iosr-JCE  Volume-28 ~ Issue-2 ~ Mar. - Apr. 2026

Abstract: The conventional moving target indicator (MTI) radars perform doppler processing using MTI filters and FFT filter bank for ground clutter rejection and estimating the moving target parameters. The MTI radar suffers from clutter ridges at zero-doppler frequency. MTI filters are used to remove these clutter ridges, but MTI filters introduce the blind speeds. If phased array antennas are used to electronically scan the beam, Space Time Adaptive Processing (STAP) can be spatially filter out the clutter. In this work, a staggered pulse repetition....

Key Word: Space Time Adaptive Processing, Staggered PRF, MTI Radar, Range-Doppler Map, Range-Angle Map, Doppler-Angle Map

[1] Merrill I. Skolnik, Introduction To Radar Systems, 2nd Edition, 1981, Mcgraw-Hill.
[2] Trunk, Gerard V. And Steven M. Brockett. “Range And Velocity Ambiguity Resolution.” The Record Of The 1993 IEEE National Radar Conference (1993): 146-149
[3] Richards M.A. Fundamentals Of Radar Signal Processing Mcgraw-Hill New York 2014
[4] Fred E Nathanson, Radar Design Principles, 2nd Edition, 1991, Mcgraw-Hill.
[5] V. C. (Chris) Sullivan, Radar Foundations For Imaging And Advanced Concepts, Scitech Publishing, IET, 2019

Abstract: The article discusses the theoretical, methodological, and technological aspects of integrating artificial intelligence into the structure of modern digital teaching materials in the context of the digitalization of higher education in 2025–2026. It is argued that digital teaching materials are transforming from static electronic resources into multi-component intelligent educational...

Key Word: artificial intelligence, digital instructional materials, digitalization of education, adaptive learning, personalized learning path, intelligent tutoring systems, machine learning, learning analytics, automated assessment, digital learning environment, higher education, pedagogical technology, teacher digital competence, ethics of artificial intelligence.

[1]. Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th Ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474
[2]. Vyugin, V. V. Mathematical Foundations Of Machine Learning And Forecasting [Text] / V. V. Vyugin. - Moscow: Moscow Center For Continuous Mathematical Education (MCCME) Publishing House, 2013. - 305 P.
[3]. Radkevich A. S., Radkevich K. A., Ryabinin N. S. Artificial Intelligence In Modern Education /
Https://Libeldoc.Bsuir.By/Bitstream/123456789/58509/1/Radkevich_Iskusstvennyj.Pdf
[4]. Khabarova N.D. Integration Of Artificial Intelligence And Neuroimaging Technologies For Personalization Of Learning / Information And Communication Technologies In Teacher Education, 2025. No. 4 (97)
[5]. Shestakova N. N., Dzhanelidze M. G. Artificial Intelligence: Impact On The Education-Labor Market System / MANAGEMENT CONSULTING. No. 5. 2025, Pp. 91-105 / Https://Www.Acjournal.Ru/Jour/Article/Viewfile/2829/2068

Abstract: This paper presents a web-based smart farming system for automated plant disease detection using Convolutional Neural Networks (CNN) integrated with the MobileNetV2 transfer learning architecture. The proposed model is trained on a labeled crop leaf dataset to enable accurate and real-time disease classification. Experimental evaluation demonstrates a classification accuracy of 96.8%, along with strong precision, recall, and F1-score values. The system also incorporates a multilingual farmer assistance module that provides treatment recommendations, preventive measures, and weather-based advisory support. The lightweight design of MobileNetV2 ensures reduced computational complexity, making the system suitable for deployment in resource-constrained agricultural environments.

[1]. S. P. Mohanty, D. P. Hughes, And M. Salathé, “Using Deep Learning For Image-Based Plant Disease Detection,” Frontiers In Plant Science, 2016.
[2]. S. Sladojevic Et Al., “Deep Neural Networks-Based Recognition Of Plant Diseases,” Computational Intelligence And Neuroscience, 2016.
[3]. K. P. Ferentinos, “Deep Learning Models For Plant Disease Detection And Diagnosis,” Computers And Electronics In Agriculture, Vol. 145, Pp. 311–318, 2018..
[4]. A. L. Too And Other People Compared Deep Learning Models For Identifying Plant Diseases. They Published Their Paper In Computers And Electronics In Agriculture In 2019.
[5]. S. Zhang And Other People Used A Method Called Representation Classification To Identify Cucumber Leaf Diseases.

