| 1 |
Author(s):
Ayush Mishra.
Research Area:
Computer Engineering
Page No:
1-13 |
Generative AI in Automated Software Testing: A Comparative Study
Abstract
Software testing is a crucial phase in the software development lifecycle, ensuring quality, reliability, and performance. Traditional automated testing tools, such as Selenium and JUnit, have improved efficiency but often require extensive manual intervention for test case creation. Recent advancements in Generative AI, particularly models like GPT-4, Codex, and CodeT5, have introduced a new paradigm in test automation by generating intelligent, dynamic test cases with minimal human involvement.
This paper presents a comparative study of Generative AI models in automated software testing, analyzing their effectiveness in terms of test coverage, accuracy, execution time, and false positive rates. We benchmark multiple AI-driven testing approaches against traditional methods and evaluate their strengths and limitations. Experimental results indicate that Generative AI significantly enhances test efficiency, with models like GPT-4 achieving up to 92% test coverage and a 95% accuracy rate. However, challenges such as AI hallucinations, dependency on training data, and ethical considerations remain critical.
| 2 |
Author(s):
Manish Kumar.
Research Area:
Civil Engineering
Page No:
14-20 |
Optimizing Construction Safety through Effective Project Management
Abstract
The construction industry is experiencing rapid growth driven by increasing demands for infrastructure, residential, and commercial spaces. However, this dynamic sector remains highly susceptible to a variety of health and safety risks, making worker protection a critical concern. Studies have shown that unsafe behaviors are the primary cause of workplace injuries, with addressing these behaviors being key to reducing accident rates and ensuring a safer work environment. This paper aims to explore effective strategies to improve safety performance in the construction industry. Specifically, it identifies the key factors influencing the successful implementation of construction safety management systems. The review highlights major challenges faced by the sector, including insufficient safety knowledge and awareness, which contribute to preventable injuries and fatalities. Additionally, the paper examines the role of traditional safety management systems and the increasing relevance of sensor-based technologies in modern safety practices. These technologies offer promising solutions for real-time data collection, accident prediction, and hazard identification, improving overall project safety. Furthermore, the paper investigates the potential of drones as a tool for enhancing safety management on construction sites. By integrating these innovative technologies, the construction industry can reduce safety risks and ensure better health outcomes for workers.
| 3 |
Author(s):
Kavya Menon.
Research Area:
Computer Science
Page No:
21-26 |
Harnessing Natural Language Processing in Clinical Practice: A Review of Unstructured Data Utilization in Healthcare
Abstract
The integration of Natural Language Processing (NLP) into clinical practice is fundamentally transforming how unstructured data is utilized within healthcare systems. This paper offers a comprehensive review of the techniques and applications of NLP in extracting meaningful insights from unstructured clinical data such as physician notes, discharge summaries, and patient-reported outcomes. By converting complex free-text data into structured formats, NLP enhances clinical decision-making processes, improves patient outcomes, and streamlines healthcare operations. The discussion covers diverse NLP applications including information extraction, predictive analytics, and patient engagement tools. Furthermore, the paper addresses the technical, ethical, and operational challenges inherent in deploying NLP in healthcare environments. Through an evaluation of methodologies and illustrative case studies, this review highlights NLP’s critical role in advancing personalized medicine and improving the efficiency and effectiveness of healthcare delivery.
| 4 |
Author(s):
Arjun Terdal.
Research Area:
Computer Engineering
Page No:
27-33 |
AI-Driven Clinical Decision Support Systems: Current Advances and Future Trajectories
Abstract
Artificial Intelligence (AI)-driven Clinical Decision Support Systems (CDSS) are revolutionizing healthcare by enhancing clinical decision-making, improving diagnostic accuracy, and optimizing patient outcomes. This paper presents an extensive review of the current advances in AI-enabled CDSS, exploring how machine learning algorithms, natural language processing, and deep learning techniques are integrated to analyze complex clinical data. It discusses the role of AI in transforming traditional CDSS from rule-based systems to adaptive, predictive, and personalized tools. The paper also examines the challenges of AI implementation, including data quality, interpretability, and clinician trust, alongside future trajectories such as real-time analytics, integration with wearable technologies, and precision medicine. By synthesizing recent research and case studies, this review highlights the critical role of AI-driven CDSS in shaping the future of patient-centered healthcare.
| 5 |
Author(s):
UMA GOWDA.
Research Area:
Medical Science
Page No:
34-41 |
Ethical AI in Medicine: Balancing Innovation with Regulation and Compliance
Abstract
Artificial intelligence (AI) is rapidly transforming medicine by offering enhanced diagnostic accuracy, personalized treatment, and operational efficiencies. However, as AI systems become more integrated into clinical workflows, ethical challenges surrounding bias, transparency, accountability, and data privacy come to the forefront. This paper presents a comprehensive examination of the ethical landscape of AI in healthcare, emphasizing the balance between fostering technological innovation and ensuring rigorous regulation and compliance. We explore foundational medical ethics principles as they apply to AI, the global regulatory environment, and the practical challenges institutions face in implementing ethical AI solutions. Through detailed case studies and emerging ethical frameworks, the paper discusses strategies to mitigate risks, promote fairness, and safeguard patient rights. Furthermore, it highlights the need for continuous oversight and collaborative governance to adapt to evolving AI technologies and maintain public trust. Ultimately, ethical AI deployment is essential for maximizing AI’s benefits while minimizing harm in medicine.
| 6 |
Author(s):
Tapan Ghosh.
