iosr-JCE   Volume-26 ~ Issue-1 ~ Jan. - Feb. 2024

Abstract: This experimental study sampled 347 students from nine urban (212 students) and nine rural (135 students) secondary schools, grouped into three and taught in; conventional classrooms, virtual reality or blended learning methods respectively. A modified Interest Scale with an alpha coefficient of 0.79 was used for data collection. It took three months to complete the study, with strict observation of COVID-19 protocols. A total of 341 students completed this study. Six subjects were discarded for missing some treatments either during or post COVID 19 period. The data were analyzed using Analyses of Covariance (ANCOVA). The pre-experimental test......

Keywords: urban, rural, conventional classroom, virtual reality, blended learning, educational interest

[1]. Adedigba, A., (2019 June, 16) Eight Million Out-Of-School Children In 10 Nigerian States And Abuja. Premium Times. Retrieved From Https://Www.Premiumtimesng.Com/News/Headlines/335352-Eight-Million-Out-Of-School-Children-In-10-Nigerian-States-And-Abuja-Unicef.Html
[2]. Ajai, J. T. And Imoko, B. I. (2013) Urban And Rural Students' Academic Achievement And Interest In Geometry: A Case-Study With Games And Simulations Method. Taraba State University. Journal Of Education Research And Production 1(2) 56-63
[3]. Aliogo, U. (2018 October, 26) Nigeria: 13.2 Million Nigerian Children Roaming The Streets, Says Falana. An Online Report Retrieved From Https://Allafrica.Com/Stories/201810260062.Html
[4]. Alokan, F. B. And Arijesuyo, A. E. (2013) Rural And Urban Differential In Student's Academic Performance Among Secondary School Students In Ondo State, Nigeria. Journal Of Educational And Social Research 3(3) 213-217.
Doi:10.5901/Jesr.2013.V4n3p213
[5]. Amorighoye, T. A., (2020) The World Economic Forum Covid Action Platform. Retrieved From
Https://Www.Weforum.Org/Agenda/2020/06/Education-Nigeria-Covid19-Digital-Divide/.

Abstract: Wind energy prediction plays a crucial role in renewable energy planning and policymaking. This study leveraged the Darts time-series framework to compare 10 different machine learning algorithms to investigate the use of solar radiation data for accurate wind energy prediction and compare with previous study results. Evaluation was conducted using two performance metrics: Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to inform on the prediction's accuracy. The best-performing model for wind speed prediction was the CatBoost model with a 12-hour lag, achieving an RMSE score of 0.877, while for wind direction prediction, CatBoost also achieved.....

Keywords: Darts; Random Forest; Neural Network; Covariates; Wind Energy; Forecasting; Nigeria

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Abstract: Enhancing the productivity of programming is an essential component of contemporary software development. In order to address the growing demand for high-quality software in shorter time limits, developers must work efficiently. As a result, numerous methods and tools targeted at raising programming productivity have been developed.
Using automated tools is one way to increase programming productivity. Many of the repetitive processes required in software development, like code generation, testing, and debugging, are automated by these technologies. Developer productivity increases when they may concentrate on more intricate facets of software development, as a result of less time and effort being needed for these jobs.......

[1]. Raychev, V., Vechev, M., &Yahav, E. (2014). Code Completion With Statistical Language Models. In Proceedings Of The 35th ACM SIGPLAN Conference On Programming Language Design And Implementation (Pp. 419-428).
[2]. Kessentini, M., Sahraoui, H., &Boukadoum, M. (2015). A Machine Learning Approach For Automatic Refactoring Suggestions. Empirical Software Engineering, 20(5), 1305-1341
[3]. Khomh, F., Padioleau, Y., Adams, B., & Hassan, A. E. (2009). An Exploratory Study Of The Impact Of Code Smells On Software Change-Proneness. In Proceedings Of The 16th Working Conference On Reverse Engineering (Pp. 75-84).
[4]. Alshammari, R., Alqahtani, M., Alharbi, M., & Alghamdi, M. (2019). A Review Of Machine Learning Techniques In Software Engineering. Journal Of Big Data, 6(1), 1-25.
[5]. Guo, Y., Zhang, H., & Sun, H. (2017). Code Completion With Neural Attention And Pointer Networks. In Proceedings Of The 26th International Conference On World Wide Web (Pp. 145-154).

Abstract: Technologies play an important role in the society. It has come with mixed feelings as disruptive in many spheres though with its benefits in accelerating growth and development. While the education sector is evolving strategies and adapting models to enhance the acceptability of their graduates by employers, the surfacing of Artificial Intelligence has come with it another dilemma. The emerging reality cannot be downplayed. The school system must accept, and remodel its curriculum to ensure it meets the requirements of the labour market for the advantage of its graduates......

Keywords: Artificial Intelligence; Technical Education; Impact; Curriculum; Remodel

[1] M. Tedre Et Al., "Teaching Machine Learning In K-12 Classroom: Pedagogical And Technological Trajectories For Artificial Intelligence Education," Ieee Access, Vol. 9, Pp. 110558–110572, 2021.
[2] I. Adamu, S. Kanbul, A. Gambo, And T. Zanna, "Technical And Vocational Education Teachers Computer Competencies Using Artificial Intelligence," J. Adv. Res. Soc. Sci. Humanit., Vol. 5, No. 6, Pp. 256–269, 2020.
[3] L. Chen, P. Chen, And Z. Lin, "Artificial Intelligence In Education: A Review," Ieee Access, Vol. 8, Pp. 75264–75278, 2020.
[4] G. J. Hwang, H. Xie, B. W. Wah, And D. Gašević, "Vision, Challenges, Roles And Research Issues Of Artificial Intelligence In Education," Comput. Educ. Artif. Intell., Vol. 1, Pp. 1–5, 2020.
[5] S. Kanbul, I. Adamu, A. G. Usman, And S. I. Abba, "Coupling Tpack Instructional Model With Computing Artificial Intelligence Techniques To Determine Technical And Vocational Education Teacher ' S Computer And Ict Tools Competence," No. March, Pp. 1–25, 2022.

