ABSTRACT
Diabetic retinopathy (DR) is an ocular condition that can affect individuals with diabetes mellitus (DM) and may lead to reduced vision or even blindness if not detected on time. Delay in diagnosis and disagreement in interpretation of retinal images by different health experts are some of the challenges that can occur during screening for DR. Deep learning (DL) techniques are currently used for classification of images across various domains including the ophthalmic imaging field. The implementation of this cutting edge technology for detection of DR could lead to improvement of existing eye care services for diabetic individuals. This paper discussed the publicly available datasets of retinal images of diabetic individuals used for training DL models. The efficiency of several convolutional neural networks (CNNs) created for the detection of different classes of DR was also reviewed. Furthermore, the achievements and the challenges faced in the application of DL techniques for the DR detection were discussed. Finally, future works that can be performed in this research area has been suggested.

ABSTRACT
Diabetic Retinopathy (DR) is an ocular condition that can manifest in individuals living with Diabetes Mellitus (DM). Retinal fundus examinations must be conducted on DM individuals as early identification and treatment of DR can reduce the risk of impaired vision or blindness. The manual diagnosis of DR conducted by eye-care professionals can be tedious and time-consuming, especially during mass screenings. Deep learning (DL) techniques are being used to provide automated diagnosis of DR. This study adopted two CNN (VGG 19 and ResNet50) models for the binary classification of DR (Non-referable DR and Referable DR). Both models were trained and validated with retinal fundus images from publicly available datasets. After training with Kaggle dataset, VGG 19 and ResNet50 models achieved accuracies of 94.3% and 96.9% respectively. For external validation, varying levels of accuracy, sensitivity and specificity were obtained for the two models on different datasets. The sensitivity of the VGG 19 model for the Messidor 2 dataset was 78.8% while the sensitivity of the ResNet50 model for the Indian Diabetic Retinopathy Image Dataset (IDRiD) was 85.7%. Findings in this study have shown that DR detection with deep learning techniques can serve as an assistive tool for eye-care professionals in the future.

ABSTRACT
This study was carried out to investigate the readiness of public higher institutions in Abia to migrate to digital education in the covid-19 pandemic era. Covid-19 pandemic era is simply a period everything and sectors of the economy was paralyzed and all activities were halted. Aimed of this study was to determine the extent to which public higher institutions in Abia to migrate to digital education in the covid-19 pandemic era. This objectives was to examined how accessibility of digital learning, and equal education opportunities. Two research questions were raised and answered for the study. Two research hypotheses were formulated and tested at .05 level of significance. Descriptive survey research design was adopted while structured questionnaire were used for data collection. The population of the study comprises 355 Students in six higher institutions in Abia State for 2022/2023 academic session in all the institution accredited by regulatory body, in Abia State. Specifically, the population consist of 71 students from State University (ABSU) Uturu, 62 students from Michael Okpara University of Agriculture (MOUAU), Umudike, 46 students from Abia State Polytechnic Aba, 53 from National Institutes for Nigerian languages (NINLAN), Abia, 47 students from College of Health and Management Technology, Aba and 76 students from Abia State College of Education Technical Arochkwu. The sample sizes for the higher institution were 116 respondents’ determined using Taro Yamane formula. Stratified random sampling was adopted while simple random sampling was used to selecting respondents from each stratum. Based on this finding, the study therefore, concluded that infrastructural facilities are very important factors that enhance effective migration to digital education in higher institution. The study recommended that, the regulatory body and accreditation panel should ensure the there is adequate facilities for digital education in all department to enhance effective ...

ABSTRACT
The study is aimed at developing a framework for implementing green building practices in Abuja with the view to create a template for sustainable building practices in Nigeria. The study assessed the potentials in adopting green building practices in Abuja; determined the drivers and barriers to adopting green building management practices; and developed a framework for implementing green building practices in Nigeria building and construction industry. Quantitative research design was adopted while an online survey research technique was conducted using a questionnaire structured on a five-point Likert scale. A total number of 560 questionnaires, comprising 80 questionnaires each were purposively sent to seven selected professional associations in the housing and construction industry in Abuja. A straightforward random technique was applied in selecting and sending online Google linked questionnaire to housing professionals through personal email addresses and social media platforms, such as WhatsApp and Instagram. Only 526 responses were received and analysis was conducted using SPSS statistical software version 24. On the potentials in adopting green building practices in Abuja, the 1st in ranking is Environmental Sustainability where Green building practices reduce the environmental impact of buildings by minimizing waste, conserving energy and water, and reducing greenhouse gas emissions with a mean score of 4.92 while Improved Health and Well-being with a Mean of 4.33 as ranked 1st emerged as the top driver for adopting green building practices. However, the most significant barrier identified is the high upfront costs associated with green building practices with a mean score of 4.49 as ranked 1st. There is a significant potential for the adoption of green building practices in Abuja and a critical need for education and awareness initiatives targeting both developers and consumers to foster a culture of sustainability.

ABSTRACT
Road traffic accidents in Nigeria represent a significant public safety concern, with thousands of lives lost annually. The Intelligent Vehicle Accident Detection System (IVADS) addresses this issue by providing a system that detects accidents automatically and instantly notifies emergency responders with real-time location information. IVADS integrates multiple sensors, GPS, and GSM technologies to achieve accurate accident detection and rapid communication. This paper provides a comprehensive review of the system’s design, implementation, testing results, and potential implications for road safety improvements in Nigeria. This work builds upon existing research on accident detection, using innovative sensor integration and machine learning for precise detection.