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The popularity of smart learning has soared due to its flexibility, ubiquity, context-awareness, and adaptiveness. In particular, video-based m-learning has the biggest impact on the learning process. Its live and realistic features make learning interactive, easy, and fast. This article establishes the importance of video-based learning and m-learning in smart learning while discussing the basics of a smart learning environment and requirements. A framework and model for smart learning is presented. A streaming video adaptation model is also proposed for mobile devices. Based on the model, an interactive video-based smart learning system has been designed, which allows for the streaming video of live as well as prerecorded lecture sessions offering an interactive teaching-learning experience. The application supports both mobile devices and desktop computers. The model is practically implemented with a group of students and their feedback shows a high rate of acceptance of the system while a sizable percentage of them acknowledged that it improved their teaching-learning process significantly.

Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance. [ABSTRACT FROM AUTHOR]

In mobile crowd computing (MCC), people’s smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today’s SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users’ mobility poses a serious issue in the QoS of MCC. To address this, before submitting a job to an SMD, we suggest estimating that particular SMD’s availability in the network until the job is finished. For this, we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time. For experimental purposes, we collected real users’ mobility data (in-time and outtime) with respect to a Wi-Fi access point. To build the prediction model, we presented a novel feature extraction method to be applied to the time-series data. The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model. [ABSTRACT FROM AUTHOR]