There is always a desire for defect-free software in order to maintain software quality for customer satisfaction and to save testing expenses. As a result, we examined various known ML techniques and optimized ML techniques on a freely available data set. The purpose of the research was to improve the model performance in terms of accuracy and precision of the dataset compared to previous research. As previous investigations show, the accuracy can be further improved. For this purpose, we employed K-means clustering for the categorization of class labels. Further, we applied classification models to selected features. Particle Swarm Optimization is utilized to optimize ML models. We evaluated the performance of models through precision, accuracy, recall, f-measure, performance error metrics, and a confusion matrix. The results indicate that all the ML and optimized ML models achieve the maximum results; however, the SVM and optimized SVM models outperformed with the highest achieved accuracy, 99% and 99.80%, respectively. The accuracy of NB, Optimized NB, RF, Optimized RF and ensemble approaches are 93.90%, 93.80%, 98.70%, 99.50%, 98.80% and 97.60, respectively. In this way, we achieve maximum accuracy compared to previous studies, which was our goal.

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction (includes shape, margin, and texture) from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a dataset of images by applying a convolutional neural network (CNN). The extraction of leaf gas enables identification of the actual level of chlorophyll (Ch) and nitrogen (N) which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color‐based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf.

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.

Breast cancer is a leading cause of mortality worldwide, emphasizing the need for accurate and timely prediction of risk factors. This study proposes a novel approach using deep learning algorithms to predict breast cancer. The self-adaptive sea lion optimization-based recurrent neural network (SA-SLnO-RNN) is developed to select optimal features that enhance prediction accuracy. Evaluated on the breast cancer Wisconsin diagnostic (BCWD) dataset, the SA-SLnO-RNN achieves superior performance compared to existing methods, with accuracy, precision, recall, specificity, and F1-score values of 98.82%, 98.78%, 98.71%, 99.06%, and 98.75%, respectively. The proposed model’s self-adaptive nature prevents overfitting, while the RNN with optimization algorithm enables quick convergence and captures complex temporal dependencies, ensuring early and accurate breast cancer prediction.

This paper proposes an intermediate solution using artificial intelligence to monitor any potential threat for SME, specifically in Malaysia. The proposed method uses Autoencoder based Deep Neural Network (AEDNN) trained with NSL-KDD dataset to efficiently detect possible cyber threats. This paper proposed AEDNN to detect automated threats cybersecurity and it does not intend to replace any existing security solutions. The proposed AEDNN is designed to detect any possible cyber threats accurately and consistently in the real-time network. The experimental results show that accurate results in the range between 96% to 99% specifically for SMEs in Malaysia.