Air Quality Index prediction using Deep learning for Lagos State in Nigeria

The index for expressing air quality is known as the air quality index (AQI). It could be used to evaluate the effect of air pollution on a one’s health over a epoch of time which provides a guide to the community on the adverse health effects of air pollution around them.  This paper focused on developing a model for AQI prediction using Deep learning for Lagos state in Nigeria. The study acquired dataset from the OpenWeatherMap API which includes historical air quality and meteorological for Lagos State in Nigeria. Data was preprocessed by handling missing values, converting data types into numerical format using one-hot encoding. The study applied SMOTE technique to ensure balanced dataset. Four distinct Models such as LSTM, CNN, Prophet and SVR were utilized to determine the AQI of Lagos State. The results of balanced datasets used revealed LSTM provides the lowest MSE, RMSE and MAE values of 0.062, 0.249 and 0.149 respectively and higher R2 value of 0.968 compared with the other model CNN, Prophet and SVR. The paper concluded that in the prediction of the AQI for Lagos State, LSTM outperformed other models such as CNN, Prophet Model and SVR on the validating metrics known as MSE, RMSE, MAE and R2. The model obtained could be subjected to further research in other geographic regions, such as Delta State or other state by state level analysis which could be expanded to forecast other pollution indices at different levels

A Review of Interpretation of Airborne Geophysical Data for Hydrocarbon, Geothermal Energy and Minerals Prospecting over Middle Benue Trough Nigeria

This paper focuses on the review of the interpretation of airborne geophysical data for hydrocarbon, geothermal energy, and mineral prospecting over the Middle Benue Trough of Nigeria by previous researchers in the field of geophysics and other geosciences. The objectives of the research include: to reveal the significance of aeromagnetic data interpretation for geothermal energy and mineral exploration, to determine the potency of spectral analysis techniques for geophysical data interpretation, and to determine the gap in geophysical techniques previously used in the area. Based on the reviewed papers, it was observed that Oasis Montaj software plays an imperative role in the area of aeromagnetic data, aero radiometric data, and aero gravity data interpretation using spectral analysis with different techniques for the identification of potential zones for hydrocarbon maturation sediment, geothermal energy, and minerals. There is possibility of hydrocarbon deposits occurrence, geothermal energy, and minerals at the Middle Benue Trough based on the research findings of the previous researchers. In order to achieve this, there is need to put more effort in the integration of geophysical techniques at the Middle Benue Trough to get detailed information about the hidden targets

Document Classification in HEIs Using Deep Learning: A CNN, RNN, and Hybrid CNN-RNN Approach

 Higher Education Institutions (HEIs) are increasingly confronted with the complexities of evolving rules and requirements, necessitating innovative technology solutions to streamline document handling processes. Traditional paperwork methods are often inefficient and error-prone, leading to potential non-compliance. This research addresses these challenges by developing an AI-powered electronic document management system designed to automate compliance checks and simplify document handling as HEIs grow. The primary objective is to create a document classification model utilizing deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and a hybrid CNN-RNN approach, to enhance document accuracy and compliance. The study involves collecting and preprocessing a substantial dataset of documents, designing and evaluating various deep learning models, and optimizing hyperparameters. Performance comparisons among the models indicate that the hybrid CNN-RNN architecture outperforms individual models, achieving superior accuracy, recall, and F1-score, alongside a significantly lower mean squared error (MSE). Initial evaluations revealed the CNN, RNN, and CNN-RNN models achieved accuracies of 73%, 44%, and 27%, respectively, on the raw dataset. However, with an upgraded dataset, these models improved to 76%, 48%, and 79% accuracy, respectively, highlighting the hybrid model's enhanced capability in accurately classifying documents. The findings revealed the effectiveness of integrating advanced deep learning techniques to improve document verification processes in HEIs, ultimately facilitating better compliance and operational efficiency

A Phenomenological Study of the Place of Human Autonomy and Agency in the Age of Artificial Intelligence

