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

Abstract: The study develops an election prediction model through Random Forest that evaluates candidate election success rates using variables including financial support and party membership together with educational background and demographic information and criminal history. The web application based on Streamlit includes interactive features that let users submit candidate data while visualizing predictions together with feature importance and calculation of win rates.......

Keywords: Election Prediction, Machine Learning, Random Forest, Streamlit, Feature Importance, Win Rate, Data Analysis, Candidate Prediction

[1] Arinze, B. (2023). Teaching Experiential Data Analytics Using An Election Simulation. Journal Of Statistics And Data Science Education, 31(3), 273-285.
[2] Alvi, Q., Ali, S. F., Ahmed, S. B., Khan, N. A., Javed, M., & Nobanee, H. (2023). On The Frontiers Of Twitter Data And Sentiment Analysis In Election Prediction: A Review. Peerj Computer Science, 9, E1517.
[3] Khan, A., Boudjellal, N., Ahmad, A., & Khan, M. (2023). From Social Media To Ballot Box: Leveraging Location-Aware Sentiment Analysis For Election Predictions. Computers, Materials & Continua, 77(3).
[4] Chen, E., Deb, A., & Ferrara, E. (2022). # Election2020: The First Public Twitter Dataset On The 2020 US Presidential Election. Journal Of Computational Social Science, 1-18.
[5] Bruno, M., Lambiotte, R., & Saracco, F. (2022). Brexit And Bots: Characterizing The Behaviour Of Automated Accounts On Twitter During The UK Election. EPJ Data Science, 11(1), 17.

Abstract: Recommendation systems have become essential components of modern e-commerce platforms, enabling personalized user experiences by suggesting suitable products based on individual choices and behaviors. This paper outlines the creation of an e-commerce website featuring a hybrid recommendation system that merges collaborative and content-based filtering methods. Developed in Jupyter Notebook using Python, the system employs Pandas for data organization, NumPy for calculations, Scikit-learn for algorithmic implementation, and Seaborn for visual insights, drawing from Walmart's online dataset of user transactions and product information to suggest personalized items........

Keywords: E-commerce, Recommendation System, Hybrid Filtering, Collaborative Filtering, Content-Based Filtering, Walmart Dataset, Machine Learning, Web Development

[1] J. Bobadilla, F. Ortega, A. Hernando, And A. Gutiérrez, "Recommender Systems Survey," Knowledge-Based Systems, Vol. 46, Pp. 109–132, Jul. 2013. Doi: 10.1016/J.Knosys.2013.03.012
[2] R. Burke, "Hybrid Recommender Systems: Survey And Experiments," User Modeling And User-Adapted Interaction, Vol. 12, No. 4, Pp. 331–370, Nov. 2002. Doi: 10.1023/A:1021240730564
[3] M. Khoali, Y. Laaziz, A. Tali, And H. Salaudeen, "A Survey Of One Class E-Commerce Recommendation System Techniques," Electronics, Vol. 11, No. 6, P. 878, Mar. 2022. Doi: 10.3390/Electronics11060878.

Abstract: Optimizing software performance is necessary for increasing efficiency, controlling resource usage, and delivering smoother user experiences. The traditional methods for software performance optimization often struggle when dealing with complexity, scalability and adaptability. Artificial Intelligence (AI) has emerged as a powerful tool for optimizing software performance, particularly in areas such as predictive tuning, automated code refactoring........

Keywords: AI-based software optimization, software performance tuning, AI in performance optimization, neural networks, AI-driven resource management

[1] Kumar, M., & Gore, M. (2024). Using AI For Dynamic Resource Allocation And Performance Optimization In Software Systems. International Journal Of Innovative Research In Engineering And Management, 12(6). Retrieved From Https://Www.Ijircst.Org/DOC/3-Using-AI-For-Dynamic-Resource-Allocation-And-Performance-Optimization-In-Software-Systems.Pdf.
[2] Garcia, A. (2023). Artificial Intelligence System Optimisation: Improving Performance And Efficiency. Hilaris Publisher. Retrieved From Https://Www.Hilarispublisher.Com/Open-Access/Artificial-Intelligence-System-Optimisation-Improving-Performance-And-Efficiency-100322.Html.
[3] Tran, T. N., Tran, H. T., & Nguyen, Q. N. (2024). Leveraging AI For Enhanced Software Effort Estimation: A Comprehensive Study And Framework Proposal. Arxiv. Https://Arxiv.Org/Pdf/2402.05484.Pdf
[4] Velaga (2020). AI-Assisted Code Generation And Optimization. Retrieved From Https://Repo.Ijiert.Org/Index.Php/Ijiert/Article/Download/1334/3299/6918
[5] Alenezi, Mamdouh, Akour, Mohammed. (2025). AI-Driven Innovations In Software Engineering: A Review Of Current Trends And Challenges. MDPI Electronics, 15(3), 1344. Https://Www.Mdpi.Com/2076-3417/15/3/1344.

