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Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

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research 2024年第1期2023卷 471-487页

作者： Lin Jiang Yueheng Peng Runyang He Qingqing Yang Chanlin Yi Yuqin Li Bin Zhu Yajing Si Tao Zhang Bharat B.Biswal Dezhong Yao Lan Xiong Fali Li Peng Xu The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Life Science and Technology Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Psychology Xinxiang Medical UniversityXinxiang 453003China School of Science Xihua UniversityChengdu 610039China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China Research Unit of NeuroInformation Chinese Academy of Medical Sciences2019RU035ChengduChina Montreal Neurological Institute and Hospital McGill UniversityMontrealQCCanada Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Radiation Oncology Key Laboratory of Sichuan Province 610041 ChengduChina Rehabilitation Center Qilu Hospital of Shandong UniversityJinan 250012China

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain *** to lacking an effective approach to quantifying the covarying of structure and functional responses,how the structural–functional circuits interact and how genes encode the relationships,to deepen our knowledge of human cognition and disease,are still ***,we propose a multimodal covariance network(MCN)construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single *** further explored the potential association between brain-wide gene expression patterns and structural–functional covarying in individuals involved in a gambling task and individuals with major depression disorder(MDD),adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent *** analysis showed a replicable cortical structural–functional fine map in healthy individuals,and the expression of cognition-and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN *** analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences.

关键词： alterations evoked spatially

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Empowering Early Detection: CervicalCareXG Revolutionizes Cervical type cancer Diagnostics 4

Empowering Early Detection: CervicalCareXG Revolutionizes Ce...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Verma, Shanti Suthar, Hiral Rami, Khyati L J University Department of Computer Applications Gujarat Ahmedabad India Silver Oak University Department of Computer Applications Gujarat India Sal Institute of Technology & Engineering Research Department of Computer Science Gujarat India

ISBN: (纸本)9798350354218

A XGBoost-Based System in Cervical type cancer Diagnostics are proposed in the current research work. Generally Cervical type of cancer will be a significant health concern worldwide, particularly in the regions with limited access on advanced healthcare facilities. On Early detection will plays a pivotal role on improving patient outcomes and by reducing mortality rates. In this study, the propose system termed CervicalCareXG, a novelistic system for cervical type cancer diagnostics depends on the XGBoost methodology. CervicalCareXG will utilizes a machine Learning-Driven approach to analyze patient data, also including demographic information, medical history, and cervical screening results, to Prediction the likelihood of cervical type cancer development. The XGBoost algorithm, known for its prediction accuracy and efficacy in handling complex health related datasets, is employed to train a predictive model with a capable of identifying high-risk individuals who may requires further diagnostic evaluation or preventive interventions. Through comprehensively evaluation using reality patient data, CervicalCareXG may demonstrates a promising performance in terms of sensitivity, specificity, and area under the receivers operating characteristic curve (AUC-ROC). Thus, it showcases its potential as a valuable tool for healthcare professionals on cervical type cancer screening programs. By harnessing the power of machine Learning-Driven and XGBoost, CervicalCareXG with an aim to contribute to the early detection and management of cervical type cancer, ultimately by improving patient outcomes and reducing the burden of this devastating disease. © 2024 IEEE.

关键词： Diseases

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1563-1570页

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Optimization of Data Memory and Safety in Clouds with Hashing Algorithm 2

Optimization of Data Memory and Safety in Clouds with Hashin...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rafi, S. Mohammed Kumar, R. Yogesh Raj Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Department of Information Technology Bharath Institute of Higher Education and Research Chennai India

ISBN: (纸本)9798350347982

Although cloud computing frameworks consume a significant amount of system resources, it is still difficult to consistently obtain precise responses to query requests. Many plans fail to address the issues raised by persistent inquiries due to a lack of space and extremely complicated progressive maintenance. Hash-based information structures are frequently utilized in the construction of records due to their rapid response times to queries and consistent scale that leans toward complexity. Sadly, information structures based on hashing take up little space and are likely to be able to handle hashing crashes. Hash tables frequently resolve crashes caused by hashing by employing open tending, binding, and mixed hashing. Cuckoo hashing, in contrast to standard hash tables, handles crashes in hashing by moving things around with simple 'throwing out' actions. © 2023 IEEE.

