The proliferation of smart systems in all spheres of society has called attention to the ethical issues of responsibility, fairness, and transparency associated with the development and deployment of AI at a global level. The more that machine learning becomes enmeshed in processes of decision-making, the more new ethical dilemmas that reflect the possibility for algorithmic bias, information vulnerability, socio-cultural exclusion, and environmental considerations begin to surface. This review provides an organised, humane, systematic examination of responsible AI, connecting ethical considerations with pragmatic concerns as derived from existing models, policies or problems in execution. Apart from standard controversies, the contribution of the paper is to single out little explored concerns that are related to non-Western ethical perspectives, sustainable AI practices, and the ethical reasoning of superintelligent systems. It is a critique of the existing limitations of ethical codes, which put forward ideas and directions to connect innovation and social good. By bringing in alternative perspectives and interdisciplinary thinking, it presents a rich base for the ethical recalibration of intelligent systems to work for human betterment, in a more inclusive, equitable and forward-looking manner. Keywords: Responsible artificial intelligence; Algorithmic fairness and non-discrimination; Transparency and accountability; Data privacy and governance; Global AI governance DOI: 10.7176/JIEA/15-2-09 Publication date: September 28 th 2025

Information and Communication Technology has woven into human lives as it is nearly inseparable from it. It has drastically affected the way things are done either by reinventing or increasing their efficiency. Inevitably, Information technologies have also reinvented how social interactions are carried out and in extreme cases introduced myriads of modernized and globalized forms of communication. The study evaluated Smartphone usage and social interaction of Delta State University students. A survey method was used to collect responses from 450 respondents from the Faculty's whole student body. The data collected was analyzed using linear regression (ANOVA). The Technology Acceptance Model and Symbolic Interaction theories were used to guide the research. The research shows that the frequency and pattern of smartphone use by students at the Faculty of Social Sciences is influenced their social interactions with family and friends; there was no significant effect of smartphone use on students' academic activities; and the brands of phones owned by students influenced their social interactions. It is recommended that students should utilize their smartphones for academic activity more; students should strike a balance between using smartphones for personal and academic objectives and University administration should give internet access for students. Keywords: Smartphones , Social Interaction , Usage, Students, Brands DOI: 10.7176/JIEA/13-1-02 Publication date: February 28 th 2023

Agriculture is undergoing a digital transformation because of population growth, climate change, and food security concerns. Agriculture is influenced by information technology in terms of cost reduction, efficiency, and sustainability. Precision agriculture employs IoT, deep learning, predictive analytics, and AI-based technologies to aid in the detection of plant diseases, pests, and poor plant nutrition in the field. The study's objectives are as follows: 1) evaluate the role of smart technologies and their impact on precision agriculture sustainability; 2) assess the typical application of IoT data analytic and deep learning in precision agriculture; and 3) investigate the barriers to the adoption of sustainable precision farming. IoT technologies collect data and relay it to data analytics and deep learning for in-depth analysis. According to the findings, data assists farmers in managing crop variety, phenotypes and selection, crop performance, soil quality, pH level, irrigation, and fertilizer application quantity. Technological issues, safety, privacy, cost, and legal issues also influence the adoption of these technologies. Keywords: IoT, Big Data Analytics, Deep Learning, Precision Agriculture, Sustainability DOI: 10.7176/JIEA/13-1-03 Publication date: February 28 th 2023

The COVID 19 pandemic led to a situation where Kenyan institutions were forced to embrace remote learning, remote working and e-commerce services. These basically led to an upsurge in financial cyber activity and with it came an upsurge in cybercrime. This paper conducted a review of available secondary data on cyber attacks in Kenya with the aim of identifying the most popular trends embraced by attackers while perpetrating cyber-attacks on financial institutions and their clients. The paper was also able to obtain a ranking of the most popular trends associated with attacks within the Kenyan cyber space. Keywords: Cybersecurity, cyber-attacks, cybercrime, malware, phishing DOI: 10.7176/JIEA/11-2-09 Publication date: November 30 th 2021

Efficient handoff prioritization schemes make mobile networks to perform better. However, most existing schemes have the common characteristics of reducing the call drop probability and increasing the call block probability; this decreases the general network performance. The reservation of channels for the use of handoff calls alone has not proved to be an efficient approach to optimally utilize the network limited channel resources. These approaches are such that even when the reserved channels are not in use, new calls could still get blocked if there are no channels among the shared channels to allocate to the new calls. In trying to maintain an improved QoS for handoff calls while ensuring an acceptable QoS for new calls, different methods have been suggested for new calls to access and use the reserved channels while priority is still given to handoff calls since it is more frustrating to have an on-going call dropped than blocking a new call. Network parameters such as call drop probability and traffic intensity had been used separately to determine when new calls could be granted access to use the reserved channels or otherwise. Handoff queuing and other call admission control approaches have also been used. This paper proposes the use of traffic intensity and call drop probability to decide whether the new calls could use the reserved channels or not. Also proposed is the need to keep the number of reserved channels fixed rather than calculating the numbers to reserve per time therefore reducing the computational complexity of the scheme. The simulation results showed an improved QoS for the handoff calls while producing a commensurate improvement for the new calls through a reduced Call drop Probability and call block probability. Keywords: Call block probability, Call drop probability, traffic intensity, handoff prioritization, mobile network DOI: 10.7176/JIEA/11-1-03 Publication date: January 31 st 2021

