ARTIFICIAL INTELLIGENCE

Fisheries and aquaculture are vital to global food security but face critical challenges, including security threats from unauthorized access, the limitations of reactive manual monitoring, and uncertainty in long-term production forecasting. Existing management tools often lack a unified approach to integrate real-time operational surveillance with predictive decision-support analytics. This study aims to address these gaps by designing and developing a hybrid methodological framework that integrates computer vision for security and advanced machine learning and time-series forecasting for production analysis and stock management. The study employed a dual-component methodology. For security, an OpenCV-based Histogram of Oriented Gradients (HOG) descriptor combined with a Support Vector Machine (SVM) was implemented to achieve automated, real-time human intrusion detection and alarm triggering. For predictive analytics, a hybrid approach was utilized, applying Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) for long-term production forecasting and various ensemble classifiers (Random Forest, Gradient Boosting, K-Nearest Neighbors) to analyze fish survivability and production trends. Results demonstrate that this integrated framework is highly effective across operational and strategic levels. The computer vision system successfully provided continuous, automated surveillance with verifiable digital evidence. The ensemble classifiers achieved near-perfect performance in survivability prediction (e.g., Random Forest Accuracy = 0.98, F1-score = 0.99, AUC ≈ 0.999). Furthermore, the SARIMAX model effectively projected a steady increase in global fish production, forecasting growth from 232.19 million tonnes in 2024 to 272.12 million tonnes by 2043—a 17.19% increase—capturing long-term temporal dynamics that static regressors fail to model. Based on these findings, it is recommended that fisheries stakeholders prioritize the deployment of computer vision-based surveillance to reduce security risks and labor costs. Additionally, management authorities should integrate ensemble classifiers into stock-management systems to optimize survivability and adopt SARIMAX-based forecasting for robust, evidence-based policy planning. The implementation of these data-driven platforms will significantly enhance sustainable fisheries governance and support proactive resource allocation.

This study examined the impact of Artificial Intelligence on the Research Productivity of Library and Information Science students in Edo State. Five research questions were raised to guide the study. The study employed a descriptive survey research design. The population of the study comprised of all undergraduate students in the Department of Library and Information Science (LIS), Edo State who are registered and actively enrolled in the 2024/2025 academic session. A sample of 205 respondents was randomly selected and used in the study and they were drawn to represent the entire population. The Instrument for data collection in this study was a structured questionnaire developed by the researcher titled "The Impact of Artificial Intelligence on Research Productivity of Library and Information Science Students in Edo State (TIAIRPQ). The data collected were analyzed using mean (x), standard deviation (SD) and percentage method. The findings from the study showed that ChatGPT is the commonly used AI tool by LIS Students. Supporting this finding, Tlili et al. (2023) reported that ChatGPT is widely adopted by university students because it integrates naturally into existing learning workflows without requiring advanced technical skills. Furthermore, it was discovered that the level of Awareness among students regarding ethical issues in AI Assisted Research is high. Based on the findings of the study, it was recommended that Nigerian tertiary institutions offering LIS programmes should formally integrate ChatGPT and similar generative AI tools into information literacy and research methodology courses through guided instructional use

This study evaluated the knowledge, perceptions, and challenges regarding the role of Artificial Intelligence (AI) in X-ray reporting among 60 radiographers professionals in Edo State, Nigeria. Data were analyzed using descriptive statistics and Pearson's correlation. The results indicated a moderate level of foundational knowledge (Mean = 6.12 out of 10), with strengths in macro- concepts but weaknesses in understanding specific AI tools ( 40.00% ) and limitations/bias ( 45.00% ). Overall perceptions were positive and optimistic (Mean = 3.65 ), with strong agreement that AI should serve as a supportive tool (Mean 4.35) to enhance workflow efficiency (Mean 4.30). Inferential analysis revealed a significant positive correlation (r = 0.52, p < 0.001) between knowledge and favorable perception, confirming that education drives professional acceptance. However, the adoption of AI faces severe structural barriers: Lack of infrastructure (e.g., high-speed internet) was ranked as the most frequent challenge (Mean 4.10), followed by high implementation costs and insufficient training. The study concludes that while the human capital in Edo State is willing and ready to adopt AI, systemic infrastructural and educational deficits represent the primary obstacles. Targeted investment in Industrial Training infrastructure and mandatory specialized training are essential to transition AI from theory to reliable clinical practice.

