iosr-JCE   Volume-27 ~ Issue-3 ~ May - Jun. 2025

Abstract: Traditionally, the manual workload and time involved in the loan approval process is intensive. In this paper, an automated approach is provided by the authors through the creation of a Loan Prediction Model, which leverages machine learning to predict the outcome of loan approvals using applicant information. The proposed model classifies the loan approval status of an applicant using Logistic Regression, XGBoost, and Decision Trees. In addition,.......

[1]
Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O’Reilly Media.
[2] VanderPlas, J. Python Data Science Handbook. O’Reilly Media.
[3] Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Education.
[4] Domingos, P. (2012). “A Few Useful Things to Know About Machine Learning.” Communications of the ACM.
[5] “Loan Default Prediction Based on Logistic Regression and XGBoost Modeling” by Yi Ouyang.

Abstract: This paper presents a comprehensive study on automatic music genre classification using machine learning techniques applied to the widely-used GTZAN dataset. We extracted 13 audio features including spectral and temporal characteristics to classify music into 10 distinct genres. Our Random Forest classifier achieved 78.2% accuracy, outperforming baseline models including Support Vector Machines (72.4%) and K-Nearest Neighbors (69.1%). Feature importance analysis revealed that Mel-Frequency Cepstral Coefficients (MFCCs) and spectral centroid are the most discriminative features.........

Key Word: Music Information Retrieval, Genre Classification, Audio Feature Extraction, Random Forest, GTZAN Dataset.

[1] Bergstra, J., Casagrande, N., Erhan, D., Eck, D., & Kégl, B. (2006). Aggregate Features And Adaboost For Music Classification. Machine Learning, 65(2-3), 473-484.
[2] Bertin-Mahieux, T., Ellis, D. P., Whitman, B., & Lamere, P. (2011). The Million Song Dataset. Proceedings Of The 12th International Society For Music Information Retrieval Conference, 591-596.
[3] Caruana, R., Karampatziakis, N., & Yessenalina, A. (2008). An Empirical Evaluation Of Supervised Learning In High Dimensions. Proceedings Of The 25th International Conference On Machine Learning, 96-103.
[4] Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2017). Convolutional Recurrent Neural Networks For Music Classification. Proceedings Of The IEEE International Conference On Acoustics, Speech And Signal Processing, 2392-2396.
[5] Costa, P. T., & Mccrae, R. R. (1992). Revised NEO Personality Inventory (NEO-PI-R) And NEO Five-Factor Inventory (NEO-FFI) Professional Manual. Psychological Assessment Resources.

Abstract: Background: Understanding algorithms poses a significant challenge to students due to their abstract nature and the limitations of traditional teaching tools such as static diagrams and textbooks. Augmented Reality (AR) offers a transformative solution by enabling learners to visualize and interact with data in real time. AlgoAR, an AR-based learning platform, addresses this issue by immersing students in a 3D environment that demonstrates how algorithms function step-by-step. The tool aims to improve engagement, comprehension, and retention in algorithm education through immersive interaction and gamified elements.........

Key Word: Augmented Reality AR; Algorithm Visualization; Interactive Learning; Gamified Education; Computer Science Education

[1]. Chen Chen, Y. T. (2018). The Exploration Of Augmented Reality Technology Applied In Colleges' Experimental Teaching . 9th International Conference On Information Technology In Medicine And Education.
[2]. Chhaya Nayak, D. A. (2023). Machine Learning Thyroid Model For Prediction System. International Conference On Device Intelligence, Computing And Communication Technologies.
[3]. Esron Rikardo Nainggolan, H. H. (2018). The Implementation Of Augmented Reality As Learning Media In Introducing Animals For Early Childhood Education. The 6th International Conference On Cyber And IT Service Management (CITSM 2018) . Inna Parapat Hotel – Medan.
[4]. Firoz Khan, J. A. (2020). Hybrid Reality Based Education Expansion System For Intelligent Learning. Seventh International Conference On Information Trends (ITT).
[5]. Guang-Jin Chen, R.-S. C. (2018). A Case Study Of The Development Of Using Augmented Reality In Teaching Nature And Life Technology To A Junior High School Students In Southern Taiwan . 1st International Cognitive Cities Conference (IC3).

Abstract: Background: The advances in the medical field and pharmaceutical sciences year on year have enhanced the human life span in developed and developing countries. A major factor that has contributed is the advancements in medical technologies, especially in the area of AI. However, these technologies are limited to treatment and care once the problem has been diagnosed. The Prediction of medical problems and the cost related to the treatment.........

