Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.

Universities around the world are keen to develop study plans that will guide their graduates to success in the job market. The internship course is one of the most significant courses that provides an experiential opportunity for students to apply knowledge and to prepare to start a professional career. However, internships do not guarantee employability, especially when a graduate's internship performance is not satisfactory and the internship requirements are not met. Many factors contribute to this issue making the prediction of employability an important challenge for researchers in the higher education field. In this paper, our aim is to introduce an effective method to predict student employability based on context and using Gradient Boosting classifiers. Our contributions consist of harnessing the power of gradient boosting algorithms to perform context-aware employability status prediction processes. Student employability prediction relies on identifying the most predictive features impacting the hiring opportunity of graduates. Hence, we define two context models, which are the student context based on the student features and the internship context based on internship features. Experiments are conducted using three gradient boosting classifiers: eXtreme Gradient Boosting (XGBoost), Category Boosting (CatBoost) and Light Gradient Boosted Machine (LGBM). The results obtained showed that applying LGBM classifiers over the internship context performs the best compared to student context. Therefore, this study provides strong evidence that the employability of graduates is predictable from the internship context.

Lifelogging is the process of digital tracking of person's daily experiences in varying amounts of details, for a variety of purposes. In recent years, lifelogging has become an increasingly popular area of research due to the growing demands from many applications, such as video surveillance, entertainment, healthcare systems, and intelligent environments. Furthermore, the advancements in devices technology offer the promise to record and store large volumes of personal data in a very cheap manner, using an inexpensive tool. However, the rapid access to this huge deluge of unlabeled and unstructured data and automatically processing it to recognize everyday experiences, still present major challenges. A large number of research have been conducted in recent years to cover different lifelogging aspects, but there is still a lack of studies that provide a comprehensive survey of the available literature, and most of the existing lifelogging surveys generally focus on only one aspect. This review highlights the advances of stateof-the-art in lifelogging from different angles, including its research history, current applications, activity recognition techniques, moment retrieval, storytelling, privacy and security issues, as well as challenges and future research trends.

Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus. In the literature, various models based on machine learning (ML) and deep learning (DL) are introduced to detect many pneumonias using chest X-ray images. The cornerstone in this paper is the use of pretrained deep learning CNN architectures to construct an automated system for COVID-19 detection and diagnosis. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pre-trained CNN models, AlexNet and Xception. Hence, we propose COVID-AleXception: a neural network that is a concatenation of the AlexNet and Xception models for the overall improvement of the prediction capability of this pandemic. To evaluate the proposed model and build a dataset of large-scale X-ray images, there was a careful selection of multiple X-ray images from several sources. The COVID-AleXception model can achieve a classification accuracy of 98.68%, which shows the superiority of the proposed model over AlexNet and Xception that achieved a classification accuracy of 94.86% and 95.63%, respectively. The performance results of this proposed model demonstrate its pertinence to help radiologists diagnose COVID-19 more quickly.