Electronic mail (email) remains a primary communication channel globally, making it a prime target for malicious actors. The continuous rise in spam emails poses severe threats, including network congestion, financial losses, and security vulnerabilities like phishing and malware distribution. Traditional rule-based spam filters struggle to keep pace with the evolving tactics of pammers, creating a critical need for more adaptive and robust solutions. This project presents the Design and Implementation of a Spam Email etection System Using Artificial Intelligence.The system leverages Natural anguage Processing (NLP) and Machine Learning techniques to accurately classify mails as "Spam" or "Ham" (legitimate). The methodology involves data collection using a standardized public dataset, followed by intensive text preprocessing including tokenization, stop-word removal, and stemming. Text data is converted into numerical vectors using Term Frequency-Inverse Document requency (TF-IDF). Multiple AI algorithms, including Naïve Bayes, Support Vector Machines (SVM), and Random Forest, are implemented and evaluated to determine the most effective classifier.The system was developed using Python, utilizing libraries such as Scikit-Learn and NLTK, and deployed with a user-friendly graphical interface. Experimental results demonstrate that the AI-powered system achieves high performance, with the top-performing model attaining an accuracy rate exceeding [Insert Accuracy, e.g., 98.2%] and a low false-positive rate. This research concludes that integrating artificial intelligence significantly enhances email security by providing dynamic, real-time protection against malicious messages, thereby ensuring a safer digital communication ecosystem.

This study investigates The Influence of Artificial Intelligence (AI)in investigative journalism in Nigeria, with a limited focus to Edo state, to know whether it serves as a Revolution or a Threat. The study examine How artificial intelligence(AI) enhance investigative journalism in Nigeria. It explores the challenges associated with the use of AI in investigative journalism. To determine if AI acts as a facilitator of press freedom or a tool for media and misinformation. The study also asses journalists' views on AI as either a groundbreaking support or a danger to their profession. To accomplish this, a mixed-method approach was employed, using survey method and in-depth interviews with journalists in Edo State. The results showed that many view AI mainly as a help to investigative journalism, particularly helping in tasks like data analysis, verification of facts, fact-checking, and the dissemination of information. Yet, respondents also pointed some possible drawbacks, including ethical concerns, misinformation, Accessibility. In summary, the study suggests that while AI marks a turning point in journalism practice, its usage in Nigeria requires vigilance and ethical guidelines to protect press freedom and maintain professional standards. The study concludes with recommendations on how to address the challenges associated with the use of AI tools. These includes Training, accessibility and availability of infrastructural equipment, human management, Privacy protection, ethical standards and regulation.

This study examined the impact of Artificial Intelligence (AI) usage on the learning engagement of Chemistry Education undergraduate students at the University of Benin. The study adopted a descriptive survey design and involved a sample of 100 students randomly selected from different levels of the Chemistry Education program during the 2024/2025 academic session. A structured questionnaire titled Artificial Intelligence Tools and Learning Engagement Questionnaire (AITLEQ) was used for data collection. The instrument was validated by experts, and its reliability was established using the Cronbach Alpha method, yielding a coefficient value above 0.7. Data collected were analyzed using mean and standard deviation to answer the research questions. Findings revealed that Chemistry Education students make frequent use of AI tools such as ChatGPT, Google Gemini, and adaptive learning platforms for academic purposes. The results showed a positive relationship between AI usage and students’ learning engagement across behavioral, emotional, and cognitive dimensions. Specifically, AI tools enhanced students’ motivation, confidence, and participation in learning activities, while helping them understand complex chemistry concepts and manage study routines effectively. However, the study also identified several challenges hindering optimal AI use, including poor internet connectivity, high data costs, and limited institutional support. Based on these findings, the study concluded that the use of Artificial Intelligence significantly improves students’ engagement, learning efficiency, and academic productivity. It recommended that universities strengthen internet infrastructure, integrate AI literacy into teacher education curricula, and provide adequate institutional support to foster effective AI adoption. The study also suggested that further research should explore AI’s long-term effects on academic achievement, cross-disciplinary comparisons, and lecturer readiness for AI integration in Nigerian higher education.

A complete system design and implementation abstract framework tailored for your AI-driven spam email detection project is detailed below.AbstractElectronic mail remains a foundational pillar of global digital communication, yet its utility is continuously threatened by the exponential growth of unsolicited messages, phishing attempts, and malware-laden spam. Traditional rule-based and heuristic filters are increasingly obsolete due to their rigidity and inability to adapt to the highly evolving and sophisticated obfuscation tactics employed by modern spammers. To address these critical vulnerabilities, this project designs and implements an intelligent, adaptive Spam Email Detection System leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques. The primary objective is to build a high-accuracy, real-time pipeline capable of automatically distinguishing legitimate messages ("ham") from malicious content ("spam") with minimal human intervention. The system's structural architecture comprises five core pipelines: data ingestion, text preprocessing, feature engineering, AI model classification, and deployment. During the design phase, raw email datasets (including text body, headers, and metadata) are subjected to rigorous NLP preprocessing, which includes tokenization, stop-word removal, lowercasing, and lemmatization to strip out textual noise. Feature extraction is then executed using TF-IDF (Term Frequency-Inverse Document Frequency) and Word2Vec embeddings to convert cleaned unstructured text into dense, high-dimensional numerical vectors. For the classification engine, a comparative implementation analysis is conducted using multiple Machine Learning (ML) and Deep Learning (DL) architectures, specifically Naive Bayes, Support Vector Machines (SVM), Random Forest, and Bidirectional Long Short-Term Memory (BiLSTM) networks. The implementation was developed using Python and integrated into a responsive web application dashboard via the Flask framework, allowing users to input raw text or connect live mailboxes for real-time scanning. Experimental evaluation of the models on standard benchmark datasets (such as the Enron and UCI Spam SMS/Email datasets) demonstrates that the BiLSTM deep learning approach, when paired with semantic word embeddings, yields the highest performance, achieving an accuracy rate exceeding 98.2%, exceptional precision, and a drastically reduced false-positive rate. The results prove that integrating AI-driven semantic understanding into email security infrastructure provides a scalable, highly adaptive defensive barrier capable of continuously learning from new spam patterns and significantly hardening enterprise cybersecurity posture.

