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.

The article reflects on the classification of brain tumors where several deep learning (DL) approaches are used. Both primary and secondary brain tumors reduce the patient’s quality of life, and therefore, any sign of the tumor should be treated immediately for adequate response and survival rates. DL, especially in the diagnosis of brain tumors using MRI and CT scans, has applied its abilities to identify excellent patterns. The proposed ensemble framework begins with the image preprocessing of the brain MRI to enhance the quality of images. These images are then utilized to train seven DL models and all of these models recognize the features related to the tumor. There are four models which are General, Glioma, Meningioma, and Pituitary tumors or No Tumor model, which helps in reaching a joint profitable prediction and concentrating solely on the strength of the estimation and outcome. This is a significant improvement over all the individual models, attaining a 99. 43% accuracy. The data used in this research was gotten from Kaggle website and comprised of 7023 images belonging to four classes. The further works will be to increase the data size more investigate other DL architectures, further enhance real time detection to enhance on the diagnostic scans’ accuracy and overall relevancy to clinical practice.

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.