One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

Millions of people around the world are infected with age-related macular degeneration (AMD) disease. It is not easily detected in the intermediate stage, as it is typically asymptomatic. Moreover, the ideal learning trends for AMD professional advice are identifying those who are at this stage of the condition, instructing them on ways to screen the early diagnosis of the choroidal neovascular level before significant vision loss is incurred, and encouraging them to think about taking nutritional supplements that may lessen the likelihood that the condition will extend from the intermediate to the mature phase. However, conventional identification of this disease can be time-intensive and necessitates knowledgeably competent individuals to carry out the task. Since retinal fundus images have proven beneficial in detecting AMD, automated methods for AMD detection from retina fundus images can be developed by applying a novel application of deep transfer learning (DTL) to leverage on the artificial intelligence advances. In this investigation, deep convolutional neural networks (DCNN) specifically trained for automated AMD evaluation were contrasted with deep transfer learning (DTL), a different deep learning (DL) technique that included common characteristics with a diagnostic grader. In order to differentiate between the age-related macular degeneration (AMD) stage and the Normal stage, automated identification was used on a 2-class classification issue. The study proposed using deep learning (DL) models, and compared them to the DTL model. The proposed DTL model with transfer learning approach yielded an accuracy of96.41%, area under receiver operator characteristics curve (AUC) of 0.9633, precision of 94.24%, specificity of 94.82% and false positive rate (FPR) of 0.0518 on the test dataset, which specified a considerable agreement with the gold standard Age-related Eye Disease investigation data set. Implementing an automatic DTL-founded AMD evaluation from fundus scans can yield outcomes that are on par with human effectiveness. This research confirms that automated techniques could perform a task in the current administration of AMD that is self-sufficient of knowledgeable human graders and could take into account the expenses of inspection or surveillance, availability to medical care, and the evaluation of effective therapeutics that resolve the advancement of AMD.

Developing an effective attendance management system has always been a difficult issue for any company, ranging from schools to universities. Smartphones have been used for attendance with technologies such as face recognition, fingerprint-based attendance, and other types. The attendance management system (AMS) and its problems are discussed in this study. The study presents a student attendance system for schools and colleges combining Geo Fencing using the geofencing API from Google Play services and Google location services dependencies along with Firebase and Geofire dependencies and Face Recognition functionality. This is performed by acquiring live location of students and a geo-fence of the class area, the system also performs a face recognition for pre-registered student and attendance is automatically taken when a student has spent over 90% of the time within the set geo-fence (Classroom). This system was developed using Android Studio Integrated Development Environment (IDE) which is being used for the development of native android applications. A questionnaire was however designed and results gotten to further explain how well the functionalities work. The responses derived were subjected to statistical examination using SPSS. Discoveries showed that attendance system was able to improve identification, verification and accuracy of student attendance. Keywords: Attendance, Geo-fence, Face Detection, Global Position System, Barcode, Database