Artificial intelligence has greatly influenced our modern society and is being incorporated into almost all facets of human life. The capabilities and possibilities of AI are endless but its use and safety has raised a lot of ethical questions. There are also concerns that AI will negatively impact humanity if it is not designed and used ethically.  Ethical considerations in the design and use of AI is a recent and pertinent issue which designers and producers of AI need to consider because of the possibilities of its tremendous impact on humanity. The dilemma of the place of human autonomy and agency in this new age of artificial intelligence is a notable ethical issue which the paper seeks to address; will human dependence on AI lead to the reduction of  human autonomy and agency  in the future? Will our preference for efficiency and optimization lead to the replacement of humanlike traits with machinelike traits? Employing the existentialist framework, this paper sets out  to explore the place of human autonomy and agency in the age of artificial intelligence, it identifies the impact of the adoption and use of AI on human freedom, it also advocates for a future without total reliance on AI. Adopting the phenomenological method, it seeks to establish that humanity must maintain control over AI  to ensure its safety and that AI must be designed and produced   in a way that it augments humanity and not made to replace humanity. This must be done to ensure that humanity does not become over dependent on AI in the  future. The paper recommends that  AI must be  designed to align with human values to ensure the preservation of human autonomy and agency in a world that  is increasingly becoming dependent on AI

Machine Learning for detecting Microbial and chemical Contaminants in sachet Water

This study addresses the critical challenge of detecting microbial and chemical contaminants in sachet water in Nigeria using machine learning (ML) techniques. Traditional methods for water quality assessment are often time-consuming, costly, and ill-suited for real-time monitoring, particularly in resource-limited settings. We propose a novel approach that leverages supervised ML algorithms, including Gradient Boosting (GBC) and Random Forest (RF), to predict water potability based on an augmented dataset of 20 parameters, encompassing both microbial contaminants (e.g., Escherichia coli, Salmonella) and chemical contaminants (e.g., lead, arsenic). The dataset was enhanced using synthetic data generation techniques to address gaps in the original dataset, which lacked comprehensive coverage of critical contaminants. Our results demonstrate that the Gradient Boosting Classifier (GBC) achieves an accuracy of 99.8% and an F1 score of 99.7% on the augmented dataset, significantly outperforming other models. Feature importance analysis revealed that Escherichia coli, Salmonella, and lead were the most critical predictors of water potability, aligning with public health concerns. This study highlights the potential of ML for enhancing water quality monitoring, offering a scalable and cost-effective solution to mitigate waterborne diseases in regions like Nigeria, Nigeria. Future work will focus on integrating real-time sensor data and validating the model in real-world scenarios to further improve its applicability and impact

A Greener Spectroscopic Approach for the Quantification of Ketoconazole in Pharmaceutical Formulations

Ketoconazole as a synthetic imidazole derivative, has emerged as a significant therapeutic agent in the field of antifungal pharmacology. Its unique mechanism of action, pharmacokinetic properties and clinical efficacy have positioned it as an essential component in the treatment of various fungal diseases, leading it to gain significant attention in medical mycology. Unfortunately, incorrect dosage or wrong pharmaceutical formulations of ketoconazole drug, can cause adverse health effects, which may include the reduced efficacy, hepatotoxicity and other unintended side effects. However, most of the conventional methods followed for the quantification of ketoconazole active ingredient in pharmaceutical samples pose significant threat to environment, and are rendered undesirable and damaging to habitable environment. Hence, there is a need for coming up with safer analytical alternatives that minimize the discharge of hazardous chemicals. This research endeavours to address the multifaceted challenges by validating a developed spectrophotometric technique, offering reliable solution in promoting a green methodology for the quantitative determination of ketoconazole active ingredient. Sample solutions of 100 ppm, 50 ppm, 25 ppm, 12.5 ppm and 6.25 ppm were prepared in water for each of the four selected brands (Axo-1, Derm-2, Ketora-3 & Keta-4) and the absorbance of each solution was taken separately at a wavelength of 240 nm, using 1 cm cell. Calibration curve for each of the tested brands indicates a linear correlation between concentrations and measured absorbance, which simply validates the accuracy of this procedure. The research finally recommends engagement of more brands on this approach for further validation and accommodation

Trace Metals in Tilapia zilli and Clarias batrachus Fishes Associated with Water and Soil Sediment from River Rukubi in Doma, Nasarawa State, Nigeria

