Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
This paper explores the utilization of artificial intelligence (AI) in preventing cyber attacks across various domains of cybersecurity. It discusses different applications of AI in cybersecurity, including intrusion detection and prevention, threat hunting, vulnerability assessment, and fraud detection. Various AI techniques, such as anomaly detection, machine learning, natural language processing, and predictive analysis, are highlighted within each application area. The paper examines specific AI-driven cyber-attack methods such as phishing attacks, voice impersonation, and the use of chatbots. The study concludes with a comparative analysis of intrusion detection methods, highlighting the effectiveness of AI-driven approaches in identifying intruders and instances of attacks, especially those with unique characteristics. The paper underscores the importance of using AI in cybersecurity defense strategies to combat evolving cyber threats effectively.
The integration of chatbot technology into human-robot interaction has emerged as a transformative approach to foster seamless communication and elevate user experiences. This paper explores the convergence of chatbots with robotics, elucidating the benefits and challenges encountered in this integration. By enabling natural language processing capabilities, chatbots facilitate more intuitive and interactive communication between users and robots. This paper examines the advantages of this integration, such as 24/7 availability, scalability, and improved efficiency, while highlighting challenges such as contextual understanding limitations, language dependencies, and ethical concerns regarding data privacy. Moreover, it delves into the implications of leveraging chatbots in diverse applications, including customer service, healthcare, and education. By comprehensively analyzing the landscape of chatbot integration in human-robot interaction, this paper aims to provide insights into maximizing the advantages while mitigating the challenges, thereby paving the way for more effective and engaging interactions between humans and robots.
This paper addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) and the efficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing.
Artificial Intelligence (AI) is a method of computation that utilizes experience or data to make certain predictions. This paper explores the utilization of artificial intelligence (AI) in lung cancer screening to enhance patient recovery by facilitating early detection, personalized treatment, predictive analytics, and decision support. The implementation of AI algorithms in critical care settings aims to monitor patient data for early complication detection, create personalized treatment plans, predict patient outcomes, and provide decision support for healthcare providers. Lung cancer screening, particularly through Low-Dose Computed Tomography (LDCT) scans, has been proven effective in improving long-term survival rates by detecting lung cancer at early stages. AI can significantly enhance the accuracy and efficiency of lung cancer screening by analyzing Computed Tomography (CT) images, assessing patient risk factors, and providing decision support for radiologists. Various machine learning algorithms such as KNN, SVM, CNN, and FFNN have been employed and evaluated for their performance in lung cancer detection, with CNN and FFNN demonstrating higher accuracy and sensitivity.
The financial software has expanded to include cryptocurrencies, which are seeing rapid adoption and are being positively received by critics. Mining is an essential part of these systems, which use a distributed ledger to store data in a trustworthy manner. The decentralized ledger, known as the blockchain is updated with information on prior transactions when mining is performed. Users are allowed to arrive at a reliable and robust agreement for each transaction. Mining can result in the generation of new wealth in the form of monetary assets, such as currency. Because cryptocurrencies were conceived from the outset to operate as decentralized, peer-to-peer networks, there is no centralized authority that can supervise the monetary transactions that take place using these currencies. Miners are accountable for ensuring that the transactions they are processing are legitimate. For crypto currencies, mining algorithms that are both dependable and strong are a fundamental must. This paper provides a comprehensive summary of crypto coins, specifically Bitcoin, Ethereum, and Litecoin, and an analysis and critique of the previous research on crypto currency trading that has been published. This paper presents a classification system that could be applied to both wellestablished standards and newly developed concepts.