Abstract: Video-based behavior recognition is essential in fields such as public safety, intelligent surveillance, and human-computer interaction. Traditional 3D Convolutional Neural Network (3D CNN) effectively capture local spatiotemporal features but struggle with modeling long-range dependencies. Conversely, Transformers excel at learning global contextual information but face challenges with high computational costs. To address these limitations, we propose a hybrid framework combining 3D CNN and Transformer architectures. The 3D CNN module extracts low........

Keywords: Video-based Behavior Recognition; 3D CNN; Transformer; Hybrid Model; Spatiotemporal Features

[1] Accattoli, S., Et Al., Violence Detection In Videos By Combining 3D Convolutional Neural Networks And Support Vector Machines. Applied Artificial Intelligence, 2020. 34(4): P. 329-344.
[2] Appavu, N. Violence Detection Based On Multisource Deep CNN With Handcraft Features. In 2023 IEEE International Conference On Advanced Systems And Emergent Technologies (IC_ASET). 2023. IEEE.
[3] Dong, J., Et Al., Action Recognition Based On The Fusion Of Graph Convolutional Networks With High Order Features. Applied Sciences, 2020. 10(4): P. 1482.
[4] Cheng, M., K. Cai, And M. Li. RWF-2000: An Open Large Scale Video Database For Violence Detection. In 2020 25th International Conference On Pattern Recognition (ICPR). 2021. IEEE.
[5] García-Gómez, J., Et Al. Violence Detection In Real Environments For Smart Cities. In Ubiquitous Computing And Ambient Intelligence: 10th International Conference, Ucami 2016, San Bartolomé De Tirajana, Gran Canaria, Spain, November 29–December 2, 2016, Part II 10. 2016. Springer.

Abstract: Understanding mechanisms of diseases, improving diagnosis, and identifying therapeutic targets have been indicated to rely on microbe-disease associations. However, determining these associations by traditional experiments methods is costly, laborious and tedious. Therefore, it is needed to have computational approaches for unveiling microbe-disease interactions. In this paper, we proposed new approach which combines a weighted K nearest known.....

Keywords: Deep learning; Graph attention networks; Multiple kernels; Predicting microbe–disease associations; WKNKN

[1] R. Ley, "The Human Microbiome: There Is Much Left To Do," Nature, Vol. 606, No. 7914. P. 435, 2022. Doi: 10.1038/D41586-022-01610-5.
[2] Z. Wen, C. Yan, G. Duan, S. Li, F. X. Wu, And J. Wang, "A Survey On Predicting Microbe-Disease Associations: Biological Data And Computational Methods," Brief. Bioinform., Vol. 22, No. 3, Pp. 1–20, (2021), Doi: 10.1093/Bib/Bbaa157.
[3] E. Holmes, A. Wijeyesekera, S. D. Taylor-Robinson, And J. K. Nicholson, "The Promise Of Metabolic Phenotyping In Gastroenterology And Hepatology." Pp. 458–471, 2015.
[4] L. Peng Et Al., "Analysis Of Ct Scan Images For Covid-19 Pneumonia Based On A Deep Ensemble Framework With Densenet.

Abstract: This paper focuses on the design of a distributed formation control law for multi-quadrotor systems (MQS), utilizing rigidity graph theory and gradient-based formation control laws, and applies these methods to the task of tracking and encircling a moving target. The MQS is represented as an undirected graph that is infinitesimally and minimally rigid. The control law comprises a formation control protocol and a target tracking and encirclement mechanism, ensuring that the formation remains stable during mission execution. To enhance efficiency and.....

Keywords: Distributed formation control, multi-quadrotor systems, Rigidity graph theory, Gradient-based formation control laws, Moving target.

[1]. Ahn, H. S. Formation Control. Springer International Publishing, 2020.
[2]. Oh, Kwang-Kyo, Myoung-Chul Park, And Hyo-Sung Ahn. "A Survey Of Multi-Agent Formation Control." Automatica 53, Pp. 424-440, 2015.
[3]. F. Michaud, M. Nicolescu, "Behavior-Based Systems" Springer Handbook Of Robotics, Pp.307–328, 2016.
[4]. G. Lee And D. Chwa, "Decentralized Behavior-Based Formation Control Of Multiple Robots Considering Obstacle Avoidance," Intelligent Service Robotics, Vol. 11, Pp. 127–138, 2018.
[5]. H. T. Do, H. T. Hua, M. T. Nguyen, C. V. Nguyen, H. T. Nguyen, H. T. Nguyen, And N. T. Nguyen, "Formation Control Algorithms For Multiple-Uavs: A Comprehensive Survey," EAI Endorsed Transactions On Industrial Networks And Intelligent Systems, Vol. 8, No. 27, Pp. E3–E3, 2021..

Abstract: As the popularity and ubiquity of Android devices continue to rise, so does the risk of malicious software targeting these platforms. Android malware poses significant threats to users' privacy, data security, and overall device performance. Therefore, effective detection and mitigation of Android malware have become essential to ensure a safe and secure user experience. In this project, a malware detection system is proposed that extracts permission and intent features from APK...