Abstract: The Ganga River is an essential source of water and livelihood for millions of people across India, yet it continues to face serious pollution problems that threaten both environmental sustainability and public health. To help address this issue, this study presents an AI-enabled decision support system designed to monitor and predict water quality conditions along the Ganga River in real time. The system uses an Exogenous Long Short-Term Memory (.....

Keywords: Alert System, Forecasting, Ganga River, Exogenous LSTM, Pollution

[1] Xu, Rui, Qingyu Xiong, Hualing Yi, Chao Wu, And Jianxin Ye. "Research On Water Quality Prediction Based On SARIMA-SARIMAX: A Case Study Of Beilun Estuary." In 2019 IEEE 21st International Conference On High Performance Computing And Communications; IEEE 17th International Conference On Smart City; IEEE 5th International Conference On Data Science And Systems (HPCC/Smartcity/DSS), Pp. 2183-2188. IEEE, 2019. Doi: 10.1109/HPCC/Smartcity/DSS.2019.00302
[2] Papacharalampous, Georgia A., And Hristos Tyralis. "Evaluation Of Random Forests And Prophet For Daily Streamflow Forecasting." Advances In Geosciences 45 (2018): 201-208. Doi: 10.5194/Adgeo-45-201-2018
[3] Kogekar, A. P., Nayak, R., & Pati, U. C. (2021). Forecasting Of Water Quality For The River Ganga Using Univariate Time-Series Models. In 2021 8th International Conference On Smart Computing And Communications (ICSCC) (Pp. 52-57). IEEE. Doi: 10.1109/ICSCC51209.2021.9528216.
[4] Ye, Q., Yang, X., Chen, C., & Wang, J. (2019). River Water Quality Parameters Prediction Method Based On SARIMAX-RNN Model. In 2019 Chinese Control And Decision Conference (CCDC) (Pp. 3024-3028). IEEE. Doi: 10.1109/CCDC.2019.8832885
[5] Zhou, Y., Zhao, Z., Wang, X., & Sun, Q. (2020, July). Water Quality Evolution Mechanism Modeling And Estimation Based On Fruit Fly Optimization Algorithm And Particle Filter. In 2020 39th Chinese Control Conference (CCC) (Pp. 1178-1183). IEEE.
Doi: 10.23919/CCC50068.2020.9188465

Abstract: Background: Cancer has become one of the leading causes of mortality worldwide, with increasing incidence and a noticeable shift toward younger age groups in many countries. Early identification of cancer risk is therefore essential for improving prevention strategies and supporting timely medical intervention. Recent advances.....

Keywords: Cancer risk prediction; Machine learning; AdaBoost; Clinical data; Lifestyle factors

[1]. N. Fatima, L. Liu, H. Sha, And H. Ahmed, “Prediction Of Breast Cancer: Comparative Review Of Machine Learning Techniques And Their Analysis,” Ieee Access, Vol. 8, Pp. 150360–150376, 2020.
[2]. J. A. M. Sidey-Gibbons And C. J. Sidey-Gibbons, “Machine Learning In Medicine: A Practical Introduction,” Bmc Medical Research Methodology, Vol. 19, No. 64, 2019.
[3]. M. Kumari And V. Singh, “Breast Cancer Prediction System,” Procedia Computer Science, Vol. 132, Pp. 371–376, 2018.
[4]. M. A. Naji, S. El Filali, K. Aarika, E. H. Benlahmar, R. Ait Abdelouhahide, And O. Debauche, “Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis,” Procedia Computer Science, Vol. 191, Pp. 487–492, 2021.
[5]. Austria, Y. D., Goh, M. L., Sta. Maria, L. B., Lalata, J. P., Goh, J. E., & Vicente, H. N. (2019). Comparison Of Machine Learning Algorithms In Breast Cancer Prediction Using The Coimbra Dataset. International Journal Of Simulation: Systems, Science And Technology. Https://Doi.Org/10.5013/Ijssst.A.20.S2.23

