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International Conference on Inventive Computation Technologies (ICICT)

作者： M. Guru Vimal Kumar revathi K Gulshan Banu A Sivaguru J r. Prasanth Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Information Technology SNS College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamilnadu India Department of Mechatronics Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Department of Catering Science and Hotel Management Dr. N.G.P. Arts and Science College Coimbatore Tamilnadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Breast cancer affects women all around the world, and it can be dangerous. As stated by clinical specialists, early detection of cancer saves deaths. Lot of the machine-learning algorithms have been created to categorize tumors to identify breast cancer, and medical imagery processing is a useful tool for improving classification accuracy. Micro classifications are among the most important indications for early detection of breast cancer. Many investigations have used machine learning methods to address this issue. Numerous classification methods are employed to detect the possibility of breast cancer, either benign or malignant. An image-enhancing technique has been designed to aid in the earlier identification and diagnosis of cancer. The present research attempts to categorize breast cancer using a machine-learning method. The goal of the current study was to use machine learning (ML) techniques to increase the reliability of disease diagnosis. This study assesses and chooses a number of breast cancer prediction methods based on their accuracy, interpretability, and ease of use.

关键词： Support vector machines Accuracy Machine learning Transforms Predictive models Feature extraction Breast cancer Medical diagnosis Medical diagnostic imaging Tumors

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data Ownership Confirmation and Privacy-Free Search for Blockchain-Based Medical data Sharing 2nd

Data Ownership Confirmation and Privacy-Free Search for Bloc...

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2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Zha, Cong Yin, Hao Yin, Bo Network and Big Data Technology R&D Center The Department of Computer Science and Technology Tsinghua University Beijing100084 China The School of Computer Science Beijing Information Science and Technology University Beijing100084 China

ISBN: (纸本)9789811592126

With the development of data mining technology and the arrival of the era of big data, people can get more and more knowledge and information from the data. However, electronic medical data are stored in isolated data silos within each hospital due to the privacy and competition. Because of the complex interests of various groups, there is a lack of data flow between medical entities. On the one hand, this leads to difficulties for patients to obtain their own electronic medical data and to referral between different hospitals. On the other hand, it has led to serious lack of actual data for medical researchers. The realization of the determination of medical data ownership is the first condition for realizing medical data circulation. How to retrieve the required medical data while protecting privacy is the second problem to be solved. In this paper, we design the medical metadata as the smallest subunit of data ownership confirmation and propose a blockchain-based medical data sharing Framework MdSF, which can be used for electronic medical data ownership confirmation and data search while protecting patient privacy. We use the petri nets to verify the system and prove its reachability and boundedness, which means it can realize its designed function and operate safely. The system provides users with a convenient and safe way to obtain their past medical data, also enables research institutions engaged in medical data mining to easily search and obtain the actual medical data needed after desensitization. Thereby the circulation of medical data between different hospitals, individuals and research institutions is realized. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical recording Systems

Certain Investigation of Optimization Methods of Sensor Node...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： N. Ashokkumar C. Kanmani Pappa Siva Kumar A dhamodharan Srinivasan M. M. Vijay Department of ECE Mohan Babu University Tirupati India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of ECE SCAD College of Engineering and Technology Tirunelveli India

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence.

关键词： Wireless sensor networks Electrocardiography Sensor systems Biosensors Synchronization Biomedical monitoring Integrated circuit modeling

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Machine Learning Approaches for Advanced Threat detection in Cyber Security

Machine Learning Approaches for Advanced Threat Detection in...

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Global Conference for Advancement in technology (GCAT)

