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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： B. Yuvasri V. Jagan raja P. Ponsudha Kamlesh Kukret Suraj Srivastava Charanjeet Singh Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Velammal Engineering College Chennai Tamil Nadu India Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computer Science and Engineering Computer Application I.K.G.PTU Punjab India Department of Electronics and Communication Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Haryana India

Ecological data, which is typically multivariate, can be collected and managed by a wireless sensor organization. A multivariate sampling algorithm is proposed in this work considering part inspection technology in wireless sensor networks. This algorithm uses a component testing approach to score the data and work it on the sample. The most agent data is kept when the agent is positioned. The reproduction findings demonstrate that our strategy reduces the information while maintaining its representativeness. According to the connection coefficient network, a few information streams are selected as the base capabilities for multivariate streams on a sensor hub. different transfers from a related hub can be conveyed similarly to one of these base capabilities via straight relapse. The approach then provides a simple log n example to address the initial information of n components. We demonstrate how, in various scenarios, our algorithm can reduce delay and conserve energy while maintaining acceptable information quality in WSN applications.

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Intelligent Time Management recommendations Using Bayesian Optimization

Intelligent Time Management Recommendations Using Bayesian O...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： G. Chandramowleeswaran M. Sindhu Haider Alabdeli Anshu Mishra Priya Vinod Harmeet Kaur Department of Commerce and Business Administration Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India School Of Business And Management Christ University Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users’ preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users’ satisfaction suggest that system helped enhance time management results. Lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others’ findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue.

关键词： Productivity Schedules Optimal scheduling Transforms User interfaces Signal processing Bayes methods Telecommunication computing reliability Usability

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Cancer Genomics: Identifying Biomarkers for Early detection and Treatment

Cancer Genomics: Identifying Biomarkers for Early Detection ...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Saif O. Husain Amandeep Kaur Chitra S Vivek Sharma Marupova Manzura Aminovna rajesh G Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Information Technology IES University Bhopal M.P. India Fergana Medical Institute of Public Health Fergana Uzbekistan Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Genomic sciences, specifically with regards to cancer, has provided optimism in early diagnosis, treatment options by pointing to the molecular basis of cancer. This work aims at defining biomarkers for cancer diagnosis and treatment – or in other words, discovering novel biomarkers for early detection and treatment of cancer using multi-omics data from TCGA and ICGC. Ing data obtained through genome, transcriptome, and proteome studies , we revealed novel biomarkers corresponding to different types of cancer. The aforementioned datasets were processed with the help of random forests and artificial neural networks because these machine learning techniques delivered high biomarker recognition rates. This will incorporate, biomarkers such as BrCA1 for breast cancer and EGFr for lung cancer, showing high sensitivity and specificity levels. It also tackles issues such as tumor heterogeneity and presents a cardiovascular treatment plan that involves multiple disciplines. Interestingly, the assessment of genomics for cancer diagnosis and treatment has outlined such possible avenues for further investigations and practical use in the future. It is positioned in the rapidly expanding field of personalized oncology; this research seeks to improve patients’ lifestyles via the early and accurate identification of cancer.

关键词： Sequential analysis Accuracy Genomics Biomarkers Oncology Turning Telecommunication computing Bioinformatics Cancer Tumors

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A dynamics Theory of Implicit regularization in deep Low-rank Matrix Factorization

arXiv

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arXiv 2022年

作者： Cao, Jian Qian, Chen Huang, Yihui Chen, dicheng Gao, Yuncheng dong, Jiyang Guo, di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Implicit regularization is an important way to interpret neural networks. recent theory starts to explain implicit regularization with the model of deep matrix factorization (dMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in dMF. We prove that, for a rank-r matrix reconstruction, dMF will converge to a second-order critical point after r stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning. Copyright © 2022, The Authors. All rights reserved.