Abstract: The traditional Intrusion Detection Systems are largely signature-based (reacting only to known threats), current IDS is increasingly behavior-based and hybrid, leveraging Deep Learning to handle large, heterogeneous data sets, decreasing false positives compared to earlier, rule-based systems. Traditional signature-based Intrusion Detection Systems (IDS) are increasingly inadequate against polymorphic and zero-day threats. This paper presents a novel approach using Deep Reinforcement Learning (DRL). By treating network security.....

Keywords: IDS, DRL, MDP, DQN, U2R, R2L

[1]. Othman Alnasser Et Al.,"Signature And Anomaly Based Intrusion Detection System For Secure Iots And V2G Communication" Alexandria Engineering Journal 125 (2025) 424–440 Https://Doi.Org/10.1016/J.Aej.2025.03.068
[2]. Mahbod Tavallaee Et Al.,"A Detailed Analysis Of The KDD CUP 99 Data Set 2009 IEEE Symposium On Computational Intelligence For Security And Defense Applications, Ottawa, ON, Canada, 2009, Pp. 1-6, Doi: 10.1109/CISDA.2009.5356528.
[3]. Yang Et Al., "A Survey For Deep Reinforcement Learning Based Network Intrusion Detection," Arxiv.Org, 2024. DOI: [10.48550/Arxiv.2410.07612](Https://Doi.Org/10.48550/Arxiv.2410.07612).
[4]. P.Sreenivasulu, Dr.M.Ussenaiah."A Review On Machine Learning And Deep Learning Based Modern Intrusion Detection Techniques For Mobile Adhoc Networks. International Journal Of Innovative Science And Research Technology (IJISRT) IJISRT25NOV173, 809-813. DOI: 10.38124/Ijisrt/25nov173. "
[5]. Y. Zhang Et Al., “Deep Learning Applications In Intrusion Detection Systems: A Review,” Appl. Sci., Vol. 15, No. 3, 2025.

Abstract: Software quality depends critically on the speed and reliability with which developers can locate and repair defects. Automated program repair (APR) techniques have made substantial progress in recent years, yet their effectiveness is often limited by the availability of realistic training data and a principled understanding of how developers fix bugs. This article presents a large, cross‑language dataset of real bug‑fixing commits together with a taxonomy of bug‑fix patterns that captures the essential repair actions observed in modern software. By aggregating more than one million fix actions across nine programming languages, we show that certain patterns—especially modifying conditional statements....

Keywords: bugfix; software; modern programming; automated program; pattern language

[1]. Li, S. (2015). Nopol: Automatic Repair Of Conditional Statements In Java Programs. In Proceedings Of ISSTA.
[2]. Pramod, D., De Silva, T., Thabrew, U., Shariffdeen, R., & Wickramanayake, S. (2024, April). Bugsphp: A Dataset For Automated Program Repair In Php. In Proceedings Of The 21st International Conference On Mining Software Repositories (Pp. 128-132).
[3]. Pham, X. (2014). Prophet: A Probabilistic Patch Generation System For Program Repair. Technical Report.
[4]. Nguyen, H. D. T., Qi, D., Roychoudhury, A., & Chandra, S. (2013, May). Semfix: Program Repair Via Semantic Analysis. In 2013 35th International Conference On Software Engineering (ICSE) (Pp. 772-781). IEEE.
[5]. Durieux, T., & Abreu, R. (2019). Critical Review Of Bugswarm For Fault Localization And Program Repair. Arxiv Preprint Arxiv:1905.09375.

Abstract: Achieving United Nations Sustainable Development Goals (SDGs) by 2030 will need innovation and collaboration. It can be accelerated by artificial intelligence (AI), which is emerging as a powerful technological tool. AI is used in addressing SDG 2 (agriculture), SDG 3 (health) and SDG 13 (climate) by converting precise data into actionable knowledge. Climate change acts as a cross-cutting stressor that impacts agricultural systems and human health....

