Afe Babalola University Based Journals
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ARTIFICIAL INTELLIGENCE AND THE LAW: AN OVERVIEW
Get PDFAI has been deployed in finance, health care, law enforcement, research, teaching, communication and even transportation. In the legal industry, AI has been useful to law students, lawyers and judges. The widespread use of AI has raised salient questions over its effects on legal concepts like human rights, intellectual property, labour and employment law, criminal law, health law and entertainment law. The need for formal regulation became more evident in recent years with the popularisation of generative AI models which have brought AI closer to the people more than ever. Regulating AI is essential to curb its adverse effects on the society. It is critical that harmonised rules and policies are made across countries, to truly harness the potential of AI in enhancing socio-economic development and mitigate the risks that are inherent in the deployment of AI. This paper serves as an overview of the relationship and impact of AI in various fields of law and provides suggestions on various thorny issues raised by the deployment of AI in law
Are Skincare Brands Turning Gen Z’s Loyalty into Profitable Ventures in Africa? A PLS-SEM Analysis of Deceptive Marketing Practice on Consumer Behaviour
Get PDFThe skincare market reached 262.5 billion in 2032 at a 6.0% growth rate. This led skincare brands to compete for dominance, using deceptive marketing tactics in advertising, labelling, product ideas, and packaging. Despite short-term financial gains, deceptive marketing practices have neglected people and planet considerations. Gen Z, raised by world-marked environmental concerns, prioritises social activism and environmental sustainability in their purchasing behaviour. The Internet has amplified these trends, disseminating deceptive ideas widely and leading to consumer confusion and complex purchase decisions. The study explored how skincare brands exploit Gen Z's loyalty for profitability by assessing the impact of deceptive marketing on consumer purchasing decisions in the cosmetics industry. The study conducted an online survey targeting Gen Z skincare consumers in economically diverse African countries, including Egypt, South Africa, Morocco, Nigeria, Uganda, and Lesotho, obtaining 352 responses worth 92% of the sample size. The survey, analysed using the PLS-SEM approach, showed high predictive power between research variables, with R2 coefficients of 55.1% for Gen Z's loyalty and 5.9%, 6.9%, and 5.6% for advertising, labelling and packaging, and product ideas, exceeding the 5% threshold. Findings suggest deceptive marketing impacts Gen Z's loyalty as a measure of purchase behaviour in Africa's cosmetics industry. Research recommends prioritising ethics in marketing practices, developing unique labelling and packaging, enforcing regulations, and investing in consumer education to empower consumers to make informed decisions. The study notes biases in data collection due to unequal online access among Gen Z consumers
Analysis of Perceived Effects of Rainfall Variability on Rice Yield in Lokoja, Kogi State, Nigeria
Get PDFThis study analyzes the perceived effects of rainfall variability on rice production in Lokoja using data on rainfall and rice production from 2013 to 2022, and data on demographic and socio-economic characteristics of farmers as well as their perception on the effects of rainfall variability. The study was done using well-structured questionnaire administered in the study area to the 140 selected respondents, and the Statistical Package for the Social Sciences (SPSS) version 24 was utilized to analyze the data. Results showed that rainfall have significant effects on rice production with varied yield based on rainfall fluctuations in the amount of rainfall with as much rice production of about 4269 tonha-1 when precipitation was suitable between about 1250 - 1350mm, but when less than normal precipitation (<1250mm), it reduces the production yield to about 2361 tonha-1. Similarly, the results revealed that when the rainfall amount was highest, the total rice production was reduced, with cultivated areas having heavy rainfall resulting in flooding. Results further showed that on average 1282mm of rainfall produced 2693 tons of rice in the study years. About 44% of the respondents refuted early rainfall onset affecting rice production with months such as June, July, and September in some of the years with high rainfall intensity having adverse effects on rice farming, with 43% of the respondent noting that it limited the amount of land available for cultivation, while 26% noted that it reduces rice yield while the remaining 31% noted that it destroys rice stands
The ‘japa’ syndrome of Nigerian youths as a survival strategy: A socio-economic perspective
Get PDFNigeria is often referred to as the giant of Africa; still, it has over the last two years experienced an alarming surge in the migration of young Nigerians to other developed states. Factors such as the chronic unemployment rate, which stands at 33.3% as of 2020, high levels of insecurity, and unfulfilled aspirations have caused increased pressure in the search for better opportunities abroad. More interestingly, the increased migration rate most likely has nothing to do with the idolisation of these states, but rather is largely due to a lack of national development and economic growth, as youths have been found to migrate to countries with better economies within Africa. Although no accurate data exists on the rate of migration, this circumstantial migration reflects the continued struggle of citizens to improve their living conditions. The sudden increase in the international migration of young Nigerians is a critical issue that has not received much attention from the Nigerian government, especially in tackling the root causes and ways to further reduce its negative effects on the socio-economic development of the nation. Given these concerns, this study aims to further explore the push and pull factors influencing the migration of Nigerian youths and their patterns. More importantly, it examines the socio-economic implications of an increase in the migration rate in Nigeria. The findings of the study will be useful for an in-depth reality check and will serve as a wake-up call for the Nigerian government.
