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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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The role of ESG rating divergence and independent directors in predicting future stock price crash risk

Journal of Derivatives and Quantitative Studies

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Journal of Derivatives and Quantitative Studies 2025年第1期33卷 45-66页

作者： Sun, Han Lee, JaeHo Kang, Hyoung-Goo Fan, Zengrui Department of Computer Science and Financial Engineering Hanyang University Seoul South Korea Department of Finance Hanyang University Business School Seoul South Korea

This study investigates the impact of ESG rating disagreements on stock performance in the Chinese A-share market, focusing on immediate and short-term market reactions and the risk of future stock price crashes. Using data from the Shanghai and Shenzhen stock exchanges, we analyze 17,006 firm-year observations from 2010 to 2021. Stock return data are sourced from the Wind database, while additional financial metrics are obtained from the China Stock Market and Accounting Research (CSMAR) database. Corporate governance information is drawn from the China National Research Data Service (CNRDS) database. Our findings indicate that higher levels of ESG divergence significantly increase the risk of future stock price crashes. Furthermore, the presence of independent directors moderates this relationship, reducing the likelihood of such crashes. Immediate market reactions to ESG rating disagreements are also significant, underscoring the need for transparency and alignment among rating agencies. The study highlights the importance of robust corporate governance and standardized ESG rating methodologies to mitigate associated risks. Policy recommendations include promoting transparency in ESG rating processes and enhancing the role of independent directors in corporate governance. © 2024, Han Sun, JaeHo Lee, Hyoung-Goo Kang and Zengrui Fan.

关键词： Corporate governance ESG rating Independent directors Market reactions Stock performance

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IoT Based Smart Framework Monitoring System for Power Station

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computers, Materials & Continua 2023年第3期74卷 6019-6037页

作者： Arodh Lal Karn Panneer Selvam Manickam R.Saravanan Roobaea Alroobaea Jasem Almotiri Sudhakar Sengan Department of Financial and Actuarial Mathematics School of ScienceXi’an Jiaotong-Liverpool UniversityChina Department of Electrical and Electronics Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Computer Science and Engineering Vel Tech Multi Tech Dr RR Dr SR Engineering CollegeChennai600062Tamil NaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Power Station(PS)monitoring systems are becoming critical,ensuring electrical safety through early warning,and in the event of a PS fault,the power supply is quickly *** technologies are based on relays and don’t have a way to capture and store user data when there is a *** proposed framework is designed with the goal of providing smart environments for protecting electrical types of *** paper proposes an Internet of Things(IoT)-based Smart Framework(SF)for monitoring the Power Devices(PD)which are being used in power substations.A Real-Time Monitoring(RTM)system is proposed,and it uses a state-of-the-art smart IoT-based System on Chip(SoC)sensors,a Hybrid Prediction Model(HPM),and it is being used in Big Data Processing(BDP).The Cloud Server(CS)processes the data and does the data analytics by comparing it with the historical data already stored in the ***-Structural Query Language Mongo Data Base(MDB)is used to store Sensor Data(SD)from the *** proposed HPM combines the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)-algorithm for Outlier Detection(OD)and the Random Forest(RF)classification algorithm for removing the outlier SD and providing Fault Detection(FD)when the PD isn’t *** suggested work is assessed and tested under various fault circumstances that happened in *** simulation outcome proves that the proposed model is effective in monitoring the smooth functioning of the ***,the suggested HPM has a higher Fault Prediction(FP)*** means that faults can be found earlier,early warning signals can be sent,and the power supply can be turned off quickly to ensure electrical safety.A powerful RTM and event warning system can also be built into the system before faults happen.

关键词： System-on-chip cloud server apache kafka apache strom NoSQL MongoDB

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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Exploring the Impact of Node Velocity on Communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Sharma, Dilip Kumar Yadav, Aaditya Patidar, Mukesh Patil, Pooja Sanjay Patidar, Praveen Kumar Mandloi, Devendra Singh Sheikh, Rashid Rajpoot, Shivshankar Ujjain Engineering College Department of Electronics and Communication Engineering Ujjain India Piet Parul University Department of Computer Science and Engineering Vadodara India Credit Acceptance Financial Institution in Southfield Michigan United States Parul Institute of Technology Parul University Department of Computer Science & Engineering Vadodara India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool. © 2024 IEEE.

关键词： Fuzzy clustering

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Optimizing Multidocument Summarization by Blending Reinforcement Learning Policies

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 416-427页

作者： Su, DiJia Su, Difei Mulvey, John M. Poor, H.Vincent Princeton University Department of Electrical and Computer Engineering PrincetonNJ08540 United States University of British Columbia Department of Computer Science VancouverBCV6Z1T8 Canada Princeton University Department of Operation Research and Financial Engineering PrincetonNJ08540 United States

We consider extractive summarization within a cluster of related texts (multidocument summarization). Unlike single-document summarization, redundancy is particularly important because sentences across related documents might convey overlapping information. Thus, sentence extraction in such a setting is difficult because one will need to determine which pieces of information are relevant while avoiding unnecessary repetitiveness. To solve this difficult problem, we propose a novel reinforcement learning-based method Policy Blending with maximal marginal relevance and Reinforcement Learning (PoBRL) for solving multidocument summarization. PoBRL jointly optimizes over the following objectives necessary for a high-quality summary: importance, relevance, and length. Our strategy decouples this multiobjective optimization into different subproblems that can be solved individually by reinforcement learning. Utilizing PoBRL, we then blend each learned policies to produce a summary that is a concise and a complete representation of the original input. Our empirical analysis shows high performance on several multidocument datasets. Human evaluation also shows that our method produces high-quality output. © 2020 IEEE.