Keywords: Artificial intelligence, SDGs, agriculture, public health, climate change, AI in SDGs, sustainable development.

[1]. Markets And Markets. 2025. Artificial Intelligence Market Size, Share, Growth, Latest Trends. Link:
Https://Www.Marketsandmarkets.Com/Market-Reports/Artificial-Intelligence-Market-74851580.Html
[2]. United Nations, N.D.. Sustainability. Link:
Https://Www.Un.Org/En/Academic-Impact/Sustainability#:~:Text=In%201987%2c%20the%20united%20nations,Development%20needs%2c%20but%20with%20the
[3]. World Food Programme (Wfp). 2026. Link: Https://Www.Wfp.Org/Global-Hunger-Crisis
[4]. Ladlee, J. R.; Murillo-Williams, A.; Spelman, R. (2025). Exploring Artificial Intelligence Applications In Agriculture. Penn State Extension. Https://Extension.Psu.Edu/Exploring-Artificial-Intelligence-Applications-In-Agriculture
[5]. Gautam, V., Gani, A., Pathak, S. Et Al. Evaluating Crop Yield Prediction Models In Illinois Using Aquacrop, Semi-Physical Model And Artificial Neural Networks. Sci Rep 15, 27494 (2025). Https://Doi.Org/10.1038/S41598-025-13453-X

Abstract: Roko’s Basilisk is commonly known as a philosophical thought experiment involving a fu- ture superintelligent artificial intelligence that may punish individuals who did not contribute to its creation. While the original concept is speculative, it reveals an important research problem in artificial intelligence safety: the emergence of coercive optimization strategies. This paper reformulates the Basilisk scenario as an AI alignment problem involving reward maximization, ethical constraints, and decision-theoretic failure modes. A mathematical framework is pro- posed to model coercive utility functions and an anti-coercion loss formulation is introduced as a solution. The study contributes to the broader domain of AI safety by examining how intelligent systems may infer fear-based strategies if alignment mechanisms are weak.

Keywords: Artificial Intelligence, AI Safety, Alignment, Reinforcement Learning, Ethics, Co- ercive Optimization, Roko’s Basilisk

[1] S. Russell, Human Compatible: Artificial Intelligence And The Problem Of Control, Viking, 2019.
[2] I. Goodfellow, Y. Bengio, And A. Courville, Deep Learning, Mit Press, 2016.
[3] N. Bostrom, Superintelligence: Paths, Dangers, Strategies, Oxford University Press, 2014.

Abstract: The Internet of Things (IoT) connects computers built into ordinary things to make smart surroundings. In this kind of environment, a user-defined job may be done by combining services from several physical devices. In every Internet of anything (IoT) system, resource discovery is a crucial first step. Resources might be anything like objects, devices,....

Keywords: Resource discovery, Network, Clusterheads, Topology, Nodes

[1] V. cp, S. Basha, M. Madiajagan, S. Jadhav, M. Matheen, and L. Maguluri, “IoT resource discovery based on multi-faceted attribute
enriched CoAP: Smart office seating discovery,” Wireless Personal Communications, vol. 146, no. 1, pp. 39–56, 2024.
[2] K. Chakraborty, D. Kapila, S. Kumar, S. Nazeer, and A. Singh, “Intelligent machine learning based Internet of Things (IoT) resource
allocation,” Engineering Proceedings, vol. 59, no. 1, pp. 1–7, 2023.
[3] M. Tamizan and A. A. Jamal, “Determination of suitable resource discovery tool and methodology for high-volume Internet of Things
(IoT),” Journal of Physics: Conference Series, vol. 1874, no. 1, pp. 1–11, 2021.
[4] M. Samadi, “A knowledge and intelligent-based strategy for resource discovery on IaaS cloud systems,” International Journal of
Grid and Utility Computing, vol. 12, no. 2, pp. 205–221, 2021.
[5] M. Elmahi, N. Osman, and H. Hamza, “Resource discovery classification for Internet of Things: A survey,” International Journal of
Digital Information and Wireless Communications, vol. 10, no. 3, pp. 35–50, 2020.

