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A Scalable Privacy Preservation Block Chain-Based Method for Smart Healthcare System 3

A Scalable Privacy Preservation Block Chain-Based Method for...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Dongre, Shubhangi Saxena, Kanak Raghuwanshi, Sandeep Dept. of Computer Science and Engineering Sati Vidisha India Synechron Pvt. Ltd India

ISBN: (纸本)9798331542375

In this study, we propose a scalable privacypreserving blockchain mechanism for smart healthcare systems. The rapid digitization of medical data has made security, scalability, and privacy vital. This approach minimizes risks associated with sensitive healthcare information by utilizing the decentralized architecture of blockchain to create a secure, transparent, and efficient healthcare information management platform. The system showed an increase in transaction throughput, a reduction of the latency, and an enhancement in energy efficiency compared with traditional approaches through simulations. Moreover, state-of-the-art cryptographic approaches provided strong privacy protection by reducing the risk of data leaks and malicious access. Smart contract implementation further optimized operations by facilitating secure and automated healthcare workflows. This method solves the growing issues in the area of managing healthcare data while delivering a much more scalable, secure, and patient-centric solution for contemporary healthcare systems. © 2025 IEEE.

关键词： Sensitive data

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An Ontology-Based Approach for Anomaly Detection in Business Processes 25th

An Ontology-Based Approach for Anomaly Detection in Business...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Musa, Tahani H. Abu Bouras, Abdelaziz Belhi, Abdelhak Information Technology Department Community College of Qatar Doha Qatar Department of Computer Science and Engineering Qatar University Doha Qatar Joaan BinJassim Academy for Defense Studies Doha Qatar

ISBN: (纸本)9789819605781

Business Process Mining is an emerging field dedicated to the analysis of Business Process Models (BPM) by deriving insights from transaction logs generated through various industrial and Supply Chain Management execution information systems, such as a ERP or an MES. This approach facilitates the examination, assessment, and improvement of business activities across the entire lifecycle, from initiation to completion. In this paper, we propose a framework to detect anomalies in business processes by analyzing event logs produced from purchasing transactions. The framework focuses on anomaly detection in the supply chain and aims to improve its accuracy, boost its workflow across multiple business process levels, and improve supply chain processes with an emphasis on security and automation. The suggested approach uses Ontology and designed SWRL rules to classify anomalies in business transactions. The framework has been evaluated using transaction logs from a simulated general a purchasing scenario, as an example of a business process mode, proving its capability to effectively identify and categorize anomalous business transactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ontology

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Welding Defect Detection Using Deep Learning and Semantic Segmentation 9

Welding Defect Detection Using Deep Learning and Semantic Se...

引用

9th international conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Nohwal, Aman Jha, Sunil Patel, Nitin Aravindan, S. IIT Delhi Department of Mechanical Engineering New Delhi India

ISBN: (纸本)9798331509262

Numerous computer-aided techniques have been created to automatically detect defects and imperfections in welding radiography images. The purpose of these strategies is to circumvent the challenges that are associated with human interpretation. In the subject of welding problem diagnostics, deep learning algorithms have garnered a substantial amount of attention due to the inherent benefits that they deliver. These advantages include powerful generalization capabilities and automated feature extraction techniques. Additional research is required, however, to investigate several various areas. These features include the gathering and improvement of welding radiography data, the selection and development of deep neural networks, and the enhancement of the model's capacity to be applied to a variety of situations while being easily understandable. This work intends to construct an automated system that can identify defects in welding by utilizing a semantic segmentation technique on radiographic images taken from a specific dataset of welds. This is taking into consideration the context in which this study is being conducted. Utilizing the UNet architecture, the procedure entails the training of several models, in addition to the incorporation of transfer learning strategies that utilize multiple backbones. A comprehensive comparative analysis is carried out after the model has been developed, and the evaluation criteria that are used are those that have been developed expressly for deep learning models. The results of the tests demonstrate that the method that was recommended is effective in precisely defining the boundaries of the various types of Defects and in faithfully identifying the various types of defects. In addition, the findings demonstrate that the system is capable of doing an automatic analysis of radiographic images to detect welding defects. Due to this fact, it is an indispensable component for quality inspection systems that are utilized in welding

关键词： Semantic Segmentation

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Advancing Visual Marketing Strategies Through Deep Learning-Based Image Recognition for Improved Customer Engagement and Brand Visibility 3

Advancing Visual Marketing Strategies Through Deep Learning-...

