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Energy inefficiency diagnosis for Android applications: a literature review

Frontiers of computer science 2023年第1期17卷 35-50页

作者： Yuxia SUN Jiefeng FANG Yanjia CHEN Yepang LIU Zhao CHEN Song GUO Xinkai CHEN Ziyuan TAN Department of Computer Science Jinan UniversityGuangzhou 510632China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China The Department of Computing Hong Kong Polytechnic UniversityHong Kong 999077China

Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.

关键词： Android applications energy defects energy inefficiency diagnosis

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SAFED: secure and adaptive framework for edge-based data aggregation in IoT applications

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Discover Internet of Things 2025年第1期5卷 1-28页

作者： Naaz, Zaineb Joshi, Gamini Sharma, Vidushi Department of Computer Science and Engineering School of ICT Gautam Buddha University Greater Noida India Department of Computer Science and Engineering PES University Electronic City Campus Bangalore India

The paper proposed a secured and efficient data aggregation mechanism leveraging the edge computing paradigm and homomorphic data encryption technique. The paper used a unique combination of Paillier cryptosystem and RSA digital signature mechanism to preserve data privacy, confidentiality, and integrity while maintaining the efficiency of the IoT network. The efficiency and implementation of the proposed scheme have been demonstrated through a case study, showcasing how the scheme can be effectively applied in real-world scenarios, such as in a smart grid network. Additionally, the future scope has been outlined, with potential techniques to address challenges that the scheme may face. © The Author(s) 2025.

关键词： Data aggregation Edge computing Internet of things Paillier cryptosystem Privacy-preserving Security

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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and Applications 2025年第18期84卷 18733-18780页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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DISTRIBUTED ARBITER A Lightweight, and Enhanced Access Control Mechanismfor Cloud computing

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Thermal science 2024年第6期28卷 4969-4977页

作者： ALMARHABI, Khalid Ali Department of Computer Science College of Engineering and Computing in Al-Qunfudah Umm Al-Qura University Makkah Saudi Arabia

Since its discovery, computer technology has played a key role in changing the lifestyles of people and continues to provide countless benefits. Nevertheless, computer technology systems that facilitate smooth integration, such as, cloud computing;are prone to cyber breaches, which has negatively affected its reputation and perception. Therefore, computer technology systems that are secure are needed to curb cyber threats and increase user trust. The primary issue in computer technology systems is that, unlike alternative methods, most cloud access control mechanisms are inadequate. Furthermore, transitioning to a trust-based mechanism is not only complicated and costly but a significantly decision intensive process. As such, this present study investigates how network risks and threats analysis, edge computing and Arbiter, a mandatory access control mechanism, can be integrated into cloud computing to prevent single points of failure. It also examines how these integrating components can decrease the costs and effort required to change an entire operating system to meet the requirements of a trusted system. © (2024), (Serbian Society of Heat Transfer Engineers). All rights reserved.

关键词： Cloud platforms

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An ensemble deep learning framework for foetal plane identification

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1377-1386页

作者： Thomas, Seena Harikumar, Sandhya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India

Antenatal (prenatal) care stipulates periodic monitoring of the foetus in alleviating risk factors and improving pregnancy outcomes. Foetal images generated from the ultrasound during prenatal care can be classified into different planes, such as the brain, abdomen, femur, etc., and can be further used for the abnormality detection, disease diagnosis, and foetal growth estimation of the foetus. Therefore, recognizing the foetal plane is one of the initial steps to automate antenatal care. This paper proposes an Ensemble Convolution Neural Network (ECNN) model that combines the base models to classify the foetal planes by using an open-access database containing 12,400 images with six foetal planes. Prior studies on this database involved state-of-the-art CNN methods, and Densenet-169 pre-trained model gave an accuracy of 93.6%. Three pre-trained CNN models (base learners) are trained using the transfer learning approach in the proposed method. The predicted features derived from the base learners are then used to train a Deep Neural Network (DNN) based meta-learner to achieve high classification rates. The stacked ensemble model resulted in an accuracy of 96 % , which is better than the accuracy of the individual pre-trained models. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Antenatal care Classification Deep neural network Ensembled convolution neural network Foetal planes

