SkillSageAI: An AI-Powered Platform for Student Career Readiness
Review of Machine Learning Algorithms for Recommender Systems in E-Commerce Platforms
Quantum AI and Deep-Tech Innovations: A Comprehensive Review of Next-Generation
AI-Based Healthcare Chatbot Using Machine Learning and NLP
Leveraging Design Patterns in Early-Stage Software Development: A Systematic Approach
Ensuring Software Quality in Engineering Environments
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
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
SkillSageAI is an AI-powered platform designed to enhance student development through virtual interviews, resume intelligence, and personalized learning assistance. The Virtual Interview Bot adapts to different subjects, difficulty levels, and time durations, providing real-time feedback using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG), referencing ideal answers from trusted sources. The Resume Intelligence module processes uploaded resumes to extract key skills, academic qualifications, and professional experiences, analyzing them for relevance to specific job roles or academic requirements. Its multi-functional design effectively bridges the gap between academic learning and industry readiness. Overall, SkillSageAI equips students with intelligent tools that support both academic excellence and career success.
Recommender systems have become a vital component of modern e-commerce platforms for improving user experience and increasing product sales. As online shopping continues to grow rapidly, the challenge of presenting relevant items to customers has intensified, making intelligent recommendation mechanisms essential. Machine learning and data mining techniques have proven highly effective in addressing this challenge by analyzing user behavior patterns, purchase history, browsing activities, and product features. Machine learning models “learn” from large-scale datasets collected through user interactions, transactional records, and online activity logs to generate accurate personalized suggestions. Numerous machine learning techniques—such as content-based filtering, collaborative filtering, hybrid models, and deep learning—are widely used to predict user preferences and recommend suitable products. The main aim of this review is to support research focused on improving accuracy and relevancy in recommendation generation using machine-learning-based techniques. Our review suggests that these approaches outperform traditional recommendation methods and emphasize that their performance depends heavily on the quality and diversity of the data on which they are trained
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next-generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application-driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
Artificial Intelligence (AI) is transforming the healthcare sector by enhancing diagnostic accuracy, improving patient care, optimizing administrative workflows, and enabling personalized medicine. With the rapid growth of medical data and advancements in machine learning, deep learning, and natural language processing, AI-driven systems are becoming integral to modern healthcare. This paper explores the role of AI in revolutionizing healthcare, highlighting key applications such as medical imaging, predictive analytics, drug discovery, virtual health assistants, and remote patient monitoring. It also discusses the challenges, ethical concerns, and future prospects of AI adoption in healthcare. The study concludes that while AI has immense potential to improve healthcare outcomes and efficiency, careful implementation, ethical governance, and collaboration between technology and healthcare professionals are essential for its sustainable growth.
The early stages of software development are often characterized by frequent changes and rapid prototyping. While this agility is essential, it can lead to codebases that are difficult to maintain and reuse. Design patterns offer structured solutions to mitigate these issues. This paper proposes a systematic and methodical approach for the early integration of design patterns, aimed at improving code maintainability, reusability, and adaptability. Unlike previous works that mainly address design patterns in later phases, our approach focuses on systematic identification, selection, and integration from the earliest iterations. A preliminary validation study conducted on a web development project demonstrates the effectiveness of the proposed method in terms of reducing complexity and improving architectural clarity.
