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.