Key Word: ICU, MIMIC, KIVA, Crowdfunding, AI-trained model, cost-prediction

[1]. Meghan Prin And Hannah Wunsch- International Comparison Of Intensive Care: Informing Outcomes And Improving Standards. National Library Of Medicine - Curropin Crit Care -2012 Dec;18(6):700-6. Doi: 10.1097/MCC.0b013e32835914d5 (Website – Https://Pubmed.Ncbi.Nlm.Nih.Gov/22954664/) Accessed On March 27, 2025
[2]. Arnaud Bruyneel, Lionel Larcin , Dimitri Martins , Julie Van Den Bulcke , Pol Leclercq And Magali Pirson – Cost Comparisons And Factors Related To Cost Per Stay In Intensive Care Units In Belgium – BMC Health Services Research – Https://Doi.Org/10.1186/S12913-023-09926-2 - Accessed On March 16, 2025
[3]. Narendra N Khanna Et Al - Economics Of Artificial Intelligence In Healthcare: Diagnosis Vs. Treatment; Healthcare (Basel) -. 2022 Dec 9;10(12):2493.Doi:10.3390/Healthcare10122493pmcid:PMC9777836 PMID: 36554017
[4]. Yin New Aung,Amrizal M Nur, Aniza Ismail, Syed M Aljunid – Determining The Cost And Length Of Stay In ICU And The Factors Influencing Them In A Teaching Hospital In Malaysia : Elsevier :Sciencedirect: Value In Health Regional Issues, Volume 21, May 2020, Pages 149-156; Web Link.

Abstract: Wind power is the leading renewable energy source, a naturally replenished, inexhaustible resource. Fossil fuels, one of the primary sources of carbon dioxide and other greenhouse gases, are being replaced by wind energy. Accurate wind power forecasting is essential for improved energy generation planning, grid scheduling, energy output generation and demand balance, trade, etc. Preprocessing the data, choosing a suitable prediction model, fine-tuning the model parameters, and assessing the forecast results are some of the procedures involved in achieving an accurate.........

Key Word: metaheuristic algorithm, optimization, wind power prediction, data preprocessing

[1] Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019). A Review Of Deep Learning For Renewable Energy Forecasting. Energy Conversion And Management, 198, 111799.
[2] "Global Electricity Review 2025" Https://Ember-Energy.Org/Latest-Insights/Global-Electricity-Review-2025/ Accessed 23 May 2025.
[3] “Yearly Electricity Data” Https://Ember-Energy.Org/Data/Yearly-Electricity-Data/ Accessed 23 May 2025.
[4] Sharma, G., Lal, M., & Singh Attwal, K. P. (2022). A Comprehensive Review On Deep Learning Algorithms For Wind Power Prediction. International Journal Of Next-Generation Computing, 13(4).
[5] Han, F., Jiang, J., Ling, Q. H., & Su, B. Y. (2019). A Survey On Metaheuristic Optimization For Random Single-Hidden Layer Feedforward Neural Network. Neurocomputing, 335, 261-273

Abstract: The healthcare chatbot system is designed to assist individuals in rural areas and government hospitals who face difficulties in obtaining appointments or medical information from doctors. By leveraging this chatbot, patients can address their healthcare concerns. With India’s growing population, rising birth rates, and declining death rates due to advancements in medical technology, there is an increasing gap between the number of doctors and the population in need of medical attention. This is especially evident in government hospitals in urban areas, where a shortage of doctors........

Key Word: Chatbot, Healthcare, Artificial intelligence, Natural language processing, Python, Symptoms

[1] Nadarzynski, O., Miles, A., Cowie, A., & Ridge, D. (2019). "Acceptability Of Artificial Intelligence (AI)-Led Chatbot Services In Healthcare." IEEE Research, 23, 101-106.
[2] Marrone, S., Piantadosi, G., Fusco, R., Petrillo, A., Sansone, M., & Sansone, C. (2014). "A Novel Model-Based Measure For Quality Evaluation Of Image Registration Techniques." International Symposium On Computer-Based Medical Systems, IEEE, 209–214.
[3] Zhang, J., Oh, Y. J., Lange, P., & Yu, Z. (2020). "Artificial Intelligence Chatbot Behavior Change Model For Designing Artificial Intelligence Chatbots To Promote Physical Activity And A Healthy Diet." Journal Of Medical Internet Research, 305-312.
[4] Boukricha, H., & Wachsmuth, I. (2011). "Modeling Empathy For A Virtual Human." International Foundation For Autonomous Agents And Multiagent Systems, 3, 1135-1136.
[5] Divya, I., Ishwarya, P., & Priyasankari, (2017). "A Self-Diagnosis Medical Chatbot Using Artificial Intelligence." Proceedings MAT Journal, 1256-1264.

Abstract: Agriculture is the backbone of many economies, providing food and raw materials essential for human survival. However, plant diseases significantly impact crop productivity, leading to financial losses and food shortages. Early detection......