 
Media Framing of Political Instability and its Impact on Developmental Policies in West Africa
Get PDFThis study explored the media framing of political instability in West Africa and its consequent impact on developmental policies. The media’s role in shaping public perceptions of political events was investigated, revealing that the portrayal of instability often influences the policy-making process. By employing a qualitative analysis of various media sources, the study examined how narratives around political unrest affect public opinion and government responses. The findings indicated that sensationalised media coverage exacerbate fear surrounding instability, leading to reactive policy measures rather than proactive strategies for development. Moreover, the research highlighted the interplay between media representations and governance, demonstrating how the framing of instability not only reflects societal anxieties but also shapes the political landscape. The study concluded that a more responsible media approach is essential for fostering informed public discourse and promoting sustainable development in the regio
Robotic Assistant for Object Recognition Using Convolutional Neural Network
Get PDFVisually impaired persons encounter certain challenges, which include access to information, environmental navigation, and obstacle detection. Navigating daily life becomes a big task with challenges relating to the search for misplaced personal items and being aware of objects in their environment to avoid collision. This necessitates the need for automated solutions to facilitate object recognition. While traditional methods like guide dogs, white canes, and Braille have offered valuable solutions, recent technological solutions, including smartphone-based recognition systems and portable cameras, have encountered limitations such as constraints relating to cultural-specific, device-specific, and lack of system autonomy. This study addressed and provided solutions to the limitations offered by recent solutions by introducing a Convolutional Neural Network (CNN) object recognition system integrated into a mobile robot designed to function as a robotic assistant for visually impaired persons. The robotic assistant is capable of moving around in a confined environment. It incorporates a Raspberry Pi with a camera programmed to recognize three objects: mobile phones, mice, and chairs. A Convolutional Neural Network model was trained for object recognition, with 30% of the images used for testing. The training was conducted using the Yolov3 model in Google Colab. Qualitative evaluation of the recognition system yielded a precision of 79%, recall of 96%, and accuracy of 80% for the Robotic Assistant. It also includes a Graphical User Interface where users can easily control the movement and speed of the robotic assistant. The developed robotic assistant significantly enhances autonomy and object recognition, promising substantial benefits in the daily navigation of visually impaired individuals
Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis
Get PDFThis paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches
Harnessing Abuja's Municipal Solid Waste as a Renewable Energy Source: Scanning Electron Microscopy Analysis
Get PDFA study of Abuja’s municipal solid waste (MSW) samples using the scanning electron microscopy analysis was undertaken in this work. In the face of the severe energy poverty being experienced in Nigeria which largely depends on diminishing fossil fuel resources coupled with the associated problem of greenhouse gas emission, the energy potential available in municipal solid wastes needs to be investigated. Using MSW as a fuel source for electric energy production will also positively impact on Abuja’s waste management. This present study requires the analysis of the MSW with aim of confirming that products of its incineration will not be hazardous to the environment. ASTM E 1508 procedures for utilizing the scanning electron microscope (SEM) were followed to identify elements that would be contained in the bottom ash of the incineration process of samples of Abuja’s municipal solid wastes obtained from selected districts of the city. Elemental composition of the bottom ash that will be formed from incineration of Abuja’s MSW was obtained by the use of energy dispersive x-ray analysis. The micrographs plotted indicate that silicon and iron are the principal elements present in the samples with values