Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.

A crop yield prediction is critical to increase agricultural sustainability because it allows for the more effective use of natural resources, including water, fertilizers, and soil. Accurate yield estimates enable farmers and governments to more accurately manage resources, decreasing waste and minimizing adverse environmental effects such as the degradation of soil and water quality issues. In addition, predictive models serve to alleviate the consequences of climate change by promoting adaptable farming techniques and improving the availability of food by means of early decision-making. Thus, including a crop yield prediction into farming practices is critical for combining productivity and sustainability. In contrast to conventional machine learning models, which frequently require long feature engineering, deep learning may obtain complicated yield-related characteristics directly from initial or merely preprocessed data from different sources. This research paper aims to demonstrate the strength of deep learning models and their ensembles in agricultural yield prediction in Saudi Arabia, where agriculture faces issues such as scarce water resources and harsh climate conditions. We first define and evaluate a Multilayer Perceptron (MLP), a Gated Recurrent Unit (GRU), and a Convolutional Neural Network (CNN) as baseline deep models for the crop yield prediction. Then, we investigate combining these three models based on stacking, blending, and boosting ensemble methods. Finally, we study the uncertainty quantification for the proposed models, which involves a discussion of many enhancements’ techniques. As a result, this research shows that, by applying the right architectures with strong parametrization and optimization techniques, we obtain models that can explain 96% of the variance in the crop yield with a very low uncertainty rate (reaching an MPIW of 0.60), which proves the reliability and trustworthiness of the prediction.

Employee attrition is considered a persistent and significant problem across all the leading businesses globally. This is evidenced by the fact that the issue negatively impacted not only production but also impeded the ability of businesses to maintain continuity and adopt strategic planning. Typically, employee attrition occurs when employees are dissatisfied with respective work experiences. To effectively address this issue, proactive measures can be implemented to enhance employee retention through early identification and mitigation of factors that contribute to perceived dissatisfaction in work places. In the current era of big data, people analytics has been widely adopted by human resource (HR) departments across various businesses with the aim of understanding the different workforces across distinct fields and reducing the attrition rate. As a result, organizations are presently incorporating machine learning (ML) and artificial intelligence (AI) into HR practices to help decision-makers make better, well-informed decisions about respective human resources. The application of ML has been confirmed to be the optimal method for predicting employee attrition, but the optimization of its hyperparameter can further improve the prediction accuracy. Therefore, this novel study aimed to tune the hyperparameters of boosting ML algorithm family and develop a potential tool for employee attrition prediction through the adoption of Bayesian optimization (BO). Using IBM HR Analytics dataset, the exploration compared the performance of six ensemble classifiers and identified categorical boosting (CB) as the superior model which achieved the highest accuracy of 95.8% and AUC of 0.98 with optimized hyperparameters, showing its comprehensiveness and reliability. The comparison results showed how various boosting ML variants could be used to build a promising tool that is capable of accurately predicting employee attrition and enabling HR managers to enhance employee retention as well as satisfaction.

Web services composition (WSC) has an enormous potential for the organizations in the B2B area. In fact, different services collaborate through the exchange of messages to implement complex business processes. BPEL is one of the most used languages to develop such cooperation. However, it has been proved that its use is complex and can require some expertise in XML syntax. Even its graphical representation is not evident to handle. This is why the authors propose to model Web services using oWF-nets, a subclass of Petri nets, and then, to translate them to BPEL. Whilst, a WSC is with added value only if the involved services are compatible. So in this context, across the translation proposed the researchers develop a verification layer of the WSC compatibility. Hence, they propose a framework named D&A4WSC which allows to model the WSC by oWF-nets, to check their compatibility with the model checker NuSMV and to translate them if they are compatible in BPEL processes using the oWFN2BPEL compiler. D&A4WSC permits, furthermore, to formally analyze a BPEL process.