iosr-JCE  Volume-28 ~ Issue-1 ~ Jan. - Feb. 2026

Abstract: The teaching of operational research and graph theory still relies heavily on a formal and abstract approach, characterized by static mathematical demonstrations and little interaction with real algorithmic processes. This situation limits a deep understanding of the resolution mechanisms, particularly for beginner students. Furthermore, existing computer solvers, although powerful, mostly function as "black boxes" and offer little educational transparency.
This article proposes.........

Key Word: OptSolver, Operational Research, Graph Theory, Linear Programming, Optimization, algorithmic pedagogy, visualization, traceability, computer- assisted learning.

[1]. Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2013). Linear Programming And Network Flows (4th Ed.). Wiley.
[2]. Beasley, J. E. (1990). OR-Library: Distributing Test Problems By Electronic Mail. Journal Of The Operational Research Society, 41(11), 1069–1072.
[3]. Berge, C. (1958). Graph Theory And Its Applications. Dunod.
[4]. Berge, C. Graphs And Hypergraphs. Dunod, Paris, 1983.
[5]. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-By-Step Data Mining Guide. The CRIPSP Consortium

Abstract: Background: Small, fast drones are now cheap and easy to use, and they are being used in hostile military or other hostile settings. These drones can be armed or used to spy on people, and they represent a real threat to our military safety and equipment. It is very important that we have good automatic systems that can find these drones, no matter how hard they try to hide them. Materials and.......

Key Word: Machine Learning Algorithms, Model Evaluation Techniques, Drone Safety, Military, Drone Detection.

[1]. E. Hetelekides, Et Al., "Early Birds And Night Owls: Distinguishing Profiles Of Cannabis Use Habits By Use Times With Latent Class Analysis," Vol. 6(1), Pp.79-98, 2022.
[2]. M. Nentwich And D. M. Hórvath, "Delivery Drones From A Technology Assessment Perspective," Overv. Report, No.2018-01, Viennaita, 2018.
[3]. M. S. Alzboon, Et Al., "Pushing The Envelope: Investigating The Potential And Limitations Of Chatgpt And Artificial Intelligence In Advancing Computer Science Research", 3rd International Conference On Emerging Smart Technologies And Applications (Esmarta), Pp 1-6, 2023.
[4]. M. S. Alzboon, Et Al., "Early Diagnosis Of Diabetes: A Comparison Of Machine Learning Methods," Int. J. Online Biomed. Eng., Vol. 19, No. 15, Pp. 144–165, 2023.
[5]. S Heimbs, Et Al., Comparison Of Drone Collision And Bird Strike On Aircraft Radome Using Experimental And Simulation Methods. Procs Of The International Conference On Impact Loading Of Structures And Materials (Icilsm 2022), Trondheim, Norway, 2022.

Abstract: This study conducts a feature-based analysis to evaluate the predictive performance of set of machine learning (ML) regression models for solar irradiance prediction using multivariate data from Chhatrapati Sambhaji Nagar, India. The dataset, sourced from NASA POWER and NOAA, spans January 2001 to July 2023 and includes various variables such as temperature, pressure, humidity, wind speed, and sunrise/sunset times. After data preprocessing and exploratory analysis, we trained six set of models—Linear Regression, Decision Tree, Random Forest.........

Key Word: Feature Selection, Meteorological Data, Machine Learning (ML) Regression, Model Performance Evaluation, Predictive Analytics, Solar Irradiance Prediction