作者： V. Saravanan Talluri Upender E. d. Kanmani ruby P. deepalakshmi desidi Narsimha reddy Ananthi S N Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of CSE CMR College of Engineering and Technology. Medchal Hyderabad Telangana Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science Technology School of Computing Director - Accreditation and Ranking Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering S.A. Engineering College Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The dynamic nature of cyber threats demands that threat detection mechanisms be continuously improved in order to protect digital assets and infrastructures. In cybersecurity, this study investigates the use of machine learning (ML) algorithms for enhanced threat detection. This work intends to improve threat detection systems' accuracy, speed, and adaptability by utilizing ML algorithms' capabilities. The abstract begins by outlining the significance of machine learning in transforming traditional cybersecurity measures. It highlights the limitations of conventional signature-based methods, which often fail to detect novel and sophisticated threats. In contrast, ML-based approaches offer dynamic and proactive defenses capable of identifying previously unseen attack vectors. Furthermore, the paper delves into the practical implementation of ML models in real-world cybersecurity environments. It covers data collection and preprocessing techniques critical for training accurate and reliable models, as well as the challenges associated with maintaining and updating these models to adapt to evolving threat landscapes. The importance of feature selection and engineering in improving model performance is also emphasized. To validate the effectiveness of ML approaches, the study presents case studies and experimental results from various cybersecurity applications. The abstract concludes by addressing the future prospects and potential advancements in machine learning for cybersecurity. It discusses the role of emerging technologies, such as deep learning and federated learning, in enhancing threat detection capabilities. Additionally, it considers the ethical and privacy concerns associated with deploying ML models in cybersecurity and the need for robust governance frameworks. In summary, this study presents a thorough analysis of machine learning techniques for enhanced threat detection in cybersecurity, including useful applications, advantages, and difficulties.

关键词： Adaptation models Machine learning algorithms Accuracy Heuristic algorithms Machine learning data collection Threat assessment data models Vectors Computer security

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Blockchain and Machine Learning Integration for Safe data Sharing in Finance and Hr

Blockchain and Machine Learning Integration for Safe Data Sh...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： T. B Sivakumar E. Prathiba Shweta Sharma rashmi Saket Prasad M. S. ramaratnam V. Krishnamoorthy Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Mailam Engineering College Mailam India Department of Business Administration Shambhunath Institute of Engineering & Technology Prayagraj India D Y Patil PGDM Institute Pune Akurdi India Department of Management Studies Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Kanchipuram India Department of Management Studies Kongu Engineering College Erode Perundurai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Modern finance and Hr businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and Hr industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system.

关键词： Industries Training Adaptation models Accuracy Heuristic algorithms data security Computational modeling Finance Blockchains Predictive analytics

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Enhancing Citywide Flow Forecasting through deep Learning and Spatial Temporal Learning

Enhancing Citywide Flow Forecasting through Deep Learning an...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： r. Parvathi Swagata Sarkar M. Supraja J P Swagatha d. Kanagajothi Srimathi S Computer Science SRM Arts and Science College Kattankulathur India Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Electronics and Communication Engineering Rajarajeshwari College of Engineering Bangalore India Department of IT Panimalar Engineering College Poonamallee India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Bio Technology Saveetha School of Engineering SIMATS Chennai India

Safe traffic management requires citywide flow projections. Transportation, public safety, and municipal planning are substantially affected. It forecasts city intake and outflow using flow data. Traditional methods are inaccurate and less scalable in complex, dynamic metropolitan settings. This study uses deep learning to predict crow traffic. deep learning can handle complicated nonlinear circumstances, making neural network crowd flow prediction more popular. However, deep learning has solved this problem, but it requires plenty of data. data shortages may develop when a city’s service or infrastructure is installed. This research introduces a deep spatial temporal transfer learning system that predicts crowd movement in a data-sparse target city using data from a data-rich source city. Geographic and temporal patterns in crow traffic data may be captured by trained algorithms, enhancing prediction accuracy. We found that our deep learning-based solution is more accurate and robust than prior methods. Crow migration patterns are properly predicted by this research, improving urban planning and management.

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Leveraging Machine Learning for Predictive Maintenance in Supply Chain Management Systems

Leveraging Machine Learning for Predictive Maintenance in Su...

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Science, technology, Engineering and Management (ICSTEM), International Conference on

作者： Bediga Sharan C. Vijai Kingshuk Srivastava Atul Singla Indira Bhati T. Ch. Anil Kumar School of CS &amp AI SR University Warangal Telangana India Department of Commerce and Business Administration Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science UPES Dehradun Uttarakhand Lovely Professional University Phagwara India Department of Management MMIM Maharishi Markandeshwar (Deemed to beUniversity). Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The purpose of this exploratory research is to investigate the incorporation of machine literacy (ML) techniques for prediction conservation in supply chain management (SCM) systems. The successful operation of force chains is essential to the success of an organization in the modern business environment, which is always shifting and transitioning. When it comes to preemptively managing outfit failures, performing in expensive time-outs, and functional dislocations, conventional conservation techniques frequently fall short unexpectedly. To analyze literal data, detector inputs, and external variables, the utilization of machine learning algorithms makes it possible to anticipate conservation requirements. Through the utilization of prophetic analytics, supply chain management systems can identify hidden faults in the outfit, which enables visionary conservation actions. This study assesses various machine learning models and algorithms that have been adapted to forecast conservation circumstances in supply chain management (SCM) systems. Specific variables such as monitoring the status of the outfit and recognizing failure patterns are taken into consideration. In the end, the purpose of the investigation is to provide insight into the actual implementation of machine learning-driven prophetic conservation methods. This will enable organizations to improve their overall performance and optimize their force chain operations.

