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Enhanced emotion recognition in an IoMT platform: leveraging data augmentation and the random forest algorithm for ECG-based E-health

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Mokhtari, Bilal Merizig, Abdelhak Zerdoumi, Hadjer Department of Computer Science Mohamed Khider University Biskra 0700 Algeria Laboratoire d’Applications des mathématiques à l’informatique et à l’électronique: LAMIE Batna 2 University N 3 RN3 Batna Route de Fesdis 05000 Algeria LINFI Laboratory Mohamed Khider University Biskra 07000 Algeria

Advancement of technologies has enabled the creation of smart solutions to improve healthcare. The primary objective is to help healthcare practitioners make efficient and fast decisions and diagnoses. Vital signs provide meaningful information while monitoring patient health, which allows assessing patients’ emotional well-being and adjust therapy. This study introduces an AI-powered patient monitoring system and healthcare platform. The proposed healthcare platform uses Internet of Medical Things (IoMT) and machine learning to monitor and classify emotional states using ECG signals. Thus, a label transformation and Random Forest classifier are used to identify patient emotions as positive or negative based on selected features from Heart Rate Variability (HRV) provided by ECG sensor. Moreover, a data augmentation technique is used to expand the primary used dataset, DREAMER which is afterward integrated with SWELL and WESAD datasets to increase data variety. The model presents emotion identification on the platform with an accuracy of 99.89%, outperforming most existing related works. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data augmentation E-health ECG Emotion recognition and classification IMoT Internet of Things Machine learning

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Swarm Intelligence Based Optimal Design of Local Volt/Var Control Function for Distributed Energy Resources 8

Swarm Intelligence Based Optimal Design of Local Volt/Var Co...

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8th International Conference on Power and Energy Engineering, ICPEE 2024

作者： Zhang, Zhengfa Dong, Yuqing Dong, Jiaojiao Dong, Jin Liu, Boming Yang, Yayu Kuruganti, Teja Liu, Yilu Oak Ridge National Laboratory Electrification and Energy Infrastructures Division Oak RidgeTN37830 United States Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak RidgeTN37830 United States University of Tennessee Knoxville Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9798331530860

The increasing penetration of renewable based distributed energy resources (DERs) in distribution network (DN) leads to larger and more frequent voltage variation in distributions network (DN), thus posing challenges on voltage control. Real-time local voltage control method is a promising solution for the above issue. However, the local voltage control function needs to be customized and optimized according to real distribution system condition. In this paper, a swarm intelligence based Volt/Var control optimal design method (SO-VVC) is proposed to optimize the control function. Compared with existing approaches, the proposed method can not only represent the nonlinear behaviour of power flow but is also computation efficient. The performance of the proposed SO-VVC is demonstrated by case studies on a modified IEEE-123 bus system. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A system capable of verifiably and privately screening global DNA synthesis

arXiv

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arXiv 2024年

作者： Baum, Carsten Berlips, Jens Chen, Walther Cui, Hongrui Damgard, Ivan Dong, Jiangbin Esvelt, Kevin M. Foner, Leonard Gao, Mingyu Gretton, Dana Kysel, Martin Li, Juanru Li, Xiang Paneth, Omer Rivest, Ronald L. Sage-Ling, Francesca Shamir, Adi Shen, Yue Sun, Meicen Vaikuntanathan, Vinod Van Hauwe, Lynn Vogel, Theia Weinstein-Raun, Benjamin Wang, Yun Wichs, Daniel Wooster, Stephen Yao, Andrew C. Yu, Yu Zhang, Haoling Zhang, Kaiyi Department of Computer Science Aarhus University Denmark DTU Compute Technical University of Denmark Denmark SecureDNA Foundation Switzerland Department of Computer Science and Engineering Shanghai Jiao Tong University China Institute for Interdisciplinary Information Sciences Tsinghua University China Media Lab Massachusetts Institute of Technology United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Applied Mathematics Weizmann Institute of Science Israel Department of Computer Science Northeastern University United States China National GeneBank China Department of Political Science Massachusetts Institute of Technology United States Shanghai Qi Zhi Institute China

Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be used to ignite a pandemic. There are three complications. First, we don't need to quickly update printers to deal with newly discovered currencies, whereas we regularly learn of new viruses and other biological threats. Second, anti-counterfeiting specifications on a local printer can't be extracted and misused by malicious actors, unlike information on biological threats. Finally, any screening must keep the inspected DNA sequences private, as they may constitute valuable trade secrets. Here we describe SecureDNA, a free, privacy-preserving, and fully automated system capable of verifiably screening all DNA synthesis orders of 30+ base pairs against an up-to-date database of hazards, and its operational performance and specificity when applied to 67 million base pairs of DNA synthesized by providers in the United States, Europe, and China. Copyright © 2024, The Authors. All rights reserved.

关键词： DNA sequences

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Scalable and flexible porous hybrid film as a thermal insulating subambient radiative cooler for energy-saving buildings

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National science Open 2023年第4期2卷 153-167页

作者： Tong Wang Shuhua Tu Yinggang Chen Qian Zhu Yinan Zhang Zhiyi Ding Xue Mei Min Chen Min Gu Limin Wu Department of Materials Science and State Key Laboratory of Molecular Engineering of Polymers Fudan UniversityShanghai 200433China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China School of Materials and Chemistry University of Shanghai for Science and TechnologyShanghai 200093China

Passive daytime radiative cooling(PDRC)is one of the promising alternatives to electrical cooling and has a significant impact on worldwide energy consumption and carbon *** real-world applications,however,the parasitic heat input and heat leakage pose crucial challenges to commercial and residential buildings *** integrating of radiative cooling and thermal insulation properties represents an attractive direction in renewable energy-efficient building envelope ***,we present a hierarchically porous hybrid film as a scalable and flexible thermal insulating subambient radiative cooler via a simple and inexpensive inverse high internal phase emulsion *** as-prepared porous hybrid film exhibits an intrinsic combination of high solar reflectance(0.95),strong longwave infrared thermal emittance(0.97),and low thermal conductivity(31 mW/(m K)),yielding a subambient cooling temperature of~8.4℃ during the night and~6.5℃ during the hot midday with an average cooling power of~94 W/m^(2) under a solar intensity of~900 W/m^(2).Promisingly,combining the superhydrophobicity,durability,superelasticity,robust mechanical strength,and industrial applicability,the film is favorable for large-scale,sustainable and energy-saving applications in a wide variety of climates and complicated surfaces,enabling a substantial reduction of energy costs,greenhouse gas emission and associated ozone-depleting from traditional cooling systems.

关键词： radiative cooling thermal insulation energy-saving buildings scalability flexibility

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Dynamical Trust Construction Schema with Fuzzy Decision in P2P Systems

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Chinese Journal of Electronics 2023年第3期18卷 417-421页

作者： Yang Wang Ruchuan Wang Zhijie Han College of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer Anhui Normal University Wuhu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The security problem for Peer-to-peer (P2P) systems has been gradually recognized as an important topic of P2P applications. Among existing solution for P2P security, how to establish trust relationship among peers is a key problem in demand. This paper proposes a novel dynamical trust construction schema based on fuzzy decision and Extended automated trust negotiation (EATN). The presented approach adopts fuzzy trust graph to describe the trust information in the P2P systems. Trust relationship among peers can be constructed by the combination of the fuzzy trust graph, fuzzy path search algorithm and EATN. The relevant application case indicates the schema is feasible. The simulations also show that the presented schema can improve the interactive success ratio by enhancing the trust relationship in the P2P systems.

