Identification and Assessment of Potholes through Geospatial Techniques and Machine Learning Kiran Kumar B Manjunatha S Volume 2, Issue 2, Pages 1-5, February 2025 The deterioration of roads, marked by problems such as potholes, cracks, and subgrade settlement, is escalating due to fluctuations in weather patterns, including heavy rainfall and seasonal temperature changes linked to global warming, as well as the reliance on traditional road construction methods. This scenario presents considerable challenges for India. Conventional strategies for managi... Read More Article DOI: doi.org/10.47001/CJESR/2025.202001
Automated Machine Learning Methods for Identification of Anomalies in IoT Sensor Networks Swapnil Deshpande Volume 2, Issue 2, Pages 6-11, February 2025 The identification of anomalies in intelligent IoT sensor networks (SISN) is important to recognize atypical events or behaviors that can indicate security gaps or operational challenges. Conventional methods based on predefined rules are often inappropriate due to the complex and constantly developing nature of IoT ecosystems. On the other hand, automatic learning techniques (ML) have occurr... Read More Article DOI: doi.org/10.47001/CJESR/2025.202002
Challenges and Technological Advancements in the Establishment of Embedded Systems for IoMT-Related Applications Do Quynh Chi Thi Dung Anh Volume 2, Issue 2, Pages 12-16, February 2025 The Internet of Medical Things (IoMT) is significantly altering the healthcare industry by merging smart devices and sensors to enhance medical monitoring, diagnosis, and treatment. This paper delves into the challenges and advancements in creating embedded systems for IoMT applications. Key obstacles include the need for effective real-time data processing, power efficiency, and compatibilit... Read More Article DOI: doi.org/10.47001/CJESR/2025.202003
Review of Energy Generation Optimization Techniques Using AI Models Collina Emmanuel M Pantanar Elizabeth S Lescano Mark Matthew Volume 2, Issue 2, Pages 17-21, February 2025 The importance of renewable energy is underscored by the existing energy deficit, which poses significant challenges to the sustainable development of the human population. In this research article, the authors have developed a renewable energy resource optimizer using a simple random forest classifier, achieving an impressive accuracy of 95% during the testing phase. The study analyzes CSV d... Read More Article DOI: doi.org/10.47001/CJESR/2025.202004
A Business Intelligence Framework Intended to Advance Quality Assurance Methodologies in Indian Higher Education Organizations Ankita S Jaiswal Volume 2, Issue 2, Pages 22-25, February 2025 This article seeks to illustrate the application of business intelligence (BI) within higher education institutions (HEIs) in India for the purpose of overseeing quality assurance (QA) initiatives. It investigates the landscape of quality assurance in Indian higher education, addresses the challenges encountered by these institutions, and analyzes how BI and analytics enhance decision-making ... Read More Article DOI: doi.org/10.47001/CJESR/2025.202005
Enhancing HR Decision-Making Using AI, Random Forests, and Exploratory Data Analysis Mohan Reddy Sareddy R.Pushpakumar Volume 2, Issue 2, Pages 26-34, February 2025 HRM is traveling on the paths of digitalization from the past tradition-based intuition-dominated processes towards the data-driven frameworks for decision making. The present study investigates the application of predictive analytics and in particular, the Random Forest algorithm in correspondence to exploratory data analysis to strengthen the decision making in HR. It has proposed an organi... Read More Article DOI: doi.org/10.47001/CJESR/2025.202006
