Paper Title

ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSIS

Abstract

Artificial Intelligence (AI) is transforming healthcare by completely changing the diagnostic process, adopting clinical decision-support tools, and personalizing various treatment strategies. The imaging application and other diagnostic tool that includes AI technology have changed areas of radiology, dermatology, cardiology, and even personalized genomics. Improvements possible for disease diagnosis and, by building look-up tables from large datasets, AI can discover patterns, predict health-related outcomes, and thereby provide actionable, patient-specific insights for use by clinicians in judgment. For instance, creating significantly larger data sets and improving the artificial intelligence algorithm allows for the creation of accurate and dynamic patient-specific profiles. It is further a focus on medical tasks, such as drug titration, a checklist-based diagnosis, and the automation of basic care processes. But there will be challenges for AI in healthcare in addressing or bypassing the presence of data bias, incomplete, and, mostly unrepresentative models, as overdiagnosis. Bias in data used for the algorithms of AI models can lead to misdiagnoses, and overdiagnosis occurs from giving excessive weight to an abnormal result of a merely single test result. Moreover, issues of confidentiality and falling into the shadow of not HIPAA may play roles in the generation of barriers surrounding AI deployment. Even faced with such categorical challenges, there is great potential in utilizing artificial intelligence to develop as efficient a clinical decision support of machine learning, such as natural language processing and deep learning technologies. Thus, such systems would improve patient care by trawling through large amounts of patient data, predicting conditions, and suggesting possible treatments. This is a review paper, tells the latest advancements in applying artificial intelligence technologies into medical imaging, diagnostic and decision-making support. They also tells about the challenges that are attached with the new AI application and ethical considerations. Last but not least would be AI\'s new role in improving diagnostic accuracy, reducing human error, and treatment optimization.
KEYWORDS : Artificial Intelligence, Clinical Decision Support, Deep Learning, Diagnosis, Healthcare, Machine Learning, Medical Imaging, Natural Language Processing (NLP), Overdiagnosis, Pathology, Radiology.

Paper Title

ENHANCING DURABILITY OF SELF-COMPACTING CONCRETE WITH HYBRID FIBER REINFORCEMENT

Abstract

This study investigates the durability of hybrid fiber-reinforced Self-Compacting Concrete (SCC) incorporating a combination of steel (1.0%), glass (0.1%), and polypropylene fibers (0.3%). The primary objective was to assess the potential of hybrid fiber reinforcement to enhance the long-term performance and sustainability of SCC in challenging environments. To evaluate the durability characteristics, several critical tests were conducted, including water absorption, chloride penetration, and abrasion resistance, which are essential for determining the material’s ability to withstand harsh environmental conditions. The results demonstrated significant improvements in the durability of hybrid fiber-reinforced SCC compared to conventional SCC. The water absorption test showed reduced porosity, chloride penetration resistance improved, indicating better protection against corrosion, and abrasion resistance was notably enhanced. This study highlights the potential of hybrid fibers in creating sustainable and resilient concrete suitable for demanding environments.
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Paper Title

A STUDY ON POTENTIAL CHALLENGES ON BLOCKCHAIN FUTURE

Abstract

Blockchain technology has emerged as a disruptive force across industries, revolutionizing financial systems, supply chains, healthcare, and beyond. As adoption continues to grow, new trends and innovations are shaping the future of blockchain, addressing scalability, interoperability, security, and sustainability challenges. This paper explores the latest advancements in blockchain technology, including Layer 2 solutions, quantum-resistant cryptography, decentralized finance (DeFi) evolution, and integration with artificial intelligence (AI). The study highlights how these innovations are enhancing blockchain efficiency, security, and usability, paving the way for widespread adoption. Additionally, challenges such as regulatory uncertainty, energy consumption, and technological limitations are discussed to provide a balanced perspective on blockchain’s future trajectory.
KEYWORDS : blockchain, challenges, future

Paper Title

NEXT-GEN SURVEILLANCE: ADVANCED MULTI-OBJECT DETECTION FOR SMART CITIES AND PUBLIC SAFETY

Abstract

The rapid advancement of urbanization and technological innovations has increased the demand for intelligent surveillance systems. Multi-object detection plays a critical role in smart city infrastructure and public safety by enabling real-time monitoring, traffic regulation, crime prevention, and emergency response. This paper explores advanced methodologies in multi-object detection that integrate deep learning, neural networks, and enhanced computer vision techniques to optimize surveillance performance. By leveraging real-time data processing, adaptive algorithms, and AI-driven analytics, the proposed approach enhances object recognition accuracy while reducing false positives. Experimental evaluations demonstrate the effectiveness of these techniques in improving surveillance efficiency and response mechanisms in dynamic urban environments. Additionally, we discuss challenges related to computational efficiency, scalability, and privacy concerns and how the proposed approach addresses these issues.
KEYWORDS : Multi-Object Detection, Smart Cities, Public Safety, Deep Learning, AI-Driven Surveillance, Real-Time Monitoring, Privacy-Preserving AI, Federated Learning.