Abstract: The advent of big data and advanced analytics has catalyzed a paradigm shift in numerous domains, notably in financial risk assessment, where traditional methodologies grapple with the complexities of modern financial markets. This paper, titled "Risk Assessment Reimagined: A Machine Learning Approach to Heterogeneous Loan Portfolios," presents a pioneering exploration into the application of machine learning (ML) techniques to revolutionize risk assessment practices, specifically targeting the heterogeneity inherent in loan portfolios. Leveraging a comprehensive.....

Keywords: Machine Learning, Financial Risk Assessment, Loan Portfolios, Predictive Analytics, Default Risk

[1]. Altman, E. I. (1968). Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy. Journal Of Finance, 23(4), 589-609. A Foundational Study That Introduced Statistical Models For Predicting Corporate Bankruptcy, Setting A Precedent For Quantitative Risk Assessment In Finance.
[2]. Bishop, C. M. (2006). Pattern Recognition And Machine Learning. Springer. This Textbook Provides Comprehensive Coverage Of Machine Learning Algorithms And Their Theoretical Underpinnings, Essential For Understanding The Methodologies Applied In This Study.
[3]. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. Introduces The Random Forest Algorithm, Highlighting Its Utility In Handling High-Dimensional Data And Its Application To Complex Prediction Problems.
[4]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Mit Press. Offers An In-Depth Exploration Of Deep Learning Techniques, Including Neural Networks, Which Are Pivotal In Advancing Ml Applications In Risk Assessment.
[5]. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements Of Statistical Learning: Data Mining, Inference, And Prediction. Springer. A Seminal Text That Outlines Statistical Learning Approaches, Crucial For The Development And Application Of Ml Models In Financial Risk Assessment.

Abstract: Watermarking is a technique used to protect the authenticity and integrity of digital data. One method commonly used in watermarking is the Least Significant Bit (LSB) method, which utilizes the last bit of digital data to insert a watermark. However, this method is vulnerable to attacks and loss of information during data compression or transformation. In order to increase robustness and ease watermarking, in this study it is proposed to use the LSB combination method with the Reed-Solomon Code. The Reed-Solomon Code is an error correcting code capable of detecting and repairing.....

Keywords: Watermarking, Least Significant Bit, Reed Solomon Code

[1] R. S. C. P.Singh, P. Singh, And R. S. Chadha, "A Survey Of Digital Watermarking Techniques, Applications And Attacks," Int. J. Eng. Innov. Technol., Vol. 2, No. 9, Pp. 165–175, 2013.
[2] A. Arya And S. Soni, "A Literature Review On Various Recent Steganography Techniques," Int. J. Comput. Technol. Appl., Vol. 4, No. 1, Pp. 143–149, 2018.
[3] T. K. Hazra, M. Haldar, M. Mukherjee, And A. K. Chakraborty, "A Survey On Different Techniques For Covert Communication Using Steganography," Iosr J. Comput. Eng., Vol. 20, No. 2, Pp. 42–52, 2018, Doi: 10.9790/0661-2002024252.
[4] S. D. Muyco And A. A. Hernandez, "Least Significant Bit Hash Algorithm For Digital Image Watermarking Authentication," Acm Int. Conf. Proceeding Ser., Pp. 150–154, 2019, Doi: 10.1145/3330482.3330523.
[5] F. Hartung, "Digital Watermarking Of Keynote Speech Iii Digital Watermarking," No. June 2002, Pp. 2–4, 2015.

Abstract: There is a great demand for an efficient facial emotion recognition (FER) technique which can recognize different emotions from facial images. The emotional state of a person can be identified through different facial expressions and automatic recognition of emotions from facial expressions can be advantageous in tasks such as human-computer interaction and computer vision applications. There are several FER models discussed in existing works. This paper provides a brief analysis of FER techniques and emphasizes the implementation of the convolutional neural.....

Keywords: Facial Emotion Recognition, Convolutional Neural Networks, CNN architecture, Recognition accuracy, Performance Evaluation

[1]. Khaireddin, Y., & Chen, Z. (2021). Facial Emotion Recognition: State Of The Art Performance On Fer2013. Arxiv Preprint Arxiv:2105.03588.
[2]. Hu, M., Wang, H., Wang, X., Yang, J., & Wang, R. (2019). Video Facial Emotion Recognition Based On Local Enhanced Motion History Image And Cnn-Ctslstm Networks. Journal Of Visual Communication And Image Representation, 59, 176-185.
[3]. Lasri, I., Solh, A. R., & El Belkacemi, M. (2019, October). Facial Emotion Recognition Of Students Using Convolutional Neural Network. In 2019 Third International Conference On Intelligent Computing In Data Sciences (Icds) (Pp. 1-6). Ieee.
[4]. Jain, D. K., Shamsolmoali, P., & Sehdev, P. (2019). Extended Deep Neural Network For Facial Emotion Recognition. Pattern Recognition Letters, 120, 69-74.
[5]. Gervasi, O., Franzoni, V., Riganelli, M., & Tasso, S. (2019, January). Automating Facial Emotion Recognition. In Web Intelligence (Vol. 17, No. 1, Pp. 17-27). Ios Press.