http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/103 Sun, 20 Sep 2026 23:04:06 GMT 2026-09-20T23:04:06Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31894 Title: Cloud-Based And AI-Enabled Career Path: A Meeting Point For Students, Parents, Higher Institution And Industries Authors: Lawal, Kehinde Hussein; Lwanga, S.Charles Abstract: Abstract Today, technology is progressing rapidly and the new profession appears almost every year, the role of the career counseling is becoming even more important. The suggested concept for developing a cloud-based AI solution for the process of career choice can be considered as the theoretical contribution of this study as it is aimed at creating an extensive platform for cooperation between students and parents, academic and industrial institutions. Basing on quantitative approaches, this model aggregates different types of information and utilizes such high-level AI techniques as machine learning to provide individualized career suggestions matched with the specific preferences, abilities, and gaps in one’s professions. Categorically, the paper identifies the factors of the proposed model as well as assesses the effectiveness of applying the model in improving career decision making based on the extant literature and statistical analysis. Consequently, the study stresses on the role of cloud-based, AI- supported solutions in transforming the landscape of career counseling services and collaboration between all the links within the education-employment chain. Majority of the participants gave their satisfaction on the possibility of the model to be used as a collaborative tool for students, parents, higher institutions, and industries so that a right career decision can be made and for education and employment to be anointed. Therefore, the study recommended the following: Here, it is imperative to assess how well the model is performing and what the users of the flow have to say about it from time to time. Engage all the stakeholders such as the students, parents, the institutions of higher learning, and industries in the current process of developing and enhancing the model. It will be necessary to consider the model’s ability to be as available to different user groups and demographics as possible. Provide the users with educational and training materials that would enable them to fully understand how to use different aspects of the model. Enhance relationships with industry associations to leverage expertise in expanding the model’s data inputs, and education institutions for immersion training opportunities and knowledge of developing trends and skills shortage. Mon, 15 Jul 2024 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31894 2024-07-15T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31546 Title: Improved 2-Level Data Security Approach using DNA Cryptography Authors: Awwal, I. M; Ojekunle, J.A; Abisoye, Opeyemi Aderiike; Babalola, G.A; Abisoye, Blessing .O; Akanji, O.S Abstract: Cryptography is the process of transforming the meaning of information with the aid of an encryption key. DNA cryptography is a new rapidly evolving technology that uses DNA computing principles: Adenine (A), Guanine (G), Cytosine (C), and Thymine (T) to encrypt or hide the meaning of information such that only the intended recipient can understand. This research aims to improve on a twolevel text data encryption system using DNA cryptography. To achieve the research goal, 2-level data security algorithms for encryption, decryption, and key generation were designed using Shift Cipher encryption technique at the first security level and One Time Pad encryption technique at the second security level of the encryption and decryption algorithms. The algorithms were implemented using the PHP programming language. The research findings revealed that the improved 2-level data security approach has better encryption and decryption execution time compared to the 2-level Text Data encryption using DNA cryptography and the encryption algorithms can be used to encrypt and decrypt alphanumeric and special symbol information Sun, 21 Nov 2021 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31546 2021-11-21T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31545 Title: Prediction Of Anxiety Disorder in Working Class Students Using Enhanced Machine Learning Algorithms Authors: Umar, Sani Alkali; Abisoye, Opeyemi Aderiike Abstract: Abstract: anxiety disorder is a mental health condition that affects millions of people worldwide and it make more impact among working-class students due to academic and work stress. Early detection of anxiety is important for easy intervention; the traditional diagnostic methods often fail due to the complex and subjective nature of the symptoms. This study includes a machine learning-based approach to predict anxiety disorder using five classification algorithms: Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). To enhance model accuracy and efficiency, Particle Swarm Optimization (PSO) was used for feature selection and hyperparameter tuning. Dataset obtained from Kaggle repository was used, it contains over 140,000 instances with 110 features spanning demographic, academic, and psychological dimensions. Exploratory Data Analysis (EDA) revealed strong associations between anxiety and factors such as work pressure, financial stress, and study satisfaction. PSO identified the most relevant 14 features, improving model interpretability and performance. Among the models evaluated, the PSO-optimized Random Forest achieved the highest accuracy (99%), followed by Decision Tree (97.3%), LightGBM (94.8%), and Naïve Bayes (88.1%). The results confirm that tree-based ensemble methods, particularly when combined with PSO, offer best solutions for anxiety prediction. This research contributes to the field of medical informatics by providing an efficient approach to predict anxiety disorder using AI. The study's findings can help in development of real-time diagnostic tools to aid clinicians and support early interventions in mental health care. Thu, 01 Jan 2026 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31545 2026-01-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31355 Title: Android-Based Food Ordering System with Feedback Mechanism Authors: Aminu, E. F.; Amedu, O. Z.; Ekundayo, A. Abstract: Mobile based transactions are among the fastest growing areas in Information Technology today. The growth introduces diverse opportunities more particularly in supply and distribution of goods and services under the wider umbrella of m-commerce for example, restaurant food ordering system. Many consumers would benefit by having a means of making orders using their mobile devices. Restaurant is a place where different people go to eat at different time of the day. Dining in restaurants has become a habit for most people over the years, where you can place order for anything you require. A major problem faced in typical ordering systems in restaurants is that customers queue for long hours in restaurants or wait for a long time so that their food can be prepared. In order to solve this problem, a mobile based solution that is used for food ordering and at the same time enabled feedback mechanism is proposed and developed to make the process of placing orders more efficient for customers, restaurant managers and chefs. The development applied an Object-Oriented Analysis and Design. The tool used for developing the Mobile Application is Android Studio which is one of the approved tools for developing Mobile Application and is also a tool that will make the application run on various android mobile phones. The system was tested by installing the mobile application on 5 users’ Android phones in which the system yielded an efficient and effective outcome in the testing and also gave an accurate result that is necessary and useful in consideration for system integration. Through using this solution, customers and restaurant operators can benefit from a seamless ecosystem concerning the processing of orders to restaurants. Description: JOSTMED Wed, 01 Jan 2020 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31355 2020-01-01T00:00:00Z