引用

3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Pandiri, Ramu Nnadhini, T.J. Velmurugan, R. Menaga, A. Karpagam, J. Vinothkumar, K. Infomatics Corp DallasTX United States Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Coimbatore India St Joseph's College of Engineering Department of Mba Chennai India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Psna College of Engineering and Technology Department of Ece Dindigul India

ISBN: (纸本)9798331542375

This paper aims at analyzing the effectiveness of utilizing deep learning-based image recognition in enhancing the visual marketing thereby determining the extent to which clients' attention and brand awareness are likely to be affected. It uses convolutional neural networking (CNN) to analyze and classify the visual content and the models used include VGG16, ResNet50, InceptionV3. An effective implementation of the data gathering, feature engineering, and algorithm development can be seen in the enhancing of the marketing effectiveness. This entails better accuracy of images in classification. This involves gaining high engagement rates from the customers. probabilities of high visibility of the brands across the various platforms. The study points at the possibility of deep learning technologies in changing visual marketing practices and therefore serves as a useful reference guide to business entity that wish to align themselves with adequate marketing practices in the growing digital landscape. © 2025 IEEE.

关键词： Convolutional neural networks

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A Grading Model for Jasmine Flowers Using Deep Learning and Quantum Machine Learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pratiksha Cauvery, K.P. Thrisha, R. Harshitha, T. Panigrahi, Suman Thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep Learning and Quantum Machine Learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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Hierarchical Multimodal Decoupling-Fusion Framework for offline Multiple Appropriate Facial Reaction Generation

Hierarchical Multimodal Decoupling-Fusion Framework for offl...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Qincheng Liu, Xiaofeng Li, Jie Ni, Rongrong Xue, Pujun Song, Siyang College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350368741

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals' facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions. © 2025 IEEE.

关键词： 2D 3D facial behaviour fusion Facial identity decoupling Multiple Appropriate Facial Reaction Generation

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Optimization Operation Strategy of Integrated Energy System Considering Load Flexibility and Energy Conversion Device

Optimization Operation Strategy of Integrated Energy System ...

引用

international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Du, Yu Yu, Yekun School of Automation and Electrical Engineering Linyi University Linyi276000 China School of Computer Electronics and Information in Guangxi University Nanning530004 China

ISBN: (纸本)9789819620456

Flexible load can actively participate in the operation control of the integrated energy system and interact with the integrated energy system. Flexible load scheduling and regulation is one of the important means to alleviate the contradiction between power supply and demand and realize the effective consumption of renewable energy. This paper presents an integrated energy system optimization strategy considering load flexibility and equipment with energy conversion characteristics. The strategy aims to improve the efficiency and flexibility of the system by optimizing the scheduling of energy production, storage and consumption within the community. At the same time, the flexible adjustment ability of the flexible load is used to adjust the real-time carbon emission economic penalty of the operator and the peak reduction economic compensation of the LAs, so as to achieve the purpose of energy saving and carbon reduction, so as to achieve the purpose of low-carbon operation of the comprehensive energy system. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Zero-carbon

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Role of Power Electronics in Power Quality Management of Smart Grid with AI Interfacing

Role of Power Electronics in Power Quality Management of Sma...