关键词： Reinforcement learning

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Evaluating the Effectiveness of Deep Learning Models in Network Intrusion Detection

Evaluating the Effectiveness of Deep Learning Models in Netw...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, Gaurav Gupta, Pankaj Yadav, Ganesh Kumar Verma, Richa Bhati, Jai Prakash Bhakuni, Vihan Singh IIMT College of Engineering Department of CSE Uttar Pradesh Greater Noida India Manager Data Engineer Discover Financial Services United States JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India Computer Science and Engineering BBDITM Lucknow India Greater Noida India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798331542344

Network security is now a significant concern in the current digital era since the increasing volume of cyber-attacks in day-to-day life has become a potential threat for the integrity and privacy of data. Most traditional intrusion detection systems face problems when it comes to detecting new and sophisticated attacks. The paper goes ahead to elaborate on how the concepts of machine learning and deep learning can be utilized to enhance the process of detecting and classifying network attacks. For instance, we experimented on the UNSW-NB15 dataset with different models: Decision Tree, Random Forest, SVM, KNN, MLP, and DNN. We have proved through our results that such kinds of models can considerably enhance the accuracy and robustness of IDS. With an average accuracy of 94.91%, the Decision Tree model took second place as the Deep Neural Network was closely followed. We will outline performance metrics based on the respective strengths and weaknesses of the models. This research is used to construct more effective and reliable network intrusion detection systems, which lead further into advancements of cybersecurity. © 2024 IEEE.

关键词： Network intrusion

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A Deep Learning Approach to Improving Patient Safety in Healthcare Using Real-Time Face Mask Detection

A Deep Learning Approach to Improving Patient Safety in Heal...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Patidar, Mukesh Bhanodia, Praveen Sethi, Kamal Kumar Rajput, Shivshankar Patil, Pooja Sanjay Tiwari, Chandrakant Gid, Aayush Gupta, Kartik Piet Parul University Department of Computer Science and Engineering Gujrat Vadodara India Cse Aitr Indore India Department of Computer Science and Engineering Indore India Credit Acceptance Financial Institution in South Field Michigan United States Sri Aurobindo Institute of Technology Department of Electronics and Communication Engineering Indore India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India

ISBN: (纸本)9798350388800

Face masks are frequently used during health an emergency, which emphasizes the need for efficient mask detection systems to guarantee public safety and adherence to health laws. The goal of this research is to accurately identify between masked and unmasked human faces using a deep learning-based approach. By employing a blend of convolutional neural networks (CNNs), the research makes use of large-scale picture datasets to train the model and improve its performance in various scenarios. The model is a useful tool for automated surveillance and public health monitoring systems, as evidenced by experimental results that show its robustness in reaching high accuracy rates. The proposed work explore by using MobileN etV2 within a Python environment. The next stage in the process is pre-processing the data, which often entails normalizing pixel values and scaling images to the input size that MobileNetV2 expects (224×224 pixels, for example). © 2024 IEEE.

关键词： Convolutional neural networks

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Digital Investigation Forensic Model with P2P Timestamp Blockchain for Monitoring and Analysis

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Journal of Electrical Systems 2024年第1期20卷 9-17页

作者： Almahadeen, Layth Pecho, Renzon Daniel Cosme Raj, Murugananth Gopal Rajesh, Nichenametla Imneef, Zainab Mohammed Yelpale, Sayali Karmode Department of Financial and Administrative Sciences Al- Balqa' Applied University Jordan Department of Biochemistry Universidad San Ignacio de Loyola Lima Peru Department of Electrical and Electronics Engineering Ahalia School of Engineering and Technology Kerala India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Dentistry Al-Zahrawi University College Karbala Iraq Department of Information Technology MGM College of Engineering Maharashtra India

Forensic analysis of Blockchain data is a new field in police work. It's now one of the largest problems facing law enforcement. The paper discussed the worldwide need for digital forensics in law enforcement and Blockchain forensics to counteract crimes committed using Blockchain technology. It's been said that we've entered a new age of technology that's heavily dependent on the principles of Blockchain. The research produced a set of guidelines for Digital Investigators. on addition, a theoretical framework grounded on the concept of regular activity has been developed, and a legislative framework has been proposed to ensure that its illegal purpose will always be punished severely. © JES 2024 on-line: ***

关键词： Digital forensics

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Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

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computers, Materials & Continua 2023年第4期75卷 133-148页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel Mona M.Abusurrah K.Vijaya Kumar E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-Commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVisakhapatnam CampusGITAM(Deemed to be a University)India Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s *** this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class *** order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

关键词： Osteosarcoma medical imaging deep learning feature vectors computer aided diagnosis image classification

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