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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Edge-Coordinated Energy-Efficient Video Analytics for Digital Twin in 6G

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China communications 2023年第2期20卷 14-25页

作者： Peng Yang Jiawei Hou Li Yu Wenxiong Chen Ye Wu School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanHubei 430074China College of Information Science and Engineering Hunan Normal UniversityChangshaHunan 410081China School of Advanced Technology Xi’an Jiaotong-Liverpool UniversitySuzhouJiangsu 215123China

Camera networks are essential to constructing fast and accurate mapping between virtual and physical space for digital *** this paper,with the aim of developing energy-efficient digital twin in 6G,we investigate real-time video analytics based on cameras mounted on mobile devices with edge *** problem is challenging because 1)mobile devices are with limited battery life and lightweight computation capability,and 2)the captured video frames of mobile devices are continuous changing,which makes the corresponding tasks arrival *** achieve energy-efficient video analytics in digital twin,by taking energy consumption,analytics accuracy,and latency into consideration,we formulate a deep reinforcement learning based mobile device and edge coordination video analytics framework,which can utilized digital twin models to achieve joint offloading decision and configuration *** edge nodes help to collect the information on network topology and task *** simulation results demonstrate that our proposed framework outperforms the benchmarks on accuracy improvement and energy and latency reduction.

关键词： mobile edge computing video analytics digital twin 6G deep reinforcement learning

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Dual-Stream Heterogeneous Graph Neural Network Based on Zero-Shot Embeddings for Predicting miRNA-Drug Sensitivity

Dual-Stream Heterogeneous Graph Neural Network Based on Zero...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Li Wang, Wang Yang, Cheng Xiao, Wenhui Fu, Xiangzheng Chen, Yifan Hunan University of Science and Technology College of Computer Science and Engineering Xiangtan China Hong Kong Baptist University College of Chinese Medicine Hong Kong Hong Kong Central South University of Forestry and Technology College of Computer and Information Engineering Changsha China

ISBN: (纸本)9798350386226

MicroRNAs (miRNAs) are a class of non-coding RNA molecules that have been shown to be closely associated with the sensitivity of chemotherapeutic drugs in cancer treatment. Given the high cost and extended duration of traditional biological experiments, there is an urgent need to develop computational models to predict the sensitivity scores between miRNAs and drugs. In this study, we proposed a dual-stream graph neural network method based on Zero-Shot Embeddings, named DSHGZS, to explore the potential sensitivity scores between miRNAs and drugs. DSHGZS first constructs two heterogeneous graphs with different isomorphic subgraphs based on zero-shot embeddings obtained from large language models (LLMs) and known miRNA-drug association data. It then utilized the enhanced LLM-derived node feature representations, embedding them into the layer feature learning process of the two heterogeneous graphs to generate high-quality vector representations of miRNAs and drugs. The learned high-quality feature embeddings are subsequently used in a segmented inner product decoder to evaluate the sensitivity association scores between miRNAs and drugs. To address the model's excessive reliance on high-quality feature representations, we employed PCA to extract the core representations of the LLM-derived node features for data augmentation. Case studies demonstrated that DSHGZS is an effective tool for predicting potential sensitivity scores between miRNAs and drugs. © 2024 IEEE.

关键词： Graph neural networks

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Weighted Fuzzy System for Identifying DNA N4-Methylcytosine Sites With Kernel Entropy Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 895-903页

作者： Wang, Leyao Tiwari, Prayag Ding, Yijie Guo, Fei College of Intelligence and Computing Tianjin University Tianjin300350 China School of Information Technology Halmstad University Halmstad30118 Sweden University of Electronic Science and Technology of China Quzhou324000 China School of Computer Science and Engineering Central South University Changsha410083 China

N4-methylcytosine (4mC) is a common DNA methylation that has been implicated in epigenetic regulation and host defense. Accurate prediction of 4mC sites in DNA sequences will help to better explore the biological processes and mechanisms. For this problem, computational methods based on machine learning and deep learning are faster, less complex, and less expensive than experimental detection methods. However, the existing computational methods are still unsatisfactory in terms of prediction accuracy, so we propose a new method with better performance. In this work, we propose a weighted fuzzy system for identifying DNA 4mC sites by kernel entropy component analysis (KECA). We named it as W-TSK-FS-KECA. This model is improved based on the Takagi-Sugeuo-Kang fuzzy system (TSK-FS). We use position-specific trinucleotide propensity to construct feature vectors on representative benchmark datasets. Then we use KECA to get the reconstruct error. Finally, we put the calculated reconstruction error add to the regular term of TSK-FS as the weights to enhance the model performance. Comparative experiments with other methods show that it has good classification perfor- mance. © 2023 IEEE.

关键词： Entropy

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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Reinforcement Learning Based Edge Microservice Migration Strategy under Resource Constraints 5

Reinforcement Learning Based Edge Microservice Migration Str...

