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- This Community holds resources in Arts and Humanity
- The community host and disseminate materials that advance understanding, skills development, and community engagement
Recent Submissions
LOSS, BEREAVEMENT AND COUNSELING STRATEGIES
(Nigerian Psychological Rescarch, 2023) Sanbe, M.T.; Badru, F.A.
Life comes with various forms of uncertainties. Homo sapiens are faced with extremities of experiences, bringing diverse forms of emotion which vary from wins and gains that come with joy, gladness and elation. Other times, loss and bereavement become inevitable leading to emotions of sadness, gloom and grief. In the face of loss and bereavement, there is the need for appropriate counselling intervention in order to rehabilitate the affected and the relations of the victims of such losses to effectively adjust to their new realities. This paper is anchored on the counselling intervention strategies on the clients and relations of the victims of Dana Air crash in 2012. Dana Air Flight 0992 crashed on the 3rd of June 2012 in Lagos, Nigeria, killing all 153 passengers on board and six on land. The ripple effect of this calamity has become a major psychological case for counseling psychologists and medical/psychiatric social workers in the bid to intervene by offering strategic and effective counseling services to the concerned. These include the families of the victims, the airline users, the service providers, and the members of the community who were traumatized with the shock of the sudden crash. The implication of the intervention and counseling strategies called for the need for group psycho-education, grief counselling, cognitive behavioural therapy and the imperativeness of deploying psychological services in disasters and traumatic situations.
The Transformative Influence of Generative AI on Teaching and Learning
(Springer, Cham, 2025-07-25) Mavundla Khulekani, Abayomi Abdultaofeek, Adetiba Emmanuel, Olaitan Olutoyin and Thakur Surendra
The emergence of Generative Artificial Intelligence (AI) has marked a transformative shift in the landscape of learning and teaching across diverse educational settings. This paper explores the influence of generative AI technologies, such as large language models and content creation tools including AI-powered virtual assistants, ChatGPT, adaptive learning platforms, and automated assessment systems, on pedagogical strategies, student engagement, and educational outcomes. We examine how generative AI facilitates personalized learning experiences by providing adaptive content, real-time feedback, and automated support for both students and instructors. Furthermore, ethical issues such as biasness, confidentiality and data privacy as well as artificial intelligence tendency to enhance the already established inequalities in the education sector are investigated. The current study examines the possibility of generative artificial intelligence in refining traditional methods of teaching and its innovative techniques in assessment, curriculum design and skill development as we leveraged on some case studies and empirical research conducted in literature. As part of our findings, the integration of artificial intelligence must be systematically handled so as to obtain fairness and equity in the outcomes even when generative artificial intelligence has a great potential in enhancing access to and effectiveness in education. In conclusion, in order to reap the full promises and capability of generative artificial intelligence in education, a deliberate thoughtful policy development, implementation and continuing research are suggested while the future direction of artificial intelligence in education is also presented.
Bio-inspired computational intelligence (BioCOIN) for cyber security during COVID-19 pandemic: A bibliometric review
(Innovative Research Publishing -International Journal of Innovative Research and Scientific Studies, 2025-10-02) Agbehadji Israel Edem, Abayomi Abdultaofeek, Aroba Oluwasegun Julius, Freeman Emmanuel, Nketsiah Richard Nana
The COVID-19 pandemic profoundly disrupted the global economy, accelerating e-commerce and online activities while increasing the vulnerability of Information Technology (IT) systems. This paper aims to review the role of bio-inspired algorithms in enhancing cyber-security during the pandemic, addressing an evident research gap in this domain. A systematic review was conducted using data obtained from an online repository with broad coverage, focusing on the period 2020–2022. This timeframe captures the onset, peak, and aftermath of the pandemic. The study analyzed computational systems and applications that incorporated bio-inspired algorithms for security purposes. The review reveals that bio-inspired algorithms were already in use for cyber-security prior to the pandemic, with notable applications in Internet of Things Cyber-Physical Systems (IoT-CPS)-based Trojan detection circuits and network-layer optimization of security settings. However, the pandemic accelerated the need for resilient cyber-security frameworks and highlighted the potential of bio-inspired methods to adapt to rapidly evolving digital threats. Bio-inspired algorithms represent a valuable approach to strengthening cyber-security in times of crisis. Their adaptability and robustness make them suitable for addressing dynamic threats in increasingly interconnected ecosystems. The findings emphasize the need for continuous integration of bio-inspired approaches into cyber-security policies and infrastructures. Doing so can support more resilient digital ecosystems, enhance organizational processes, and promote better work practices in both crisis and post-crisis environments.
Development of a Restricted Access and Energy-Efficient IoT-Based Greenhouse System for Tropical Climates
(Springer Nature Switzerland AG 2025, 2025-10-01) Erinosho C. Tolulope, Amusa A. Kamoli, Abayomi Abdultaofeek, and Lawal Azeez
Agriculture as a cornerstone of global food production, faces sustainability and environmental impact issues. Conventional greenhouses while aiding
crop production, often contribute to high energy consumption and substantial greenhouse gas (GHG) emissions. They are also vulnerable to the introduction of diseases due to uncontrolled access. Theoretical frameworks highlight the greenhouse effect and the impact of climate change on traditional agricultural methods, emphasizing the urgency of alternative sustainable practices. This paper introduces and implements a Radio Frequency Identification (RFID) and Internet of Things (IoT)-based energy-efficient greenhouse system for tropical climates applications.
The system employs a solar energy source, an Arduino microcontroller, and various units for soil moisture, temperature monitoring, and RFID-based access control. The Arduino system enhances security by managing a list of approved RFID card (UIDs), granting access only to authorized users, and activating a buzzer for unauthorized access attempts. Performance tests of the prototype demonstrated superior results in temperature and soil resistance management compared to open-field farming, thus highlighting its potential for increased yield, resource efficiency, and enhanced farm security. This eco-friendly system is particularly suitable for deployment in energy-deprived tropical regions, offering improved farm yield and robust protection for greenhouse operations.
Speech to speech translation with translatotron: A state of the art review
(Elsevier B.V., 2025-10-20) Kala R. Jules, Adetiba Emmanuel, Abayomi Abdultaofeek, Oluwatobi E. Dare, Ifijeh H. Ayodele
A speech-to-speech translation using cascade-based methods has been considered a benchmark for a very long time. Still, it is plagued by many issues, like the time to translate a speech from one language to another and compound errors. These issues are because cascade-based methods use a combination of other methods, such as speech recognition, speech-to-text transcription, text-to-text translation, and finally, text-to-speech transcription. Google proposed Translatotron, a sequence-to-sequence direct speech-to-speech translation model that was designed to address the issues of compound errors associated with cascade-based models. Today, there are 3 versions of the Translatotron model: Translatotron 1, Translatotron 2, and Translatotron 3. Translatotron 1 is a proof of concept to demonstrate direct speech-to-speech translation. This first approach was found to be less effective than the cascade model, but it was producing promising results. Translatotron 2 was an improved version of Translatotron 1 with results similar to the cascade-based model. Translatotron 3, the latest version of the model, significantly improves the translation and is better than the cascade model at some points. This paper presents a complete review of speech-to-speech translation using Translatotron models. We will also show that Translatotron is the best model to bridge the language gap between African Languages and other well-formalized languages.