorithms, and suggest ways to increase the security and communication speed in the vehicle
Smart Drone with Renewable Smart System
Full text link In order to lessen its negative effects on the environment and to maintain its future operations in a clear, renewable, and sustainable manner, the aviation industry has begun developing designs that are dependent on alternative energy sources but also friendly to the environment and conventional energy. Solar energy has been suggested as a potential remedy. Aerial vehicles driven by solar energy are viewed as essential to limiting the consequences of global warming. In this study, a MATLAB/Simulink environment is used to simulate a mathematical model of a solar-powered BLDC motor of a UAV. under photovoltaic (PV) array systems, the phrase "maximum power point tracking" (MPPT) is crucial to ensuring that, under specific circumstances, the connected systems receive the greatest power output. This study simulates "fuzzy logic control," one of the preferred MPPT methods, using a solar-powered BLDC motor for an unmanned aerial vehicle (UAV) design. The PV cell, MPPT, buck-boost converter, and BLDC motor models in the cascade structure are simulated, tested, and the results are compared to the DC motor technical data. As a result, despite changes in irradiance, the results of mathematical model simulation overlap with motor technical reference values. A mathematical model of a solar-powered BLDC motor for a UAV is created and simulated using the MATLAB/Simulink environment, in contrast to prior solar-powered BLDC motor literature efforts. The fuzzy logic control MPPT technique is preferred for adjusting the maximum power output at the solar cell, and a buck-boost converter structure is connected between the MPPT and the BLDC motor mathematical model. It is recommended for usage in solar-powered UAV designs in the future
Relevance of Principals’ Technology Leadership and Management on 21st Century Teacher Preparation: A Reflection on Ghanaian Colleges of Education
Full text linkThis study aimed to identify the relevance of Principals’ Technology Leadership on Tutors’ Technology Integration in the Ghanaian Colleges of Education. This is a cross-sectional survey where simple random sampling was employed to select 13 principals and 229 tutors from the Colleges of Education in the Ashanti, Ahafo and Bono regions of Ghana. A two-part structured questionnaire guided by Principals’ Technology Leadership Assessment (PTLA), which is based on the International Society for Technology in Education (ISTE)-Standards for Administrators [30] was used. Descriptive analysis was carried out using SPSS Version 25. Although the findings showed that the levels of Technology Leadership; the five constructs of ISTE [30], and Tutors’ Technology Integration were essential but, much needs to be done to improve relationship between Principals’ Technology Leadership and Tutors’ Technology Integration in the selected colleges in Ashanti, Ahafo and Bono regions of Ghana. Principals’ preparatory programmes should emphasize leadership based on technology to enhance the integration of technology in classrooms. Further research on professional development for principals is recommende
Role of RFID Technology in Engineering Applications
Full text linkThis paper provides an extensive overview of Radio Frequency Identification technology (RFID) and its applications across different industries, rather than focusing on recent developments alone. Due to the multidisciplinary nature of RFID, proficiency in various engineering fields is necessary to fully comprehend this technology. As the use of RFID continues to expand in various industries, it is imperative to educate engineers and professionals to better understand this technology. An RFID system typically consists of tags, readers, antennae, and software, and various factors such as reading range, frequency range, and environmental considerations must be taken into account when designing such a system. Security must also be a key consideration in the development of an RFID system. To incorporate RFID technology into traditional engineering curricula, this study proposes emphasizing its connection to traditional fields such as electrical, computer, and artificial engineering