Abstract: The Parkinson's Disease (PD) is a chronic and progressive neurodegenerative disorder. It affects motor skills and influences the patient's life quality. Early and accurate stage classification of Parkinson's is important to diagnose and to treat the PD. Previously, the diagnosis was based on clinical examination, which may include a margin of error depending on the observer's judgement. In this paper, an automatic classification system based on the MRI brain....

Keywords: Parkinson's Disease, Machine Learning, MRI, Histogram of Oriented Gradients (HOG), Random Forest, Disease Staging, Medical Imaging

[1] Y. Guo Et Al., “Characterization Of Early-Stage Parkinson's Disease From Resting-State Fmri Data Using A Long Short-Term Memory Network,” Frontiers In Neuroimaging, Vol. 1, 2022.
[2] C. Cui Et Al., “Diagnosis Of Parkinson's Disease Based On Feature Fusion On T2 Mri Images,” International Journal Of Intelligent Systems, Vol. 37, No. 12, Pp. 11054–11075, 2022.
[3] S. Mozhdehfarahbakhsh Et Al., “An Mri-Based Deep Learning Model To Predict Parkinson’s Disease Stages,” Medrxiv, 2021.
[4] N. J. Dhinagar Et Al., “Curriculum-Based Multi-Task Learning For Parkinson’s Disease Detection,” In Proc. Ieee International Conference On Bioinformatics And Computational Biology (Icbcb), 2023.
[5] “A Fully Automated Approach Involving Neuroimaging And Deep Learning For Parkinson’s Disease Detection And Severity Prediction,” Peerj Computer Science, Vol. 9, P. E1485, 2023..

Abstract: Urbanization has made managing municipal waste more difficult. There are many cities across the globe where conventional models of waste collection are followed, based on scheduled trips to dispose the waste. This results in overflowing of bins, addition of unnecessary trips, pollution of the environment etc. In this project, we propose a smart waste management system using IoT and machine learning. The fill level of the waste bins are monitored by ultrasonic sensors placed in the bins. The data is transferred to a cloud based server which uses machine learning predictive analyti....

Keywords: : Intelligent Waste Management, IoT, Predictive Analytics, Urban Sustainability

[1] M. Alqahtani And H. Mathkour, “Iot-Based Intelligent Waste Monitoring System Using Deep Learning,” Sensors, Vol. 22, No. 14, P. 5321, Jul. 2022.
[2] S. Sharma, R. Gupta, And A. Kumar, “Ai-Enabled Smart Waste Management System For Sustainable Smart Cities,” Ieee Access, Vol. 10, Pp. 102345–102358, 2022.
[3] J. Kim, D. Lee, And H. Park, “Deep Learning-Based Waste Classification Using Efficientnet For Smart Recycling,” Expert Syst. Appl., Vol. 198, P. 116824, 2022.
[4] P. Singh And V. K. Sharma, “Predictive Waste Collection Using Machine Learning And Iot Data Analytics,” Sustain. Cities Soc., Vol. 82, P. 103875, 2023.
[5] L. Chen, Y. Zhao, And X. Wang, “Vision-Based Automated Waste Sorting Using Transfer Learning,” Resour. Conserv. Recycl., Vol. 188, P. 106700, 2023.

Abstract: There has been unprecedented increase in the number of Distributed Denial of Service (DDoS) attacks and network intrusions to the extent that most nodes associated with the IoT are prone to attacks. Dynamics of the threat scenario dictate the need to develop effective and accurate intrusion detection systems. With the help of the RT-IoT2022 data set, this paper presents a deep evaluation of three machine learning models of intrusion detection in IoT. Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The research invo....

