iosr-JCE   Volume-27 ~ Issue-4 ~ Jul. - Aug. 2025

Abstract: This paper considers the system of examination invigilation, monitoring and malpractice mitigation in Nigeria universities with particular reference to Ebonyi State University, Abakaliki. Over the years, the extent of students’ malpractice in examinations and the resultant effects when confronted by invigilators or university security agents have been on the increase and devastating. Generally, moral decadence and the high premium placed on achievement and certificates by Nigerians has in recent times spawned examination fraud. There arose the need for an examination.....

Keywords: Examination Malpractice; Malpractice Mitigation; Webcam; Monitoring; Exam Ethics; Exam Malpractice Panel; Solar Panel; Dedicated System

[1] Abdulkareem, A. Y. (2003). Examination Malpractice: The Way Out. A Paper Presented At The 36th Founders Day Anniversary Of Ilorin Grammar School, Ilorin, Thursday February 6th, 2003.
[2] Amanze Et Al. 2016). “Video Survellance And Monitoring System For Examination Malpractice In Higher Institution”. IJECS 2016; 5(1): 15560-15571
[3] Ejiogu, A. (2001). Morality And National Development: A Case For National Rebirth. Abuja: National Orientation Agency.
[4] Liman, M. T. (1996). Promoting Examination Ethics. Nigeria: An Examination Ethics Publication.
[5] Matthew J. E. And Akosubo E. 2021). “Management Of Examination Malpractice Problems In The Universities In Niger Delta Region”. International Journal Of Educational Research And Management Technology. Vol 6, No 2. June 2021. ISSN: 2545-5893(Print) 2545-5877 (Online)

Abstract: Generative AI – models that synthesize novel data, have transformed fields from art to science. Yet without common standards, its development suffers from inconsistent evaluation, poor reproducibility, and unchecked ethical risks. We propose a multi-pillar framework for generative AI standardization integrating
(1) benchmark datasets and evaluation metrics, (2) algorithmic transparency and maintainability, (3) ethical frameworks and guidelines, and (4) continuous community dialogue and iteration. We illustrate the framework with a case study in environmental.....

Keywords: Generative AI, standardization, audio synthesis, watermarking, benchmarks, ethics.

[1] D. Amodei et al., "Concrete Problems in AI Safety," arXiv:1606.06565, 2016.
[2] E. M. Bender et al., "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?", Proc. FAccT, 2021, pp. 610–623.
[3] H. F. Dacre et al., "Standardization in machine learning: Challenges and opportunities," arXiv:2202.00573, 2022.
[4] A. Gui et al., "Adapting Frechet Audio Distance for Generative Music Evaluation," ICASSP, 2024.
[5] M. Lagrange et al., "Sound Scene Synthesis at the DCASE 2024 Challenge," arXiv:2501.08587, 2025.

Abstract: In engineering vector calculus courses, one of the most recommended tools for re-constructing three-dimensional elements is the use of contour lines. This is due to their versatile and simple elements, which allow for appropriate didactic guidance for the assimilation of their underlying concepts. This research article provides three different computational tools for constructing contour lines. These tools were tested with university students and improved their academic performance and understanding of the concepts associated with interpreting curve equations.

Keywords: Contour lines, vector calculus, three-dimensional elements, academic performance, computational tools

[1] Atit, K., Weisberg, S. M., Newcombe, N. S., & Shipley, T. F. (2016). Learning To Interpret Topographic Maps: Understanding Layered Spatial Information. Cognitive Research: Principles And Implications, 1(1), 2.
[2] Pick, H. L., Heinrichs, M. R., Montello, D. R., Smith, K., Sullivan, C. N., & Thompson, W. B. (2018). Topographic Map Reading. In Local Applications Of The Ecological Approach To Human-Machine Systems (Pp. 255-284). CRC Press.
[3] Wu, Q., Liu, H., Wang, S., Yu, B., Beck, R., & Hinkel, K. (2015). A Localized Contour Tree Method For Deriving Geometric And Topological Properties Of Complex Surface Depressions Based On High-Resolution Topographical Data. International Journal Of Geographical Information Science, 29(12), 2041-2060.
[4] Mostafavi, M. (2016). Cartographic Grounds: Projecting The Landscape Imaginary. Chronicle Books.
[5] Telbisz, T., Látos, T., Deák, M., Székely, B., Koma, Z., & Standovár, T. (2016). The Advantage Of Lidar Digital Terrain Models In Doline Morphometry Compared To Topographic Map Based Datasets–Aggtelek Karst (Hungary) As An Example. Acta Carsologica, 45(1)..