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A Comparative Exploration of Time Series Models for Wild Fire Prediction 4

A Comparative Exploration of Time Series Models for Wild Fir...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sri Sowmya, V. Sasikala, D. Theetchenya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350343670

Wildfires pose a great threat to human safety and property arising from both natural and human causes. According to a technical assessment by the Forest Survey of India more than 95% of fires are of anthropogenic origin. Wildfires erupt due to burning of fossils by local communities for crop rotation, camp fires without proper supervision etc. Climate change further elevates the risk by fostering dry conditions. Traditionally, wildfire prediction relied on statistical models and expert judgment. However, the emergence of Machine Learning (ML) and Deep Learning (DL) techniques has significantly improved the accuracy of forest fire prediction. The objective of this work is to prevent wildfires and save the ecosystem. In this work, LightGBM(Light Gradient Boosting Machine) and LSTM(Long Short-Term Memory) machine learning models are utilized to predict the forest fire. Both models exhibit high F1 scores of 97% and 95% accuracy in forest fire prediction, enabling the development of reliable prediction systems. The results of these ML-based models may aid in identifying highrisk areas, optimizing prevention measures, refining evacuation plans, and guiding firefighting efforts. © 2024 IEEE.

关键词： Deep Learning(DL) LightGBM LSTM Machine learning(ML) Wildfires

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A comprehensive systematic review of machine learning in the retail industry: classifications, limitations, opportunities, and challenges

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Neural computing and Applications 2024年第4期37卷 2035-2070页

作者： Hassan, Dler O. Hassan, Bryar A. Department of Computer Science College of Science Charmo University Kurdistan Region Chamchamal Sulaimani46023 Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

Machine learning has profoundly transformed various industries, notably revolutionizing the retail sector through diverse applications that significantly enhance operational efficiency and performance. This comprehensive review examines the state-of-the-art machine learning applications in the retail sector from 2019 to 2024, focusing on supervised learning, unsupervised learning, and ensemble methods. It aims to identify and categorize recent machine learning applications in retail, evaluate the performance of machine learning algorithms, and determine the most suitable algorithms for specific retail use cases. This review article examines 56 studies and identifies 20 unique machine learning applications within the retail sector. This review also discusses the challenges and opportunities of implementing machine learning in retail, offering valuable insights to guide future research and enhance retail performance and customer satisfaction. The findings highlight the strengths and limitations of different machine learning methods, providing insights into their practical applications and future potential. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Ensemble methods Machine learning Retail Supervised learning Unsupervised learning

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Survey on Video Security:Examining Threats,Challenges,and Future Trends

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computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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Resource Allocation in Edge computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey

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SN computer science 2024年第1期5卷 99页

作者： Purushothaman, Sony Shanmugam, G. Siva Nagarajan, Sureshkumar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science Govt. Model Engineering College Kerala Kochi India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnakovil India

Achieving semantic interoperability in healthcare is one of the significant challenges in the rapidly expanding healthcare sector. On the other hand, IoT in healthcare can provide patient-centric treatment mechanisms to help resolve these issues. When several healthcare organizations are generating heterogeneous medical information, semantic interoperability becomes essential to develop better treatment plans and better medical research. In this article, we discuss the applications of healthcare IoT, a comparative study of existing solution paradigms for achieving semantic interoperability and its enabling technologies. The comprehensive analysis of semantic interoperability has versatile dimensions to meet this, and it establishes deep learning-based directions. In continuing the background work, we proposed a new deep learning model called MedRelBERT with six embeddings to remove linguistic ambiguities in medical documents. The survey also examines the bibliographic evaluation of Web of science articles discussing semantic interoperability solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Internet of medical things Privacy Semantic interoperability

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