引用

international conference on Computational Intelligence and Smart Technologies in Electrical engineering, CISTEE 2023

作者： Rai, Shiwani Shukla, Hiramani Pathre, Ayonija Kumar, Alok Jaiswal, Pooja Ansari, Aftab Ahmed Department of Electrical and Electronic Engineering IES Institute of Technology and Management IES University Bhopal MP Bhopal India Department of Electrical Engineering U.V. Patel college of Engineering Ganpat University Gujarat Mehsana-Gozaria Highway Ganpat Vidyanagar India Department of Computer Science and Engineering JNCT MP Bhopal India Department of Electrical and Electronics Engineering Trinity Institute of Technology and Research Bhopal India Aspen Enggicon Pvt. Ltd. Jharkand Ranchi India Vidya Pratishthans Kamalnayan Baja Institute of Engineering and Technology Maharashtra Baramati India Department of Electrical and Electronics Engineering Bansal Institute of Research Technology and Science MP Bhopal India

ISBN: (纸本)9789819604753

With continual evolution in power sector and Power Electronics (PE) and the need for better performance with less human interfacing has raised the necessity for AI interfacing in power sector. AI, i.e., Artificial Intelligence is a computer software or machine which is capable of replicating human expertise without any human interference. PE, plays a dominant role in transmitting, converting, and controlling power from kilowatts to gigawatts, efficiently and flexibly between the power grids with 95% efficiency. Its interference with AI can take modern smart grid system into the next level of techno-automation without any human interference. The application of PE is at its peak for enhancing the Power Quality (PQ) of the system. This paper presents a review of transformation of the existing system into hi-tech PE integration to obtain high power quality with AI interfacing to reduce human supervision and to reduce the PQ events to take place in power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric power transmission networks

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Touch Sensors for Safety in Industrial automation Systems

Touch Sensors for Safety in Industrial Automation Systems

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Tanapong Khumyat Preecha Mahamai Electrical Engineering Rajamangala University of Technology Lanna Tak Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper designs and evaluates a touch sensor based on a MOSFET that detects electric fields generated between mechanisms in industrial automation systems and human skin. The sensor aims to enhance safety in industrial operations. When the sensor installed on an automated mechanism comes into contact with human skin, it detects the electric field and electrostatic discharge. The processor immediately halts the automation or robotic system to ensure worker safety. The best test results indicate that the sensor operates with a response time of 3 milliseconds.

关键词： MOSFET automation Contacts Tactile sensors Switches Skin Safety Steel Time factors Electric fields

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Towards Precision Cardiovascular Medicine - A Novel Framework for Risk Prediction Using MultiOmics and Imaging Data 3

Towards Precision Cardiovascular Medicine - A Novel Framewor...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Lalitha, S. Sujay, V. Krishna, A.P. Jaya Navaneethan, S. Abijith, G.R. Priya, S. Sugantha Department of Cse Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Krishna University College of Engineering and Technology Department of Computer Science and Engineering Krishna Andhra Pradesh Machilipatnam India Sona College of Technology Department of Electronics and Communication Engineering TamilNadu Salem India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology St.Joseph's Institute of Technology Tamil Nadu Chennai India Department of Computer Science Dr Sns Rajalakshmi College of Arts and Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331542375

The framework integrated multi-omics data and imaging characteristics to boost predictive precision for cardiovascular risk. Existing studies chiefly relied on restricted clinical information using common logistic regression designs unable to capture cardiovascular diseases (CVD's) intricacy. Such dated techniques experienced diminished accuracy and lack personalized risk prediction. But these restrictions are surpassed by the suggested system since it uses deep learning algorithms such as Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for structured data and visual information. To prepare the data, standardization and simplification of complexity firstly applied preprocessing techniques such as PCA and t-SNE first, before utilizing feature selection via RFE and LASSO. Results demonstrated substantially improved outcomes with 0.85 accuracy, 0.82 sensitivity and an AUC-ROC of 0.90, delivering a more trustworthy tool for early intervention and customized cardiovascular care. Complex sentences mixed with shorter, simpler constructions to vary structure and keep readers engaged throughout the lengthier description. © 2025 IEEE.

关键词： Recurrent neural networks

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