Abstract: The integration of the Internet of Things (IoT) with fifth-generation (5G) networks represents a transformative leap in digital connectivity, enabling ultra-low latency, high bandwidth, and massive device connectivity. This study examines the technical capabilities of 5G for large-scale IoT deployment, the benefits across key sectors such as healthcare, manufacturing, transportation, and smart cities, and the challenges relating to security, interoperability,......

Keywords: Internet of Things, 5G, ultra-low latency, network slicing, cybersecurity, smart cities, Industry 4.0

[1] Abdalla, A., Omar, M., & Hassan, M. M. (2022). Security Challenges In 5G-Enabled Iot Networks: A Comprehensive Survey. IEEE Access, 10, 125471–125494. Https://Doi.Org/10.1109/ACCESS.2022.3210164
[2] Abdel-Basset, M., El-Shahat, D., & Chakrabortty, R. K. (2021). Trust Management In Iot: State-Of-The-Art And Future Directions. IEEE Internet Of Things Journal, 8(10), 7782–7795. Https://Doi.Org/10.1109/JIOT.2020.3036489
[3] Ahmed, E., Gharavi, H., & Alam, M. M. (2021). Role Of 5G In The Internet Of Things (Iot). IEEE Access, 9, 70150–70164. Https://Doi.Org/10.1109/ACCESS.2021.3086632
[4] Agiwal, M., Saxena, N., & Roy, A. (2022). Towards Connected And Autonomous Systems: Integrating Iot And 5G Networks. IEEE Internet Of Things Journal, 9(15), 13102–13115. Https://Doi.Org/10.1109/JIOT.2022.3148572
[5] Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2021). Internet Of Things: A Survey On Enabling Technologies, Protocols, And Applications. IEEE Communications Surveys & Tutorials, 23(4), 2131–2161.
Https://Doi.Org/10.1109/COMST.2021.3101732.

Abstract: Artificial intelligence has changed how we write code. AI-based code generation tools now help developers work faster. These tools, such as GitHub Copilot, OpenAI Codex, ChatGPT 4.5, Claude, and DeepSeek R1, generate code by learning from millions of examples. They improve productivity and reduce repetitive tasks. However, their impact on software quality varies. Some AI-generated code is clean and efficient, while other outputs need human refinement. This.....

Keywords: AI-generated code, software quality, developer productivity, GitHub Copilot, ChatGPT 4.5, OpenAI Codex, Claude, DeepSeek R1, code generation tools, developer trust, technical analysis

[1] B. K. Deniz, C. Gnanasambandam, M. Harrysson, A. Hussin, And S. Srivastava, “Unleashing Developer Productivity With Generative Ai,” Mckinsey & Company, Jun. 27, 2023.
Https://Www.Mckinsey.Com/Capabilities/Mckinsey-Digital/Our-Insights/Unleashing-Developer-Productivity-With-Generative-Ai
[2] A. Soni, A. Kumar, R. Arora, And R. Garine, “Integrating Ai Into The Software Development Life Cycle: Best Practices, Tools, And Impact Analysis,” Ssrn, Jan. 2024, Doi: 10.2139/Ssrn.4918992.
[3] C. Pantin, “The Impact Of Ai-Generated Code On The Future Of Junior Developers,” Theseus, 2024.
Https://Www.Theseus.Fi/Handle/10024/866717
[4] “Automatic Generation Of Algorithms,” Google Books. January, 2025
Https://Books.Google.Com.Pk/Books?Hl=En&Lr=&Id=Ke04eqaaqbaj&Oi=Fnd&Pg=Pp1&Dq=When+The+Ai+Tackles+More+Complex+Tasks,+It+May+Insert+Redundant+Checks+Or+Use+Non-Optimal+Algorithms+&Ots=M0bvnrgd-7&Sig=F6ro-F8onajepnqpa7bsvbqdsmc&Redir_Esc=Y#V=Onepage&Q&F=False

Abstract: AI-driven code review tools are transforming how development teams engage in joint development to guarantee code reliability. This article investigates how models like ChatGPT-4.5, Gemini, and Deepseek R1 automate repetitive tasks, enforce standards, and detect defects without replacing human expertise. Studies show AI reduces code review time by 40–60% while flagging 30% more edge-case bugs than manual reviews. However, teams using AI report 25% higher initial setup costs for integration pipelines. The analysis covers technical workflows, collaboration patterns, and,......