[1] M. J. Samma Et Al., “Illuminating The Future: A Comprehensive Review Of Ai-Based Solar Irradiance Prediction Models,” Ieee Access, Vol. 12, Pp. 114394–114415, 2024, Doi: 10.1109/Access.2024.3402096
[2] A. Javed, B. Kasi, And F. Khan, “Predicting Solar Irradiance Using Machine Learning Techniques,” In International Conference On Wireless Communications And Mobile Computing, Jun. 2019. Doi: 10.1109/Iwcmc.2019.8766480
[3] E. Abrahamsen, O. M. Brastein, And B. Lie, “Machine Learning In Python For Weather Forecast Based On Freely Available Weather Data,” The 59th Conference On Imulation And Modelling (Sims 59), Pp. 169–176, Sep. 2018,
Doi: 10.3384/Ecp18153169. Available: Https://Ep.Liu.Se/En/Conference-Article.Aspx?Series=Ecp&Issue=153&Article_No=24
[4] A. H. M. Jakaria, M. M. Hossain, And M. Rahman, “Smart Weather Forecasting Using Machine Learning: A Case Study In Tennessee,” 2018.
[5] M. Holmstrom, D. Liu, And C. Vo, “Machine Learning Applied To Weather Forecasting,” Course Project Report, Dec. 2016, Available: Https://Cs229.Stanford.Edu/Proj2016/Report/Holmstromliuvo-Machinelearningappliedtoweatherforecasting-Report.Pdf

Abstract: The rapid growth of big data has led to the adoption of NoSQL databases due to their scalability and flexibility in handling structured, semi-structured, and unstructured data. However, many NoSQL systems lack strong security features, such as authentication, authorization, and encryption, which increase the risk of data breaches and unauthorized access. Addressing these security issues is essential to ensure safe and reliable data management in big data environments.

Key Word: RDBMS,NoSQL, MONGODB

[1]. Yesha Mehta, And Sanjay Buch, “Big Data Mining And Semantic Technologies: Challenges And Opportunities”, Int. J. On Recent And Innovation Trends In Computing And Communications (ISSN 2321- 8169) Vol.3,No 7,2015.
[2]. Rodrigues M., Santos M.Y., Bernardino J., “Describing And Comparing Big Data Advances In Information Systems And Technologies”. Worldcist 2017 Advances In Intelligent Systems And Computing, Vol 569. Springer, Cham,2017
[3]. Lomotey ,RK And Deters, R , “Data Mining From Document – Append Nosql”, Int.J.Of Services Computing(ISSN 2330- 4472),Vol.2,No.2,2014.
[4]. Nadeem Qaisar Mehmood,Rosario Culmone And Leonardo Mostarda,“Modeling Temporal Aspects Of Sensor Data For Mongodb Nosql Database”,Journal Of Big Data,Springer,2017.
[5]. A. Nayak, A. Poriya, And D. Poojary, “Type Of Nosql Databases And Its Comparison With Relational Databases,” International Journal Of Applied Information Systems (IJAIS), Vol. 5 - No. 4, Pp. 16-19, March 2013.

Abstract: The paradigm shift around the extraction of value out of information basing on big data has been triggered by its exponential growth, and has turned large-scale data mining into a pillar of contemporary innovation in health care, finance, and governance. This utility has however been an unsafe cost to personal privacy. With data mining algorithms becoming more advanced, even able to deduce sensitive qualities out of apparently harmless patterns of behavior, the classical anonymisation methods, including k- anonymity and data masking, have been shown to be mathematically ineffective in repelling re-identification attacks. The paper presents a critical, systematic discussion of the issues of privacy involved in massive data mining. It is a critical review of the development....

[1]. Dwork, C. (2006). "Differential Privacy." Proceedings Of The 33rd International Colloquium On Automata, Languages And Programming (ICALP), Pp. 1-12.
[2]. Sweeney, L. (2002). "K-Anonymity: A Model For Protecting Privacy." International Journal Of Uncertainty, Fuzziness And Knowledge-Based Systems, 10(5), 557-570.
[3]. Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007). "L- Diversity: Privacy Beyond K-Anonymity." ACM Transactions On Knowledge Discovery From Data (TKDD), 1(1), 3.
[4]. Li, N., Li, T., & Venkatasubramanian, S. (2007). "T-Closeness: Privacy Beyond K- Anonymity And L-Diversity." IEEE 23rd International Conference On Data Engineering (ICDE), Pp. 106-115.
[5]. Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). "Membership Inference Attacks Against Machine Learning Models." IEEE Symposium On Security And Privacy (SP), Pp. 3-18..