关键词： Query processing

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Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic 1

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Second International Conference on Security, Privacy and Data Analytics, ISPDA 2022

作者： Kashyap, Himanshu Pais, Alwyn Roshan Kondaiah, Cheemaladinne Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India

ISBN: (数字)9789819935697

ISBN: (纸本)9789819935680

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer Security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Speech Enhancement Using U-Net-Based Progressive Learning with Squeeze-TCN 5th

Speech Enhancement Using U-Net-Based Progressive Learning wi...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Vanambathina, Sunny Dayal Nandyala, Sivaprasad Jannu, Chaitanya Sirisha Devi, J. Yechuri, Sivaramakrishna Parisae, Veeraswamy School of Electronics Engineering VIT-AP University Amaravati India Secure Systems Research Center Technology Innovation Institute Masdar City United Arab Emirates NRI Institute of Technology Vijayawada Agiripalli India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9789819735228

Speech enhancement is crucial in many speech processing applications. Recently, researchers have been exploring ways to improve performance by effectively capturing the long-term contextual relationships within speech signals. Nevertheless, these investigations often neglect the significance of the time-frequency (T-F) distribution details of speech spectral elements, which are also important for improving speech quality. Moreover, accurately mapping spectral information in supervised methods requires both global and local information. This paper presents a novel speech enhancement model that utilizes long-term and time-frequency (T-F) information of speech spectral elements for enhancing target speakers. The system is composed of a feature extractor and reconstructor. The feature extractor introduces the U-Net block to enable feature recalibration from multiple scales. Furthermore, the time-frequency attention (TFA) within the U-net’s feature extractor delineates the prominent time-frequency distribution of speech through the simultaneous utilization of two branches: one for time-frame attention and another for frequency channel attention. TFA involves the assignment of distinct attention weights to individual time-frequency spectral components, empowering the models to concentrate on both the "when" (time frames) and the "what" (frequency-wise channels). In the reconstructor, several filtering modules (FMs) are stacked to facilitate the reconstruction of a spectrum in a progressive manner to obtain a coarse estimation by reducing noise in the magnitude domain. Furthermore, the intermediate outcome can be progressively improved across stages by unfolding the filtering modules repeatedly, ultimately resulting in the estimation of the spectrum. The proposed approach is tested on the Librispeech and Voicebank datasets. The findings indicate that this method surpasses earlier models and attains the state-of-the-art performance on these datasets. © The Author(s), under

关键词： Deep neural networks

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction Systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

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science China(Information sciences) 2023年第4期66卷 301-302页

作者： Hongbo GAO Juping ZHU Fei ZHANG Ruidong YAN Junjie ZHOU Lei JIANG Jianqiang WANG Keqiang LI Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer Science and Technology Zhejiang University China North Vehicle Research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(RHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, RHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use RHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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Utilizing Machine Learning to Develop an Improved Real-Time Data Analysis System 3

Utilizing Machine Learning to Develop an Improved Real-Time ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khandari, Harsimrat Sahoo, Girija Shankar Raja Praveen, K.N. Shree, Ritu Umamaheswari, B. Yashwant, V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records © 2023 IEEE.

关键词： Statistics

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Traceable ring signature schemes based on SM2 digital signature algorithm and its applications in the data sharing scheme

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Frontiers of computer science 2024年第2期18卷 251-253页

作者： Yongxin ZHANG Hong LEI Bin WANG Qinghao WANG Ning LU Wenbo SHI Bangdao CHEN Qiuling YUE R&D Department SSC Holding CompanyLtd.Chengmai 571924China School of Cyberspace Security(School of Cryptography) Hainan UniversityHaikou 570228China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Computer Science and Technology Xidian UniversityXi'an 710071China Blockchain Research Group Oxford-Hainan Blockchain Research InstituteChengmai 571924China

1 Introduction Identity privacy concerns hinder data sharing by casting doubt on the safeguarding of personal information,eroding trust,and impeding the willingness of individuals and organizations to exchange their data[1,2].The traceable ring signatures(TRSs)addresses the contradiction between identity privacy and regulation[3],no scheme has been developed thus far that is based on SM2,the Chinese cryptographic public key algorithm standard,without relying on centralized trust.

关键词： SM2 signature scheme

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