关键词： Supply chain management Machine learning algorithms Force Organizations Machine learning Predictive models Pattern recognition

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Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN

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Measurement: Sensors 2024年 35卷

作者： Anto Pravin, r. Murugan, K. Thiripurasundari, C. ranjith Christodoss, Prasanna Puviarasi, r. Abdul Lathif, Syed Ismail Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu India Department of Electronics and Communication Engineering KSK College of Engineering and Technology Tamil Nadu India Department of Computing Mathematics and Physics Messiah University Mechanicsburg PA United States Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness. © 2024 The Authors

关键词： Internet of things

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Exploring the thermodynamic stability and electronic structure of ZnNb₂O₆(001) surface through first-principles calculations

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Chemical Physics 2025年 597卷

作者： Yang, Anqi Chen, Qian Zhang, Lilong Zhu, Yao Xie, Quan Institute of New Type Optoelectronic Materials and Technology College of Big Data and Information Engineering Guizhou University Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University 550025 China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural Bioengineering Ministry of Education State-Local Joint Laboratory for Comprehensive Utilization of Biomass Center for R&D of Fine Chemicals of Guizhou University Guiyang 550025 China

The polar surface of ZnNb₂O₆ material holds considerable importance and practical significance. Using first-principles calculations, the structure, charge redistribution, electronic properties, and thermodynamic stability of the ZnNb₂O₆(001) surface were examined. The crystal was cleaved along the (001) plane to form nine surface slab models, of which five surface terminations are feasible under specific chemical conditions. The optimized structural configurations of these terminations illustrate the charge transfer due to polarity compensation and the differences in electronic structure caused by surface reconstruction. Surface metallization, resulting from the atypical saturation of surface states, acts as a compensatory mechanism for pronounced surface polarity. The ZnNb₂O₆(001) surface slab exhibits a shift in its electronic band structure, transitioning from semiconductor characteristics to metallic properties. The nature of the charge carriers (p-type or n-type) depends on the specific surface termination. The unique electronic properties of the ZnNb₂O₆(001) surface arise from significant variations in volume and polarity compensation. This detailed atomic-level understanding is expected to stimulate further theoretical and experimental investigations into this novel photocatalytic material. © 2025

关键词： dFT Electronic structure Surface Thermodynamic stability ZnNb2O6

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Contrastive Learning for Improved Abstractive Sentence Summarization

Contrastive Learning for Improved Abstractive Sentence Summa...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Ulligaddala Srinivasarao Karthikeyan r Mohamed Sithik M Faizal Mukthar Hussain S Ohammed Uveise S A Hariharan Shanmugasundaram Department of CSE GITAM (Deemed to be) University Hyderabad India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Mohamed Sathak Engineering College Ramanathapuram India Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Text summarizing aims to overview the material while maintaining essential details concisely. Lately, summary tasks have been included in the contrastive learning process in addition to visual representation. To optimize their similarities, contrastive learning summarizing algorithms currently concentrate on modelling and semantic similarity in all documents, targets, and data summaries. They do not, however, consider the impact that sentence semantics have on the text. In this study, we propose an rNN technique to develop the model for detecting the salient information. The sentence salience is based on the semantic similarity between the summary sentences of the original content, taking into account the distance of the loss of contrast in the form of soft weights functions. Consequently, our proposed model minimizes the semantic similarity given the textual summaries and possible noise and maximizes the semantic words between summaries and relevant information. Three popular datasets—multi news, CNN/daily Mail, and Booksum datasets achieved the highest accuracy of 0.69% in the news datasets. The experiment outcomes demonstrate that the suggested strategy can considerably raise baseline performance and outperform other contrastive learning techniques.

关键词： Computers Visualization Semantics Noise reduction Noise Contrastive learning Encoding data models data mining Postal services

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