This study examines the influence of Artificial intelligence on jobs: unemployment and displacement. It captures the implication of Artificial intelligence on the workforce as a whole. The Literature review of the study was segmented into three(3) sections namely; Conceptual review, Theoretical review, and Empirical review. Major Statistical tools of analysis used includes; data visualizations using histograms and the multinomial logistic regression. All tests done were conducted at the 0.05 level of significance. Major findings show that python, AI algorithms, certifications, problem solving, mathematics, and the other skills have a significant effect on getting an AI-related job. The study concludes that the impact AI has on jobs, is more on job loss as regards low-skilled workers, however AI has the ability to complement human workers, which will in turn lead o increased efficiency and productivity. Also as regards job displacement, it will affect mainly low-skilled and routine jobs.

The main purpose of this study was to examine the impact of artificial intelligence on the accounting profession. It examines the usefulness of artificial intelligence to the accounting profession. The findings indicate that artificial intelligence and the accounting profession are positively correlated, and that AI will have an impact on the accounting profession in the future.The accounting profession's adoption of artificial intelligence has improved the quality of financial information, relevance, faithful representation, efficiency, and corporate governance information. However, it is advised that a comparison of the use of the accounting profession in other fields and in other accounting professions could offer some insights into institutional and cultural factors that influence the decision to use artificial intelligence. Additionally, the use of AI technology can help improve the quality of their asset base and lower leverage ratios by reducing debt. A survey research design was used in the study. A total of 50 questionnaires were distributed equally among penultimate, final-year students and faculty members working in the accounting department of the University of Benin in Benin City, Edo State, as the primary method of data collection. Regression analysis was used to formulate and test five hypotheses.The analysis's findings led to the acceptance ofthe alternative hypotheses and the rejection ofthe five null hypotheses. Thus, it was determined that artificial intelligence significantly affects how the accounting profession is perceived. According to the study, the accounting profession should implement stronger artificial intelligence procedures in order to enhance the caliber of their financial reporting and, consequently, their overall worth.

The Purpose of this study was to assess the influence of generative artificial intelligence on business education students' academic performance, in university of benin, benin city, Edo state. The total population for this study is one hundred and twenty (120) Business Education students from 100 level to 400 level of the 2022/2023 set, from the department of vocational and Technical Education, Faculty of Education, University of Benin, Benin City and a sample size of One hundred and sixteen (116) was used, using the proportionate stratified random sampling techniques. Five research questions were raised to guide the study, with a 0.05 significant level. A descriptive survey design was adopted. A questionnaire containing thirty-three (33) items was the instrument used in obtaining responses from the respondents. The instrument was validated by experts, using the T-Retest method. Its reliability co-efficient was 0.89. The data collected were analyzed using frequency counts, mean and standard deviation, while independent sample T-test will be used to address the research Hypothesis. The analysis of the data revealed the current state of student's exposure and experience with artificial intelligence in the realm of education in improving business education student’s academic performance. The findings also underscore the awareness of business education students on various generative artificial intelligence and how it is being used to improve their academic performance. It was evident that AI has a substantial impact on various facets of business education students learning, including access to learning materials, customization of learning experience, collaboration, instant
feedback, and the development of critical thinking and problem solving skills. The study also identified some key challenges such as laziness, lack of originality, examination malpractices, overdependence on technology, the ability to reduce classroom attendance and developing Erosion of Human Creativity. This research underscores the potential of generative AI in education and highlights areas for improvement in addressing the challenges that arise with its implication. The following recommendations were made based on this finding; Authorities in Business Education department should recognize the challenges associated with the integration of AI in Business Education. Business Education department should invest in technical support and provision of materials needed for administering generative AI to business education students. Educators in business education department should receive trainings to adapt to new AI-based teaching methods, and a balance should be struck between traditional teaching and AI-based methods to ease the transition to harness the full potential of generative AI among business education students, Business Education department should focus on creating more customized learning platforms and environments. This involves developing AI-driven tools and platforms that cater to individual learning needs and preferences, ultimately a more personalized and effective learning experience for each student in business education.