Keyword: AI/ML, Python, Frameworks, et

[1] M. A. Naidu And G. Kumaravelan, "A Review Of Machine Learning Approaches In Plant Leaf Disease Detection And Classification," International Journal Of Ai Research, Vol. 12, No. 4, Pp. 120-134, 2023.
[2] B. Liu And C. Tan, "A Data Augmentation Method Based On Generative Adversarial Networks For Grape Leaf Disease Identification," Ieee Transactions On Agriculture Ai, Vol. 5, No. 2, Pp. 45-58, 2022.
[3] P. Kumaran, U. Kumar, And A. Tirkey, "Anthracnose Disease Detection In Papaya Fruit Using Machine Learning Techniques," Journal Of Agricultural Informatics, Vol. 9, No. 3, Pp. 75-89, 2021.
[4] N. Saranya And L. Pavithra, "Detection Of Banana Leaf And Fruit Diseases Using Neural Networks," International Conference On Smart Farming Systems, Pp. 134-142, 2022.
[5] V. K. Vishnoi And K. Kumar, "Detection Of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network," Neural Computing & Applications, Vol. 32, Pp. 221-233, 2021

Abstract: The growing adoption of multi-cloud strategies by organizations introduces both flexibility and complexity in managing cloud services. While leveraging multiple cloud providers improves redundancy, performance, and cost optimization, it also amplifies security challenges such as data leakage, compliance inconsistency, and inter-cloud communication vulnerabilities. This paper conducts a comparative study of security mechanisms implemented by major cloud service providers (AWS, Microsoft Azure, and Google Cloud) in multi-cloud architectures. Using a mixed-method approach, including literature analysis and case studies, the paper evaluates identity and access management (IAM), data encryption, and security orchestration capabilities across platforms......

Keyword: Cloud Computing, Multi-Cloud, Security, Data Privacy, IAM

[1] Use IEEE, APA, or the journal’s required style. Cite peer-reviewed sources, standards (NIST), and vendor whitepapers

Abstract: The research explores the transformative impact of artificial intelligence (AI) on software development, highlighting the specific advancements in code era, evaluation, and maintenance. It discusses the abilities of numerous AI-powered tools, which includes ChatGPT, GitHub Copilot, and Google Bard, which assist developers with the aid of automating repetitive tasks, generating test cases, and identifying bugs and security vulnerabilities. While these tools enhance......

Keyword: AI Generated Code , Code Quality, Leetcode, Reliability,Metrics and Errors

[1]. Aillon, S., Garcia, A., Velandia, N., Zarate, D. and Wightman, P., 2023, November. Empirical evaluation of automated code generation for mobile applications by AI tools. In 2023 IEEE Colombian Caribbean Conference (C3) (pp. 1-6). IEEE.
[2]. Barenkamp, M., Rebstadt, J. & Thomas, O. Applications of AI in classical software engineering. AI Perspect 2, 1 (2020). https://doi.org/10.1186/s42467-020-00005-4
[3]. Bucaioni, A., Ekedahl, H., Helander, V. and Nguyen, P.T., 2024. Programming with ChatGPT: How far can we go?. Machine Learning with Applications, 15, p.100526.
[4]. Clark, A., Igbokwe, D., Ross, S. and Zibran, M.F., 2024, April. A quantitative analysis of quality and consistency in ai-generated code. In 2024 7th International Conference on Software and System Engineering (ICoSSE) (pp. 37-41). IEEE.
[5]. Corso, V., Mariani, L., Micucci, D. and Riganelli, O., 2024, April. Assessing AI-Based Code Assistants in Method Generation Tasks. In Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (pp. 380-381).

Abstract: Purpose: This study develops machine learning (ML) models using Orange Data Mining to predict CO₂ emissions from oil tankers, offering a scalable and user-friendly approach to emissions monitoring......

Keyword: CO2 emissions prediction, maritime decarbonization, machine learning in shipping, orange data mining, emission monitoring systems, sustainable shipping, big data in maritime transport

[1] European Union, Climate action, 2023. Retrieved from https://climate.ec.europa.eu/eu-action/climate-strategies-targets/progress-made- cutting-emissions_en#:~:text=The%20EU%20has%20put%20in,AgreementEN%E2%80%A2%E2%80%A2%E2%80%A2.
[2] IMO, IMO’s work to cut GHG emissions from ships, 2023. Retrieved from https://www.imo.org/en/MediaCentre/HotTopics/Pages/Cutting-GHG-emissions.aspx
[3] Y. Cao, K. Yin, X. Li, and C. Zhai, Forecasting CO₂ emissions from Chinese marine fleets using multivariable trend interaction grey model, Applied Soft Computing, 2021.
[4] M.S. Reis, R. Rendall, B. Palumbo, A. Lepore, and C. Capezza, Predicting ships' CO₂ emissions using feature‐oriented methods, Applied Stochastic Models in Business and Industry, 2020, 110–123.
[5] J. Dean, Big data, data mining, and machine learning: value creation for business leaders and practitioners (Hoboken, NJ: John Wiley & Sons, 2014