Lecturers\u27 Awareness, Perception, and Utilization of Institutional Repositories in Two Universities in Nigeria

The purpose of this study was to examine the lecturer\u27s awareness, perception, and utilization of institutional repositories in two universities in Nigeria. The universities include Ahmadu Bello University, Zaria, and the University of Jos. The research design adopted for the study was correlational research design, while the questionnaire was the instrument used for data collection. The questionnaire was grouped into five sections and most of the questions were close-ended. Also, the validity and reliability of the questionnaire were done in order to obtain the desired results. Out of 642 questionnaires distributed, 473 were completed, returned, and found suitable for use. Descriptive and inferential statistics were then used to analyze the data. The findings revealed that lecturers\u27 level of awareness of IRs was moderate, this was indicated by a weighted average of 2.58 (65%). The findings also revealed the lecturer\u27s major sources of awareness of IRs were through University\u27s website 417 (88.2%), library sensitizations 286 (60.5%), and colleagues 288 (60.9%).The Majority of the lecturers slightly utilized IRs 243 (51.4%) for depositing scholarly materials. However, the majority of the lecturers indicated that they did not encounter many constraints when utilizing the repositories. In the course of the study, three hypotheses were tested and the findings from the hypotheses revealed that there was a positive significant relationship between awareness and utilization of IRs, perception, and utilization of IRs, awareness, and perception of the importance of IRs. It was concluded that an increase in the awareness and perception of IRs by lecturers increases their utilization of the IRs. Hence, recommendations were made to the library management of the two universities to intensify their advocacies, awareness campaigns, and sensitization excises in order to increase awareness and perception of lecturers which would in turn increase the utilization of the repositories

Microbial and physicochemical properties of ground water of Ilaro, South-West, Nigeria

The present study was carried out to determine the microbial quality and physicochemical properties of ground water in Ilaro, a semi-urban settlement. Plate count agar (PCA), MacConkey broth and eosin methylene blue agar (EMB) were used in the microbial analysis. The results showed that the most probable number (MPN) ranges from 0 – 50 per 100 ml while the total viable count (TVC) ranged from 0.07 x 10² – 4.12 x 10² cfu ml-1. Bacillus subtilis, Bacillus cereus, Staphylococcus aureus, Streptococcus faecalis, Escherichia coli,Enterobacter aerugenosa and Micrococcus luteus were isolated. The physicochemical properties measured using their respective meters showed that the water was acidic. It was concluded that treatment before consumption is necessary to avoid borne diseases

Investigating a deep learning approach to real-time air quality prediction and visualisation on UK highways

The construction of intercity highways by the United Kingdom (UK) government has resulted in a progressive increase in vehicle emissions and pollution from noise, dust, and vibrations amid growing concerns about air pollution. Existing roadside pollution monitoring devices have faced limitations due to their fixed locations, limited sensitivity, and inability to capture the full spatial variability, which can result in less accurate measurements of transient and fine-scale pollutants like nitrogen oxides and particulate matter. Reports on regional highways across the country are based on a limited number of fixed monitoring stations that are sometimes located far from the highway. These periodic and coarse-grained measurements cause inefficient highway air quality reporting, leading to inaccurate air quality forecasts. Multi-target neural network is a type of machine learning algorithm that offers the advantage of simultaneously predicting multiple pollutants, enhancing predictive accuracy and efficiency by capturing complex interdependencies among various air quality parameters. The potentials of this and similar multi-target prediction techniques are yet to be fully exploited in the air quality space due to the unavailability of the right data set. To address these limitations, this doctoral thesis proposes and implements a framework which adopts cutting-edge digital technologies such as Internet of Things, Big Data and Deep Learning for a more efficient way of capturing and forecasting traffic related air pollution (TRAP). The empirical component of the study involves a detailed comparative analysis of advanced predictive models, incorporating an enriched dataset that includes road elevation, vehicle emission factors, and background maps, alongside traditional traffic flow, weather, and pollution data. The research adopts a multi-target regression approach to forecast concentrations of NO2, PM2.5, and PM10 across multiple time steps. Various models were tested, with Fastai's tabular model, Prophet's time-series model, and scikit-learn's multioutput regressor being central to the experimentation. The Fastai model demonstrated superior performance, evidenced by its Root-Mean Square Error (RMSE) scores for each pollutant. Statistical analysis using the Friedman and Wilcoxon tests confirmed the Fastai model's significance, further supported by an algorithmic audit that identified key features contributing to the model's predictive power. This doctoral thesis not only advances the methodology for air quality monitoring and forecasting along highways but also lays the groundwork for future research aimed at refining air quality assessment practices and enhancing environmental health standards

