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Mobile Edge Computing (MEC) enhances social media customer reviews by providing real-time processing and analysis at the network edge. Social media platforms have revolutionized how users communicate their thoughts, ideas, and opinions on various issues, yielding a wealth of valuable data that can be used to get insights into people’s attitudes toward various items. This data is especially relevant for Aspect Category Detection (ACD), which is finding specific aspects or features that people discuss or mention concerning products. These systems are used in various organizations that operate on various platforms. However, current approaches frequently need to generate promising and accurate outcomes. Thus, this research presents an innovative strategy for detecting ACD in user reviews of restaurants using federated learning. The approach harnesses the power of a federated deep neural network for accurate classification. Various data preparation methods were employed to preprocess the data before model training to ensure the construction of a reliable dataset for classification. The proposed approach includes data cleaning, balancing, TF-IDF feature extraction, and model prediction using federated learning. The experimental results demonstrate that the proposed approach achieved an impressive accuracy of 88.38%, recommending the proposed approach for aspect category detection.

The prime focus on the deliberations related to Industry 5.0 lies in creativity, efficiency and resilience for adapting various organic components such as accurate decisions, customized demands in consumer electronics with promising/sustainable solutions. This transformation and paradigm shift inside the Industry 5.0 frame can be comprehensively understood by integrating the factors related to human values with the cutting-edge technologies of consumer electronics for compatibility with the issue of sustainable development. The present work proposes an integrated approach for extensive study with a set of criteria and possible Industry 5.0 enablers in consumer electronics using a novel decision-making approach. The approach comprises of $T$ -spherical fuzzy information containment/processing through an analytic hierarchy process (AHP) and then utilizing the weighted aggregated sum product assessment (WASPAS) sequentially to obtain the necessary weights of selected criteria. Further, the obtained weights from AHP would be used in WASPAS for the computational assessment while finding the prioritization/ranking of industry 5.0 enablers. The results with the sensitivity analysis validate the robustness-cum-resilience of the proposed integrated approach with standard managerial implications and the findings take care of cognitive in the human socio-technical environment. The regulated adoption of Industry 5.0 enablers would help consumer electronics manufacturers in finding optimized solutions.

The increasing popularity of mobile phones has led to an abundance of online reviews, making it challenging for consumers to make well-informed purchasing decisions. This study proposes a novel recommendation system-based mobile phone rating classification approach using federated learning and Term Frequency-Inverse Document Frequency (TF-IDF) features. We created a novel dataset by scraping over 13,000 mobile phone reviews from Flipkart’s website. The proposed approach involves the development of a federated deep neural network (FDNN) to classify the newly created Flipkart dataset. This approach includes data cleaning, balancing, TF-IDF feature extraction, and prediction using federated learning. We employ two clients and one server and conduct three rounds of experiments. The experimental results demonstrate that the proposed approach achieved an accuracy of 96.68% on the aggregated server side while maintaining the security of customer data on their local devices. The proposed approach has the potential to assist consumers in making well-informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.

Pneumonia is a significant global health concern that can lead to severe and sometimes fatal consequences. Timely identification and classification of pneumonia can substantially improve patient outcomes. However, the disclosure of sensitive medical data for diagnostic purposes raises important issues regarding patient privacy and data security. The Federated Multi-Party Computing (FMPC) technique has emerged as a promising solution to these challenges, enabling multiple parties to collaborate and compute a function over their private data while maintaining data privacy. This paper presents a novel Internet of Things (IoT)-enabled FMPC framework that significantly enhances the accuracy of pneumonia categorization while preserving patient privacy and data security. Using publicly available Kaggle datasets, the efficacy of the proposed strategy is assessed, and a comparison with contemporary deep learning models is made. The study demonstrates the remarkable efficacy of the IoT-enabled FMPC approach in pneumonia recognition within the industry 5.0 landscape. With 96.67% accuracy achieved for the unbalanced dataset and 97.84% accuracy for the balanced dataset, the results of the proposed algorithm demonstrate the potential for improvement. This approach ensures the privacy of sensitive medical information, aligning with the core principles of Industry 5.0 that emphasize the harmonious integration of advanced technologies and human-centric values.

Product reviews are critical in informing customers about products and services and are crucial to customer inclinations and business success. Active learning enables Artificial Intelligence (AI) algorithms to learn more efficiently by actively selecting the most informative data samples for training. This study proposes a state-of-the-art approach that utilizes active learning (AL) based machine learning algorithms for Flipkart’s smartphone customers’ reviews sentiments analysis. The proposed study adopts rigorous data cleaning, data balancing, and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction techniques, followed by five AL-based machine learning algorithms, including Random Forest (AL-RF), Decision Tree (AL-DT), K-Nearest Neighbor (AL-KNN), Logistic Regression (AL-LR), and Gradient Boost (AL-GB), to classify sentiment analysis based on product reviews. The experiment spanned over three iterations with a chunk size of 200, and an active learning strategy was applied to the process. The results demonstrate the effectiveness of active learning in improving the accuracy of sentiment analysis on unbalanced datasets. Logistic Regression has demonstrated an accuracy rating of 89%. Collectively, the study showcases the potential of active learning techniques for sentiment analysis in product reviews and serves as a valuable tool for guiding customers in making informed decisions regarding their choice of products.

Nowadays, developing environmental solutions to ensure the preservation and sustainability of natural resources is one of the core research topics for providing a better life quality. Using renewable energy sources, such as solar energy, is one of the solutions that can reduce the overuse of natural resources. This research aims to boost the efficiency of solar energy plants by proposing a novel approach to optimize the total flying time of battery-based drone systems to enhance the performance of solar plant systems. The contribution of the proposed approach is to solve scheduling problems based on timing constraints to monitor the solar plant. The main objective of the proposed approach is to maximize the drone’s minimum total flying time, which will increase the availability and reliability of the solar plant monitoring system. Time to empty values is calculated based on battery degradation rates. This problem is proven to be NP-hard. Four categories of enhanced algorithms were developed to solve drones’ scheduling problems in handling various tasks within multiple errands in the extent of solar parks in the monitored power plant to achieve the desired objective. Experimental results of the presented algorithms showed that the $M2S$ algorithm has a stable performance behavior in all conducted experiments.