Diabetes is a common chronic illness or absence of sugar in the blood. The early detection of this disease decreases the serious risk factor. Nowadays, Machine Learning based cloud environment acts as a vital role in disease detection. The people who belong to the rural areas are not getting the proper health care treatments. So, this research work proposed an automated eHealth cloud system for detecting diabetes in the earlier stage to decrease the mortality rate and provides health treatment facilities to rural peoples. Extreme Learning Machine (ELM) is a type of Artificial Neural Network (ANN) that has a lot of potential for solving classification challenges. This research work is consisting of several activities like feature normalization, feature selection and classification. We have employed principal component analysis (PCA) for feature selection and extreme learning machine (ELM) for classification. Finally, a cloud computing-based environment with three numbers of virtual machines (vCPU-4, vCPU-8, and vCPU-16), is used for the detection of diabetes. The efficacy of the proposed model has been evaluated with the PIMA dataset in both standalone and cloud environments and achieved 90.57 % accuracy, 82.24 % sensitivity, 73.23 % specificity, and 75.03 % F-1 score with the virtual machine vCPU-16. The experimental results define the proposed model as superior to other state-of-art models with better classification accuracy and less number of features.

Ensuring the dependability of software before its public release is of utmost importance. Many software issues arise due to human errors made throughout the development process, highlighting the importance of addressing these errors early. It is crucial to incorporate testing resources at the beginning of development to minimize potential issues. Utilizing an approach that identifies modules susceptible to errors helps potential problems. With an understanding of the significance of precisely anticipating module failures, multiple automated solutions are already emerging. This work presents a refined software defect prediction model that utilizes a meta-heuristic optimization technique. Our methodology integrates NASA’s data collection procedure, which includes data cleansing, reducing the dimensionality of features, and predicting defects. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to decrease the feature dimensionality, while an Extreme Learning Machine (ELM) is utilized for forecasting defects. The ELM parameters, such as weight and biases, are ideally chosen using the suggested improved JAYA optimization (IMJAYA) method. The model’s validation involves assessing its accuracy, sensitivity, specificity, F1 score, and MCC metrics using a 10x5 cross-validation. The model is verified using NASA datasets that consist of several classes, such as CM1, KC2, KC3, MC1, PC1, and JM1. The PCA-LDA+ IMJAYA-ELM model yields defect prediction accuracies of 95.73%, 98.08%, 94.87%, 96.23%, 97.10%, and 97.46% for the CM1, KC2, KC3, MC1, PC1, and JM1 datasets, respectively. The research outcomes show encouraging outcomes when using a meta-heuristic optimization technique with smaller feature sets for studies on predicting software defects.

In aquatic environments, the health of fish populations is crucial for maintaining ecological balance and sustaining aquaculture industries. Timely and accurately detecting fish diseases is paramount for effective management and mitigation strategies. This paper presents a novel approach for fish disease detection leveraging transfer learning and ensemble techniques. Our method combines features extracted from three pre-trained deep learning models, namely VGG-16, MobileNet-V2, and Inception-V3, with a Support Vector Machine (SVM) for classification. Through empirical experimental evaluation on a comprehensive dataset, we demonstrate the effectiveness of our proposed model in accurately detecting various fish diseases. The results showcase significant improvements in sensitivity and specificity compared to existing approaches. Additionally, we analyze the impact of different transfer learning strategies and feature fusion techniques on the model’s overall performance. Our findings underscore the potential of transfer learning and ensemble methods in enhancing fish disease detection systems, offering promising avenues for future research in aquatic health management and aquaculture.