Mel-Frequency Cepstral Coefficients and Convolutional Neural Network for Genre Classification of Indigenous Nigerian Music

Music genre classification is a field of study within the broader domain of Music Information Retrieval (MIR) that is still an open problem. This study aims at classifying music by Nigerian artists into respective genres using Convolutional Neural Networks (CNNs) and audio features extracted from the songs. To achieve this, a dataset of 524 Nigerian songs was collected from different genres. Each downloaded music file was converted from standard MP3 to WAV format and then trimmed to 30 seconds. The Librosa sc library was used for the analysis, visualization and further pre-processing of the music file which includes converting the audio signals to Mel-frequency cepstral coefficients (MFCCs). The MFCCs were obtained by taking performing a Discrete Cosine Transform on the logarithm of the Mel-scale filtered power spectrum of the audio signals. CNN architecture with multiple convolutional and pooling layers was used to learn the relevant features and classify the genres. Six models were trained using a categorical cross-entropy loss function with different learning rates and optimizers. Performance of the models was evaluated using accuracy, precision, recall, and F1-score. The models returned varying results from the classification experiments but model 3 which was trained with an Adagrad optimizer and learning rate of 0.01 had accuracy and recall of 75.1% and 84%, respectively. The results from the study demonstrated the effectiveness of MFCC and CNNs in music genre classification particularly with indigenous Nigerian artists

Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data

Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitizing clinical and non-clinical functions like preoperative planning, context-aware decision-making, and operating skill assessment. However, this field is still in its infancy and lacks representative, well-annotated datasets for training robust models in intermediate ML tasks. Also, existing datasets suffer from inaccurate labels, hindering the development of reliable models. In this paper, we propose a systematic methodology for developing robust models for surgical tool detection using noisy data. Our methodology introduces two key innovations: (1) an intelligent active learning strategy for minimal dataset identification and label correction by human experts; and (2) an assembling strategy for a student-teacher model-based self-training framework to achieve the robust classification of 14 surgical tools in a semi-supervised fashion. Furthermore, we employ weighted data loaders to handle difficult class labels and address class imbalance issues. The proposed methodology achieves an average F1-score of 85.88\% for the ensemble model-based self-training with class weights, and 80.88\% without class weights for noisy labels. Also, our proposed method significantly outperforms existing approaches, which effectively demonstrates its effectiveness

Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning

Coronary angiography analysis is a common clinical task performed by cardiologists to diagnose coronary artery disease (CAD) through an assessment of atherosclerotic plaque's accumulation. This study introduces an end-to-end machine learning solution developed as part of our solution for the MICCAI 2023 Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) challenge, which aims to benchmark solutions for multivessel coronary artery segmentation and potential stenotic lesion localisation from X-ray coronary angiograms. We adopted a robust baseline model training strategy to progressively improve performance, comprising five successive stages of binary class pretraining, multivessel segmentation, fine-tuning using class frequency weighted dataloaders, fine-tuning using F1-based curriculum learning strategy (F1-CLS), and finally multi-target angiogram view classifier-based collective adaptation. Unlike many other medical imaging procedures, this task exhibits a notable degree of interobserver variability. %, making it particularly amenable to automated analysis. Our ensemble model combines the outputs from six baseline models using the weighted ensembling approach, which our analysis shows is found to double the predictive accuracy of the proposed solution. The final prediction was further refined, targeting the correction of misclassified blobs. Our solution achieved a mean F1 score of $37.69\%$ for coronary artery segmentation, and $39.41\%$ for stenosis localisation, positioning our team in the 5th position on both leaderboards. This work demonstrates the potential of automated tools to aid CAD diagnosis, guide interventions, and improve the accuracy of stent injections in clinical settings.Comment: Submission report for ARCADE challenge hosted at MICCAI202