Keywords: IoT, machine learning, intrusion detection, Random Forest, artificial neural network, and support vector machine

[1] M. B. Bankó Et Al., "Advancements In Machine Learning-Based Intrusion Detection In Iot: Research Trends And Challenges," Algorithms, Vol. 18, No. 4, P. 209, 2025. [Online]. Available: Https://Www.Mdpi.Com/1999-4893/18/4/209.
[2] N. M. Imam, M. Ali, And A. Bhatnagar, "Investigation Of Machine Learning Models For Multi-Class Detection And Classification Of Ddos Attack," In 2025 International Conference On Communication And Smart Devices (Iccosd), 2025, Vol. 1: Ieee, Pp. 1-6.
[3] A. Zadeh And A. Jeyaraj, "A Multistate Modeling Approach For Organizational Cybersecurity Exploration And Exploitation," Decision Support Systems, Vol. 162, P. 113849, 2022.
[4] B. Yang, M. H. Arshad, And Q. Zhao, "Packet-Level And Flow-Level Network Intrusion Detection Based On Reinforcement Learning And Adversarial Training," Algorithms, Vol. 15, No. 12, P. 453, 2022.
[5] M. S. El Sayed, N.-A. Le-Khac, M. A. Azer, And A. D. Jurcut, "A Flow-Based Anomaly Detection Approach With Feature Selection Method Against Ddos Attacks In Sdns," Ieee Transactions On Cognitive Communications And Networking, Vol. 8, No. 4, Pp. 1862- 1880, 2022.

Abstract: Recent advancements in artificial intelligence have significantly influenced creative fields such as music composition. However, many individuals who possess musical ideas often lack the technical knowledge required to convert those ideas into structured musical compositions using professional tools. This paper presents Hum2Song, an AI-powered web-based system designed to transform hummed melodies into complete musical compositions. The proposed system captures a short vocal melody from the user through a browser-based interface and processes the audio using machine learning techniques to extract musical features such as pitch, timing, and note duration...

[1]. Blanchard, L., Et Al. (2025). AI Harmonizer: Expanding Vocal Expression With A Generative Neurosymbolic Music AI System. Arxiv Preprint Arxiv:2506.18143.
[2]. Yuan, Y., Et Al. (2025). Midi-LLM: Adapting Large Language Models For Text-To-MIDI Music Generation. Arxiv Preprint Arxiv:2511.03942.
[3]. Goot, D. K. (2025). Reimagining Musical Collaboration: AI And Music Real-Time Communication. Journal Of The Association For Technology In Music Instruction, 5(1).
[4]. Symbolic Music From Natural Language Prompts Using An LLM-Enhanced Dataset. International Society For Music Information Retrieval Conference (ISMIR).
[5]. Copet, J., Kreuk, F., Gat, I., Remez, T., Kant, D., Synnaeve, G., Adi, Y., & Défossez, A. (2023). Simple And Controllable Music Generation (Musicgen). Advances In Neural Information Processing Systems (Neurips).
[6]. Donahue, C., Caillon, A., Roberts, A., Manilow, E., Esling, P., & Raffel, C. (2023). Singsong: Generating Musical Accompaniments From Singing. Arxiv Preprint Arxiv:2301.12662..

Abstract: This pilot study investigates the impact of financial technology (FinTech) on advancing financial inclusion among Micro, Small, and Medium Enterprises (MSMEs) in the textile and handloom industry of Panipat district, Haryana. The primary objective of the study was to examine the level of FinTech adoption, its influence on financial accessibility and business performance, and the barriers that limit its effective implementation. A structured questionnaire was administered to a sample of 135 MSME owners and managers to assess patterns of digital f...

Keywords: Financial Technology (FinTech), Financial Inclusion, MSMEs, Digital Payments Textile and Handloom Industry

[1]. Arner, D. W., Barberis, J., & Buckley, R. P. (2016). The Evolution Of Fintech: A New Post-Crisis Paradigm. Georgetown Journal Of International Law, 47(4), 1271–1319.
[2]. Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2018). The Global Findex Database 2017: Measuring Financial Inclusion And The Fintech Revolution. World Bank.
[3]. Gomber, P., Koch, J. A., & Siering, M. (2017). Digital Finance And Fintech: Current Research And Future Research Directions. Journal Of Business Economics, 87(5), 537–580.
[4]. Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, Business Models, Investment Decisions, And Challenges. Business Horizons, 61(1), 35–46.
[5]. Nicoletti,B.(2017). The Future Of Fintech:Integrating Finance And Technology In Financial Services. Springer...