After the novel coronavirus outbreak, many countries closed universities. This situation urges to implement online delivery as an alternative method. This study aimed assessing the access to and use of ICT by students, the ICT competencies possessed by the students, and assesses the current level of students’ readiness for online education methods. Sample of 106 undergraduate students were selected. Semi-structured survey questionnaire was used for data collection. The findings revealed that most of the students have very limited access to and use of different types of ICTs. Mobile phones are the most popular ICT tool used by students. Poor ICT using competencies is another problem observed. ICT experts in the field of online education need to plan smartphone-based technologies, and it is recommended to offer zero-rated access to specific educational websites, and offer free or discounted mobile Internet packages to all students who need it to switch to online classes. Keywords : Online education; Coronavirus; Students; University; Mobile phones; ICT DOI: 10.7176/JIEA/10-4-04 Publication date: September 30 th 2020

Automatic street lightning system is very important particularly in urban and industrialized areas because of its ability to provide illumination and safety for vehicles and pedestrians throughout the night. It is relatively efficient as it consumes lesser amount of power with pre-determined operation times that are non-optimal for the surrounding environment. It is also a self-sustaining device. This research paper designed and implemented an improved four directional automatic street lightning system. It involved calculating the design parameters after selecting the appropriate devices and the cables. A solar street light is a form of renewable energy which is a stand-alone system in supply of electrical energy to be used for lightning. Solar radiation energy is used to charge the battery during daytime, and offer energy to the LEDs light equipment at night. A dimmable Modula is designed and integrated to the system to dim the LEDs at night from 01:00-06:00am.This system has a double advantage as it utilizes renewable energy and also reduces cost. Keywords: Automatic , Four Directional, Solar Power, Renewable, Street Light, Utility Grids, Light Emitting Diodes (LEDs) DOI: 10.7176/JIEA/10-4-05 Publication date: September 30 th 2020

The study investigated the health information-seeking behaviour of students at the Federal University of Technology, Akure in Nigeria. Information resources exploited by university students to meet health information needs were also examined. The study made use of the descriptive survey design, randomly sampling respondents’ opinions on reasons for seeking health information and factors that militate against the effective use of health information. Of 377 respondents, 239 (63.4%) of them were disposed to seeking information on physical fitness or exercise and cold or flu. Newspaper (283 or 75.1%) was identified as respondents’ commonest source of health information. For university students, a major inhibitor of health information seeking outside the library is the high cost of accessing health information for personal use (318 or 84.4%). But within the library, the uncooperative attitude of library personnel (240 or 63.7%) was identified as respondents’ major hindrance in accessing health information in the library. The study recommended adequate provision of health information resources for students’ use and regular library personnel training to improve professional skills required for effective dissemination of health information, particularly to university students. Keywords: information, information need, health information, health information-seeking behaviour, university students, library personnel DOI: 10.7176/JIEA/10-2-03 Publication date: March 31 st 2020

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnet-based totally system that permits the teach to levitate vertically up and down. Fuzzy logic, PID and Mras controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller. Index Terms: Magnetic Levitation (MAGLEV) train, Fuzzy logic, PID, MRAS DOI: 10.7176/JIEA/10-2-01 Publication date: March 31 st 2020

Background : Data mining techniques such as clustering and classification are used to explore patient's data and extract a predictive model. Medical data set are often classified by a large number of irrelevant disease measurements(features). Feature selection is one of the most common tasks which reduces the computational cost by removing insignificant features. Method : This paper presents a graph-based Louvain algorithm for mental illness dataset clustering and a particle swarm optimization combined with a decision tree as the classifier to select the small number of an informative feature from the thousands of features were collected from health centers consist of 1060 people in two groups of 550 patients and 510 healthy. Result: The results show that "aggression" Finding the greatest impact on the diagnosis of mental disorders has been observed in the number of 65. After that, the features such as "prisoner in the family" and "hard labor" with 63 observations had a greater impact on the disease also the third ranking "illiterate" and "elation and euphoria" had 61 and 58 observations. Conclusions: The classification accuracy shows that the proposed method is capable of producing good results with fewer features than the original datasets. Keywords: Mental illness, Graph clustering, Particle swarm optimization, ID3 DOI : 10.7176/JIEA/9-7-03 Publication date: December 31 st 2019