This study was conducted to investigate the pollution level of heavy metals such as Pb, Cr, Ni, Mn, Zn and Fe in the water, sediment and body organs (head, intestine & tile) of Tilapia zilli and Clarias batrachus from River Rukubi Doma, Nasarawa State. This was carried out using Atomic Absorption Spectrophotometer. Magnesium was the most highly concentrated in the various matrices.  Metal levels in the various body organs of the two fishes studied were found to be highly concentrated in the head.  Comparison of metal levels in the body parts of the two fishes, sediment and water showed that Cu (85.9%) had the widest variability while Mg (13.1%) was the least. Also comparison of metal levels in the body parts of the Tilapia zilli and Clarias batrachus showed that Mg has the highest mean metal concentration in Clarias batrachus (655.85 µgg-1) than in Tilapia zilli (608.40 µgg-1). Iron showed the highest mean metal concentration in Tilapia zilli (166.51 µgg-1) than in Clarias batrachus (57.20 µgg-1). The heavy metals determined were mostly within the ranged of acceptable limit with exception of Pb, Cr and Mn.  In view of this study it is recommended that biological monitoring of fishes meant for consumption from this water body be carried out regularly to ensure human safety. This study contributes to understanding the evaluation of metals level in fishes and their importance in food balanced approach for essential benefits

Effect of Magnesium Oxide-Carbonized Hibiscus Sabdariffa (Zobo) Waste and Magnesium–Zinc Binary Oxide Nanocomposites in Mitigating Cadmium and Bacteria in Contaminated Water.

The need for active and inexpensive materials in combating menace of bacterial as well as heavy metals is very essential to the longevity of man and protection of his environment against deleterious substances such as bacterial and heavy metals. This research was designed to study the mitigative effect of MgO-carbonized zobo waste nanocomposite (MOCZWNC) and Mg-Zn binary oxide nanocomposite (MgZBONC)  on Salmonella typhi – gram negative organism and Staphylococcus aureus – gram positive organism as well as eradication of cadmium ions in contaminated water. MgZBONC and MgOCZWNC was prepared, characterized and their adsorption ability was studied using adsorption isotherm evaluation. The mitigative effect of MgOCZWNC and MgZBONC on bacteria was evaluated using turbidimetric method. The maximum sorption capacity of MgOCZWNC and Mg-ZBONC for cadmium was 188.68mg/g and 192.31mg/g respectively. The average crystallite size of MgOCZWNC and MgZBONC was 28.61nm and 59.42nm respectively. The treated samples brought about a steady decrease in the population of Staphylococcus and Salmonella within 2hrs, after about 2hrs, the samples of staphylococcus and salmonella treated with MgOCZWNC continued to show continuity in its mitigative effect at decreasing the population of the bacteria with time while the staphylococcus and salmonella sample treated with MgZBONC experienced a slightly steady drop in their effectiveness after same Nanocomposite period. The MgZBONC adsorbed cadmium ions more effectively and the MgOCZWNC was more efficient in severely reducing the population of staphylococcus and salmonella in contaminated water

Electronic and Optical Properties of Rare Earth Atoms Doped Niobium Dichalcogenides NbX₂ (X=S, Se) Monolayers: A First Principle Study

A First-principles calculation has been carried out to study the electronic, and optical properties of RE (La and Sm) atoms doped monolayer NbX2 (X=S, Se). The properties are studied through the use of density functional theory (DFT) with Perdew–Burke–Ernzerhof-generalized gradient approximation (PBE-GGA) as exchange correlation functional and Time dependent density functional theory (TDDFT) as implementation in Quantum ESPRESSO code. The electronic band structures, as well as density of states (DOS) of these structures, show both are metallic in nature. The analysis of optical properties reveals that both La and Sm doped NbX2 (X= S, Se) Monolayer possess maximum absorptivity in the UV range of incident photon’s energy with minimum energy loss and decrease in reflectivity. Our complete study about the considered compounds describes them as potential candidates for technological applications in plasmonic and optoelectronic devices

A Joint Optimization Scheme for Enhanced Breast Cancer Diagnosis Using Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO)

One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combatting breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.