for silicon and iron being highest at 49.5 and 19.55%, respectively, for the sample from Dutse-Alhaji. The tests also show the presence of silver in the organic wastes generated in Abuja, while presence of sulphur is very minimal. The silicon levels present in Abuja’s municipal solid waste compare well with values for Nigerian coals which have percent silicon contents ranging from 39.0 – 49.4% (Enugu coal – 39.0%; Okaba – 44.8%; Maiganga – 49.4%). The test results also show that Abuja’s MSW samples had grain sizes ranging from 3.5 mm 16 mm. The results indicate Abuja’s MSW combustion rate will be lower than for pulverised coal which is known to have much lower grain size in the range of 75 μm to 106 μm and will need shredding before firing since grain size is a very critical determinant factor in solid fuel combustion rate and burn-out time. The tests conclusively show that Abuja’s MSW will be a more environmentally friendly fuel than coal because of its lower sulphur content
Discovering the Macro-Elements Presence in Biochar Produced Indigenously
Get PDFBiochar boosts soil fertility and helps plants to withstand drought. Its production locally has been a challenge and that is why an Indigenous Biochar Production Kiln (IBPK) was conceived, designed and fabricated at the Workshop of the Agricultural Technology Department, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria. IBK convert biomass to carbon-rich organic material through thermal energy. The IBPK has two drums, the Internal Retort Drum (IRD) and External drum of diameters and heights of 350 mm x 600 mm and 500 mm x 800 mm respectively. The total weight of the IBPK was 82.50 kg. The IRD of 116 kg/m3 volume was loaded with 55 kg biomass from wood waste, covered, and placed inside the external drum. The space between the outer wall of the IRD and the inner wall of the outer drum was 75 mm enough to contain firewood lighted and covered to produce the heat needed for the wood waste inside the IRD to convert it to Biochar. Smoke from the IBK escaped through the chimney attached to the external drum’s lid. The operating time for the carbonization was 182 minutes and the conversion efficiency of the IBPK was 71 %. The average temperature of the IBPK during the conversion was 269 °C. The test carried out on the produced Biochar showed the presence of macro elements that included Nitrogen (2.95%), Phosphorus (21.79%), Potassium (4.95%) and Carbon (70.31%). The fabrication cost was Fifty-Two Thousand, Two Hundred Naira only (₦52,200:00). The IBPK is recommended for farmers to produce Biochar as needed for improved farm yield, and young graduates who want to go into Biochar production as a way out of unemployment
Mitigating the Impact of Climate Change on Vegetable Farming: An Evaluation of Artificial Planting Technique
Get PDFA worldwide issue, global warming results from human activity changing the climate and having a negative impact on people, animals, and plants. However, in terms of plants, the sun provides the primary elements required for healthy growth of photosynthetic plants, which use the energy from the sun to create food for themselves. Light with varying wavelengths that serve distinct functions during the photosynthetic process are the essential elements that are captured from the sun. The wavelength of the ultraviolet (UV) component of sunlight varies, characterized as UV A (315–400 nm) and UV B (280–315 nm) are the primary components that must be precisely proportioned for a profitable farming. In order to lessen the impact of climate change on vegetable farming, this research suggests integrating light emitting diodes (LEDs) in artificial growing machines as well as planned irrigation systems as an alternate source of ultraviolet sunshine. To provide the necessary UV light combination, blue, red and white colours of light-emitting diodes (LEDs) were combined using diffusers. The red, blue, and white LEDs were used for two weeks, each 12 hours a day, to influence the plants growth, with red promoting photosynthesis, white improving it, and blue encouraging stem and leaf growth. An Arduino Uno was used to program both the hardware and software components of the automated growth machine. The outcome of planting varied vegetable plant under LED lights was contrasted with the outcome of planting the identical set of plants under direct sunlight. After the first and second weeks of planting, the plants' performances under both circumstances are comparable