关键词： deep learning

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radar target detection method based on improved YOLOv3

Radar target detection method based on improved YOLOv3

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IET International radar Conference (IET IrC 2020)

作者： L. Zhongwei G. Hongwei L. Yingchun X. rongrong S. Shuyan Z. dongming Y. Jizhou Beijing Institute of Radio Measurement Radar R&D Center Beijing China Beijing Institute of Computer Technology and Application Department of Network and Information Security Beijing China

Aiming at the problems of low detection rate and high false alarm rate in radar target detection under low signal-to-noise ratio environment, a radar target detection method based on improved YOLOv3 is proposed. Considering the characteristics of small scale and few features of radar targets, the original YOLOv3 backbone network is pruned and nonlinear features are added, and the darknet13 network structure is proposed. The multiple outputs of the darknet13 network are fused in two scale, deep fusion is used to detect large-scale targets, and shallow fusion is used to detect small-scale targets. In order to solve the problem of imbalance between positive and negative samples of radar data, the focus loss function is used to strengthen the attention to the hard to distinguish samples. Meanwhile, warm up technology is used to optimize the learning rate adjustment strategy to improve the target detection performance and network training speed. The experimental results show that compared with the original YOLOv3 algorithm, the proposed method can improve the average detection rate by 8.4% and reduce the average false alarm rate by 79.8% when the signal-to-noise ratio is 10 dB.

关键词： object detection learning (artificial intelligence) radar detection neural nets optimisation

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Federated Learning-Based Intelligent Indoor Smoke and Fire detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1d for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable dL-CNN1d model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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ru Single Atoms and Sulfur Anions dual-doped NiFe Layered double Hydroxides for High-Current-density Alkaline Oxygen Evolution reaction

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Advanced Energy Materials 2025年第0期

作者： Zhu, Yiming Wang, Jiaao Weiser, Gregor Klingenhof, Malte Koketsu, Toshinari Liu, Shangheng Pi, Yecan Henkelman, Graeme Shi, Xinyue Li, Jiayi Pao, Chih-Wen Yeh, Min-Hsin Huang, Wei-Hsiang Strasser, Peter Ma, Jiwei Shanghai Key Laboratory for R&D and Application of Metallic Functional Materials Institute of New Energy for Vehicles School of Materials Science and Engineering Tongji University Shanghai201804 China Department of Chemistry and the Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712-0165 United States Department of Chemistry Technische Universität Berlin Berlin10623 Germany State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China School of Chemistry and Chemical Engineering Yangzhou University Jiangsu225002 China National Synchrotron Radiation Research Center Hsinchu300092 Taiwan Center Department of Chemical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan

New anodic electrocatalysts with high performance and cost-effectiveness at large current densities help advance the emerging anion exchange membrane water electrolyzer (AEMWE) technology. To this end, a ruthenium (ru) single atoms and sulfur (S) anions dual-doped NiFe layered double hydroxides (ru-S-NiFe LdH) catalyst is reported with remarkably low alkaline oxygen evolution reaction (OEr) overpotentials, high mass activities and prolonged stabilities at high current densities. Inspiringly, the AEMWE performance on ru-S-NiFe LdH is also superior to the NiFe LdH. In-depth mechanism investigations reveal that ru single atoms not only act as the highly active sites, but also facilitate the conductivity of NiFe LdH. Meanwhile, S anions accelerate the electrochemical reconstruction of NiFe LdH to OEr-active NiFeOOH and alleviate the over-oxidation issue on ru active sites. Benefiting from these, ru-S-NiFe LdH shows significantly enhanced OEr activity and stability. Theoretical calculations further validate the decreased OEr free energy difference brought about by the ru single atoms and S anions dual-doping. This study offers a proof-of-concept that the noble metal single atoms and anions dual-doping is a feasible strategy to construct the promising 3d transition metal-based electrocatalysts toward the practical alkaline water electrolyzer. © 2025 The Author(s). Advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Cost effectiveness

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Study on Vapor-Liquid Equilibrium and Microscopic Properties of Glycine + H2o, L-Histidineand + H2o and L-Tryptophan + H2o

SSRN

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SSrN 2023年

作者： Li, Xia Liu, Weiping Yin, Yahui Zhou, Yu Xu, Xianzhen Xiang, Hongfei College of Chemistry and Chemical Engineering Qingdao Application Technology Innovation Center of Photoelectric Biosensing for Clinical Diagnosis and Treatment Shandong Sino-Japanese Center for Collaborative Research of Carbon Nanomaterials Qingdao University Shandong Qingdao266071 China Qingdao Marine Hydrogen Equipment Technology Innovation Center Qingdao Boting Hydrogen Age Ocean Technol R&D Ctr Qingdao Boting Technol Co Ltd Shandong Hydrogen Times Marine Technology Co Ltd Shandong Qingdao266100 China Department of Orthopedics Affiliated Hospital of Qingdao University Shandong Qingdao266003 China

