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A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm

International Journal of computers and Applications 2022年第5期44卷 465-479页

作者： Noureddine, Samia Zineeddine, Baarir Toumi, Abida Betka, Abir Benharkat, Aïcha-Nabila Department of Electrical Engineering LESIA Laboratory University of Biskra Biskra Algeria Department of Computer Science INSA of Lyon Lyon France

Handling very large data, in order to make the best decision, is only possible through an extraction of knowledge. Data mining has become a widely used process in data analytics to extract the most important knowledge for predictive decision making. One of the important types of data mining is clustering mechanism;its purpose is dividing data into a set of clusters with very large data, the numbers of parameters are very high, and the clustering problem is more difficult. Metaheuristics have been widely used in clustering;they can provide satisfactory solutions for complex problems. The main objective of this paper is to propose a new clustering algorithm based on a metaheuristic technique called Symbiotic Organisms Search (SOS), it was inspired from a biological process, and it simulates the symbiotic interaction between organisms of the same population. The SOS method is used to find the optimal centers of a number of clusters, as a supervised data mining technique. Experimental results have been performed through two phases. Firstly, the SOS technique is benchmarked with six well-known test functions. Secondly, different medical datasets have been used to test our proposed clustering method based on SOS, and show its credibility of treatment. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Data mining

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Security challenges and defense approaches for blockchain-based services from a full-stack architecture perspective

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Blockchain(Research and Applications) 2023年第3期4卷 18-38页

作者： Hongsong Chen Xietian Luo Lei Shi Yongrui Cao Yongpeng Zhang Department of Computer Science University of Science and Technology Beijing(USTB)Beijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China

As an advantageous technique and service,the blockchain has shown great development and application ***,its security has also met great challenges,and many security vulnerabilities and attack issues in blockchain-based services have ***,security issues of blockchain have attracted extensive ***,there is still a lack of blockchain security research from a full-stack architecture perspective,as well as representative quantitative experimental reproduction and *** aim to provide a security architecture to solve security risks in blockchain services from a full-stack architecture ***,we propose a formal definition of the full-stack security architecture for blockchain-based services,and we also propose a formal expression of security issues and defense solutions from a full-stack security *** use ConCert to conduct a smart contract formal verification experiment by property-based *** security vulnerabilities of blockchain services in the Common Vulnerabilities and Exposures(CVE)and China Nation Vulnerability Database(CNVD)are selected and ***,three real contract-layer real attack events are reproduced by an experimental *** Alibaba's blockchain services and Identity Mixer in Hyperledger Fabric as a case study,the security problems and defense techniques are analyzed and *** last,the future research directions are proposed.

关键词： Security and privacy Blockchain-based services Full stack Architecture perspective Case and experimental study Formal verification

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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Science China(Information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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OFDM-structure based waveform designs for integrated sensing and communication

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Science China(Information Sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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24.4 THz·V fT×BV figure-of-merit AlN/GaN/AlN MISHEMTs with thin AlN buffer layer

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Science China(Information Sciences) 2025年第3期68卷 403-404页

作者： Chaoqun ZHANG Hong ZHOU Sami ALGHAMDI Kun ZHANG Zhihong LIU Yue HAO Jingcheng ZHANG State Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology Xidian University Guangzhou Wide Bandgap Semiconductor Innovation Center Guangzhou Institute of TechnologyXidian University Electrical and Computer Engineering Department King Abdulaziz University

Gallium nitride-based high-electron-mobility-transistors(HEMTs) have gained widespread interest and become primary candidates for next-generation high-frequency and high-power RF electronics, due to their wide bandgap,high breakdown field, and strong polarization-induced high-density 2-dimensional electron gas(2DEG) at the heterojunction interface.

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Cluster-HSFL: A Cluster-Based Hybrid Split and Federated Learning

Cluster-HSFL: A Cluster-Based Hybrid Split and Federated Lea...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Songge Tu, Haoyu Li, Zuguang Liu, Shengbo Li, Shaofeng Wu, Wen Shen, Xuemin Sherman Peking University School of Electronic and Computer Engineering Shenzhen China Frontier Research Center Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering Sun Yat-Sen University Guangzhou China Shenzhen China Department of Electrical and Computer Engineering University of Waterloo ON Canada

ISBN: (纸本)9798350345384

Federated learning (FL) and split learning (SL) are two machine learning paradigms that can distributedly train artificial intelligence (AI) models while guaranteeing client privacy. FL can parallelly train AI models based on clients' dataset, but it suffers from considerable communication overhead for model uploading and imposes a significant on-device computation workload. SL can mitigate the computation workload of the devices by offloading part of the AI model training to an edge server, but it suffers from long training delay for a large number of clients. To address these challenges, in this paper we present a cluster-based hybrid split federated learning (Cluster-HSFL) scheme by integrating FL and SL paradigms. This scheme can reduce training latency and alleviate on-device computational constraints while preserving accuracy performance. We establish a testbed for the Cluster-HSFL scheme based on the NVIDIA Jetson series hardware. The experimental results demonstrate that the Cluster-HSFL surpasses FL and SL by 6.3× and 1.8×, respectively, in terms of delay performance. Our results provide insight into the practical performance of the Cluster-HSFL scheme and can help guide future experimental research on SL in resource-limited wireless networks. © 2023 IEEE.

关键词： Artificial intelligence

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A Hybrid Security Framework for Medical Image Communication

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computers, Materials & Continua 2022年第11期73卷 2713-2730页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Ashraf A.M.Khalaf Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Authentication of the digital image has much attention for the digital *** image authentication can be verified with image watermarking and image encryption *** schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful *** on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this *** framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using *** the encrypted image is watermarking based on *** combination between watermarking and encryption increases the security and robustness of transmitting an *** performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

关键词： Walsh hadamard transform watermarking encryption double random phase encoding structural similarity index

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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