Keywords: AI code review, collaboration, coding standards, defect detection, technical debt, static analysis

[1] “Improving The CPU And Latency Performance Of Amazon Applications Using Amazon Codeguru Profiler | Amazon Web Services,” Amazon Web Services, Mar. 31, 2021. Https://Aws.Amazon.Com/Blogs/Devops/Improving-The-Cpu-And-Latency-Performance-Of-Amazon-Applications-Using-Amazon-Codeguru-Profiler/
[2] Velaga, S. P. (2020). Ai-Assisted Code Generation And Optimization: Leveraging Machine Learning To Enhance Software Development Processes. International Journal Of Innovations In Engineering Research And Technology, 7(09), 177-186.
[3] V. J. Owan, “Exploring The Potential Of Artificial Intelligence Tools In Educational Measurement And Assessment,” Eurasia Journal Of Mathematics, Science And Technology Education, Vol. 19, No. 8, Art. No. Em2307, Jun. 2023, Doi: 10.29333/Ejmste/13428.
[4] S. Lund Et Al., “The Postpandemic Economy: The Future Of Work After COVID-19,” Feb. 2021. [Online]. Available: Https://Www.Mckinsey.Com/Ch/~/Media/Mckinsey/Featured%20insights/Future%20of%20organizations/The%20future%20of%20work%20after%20covid%2019/The-Future-Of-Work-After-Covid-19-Report-Vf.Pdf
[5] M. Chui, B. Hall, H. Mayhew, A. Singla, And A. Sukharevsky, “The State Of AI In 2022—And A Half Decade In Review,” Mckinsey & Company, Dec. 06, 2022. Https://Www.Mckinsey.Com/Capabilities/Quantumblack/Our-Insights/The-State-Of-Ai-In-2022-And-A-Half-Decade-In-Review

Abstract: The rapid expansion of digital infrastructure has intensified global energy consumption, placing significant pressure on environmental sustainability. This study investigates the role of Green Cloud Computing, Artificial Intelligence (AI) for Energy Optimization, and Carbon-Aware Software Development in reducing the environmental footprint of IT operations. Using a mixed-method approach, primary data were collected through surveys of IT professionals and secondary data from sustainability reports, focusing on key performance indicators such......

Keywords: Green Cloud Computing, AI for Energy Optimization, Carbon-Aware Software Development, Sustainability Metrics, Renewable Energy, Digital Transformation.

[1] Ahmed, S., Rahman, M., & Chowdhury, M. (2022). Energy-Efficient Cooling Solutions For Modern Data Centres: A Review. Journal Of Sustainable Computing, 35, 100690. Https://Doi.Org/10.1016/J.Suscom.2022.100690
[2] Anupong, W., Azhagumurugan, R., Sahay, K. B., Dhabliya, D., Kumar, R., & Vijendra Babu, D. (2022). Towards A High Precision In AMI-Based Smart Meters And New Technologies In The Smart Grid. Sustainable Computing: Informatics And Systems, 35, Article 100690. Https://Doi.Org/10.1016/J.Suscom.2022.100690
[3] Australian Government Clean Energy Regulator. (2023). National Greenhouse And Energy Reporting (NGER) Scheme. Https://Www.Cleanenergyregulator.Gov.Au
[4] Baliga, J., Ayre, R., Hinton, K., & Tucker, R. S. (2022). Green Cloud Computing: Balancing Energy Savings And Performance. IEEE Transactions On Sustainable Computing, 7(3), 450–462. Https://Doi.Org/10.1109/TSUSC.2021.3112586
[5] Belady, C., Rawson, A., Pfleuger, J., & Cader, T. (2008). Green Grid Data Centre Power Efficiency Metrics: PUE And Dcie. The Green Grid. Https://Www.Thegreengrid.Org