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5th International science and technology Conference Modern Network Technologies, MoNeTec 2024

作者： Dong, Mengxue Huo, Dongkun Zhang, Hong Zhang, Yan Hao, Yixue Hu, Long Chen, Min Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China South China University of Technology School of Computer Science & Engineering Guangzhou China National University of Defense Technology College of Information and Communication Changsha China

ISBN: (纸本)9798350369007

Service migration can solve the issue of frequent user switching caused by the restricted coverage of edge cloud, which is currently a major focus of research. Simultaneously, microservice architecture gains traction in businesses for its benefits in maintainability and scalability. Existing migration strategies mainly target monolithic services, overlooking the complexities of microservice migration scenarios. Considering the limited edge cloud resources and the flexible combination of microservices to be migrated individually in the microservice migration process, we construct a microservice migration system architecture to solve this problem with the goal of minimizing latency and energy consumption. We transform the microservice migration problem to a Markov decision process and propose an intelligent microservice migration algorithm under resource constraints based on reinforcement learning algorithm. Simulation results indicate that our algorithm reduces user request latency and system energy consumption, while enhancing QoS compared to current approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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An Intelligent Energy Optimization Strategy for the Multi-Energy Microgrid with the Carbon Trading under the Step Price 7

An Intelligent Energy Optimization Strategy for the Multi-En...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Liu, Weirong Tang, Nvzhi Rong, Jieqi He, Hongjiang Chen, Bin School of Computer Science and Engineering Central South University Changsha China School of Electronic Information Central South University Changsha China School of Automation Central South University Changsha China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9798350359558

It is promising to reduce carbon emission by incor-porating carbon trading into the multi-energy microgrid which can exploit the complementary advantages among various energies. Currently, most carbon trading uses the fixed price, limiting the prohibition effect on carbon emission. Introducing the step price can bring the indifferentiable characteristic of the cost function, which is difficult to solve. To address the issue, in this paper, an energy optimization strategy is proposed for the multi-energy microgrid based on the intelligent meta-heuristic algorithm. Firstly, the models of the electric, heat, gas devices, and energy converters are constructed. Then the energy optimization problem for multiple energies with the carbon trading under step price is formulated. Afterward, an improved grey wolf optimization algorithm with fast convergence is utilized to reduce the operation cost under the lowest carbon emission. The Simulations conducted on the real-world data verify the effectiveness of the proposed strategy. © 2024 IEEE.

关键词： Carbon

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Big Data Mining of Maritime Shipping Flows Based on Dependency Relationships 9

Big Data Mining of Maritime Shipping Flows Based on Dependen...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Fu, Yude Li, Xiang Lu, Xiongyi Yu, Mengjun Huangpeng, Qizi Duan, Xiaojun College of Science National University of Defense Technology Changsha China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9798350376548

This research utilizes higher-order dependency relationships for the modeling of higher-order dependency networks from voluminous maritime flow data and introduces higher-order dependency mining to uncover critical port information in the China-Southeast Asia region as well as the coopetition relationships between ports. Analysis of maritime data from the years 2018 and 2023 indicates that global ports maintain stability and competitiveness through their evolution, with Singapore serving as the central hub connecting China and Southeast Asia. Subsequent analysis employing dependency paths indicates that in recent years, the dependency relationships among ports between China and Southeast Asia have achieved higher orders and greater complexity. © 2024 IEEE.

关键词： competition and cooperation dependency relationships flow data maritime network

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CBi-GRU: Post-route timing path analysis method based on combined bidirectional GRU 8

CBi-GRU: Post-route timing path analysis method based on com...

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8th International Conference on Integrated Circuits and Microsystems, ICICM 2023

作者： Wang, Bin Huang, Pengcheng You, Huiqing Wang, Yaohua Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China National University of Defense Technology College of Computer Science Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350318517

As the level of circuit integration increases, the physical design becomes more and more complex, iterative static timing analysis(STA) is one of the most time-consuming task to achieve timing closure. After the route stage, fast and accurate timing analysis prediction can effectively reduce the number of static timing analysis in iterations, thereby improving design efficiency and reducing design cycle. In order to quickly predict the timing results after route, we propose a fast timing analysis predictor based on gate recurrent unit (GRU) neural network. This method extracts the features of combinations of cells and nets, assigns attention weights to different combinations, then predicts the hold slacks and setup slacks of each path in multi-corners. The experimental results in the sub-30nm show that the standard deviation of the prediction error ranges 5.41∼16.44ps, and the mean absolute error ranges 6.87∼18.55ps, and the prediction correlation is more than 0.96. Moreover, the speed of timing path prediction is more than 197 times faster than the analysis by STA tools, so that it can effectively and accurately accelerate the process of timing analysis and improve the efficiency of physical design. © 2023 IEEE.

关键词： Forecasting

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