Deep Learning-Based Multi-Target Regression for Traffic-Related Air Pollution Forecasting

Data availability: The authors do not have permission to share dataCopyright © 2023 The Author(s). Traffic-related air pollution (TRAP) remains one of the main contributors to urban pollution and its impact on climate change cannot be overemphasised. Experts in developed countries strive to make optimal use of traffic and air quality data to gain valuable insights into its effect on public health. Over the years, the research community has developed advanced methods of forecasting traffic-related pollution using several machine learning methods albeit with persistent accuracy and insufficient data challenges. Despite the potentials of emerging techniques such as multi-target deep neural network to achieve optimal solutions, they are yet to be fully exploited in the air quality space due to their complexity and unavailability of the right training data. It is to this end that this study investigates the impact of integrating an updated data set including road elevation, vehicle emissions factor and background maps with traffic flow, weather and pollution data on TRAP forecasting. To explore the robustness and adaptability of our methodology, the study was carried out in one major city (London), one smaller city (Newport) and one large town (Chepstow) in the United Kingdom. The forecasting task was modelled as a multi-target regression problem and experiments were carried out to predict NO2, PM2.5 and PM10 concentrations over multiple timesteps. Fastai’s tabular model was used alongside prophet’s time-series model and scikit-learn’s multioutputregressor for experimentation with fastai recording the overall best performance. Statistical tests run using Friedman and Wilcoxon test also revealed the significance of the fastai model with a p-values < 0.05. Finally, a model explanation tool was then used to reveal the most and least influential features from the newly curated data set. Results showed traffic count and speed were part of the most contributing features. This result demonstrates the impact of these and other introduced features on TRAP forecasting and will serve as a foundation for related studies.InnovateUK (Grant Application No 10137 and File No 104367)

Deep learning-based multi-target regression for traffic-related air pollution forecasting

Traffic-related air pollution (TRAP) remains one of the main contributors to urban pollution and its impact on climate change cannot be overemphasised. Experts in developed countries strive to make optimal use of traffic and air quality data to gain valuable insights into its effect on public health. Over the years, the research community has developed advanced methods of forecasting traffic-related pollution using several machine learning methods albeit with persistent accuracy and insufficient data challenges. Despite the potentials of emerging techniques such as multi-target deep neural network to achieve optimal solutions, they are yet to be fully exploited in the air quality space due to their complexity and unavailability of the right training data. It is to this end that this study investigates the impact of integrating an updated data set including road elevation, vehicle emissions factor and background maps with traffic flow, weather and pollution data on TRAP forecasting. To explore the robustness and adaptability of our methodology, the study was carried out in one major city (London), one smaller city (Newport) and one large town (Chepstow) in the United Kingdom. The forecasting task was modelled as a multi-target regression problem and experiments were carried out to predict N O 2 , P M 2 . 5 and P M 10 concentrations over multiple timesteps. Fastai’s tabular model was used alongside prophet’s time-series model and scikit-learn’s multioutputregressor for experimentation with fastai recording the overall best performance. Statistical tests run using Friedman and Wilcoxon test also revealed the significance of the fastai model with a p-values < 0.05. Finally, a model explanation tool was then used to reveal the most and least influential features from the newly curated data set. Results showed traffic count and speed were part of the most contributing features. This result demonstrates the impact of these and other introduced features on TRAP forecasting and will serve as a foundation for related studies