IJEME Vol. 12, No. 5, Oct. 2022
Cover page and Table of Contents: PDF (size: 542KB)
This study examined the efficacy of assistive technology (AT) for improved teaching and learning in computer science (a case study of an inclusive educational system). Two (2) hypotheses were formulated and tested for this study. A descriptive survey method was adopted for this study, the population of this study comprises all Students with special needs and all teachers teaching at Durbar Grammar School, Oyo, Oyo State, Nigeria. A purposive sampling technique was used to select twenty (20) respondents (teachers) and all the Students with special needs were involved (40). A structured questionnaire of two sections (sections A and B to be answered by the Teachers and the Students respectively) which was validated and tested for reliability was used; a reliability coefficient of 0.81 was obtained. Simple percentages and the Chi-square statistical method were used to analyze the collected data which was tested with this study’s hypotheses. The results of this study revealed that AT is capable of improving the teaching and learning of computer science for Students with special needs in an inclusive education if AT is allowed to play its role. It was also discovered that both the teacher and students with special needs were exposed to very little AT and there was no periodical training programme for both the teachers and the students with special needs on the use of AT which has affected their teaching and learning ability. This paper, therefore recommends that a periodical training programme on the use of AT be organized by all the stakeholders in inclusive education for both the students with special needs and all the teachers teaching them.[...] Read more.
According to UNICEF, the latest estimate states that there are about 2.7 million children in orphanages. Orphanage is a residence for people who are without parental support or any moral support from anyone. Such orphans require help from people who are in a good financial state to donate them. Generally, in orphanage records are usually maintained for future reference, retrieval, and easy management. The objective of this paper is to help the orphans from different orphanages to get help from the donors who wish to donate them by using our web application. The proposed system helps the staff in reducing manual paper work and enhances tidiness in record keeping since the existing one uses manual keeping, i.e., the use of files and papers. The system allows the orphanage owner to add and modify the orphan records. The system provides suggestions for assignment of these orphans to the caretakers/donors by using SVM (Support Vector Machine) algorithm. Donor can select the orphan and request for adoption from the orphanage owner. The Orphanage owner can accept or reject help from the donor. The proposed system is aimed to facilitate donors with the details of an orphan and providing fund specifically to that orphan.[...] Read more.
This paper proposes multiple optimal combinations of renewable and nonrenewable energy systems for Nilphamari, Bangladesh. The Nilphamari relies mainly on on-grid electricity system. Therefore, the optimal combination of hybrid energy systems related to renewable and nonrenewable options is proposed to mitigate grid dependency. This hybrid energy system generates electricity for the consumption loads of the project area in which the natural resource potentials like solar, wind, hydro, and diesel are available. All power sources and resources data are included in the Homer software carefully. Finally, the Homer software is applied for viable techno-economic investigations especially cost of energy (COE) and net present cost (NPC) for the proposed hybrid system in Nilphamari, Bangladesh. The optimization result indicates that $0.224/kWh is the minimal COE. The proposed system has an operating cost of $16,156.16, a COE of $0.241/kWh, an NPC of $2,961,790.00, and a CO2 output of 3,373 kg per year. The proposed system's legitimacy, as determined by the LCOE and the NPC, was confirmed by optimization analysis. Within a few years of the project's lifetime, the system is estimated to pay for itself completely. The evaluations yielded the best system configurations, hybrid system costs, fuel savings, and CO2 emission reductions.[...] Read more.
Skin Lesion is a part of the skin that can be caused by abnormal growth in the epithelium layer on the skin. There are nine types of skin lesion like Actinic Keratoses (AK), Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevi (MV), Benign Keratosis (BK), Vascular Lesions (VASC), Squamous Cell Carcinoma (SCC), and Pigmented Benign Keratosis (PBK). The aim of this study is to spotlight on the problem of skin lesion classification based on early detection of the disease using deep learning techniques. This approach is used to work out the problem of classifying a dermoscopic image. The dermoscopic is a digital device; in this case Smartphone is attached to a lens and collects the images through the device. The proposed spotlight is built in the region of using Convolutional neural network architecture and ResNet-50 module is used to predict Skin-Lesion classification. The dataset used in this research was taken from kaggle repository. The proposed work uses ResNet-50 CNN model which has yielded 93% of accuracy for detecting Skin Cancer, previous work was carried out using Visual Geometry Group model which yielded 73% accuracy. In the proposed work we have considered 25,000 images of skin lesion. Hence we are able to attain this accuracy with more reliable Machine Learning algorithms compared to the previous work.[...] Read more.