Vapor-liquid equilibrium (VLE) is essential for separation processes such as distillation and absorption in chemical production. In this work, the VLE of glycine, L-histidine and L-tryptophan aqueous solutions was measured at pressures ranging from 5.3 kPa to 101.1 kPa. The NrTL model was used, which has been successfully correlated with VLE data at temperatures between 298.15 K and 373.85 K, and the model parameters were obtained. In addition, the absorption peaks of three amino acid aqueous solutions were analyzed by Fourier transform infrared spectroscopy (FTIr) from a microscopic perspective. The results showed that the higher the solubility of amino acid in water, the stronger the hydrogen bond interaction with water. Furthermore, the total mean interaction energy of water in the systems was studied by COSMOthermX. Based on the calculations, the total mean interaction energy was little affected by concentration. The structure–activity relationship was established between the microscopic interactions and the macroscopic VLE. © 2023, The Authors. All rights reserved.

关键词： Amino acids

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Metabolite Profiling of Herbal Medicines using an Artificial Intelligence Approach-A Concise review

Metabolite Profiling of Herbal Medicines using an Artificial...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Ajay S Shet Somasundaram r Sravan Kumar G S. Kaliappan S.r. ramprasad Maloth Srinivas Department of CSE Canara Engineering College Mangalore Karnataka India Management Studies Department Kongu Engineering College Erode TN India Dept. of CSE (Cyber Security) CVR College of Engineering Hyderabad Telangana India Division of Research and Development Lovely Professional University Jalandhar Punjab India Dept. of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept. of CSE (AIML) St. Martin's Engineering College Secunderabad Telangana India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Artificial intelligence (AI) is being developed extremely swiftly in medicine. A structured review should be conducted to comprehend the current status of AI models since they have been introduced in Traditional medicine to Investigate and classify the present applications of AI in TM. Following the technique suggested by the Joanna Briggs Institute, a thorough scoping review was conducted. To find papers on AI and CAM, three databases were searched: PubMed, Embase, and Cochrane Library. Studies published only in English since 2000 are taken into account. Those without mention of traditional medicine or AI methods were also disqualified, as were studies that were not peer-reviewed. All pertinent studies were located using a comprehensive search technique. This review demonstrated the potential use of AI models in TM; nevertheless, to develop AI in TM in the era of digital health, future research should concentrate on validating and verifying the models by carrying out a sizable clinical investigation.

关键词： reviews databases Machine learning Predictive models Prediction algorithms Knowledge discovery data models Libraries data mining Artificial intelligence

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Multiphoton emission of x-rays from cooperative resonant nuclei

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Physical review research 2025年第1期7卷 013030-013030页

作者： Xiangjin Kong Yue Chang Lida Zhang Jianmin Yuan Yu-Gang Ma Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 Peoples Republic of China Research Center for Theoretical Nuclear Physics NSFC and Fudan University Shanghai 200438 China Beijing Automation Control Equipment Institute Beijing 100074 People's Republic of China Quantum Technology R&D Center of China Aerospace Science and Industry Corporation Beijing 100074 People's Republic of China School of Physics East China University of Science and Technology Shanghai 200237 People's Republic of China Department of Physics Graduate School of China Academy of Engineering Physics Beijing 100193 Peoples Republic of China Institute of Atomic and Molecular Physics Jilin University Changchun 130012 Peoples Republic of China

The advent of x-ray free electron lasers (XFELs) has opened the door to exploring the x-ray-nuclei interaction in the multiexcitation regime. Here, we show the dependence of emission properties of x-ray photons from an ensemble of identical resonant nuclei on the number of excitations in the system, employing the theoretical methods we have introduced. Under the multiexcitation condition, the detection of x-ray quanta is emission-order selective. Our work presents an efficient approach to investigating the multiphoton emission of x-rays from cooperative resonant nuclei within and beyond weak-excitation regimes, offering valuable insights into nonlinear x-ray optics and many-body nuclear physics.

关键词： Nonlinear optics

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