Keywords: Machin Learning, Android Malware, Malware Detection.

[1]. J. Lee, H. Jang, S. Ha, And Y. Yoon, "Android Malware Detection Using Machine Learning With Feature Selection Based On The Genetic Algorithm," Mathematics, Vol. 9, No. 21, 2021.
[2]. N. Peiravian And X. Zhu, "Machine Learning For Android Malware Detection Using Permission And Api Calls," In 2013 Ieee 25th International Conference On Tools With Artificial Intelligence, Pp. 300–305, 2013.
[3]. M. Bhatt, H. Patel, And S. Kariya, "A Survey Permission Based Mobile Malware Detection," International Journal Of Computer Technology And Applications, Vol. 6, P. 2, 10 2015.
[4]. J. Lee, H. Jang, S. Ha, And Y. Yoon, "Android Malware Detection Using Machine Learning With Feature Selection Based On The Genetic Algorithm," Mathematics, Vol. 9, No. 21, P. 2813, 2021.
[5]. N. Tarar, S. Sharma, And R. Challa, "Analysis And Classification Of Android Malware Using Machine Learning Algorithms," Pp. 738–743, 11 2018..

Abstract: Matching the right person to the right job is a complex task, and recommendation systems have become an essential tool in streamlining this process. While many existing models focus on either the job seeker's or the recruiter's behavior in isolation, they often miss the richer signals that emerge from the interaction between both sides. In reality, both users and jobs exhibit behavioral sequences—browsing histories, application patterns, and engagement.......

Key Word: person-job fit recommendation; bilateral behavior sequences; Transformer

[1]. Okolie, U. C., & Irabor, I. E. (2017). E-Recruitment: Practices, Opportunities And Challenges. European Journal Of Business And Management, 9(11), 116-122.
[2]. Malinowski, J., Keim, T., Wendt, O., & Weitzel, T. (2006, January). Matching People And Jobs: A Bilateral Recommendation Approach. In Proceedings Of The 39th Annual Hawaii International Conference On System Sciences (HICSS'06) (Vol. 6, Pp. 137c-137c). IEEE.
[3]. Gu, Y., Ding, Z., Wang, S., Zou, L., Liu, Y., & Yin, D. (2020, October). Deep Multifaceted Transformers For Multi-Objective Ranking In Large-Scale E-Commerce Recommender Systems. In Proceedings Of The 29th ACM International Conference On Information & Knowledge Management (Pp. 2493-2500).
[4]. Liu, C., Li, X., Cai, G., Dong, Z., Zhu, H., & Shang, L. (2021, May). Noninvasive Self-Attention For Side Information Fusion In Sequential Recommendation.
[5]. Li, C., Liu, Z., Wu, M., Xu, Y., Zhao, H., Huang, P., ... & Lee, D. L. (2019, November). Multi-Interest Network With Dynamic Routing For Recommendation At Tmall. In Proceedings Of The 28th ACM International Conference On Information And Knowledge Management (Pp. 2615-2623)..

Abstract: With artificial intelligence (AI) continually revolutionizing software development lifecycle (SDLC), the need for secure, transparent, and accountable systems could not be graver. The majority of traditional security modelswhich, if, are retrofitted in their later stages of development-will not come up with the dynamism characterized by threats in putting any of the agile and perennial security assurances to the test. The Trust-by-Design framework proposed in this study seeks to embed AI-based security checks to be a part of the software quality workflow such that it prioritizes early-stage investigations, in time and continuing assurance, and transitional explainability. Intelligent.......

Key Word: Trust-by-Design, AI-Powered Security, Software Quality, Secure Software Development, CI/CD, Explainable AI, Static Code Analysis, DevSecOps, Anomaly Detection, Software As

[1]. Goniwada, S. R. (2021). AI-Driven Development. In Cloud Native Architecture and Design: A Handbook for Modern Day Architecture and Design with Enterprise-Grade Examples (pp. 555-570). Berkeley, CA: Apress.
[2]. Raheman, F. (2024). From Standard Policy-Based Zero Trust to Absolute Zero Trust (AZT): A Quantum Leap to Q-Day Security. Journal of Computer and Communications, 12(3), 252-282.
[3]. Kanwal, M., & Siddiqui, M. S. (2025). Generative AI in Modern Healthcare: Benefits, Challenges, and Ethical Implications. In Intersection of Human Rights and AI in Healthcare (pp. 117-146). IGI Global Scientific Publishing.
[4]. Anyonyi, Y. I., & Katambi, J. (2023). The Role of AI in IoT Systems: A Semi-Systematic Literature Review.
[5]. Mohamed, M. A., Chaudhry, B. M., Chakraborty, J., & O’Sullivan, K. J. The Missing Piece in the Zero Trust Sphere Knowledge Management Perspectives on Safeguarding Business Data. Available at SSRN 4766980