关键词： Simulation Heuristic algorithms Education Telecommunication traffic Peer-to-peer computing Security Trust management

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Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network 23

Battle Against Fluctuating Quantum Noise: Compression-Aided ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zhirui Hu Youzuo Lin Qiang Guan Weiwen Jiang Electrical and Computer Engineering Department George Mason University Fairfax VA US and Quantum Science and Engineering Center George Mason University Fairfax VA US Earth and Environmental Sciences Division Los Alamos National Laboratory NM US Department of Computer Science Kent State University Kent OH

ISBN: (纸本)9798350323481

Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instability) can easily collapse the performance of error-aware designs. What's worse, users can even not be aware of the performance degradation caused by the change in noise. To address both issues, in this paper we use Quantum Neural Network (QNN) as a vehicle to present a novel compression-aided framework, namely QuCAD, which will adapt a trained QNN to fluctuating quantum noise. In addition, with the historical calibration (noise) data, our framework will build a model repository offline, which will significantly reduce the optimization time in the online adaption process. Emulation results on an earthquake detection dataset show that QuCAD can achieve 14.91% accuracy gain on average in 146 days over a noise-aware training approach. For the execution on a 7-qubit IBM quantum processor, ibm-jakarta, QuCAD can consistently achieve 12.52% accuracy gain on earthquake detection.

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Perivascular spaces relate to the course and cognition of Huntington’s disease

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Translational Neurodegeneration 2023年第1期12卷 546-549页

作者： Xiao-Yan Li Juan-Juan Xie Jin-Hong Wang Yu-Feng Bao Yi Dong Bin Gao Ting Shen Pei-Yu Huang Hao-Chao Ying Han Xu Anna Wang Roe Hsin-Yi Lai Zhi-Ying Wu Department of Medical Genetics and Center for Rare Diseases Department of Neurology in Second Affiliated HospitalKey Laboratory of Medical Neurobiology of Zhejiang ProvinceZhejiang University School of MedicineHangzhouChina Interdisciplinary Institute of Neuroscience and Technology College of Biomedical Engineering and Instrument ScienceKey Laboratory for Biomedical Engineering of Ministry of EducationZhejiang UniversityHangzhouChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Department of Radiology Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina School of Public Health Zhejiang UniversityHangzhouChina MOE Frontier Science Center for Brain Research and Brain-Machine Integration School of Brain Science and Brain MedicineZhejiang UniversityHangzhouChina CAS Center for Excellence in Brain Science and Intelligence Technology ShanghaiChina

Huntington’s disease(HD)is an autosomal dominant neurodegenerative disease that is caused by a cytosine-adenine-guanine(CAG)expansion in the first exon of the huntingtin(HTT)gene,which codes for the hun-tingtin *** typically manifests with a triad of symptoms,including motor disorders,cognitive impair-ment and psychiatric disturbances[1].HD primar-ily affects the basal ganglia(BG),especially the caudate and putamen,after which it extends to more widespread gray and white matter[2].Perivascular spaces(PVSs)are fluid-filled extensions of the subarachnoid spaces that enclose cerebral blood vessels and extend from the cer-ebral cortex through the brain *** physi-ological role of PVSs is the drainage of brain interstitial fluid into perivascular pathways for the elimination of waste products through the glymphatic drainage *** increasing number of studies have demonstrated that enlarged PVSs indicate glymphatic dysfunction and are associated with many neurological diseases,such as Alzheimer’s disease,Parkinson’s disease and small vessel disease[3].With the advantage of strong field strengths,7.0 T images show superior resolution and signal-to-noise ratios than 3.0 T,which facilitate the visualization of *** automated segmentation methods could accurately identify PVS in a short time with no inter-rater *** the current study,we used U-shaped networks(U-net),a class of deep learning methods,to explore the PVS distribution in HD and *** date,PVS distribution in HD is still *** two studies have investigated PVSs in HD,and both dem-onstrated increased visible PVS burden in manifest HD compared to controls[4,5].However,whether PVS bur-den increases in premanifest HD(pre-HD)individuals remains unknown,and the relationship of PVS with cog-nitive decline has never been studied.

关键词： Huntington filled elimination

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise Knowledge graphs Predictive models Fingerprint recognition Transformers

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