Research Area:
Computer Science
Page No:
42-47 |
Artificial Intelligence in Rare Disease Diagnostics: Shortening the Path to Early Detection
Abstract
Rare diseases, despite individually affecting a small number of patients, collectively impact millions worldwide, often presenting significant challenges in timely diagnosis and treatment. The complexity, heterogeneity, and scarcity of data related to rare diseases contribute to frequent diagnostic delays, misdiagnoses, and prolonged patient suffering. Artificial Intelligence (AI) has emerged as a powerful tool to address these challenges by analyzing vast and complex datasets, uncovering subtle clinical patterns, and supporting clinicians in decision-making processes. This paper provides an extensive review of AI applications in rare disease diagnostics, highlighting machine learning algorithms, natural language processing (NLP), and predictive modeling techniques. The integration of AI with genomic sequencing data and electronic health records (EHRs) facilitates personalized and accurate diagnostic pathways, ultimately shortening the time to detection and improving patient outcomes. We also discuss the challenges posed by limited data availability, model interpretability, privacy concerns, and ethical issues. The paper concludes by exploring future prospects for AI in rare disease diagnosis, emphasizing collaborative efforts, advanced computational methods, and patient-centric approaches.
| 7 |
Author(s):
Veer Raghavan.
Research Area:
Computer Science
Page No:
48-53 |
Simulation-Based AI Training for Surgeons: Redefining Medical Education and Skill Development
Abstract
The advent of Artificial Intelligence (AI) in surgical education has revolutionized traditional training methodologies by introducing simulation-based learning platforms that provide realistic, adaptive, and personalized experiences for surgeons. These AI-driven simulation systems enhance the acquisition of technical skills, decision-making capabilities, and procedural competence without risking patient safety. This paper presents a comprehensive review of AI applications in simulation-based surgical training, examining how machine learning, computer vision, and virtual reality are integrated to create immersive learning environments. The paper highlights the benefits of these technologies, including objective performance assessment, tailored feedback, and continuous skill refinement. Additionally, it discusses the challenges related to system development, ethical considerations, and integration into existing curricula. By analyzing case studies and emerging trends, this review underscores the transformative potential of AI-driven simulation in redefining medical education and advancing surgical skill development.
| 8 |
Author(s):
Satish Ingale, Santosh Kataria, Aadesh M. More, Rahul S. Yadav, Sajid M. Mansoori, Jyotiba V. Pawar.
Research Area:
Chemistry
Page No:
54-61 |
Biosynthesis of Cobalt Oxide Nanoparticles Using by Costus igneus Plant
Abstract
Nanotechnology is gaining popularity in the 21st century for its capacity to modify metals into nanoparticles. Nanotechnology research suggests that green chemistry can manufacture valuable nanomaterials. The main of the study was to produce cobalt nanoparticles to determine its antibacterial property and to check its photocatalytic activity for degradation of dyes. It has become crucial to biosynthesize efficient, secure, and affordable nanoparticles that we use for the treatment of various infections, including surgical site infection and wound infection, due to the rapid development of microbial resistance to numerous antibiotic drugs. Cobalt oxide, a multifunctional, anti-ferromagnetic p-type semiconductor with an optical bandgap of ~2.00 eV, exhibits remarkable catalytic, chemical, optical, magnetic, and electrical properties. In our study, cobalt oxide nanoparticles were prepared by the green synthesis method using leaves of Costus igneus plant which also known as insulin plant. Costus igneus belongs to Costaceae family. X-ray diffraction and SEM techniques were used to confirm the synthesis of cobalt nanoparticles (XRD).
| 9 |
Author(s):
Subha Gaurab Roy, Surya Shekhar Deb, Pranjal Bhattacharjee, Nandini Roy.
Research Area:
Physics
Page No:
62-71 |
An Automatic Street Light With Motion Activation
Abstract
As a focus on power-saving and intelligent city, the demand of intelligent lighting is becoming more and more. The design For the SMART street light — A cost effective, efficient and Robust design This paper only deals with the replacement of Power wasting street lights by completely automated system based on Motion Detection, which needs to be turned ON only in Dusk. Built with parts including an LDR, HC-SR04 ultrasonic ranging sensor, and a set of LED modules controlled by an Arduino microcontroller, the system adjusts lighting in the room according to current environmental states. The authors highlight the use in smart city-rich contexts, thus aiding in the sustainable urban development.
