The distinctive feature of eBusiness applications is their complexity due to the large number of factors that have to be taken into consideration and aligned with regard to the products and services offered. Besides, the rapid adoption of eBusiness has resulted in an ever-increasing set of complex applications and IT infrastructure components to be managed. Thus, in this paper, we address the substances specific for the complex service management and identify some possible problem areas arising from contractual agreements between involved parties as well as from the directions of where service management is heading nowadays. Initially we examine what may be perceived as heterogeneous and agile eBusiness. We perform an aspect-based analysis of researched scientific literature: a detailed assessment of various definitions, requirements and other facets specific to the dynamic nature of service management in heterogeneous e-Business environment. Furthermore, we evaluate business-driven against conventional approaches to design SLAs; finally we examine various service management models giving priority to on SOA architecture

The most common problems that arise when working with big data for intelligent production are analyzed in the article. The work of recommendation systems for finding the most relevant user information was considered. The features of the singular-value decomposition (SVD) and Funk SVD algorithms for reducing the dimensionality of data and providing quick recommendations were determined. An improvement of the Funk SVD algorithm using a smaller required amount of user data for analysis was proposed. According to the results of the experiments, the proposed modification improves the speed of data processing on average by 50–70% depending on the number of users and allows spending fewer computing resources. As follows, recommendations to users are provided in a shorter period and are more relevant. The faster calculation of modified Funk SVD to exchange the optimal parameters between nodes was proposed. It was determined that execution time can be reduced on average by 75% for using ten nodes exchanging the optimal decomposition parameter compared to using one. Using Spark technology for faster calculation on average by 20% compared to Hadoop was proposed. The architecture of the IIoT system was proposed, which uses a modified Funk SVD algorithm to optimize data on edge devices and monitors the effectiveness of providing recommendations using control centers and cloud resources.

Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives. Despite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). An effective remedy is the incorporation of active learning strategies. This study explores this junction, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the dataset used in our article did not provide results with a higher accuracy, which could make it difficult to make predictions. This paper proposes a novel approach to predict human activity from the Heterogeneity human activity recognition (HHAR) dataset that joins active learning with machine learning models: Random Forest (RF),Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict heterogeneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope that were present in smartwatches. The dataset was evaluated on 3 iterations; the evaluation measures demonstrated that we can predict human activity with the highest accuracy and F1-Score of 99.99%. The results indicate that this approach is the most accurate and effective compared to the conventional machine learning approaches.

Since the vast development of the mobile and e-Business technologies, there has been growing need to advance new methods - to enable the simultaneous collaboration of two or more individuals. Besides, the business environment becomes harder and faster and needs more sophisticated technologies to enable simultaneous brainstorming, which means for collaboration team members more traveling and therefore increase in time and money. Thus, this paper deals with the meaning of mobile technologies in organizations, especially for collaboration needs. Accordingly, we present here various benefits of using mobile technologies by companies. We also outline the different strategies of companies to use these technologies for better intra/inter collaborations. And, we conclude our paper with the suggestions of further developments in mobile applications for me efficient collaboration by the enterprises.

The increasing demand for mobile phones has resulted in abundant online reviews, making it challenging for consumers to make informed purchasing decisions.In this study, we propose Graph Neural Network (GNN) models to classify mobile phone ratings using Term Frequency-Inverse Document Frequency (TF-IDF) features. We collected a dataset of over 13,000 mobile phone evaluations from the Flipkart website. The proposed method includes data purification, balancing, feature extraction from the TF-IDF, and model prediction using deep learning models. The proposed approach utilized other models such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM to compare other classifiers. The experiments’ outcomes demonstrate that the suggested model performs better than conventional deep learning methods regarding accuracy and efficiency. The GNN model achieved the best 99.0% accuracy rate. The proposed approach can help consumers make informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.