App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(M...
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App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.
Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech r...
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Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i
The factory has adopted an extensive ecosystem of connected devices and IoT sensors, utilizing cloud computing for real-time decision-making. Secure cloud storage serves as the backbone, managing vast datasets and ena...
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The factory has adopted an extensive ecosystem of connected devices and IoT sensors, utilizing cloud computing for real-time decision-making. Secure cloud storage serves as the backbone, managing vast datasets and enabling centralized control. By leveraging advanced analytics and machine learning on the cloud, the factory has implemented predictive maintenance, minimizing downtime and optimizing production. The integration of Hybrid PSO-GA for machines and supply chain processes streamlines operations, allowing for remote monitoring and control to enhance operational agility. Cutting-edge advancements in New Generation Information Technologies (New IT) are crucial in driving the evolution of smart manufacturing. The proliferation of Internet-connected devices in these environments generates substantial data throughout the product lifecycle. Adopting a cloud-based smart manufacturing strategy provides numerous services and applications for analysing massive datasets and fostering significant cooperation in manufacturing operations. However, challenges such as latency, bandwidth congestion, and network unavailability hinder its effectiveness for real-time applications requiring fast, low-latency performance. These issues are efficiently addressed by integrating cloud computing with edge computing, extending the cloud’s capabilities to the edge. This paper presents a hierarchical reference architecture for smart manufacturing, leveraging cloud computing. The proposed approach employs a hybrid PSO-GA scheduling function that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to optimize task start times and reduce latency. The optimal solution from this hybrid approach updates task start times, with subsequent scheduling performed using a selected algorithm. The proposed novel hybrid PSO-GA model integrates AI-driven optimization, IoT, and digital twins to enhance real-time decision-making and adapt to dynamic data streams in smart manufacturing. Its
Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content i...
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Phishing attacks are among the persistent threats that are dynamically evolving and demand advanced detection mechanisms to counter more sophisticated techniques. Traditional detection approaches are usually based on ...
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Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with...
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Digital signatures, essential for establishing trust in the digital realm, have evolved in their application and importance alongside emerging technologies such as the Internet of Things (IoT), Blockchain, and cryptoc...
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Predicting energy consumption has become crucial to creating a sustainable and intelligent environment. With the aid of forecasts of future demand, the distribution and production of energy can be optimized to meet th...
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This research focuses on abstractive text summarization techniques for regional languages, specifically Hindi. It employs a Transformer-based model to generate rephrased summaries from datasets of local language news ...
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With the growing trend of cloud computing, the necessity for secure data storage arises as traditional security measures fail with different types of new emerging cyber threats. The paper introduces a file storage sys...
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