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IEEE Transactions on Green Communications and Networking 2024年第2期8卷 635-644页

作者： Shabestari, Fatemeh Jafari Navimipour, Nima Islamic Azad University Sofian Branch Department of Computer Engineering Sofian Iran Kadir Has University Department of Computer Engineering Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines. © 2017 IEEE.

关键词： Energy utilization

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Reversible Data Hiding in Shared Images Based on Syndrome Decoding and Homomorphism

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IEEE Transactions on Cloud Computing 2023年第3期11卷 3085-3098页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing210044 China Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education CICAEET Jiangsu Nanjing210044 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974 Taiwan Fudan University School of Computer Science Shanghai200433 China

Reversible Data Hiding in Encrypted Images (RDHEI) has drawn increasing concern in multimedia cloud computing scenarios. It embeds secret message into the encrypted carrier while preserving the confidentiality of the image. However, most RDHEI schemes have only one hider and one image carrier which are not efficient for the distributed system with multiple participants. Moreover, if the stego-image is lost, the cover image and the embedded data cannot be restored. To solve above issues, this paper proposes Reversible Data Hiding in Shared Images based on syndrome decoding and homomorphism (RDHSI). In RDHSI, the cover image and secret data are distributed using secret sharing. Multiple data hiders embed shared data into the shared images based on the Syndrome Decoding of Hamming codes and the additive homomorphism of the polynomial. On the receiver side, the lossless cover image and secret data are obtained. The reversibility and privacy preserving of the cover image, the complete extraction of secret data, and the fault-tolerance of the proposed schemes are achieved. The experimental results show that PSNR of the marked image is above 54 dB at the embedding rate of 0.43 bpp. In order to improve the payload of secret data, the Modified RDHSI is further proposed. Generally, the proposed schemes are secure, effective and fault-tolerant. © 2013 IEEE.

关键词： Data privacy

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Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

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computers, Materials & Continua 2023年第7期76卷 849-859页

作者： Baowei Wang Neng Wang Yuxiao Zhang Zenghui Xu Junhao Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044JiangsuChina Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044JiangsuChina

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data *** speaking,EHRs are widely used in blockchain-based medical data *** are valuable private assets of patients,and the ownership belongs to *** recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit *** order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate *** paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted *** the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical *** addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical *** the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the *** analysis and security analysis illustrate the completeness and feasibility of the scheme.

关键词： Electronic health records cross-chain medical certificate blockchain data deletion and recovery

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues

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ACM Computing Surveys 2023年第12期55卷 1-45页

作者： Heidari, Arash Navimipour, Nima Jafari Unal, Mehmet Zhang, Guodao Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul34083 Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey Department of Digital Media Technology Hangzhou Dianzi University Hangzhou310018 China

Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with

关键词： Drones

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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PerfCam: digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models

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IEEE Access 2025年 13卷 85390-85406页

作者： Khan, Michel Gokan Guarese, Renan Johnson, Fabian Wang, Xi Vincent Bergman, Anders Edvinsson, Benjamin Romero, Mario Vachier, Jeremy Kronqvist, Jan KTH Royal Institute of Technology School of Engineering Sciences Stockholm Sweden KTH Royal Institute of Technology Digital Futures Stockholm Sweden KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden AstraZeneca Slovakia KTH Royal Institute of Technology School of Industrial Engineering and Management Stockholm Sweden Linköping University Department of Science and Technology Sweden

We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling highly accurate digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam’s ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics. © 2013 IEEE.

关键词： Convolutional neural networks

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Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

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computers, Materials & Continua 2024年第5期79卷 3397-3416页

作者： Umi Salma Basha Shashi Kant Gupta Wedad Alawad SeongKi Kim Salil Bharany Computer Science and Information Technology Jazan UniversityJazanSaudi Arabia CSE Eudoxia Research UniversityNew CastleUSA Department of Information Technology College of Computer ScienceQassim UniversityBuraydahSaudi Arabia National Center of Excellence in Software Sangmyung UniversitySeoulKorea Department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjabIndia

A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity *** use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional *** suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

关键词： Cloud computing healthcare data security enhanced parallel multi-key encryption algorithm(EPM-KEA)

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Semantic and secure search over encrypted outsourcing cloud based on BERT

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Frontiers of computer science 2022年第2期16卷 152-159页

作者： Zhangjie FU Yan WANG Xingming SUN Xiaosong ZHANG Engineering Research Center of Digital Forensice Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Searchable encryption provides an effective way for data security and privacy in cloud *** can retrieve encrypted data in the cloud under the premise of protecting their own data security and ***,most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the *** this paper,we propose two secure and semantic retrieval schemes based on BERT(bidirectional encoder representations from transformers)named SSRB-1,*** training the documents with BERT,the keyword vector is generated to contain more semantic information of the documents,which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s ***,through testing on real data sets,it is shown that both of our solutions are feasible and effective.

关键词： cloud computing semantic search BERT model searchable encryption

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Reinforcement Learning Double DQN for Chip-Level Synthesis of Paper-Based digital Microfluidic Biochips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第8期43卷 2465-2478页

作者： Li, Katherine Shu-Min Wu, Fang-Chi Li, Jian-De Wang, Sying-Jyan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung402 Taiwan Realtek Semiconductor Corporation Corporate Technology Center Hsinchu300 Taiwan

digital microfluidic biochips (DMFBs) can effectively reduce the cost of biochemical analysis and improve experimental efficiency, as they are easy to carry, use fewer reagent samples and have high precision. Paper-based DMFBs (PB-DMFBs) are a branch of microfluidic biochips. This technology prints ink containing carbon nanotubes on special paper to form electrodes and control wire, so the manufacturing cost and time required are far less than the traditional digital microfluidic chip, in which droplets move between two control layers. However, the chip-level synthesis of PB-DMFBs becomes more challenging because all circuits of PB-DMFBs are printed on a single paper layer. Furthermore, current PB-DMFB designs must address various issues, including fabrication cost, reliability, and safety. Therefore, a more flexible method for the chip-level synthesis of PB-DMFBs is needed. In this article, we propose a chip-level synthesis method of PB-DMFBs based on reinforcement learning. Double deep Q -learning networks (Double DQN) are suitable for agents to select actions and estimate actions, and then obtain optimized comprehensive results. Experimental results demonstrate that the proposed method is not only effective and efficient for chip-level synthesis but also scalable to applications with high reliability and safety requirements. © 1982-2012 IEEE.

关键词： Electrodes

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