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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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Falcon Optimized CNN for the detection of Alzheimer's disease in MrI Image

Falcon Optimized CNN for the Detection of Alzheimer's Diseas...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： S. Febeena Ezhil Jothi L. Mahalakshmi P. Elamurugan Naveenkumar Kaliyan J. Swapna r. rajeshwari Department of Artificial Intelligence and Data Science Adhi College of Engineering And Technology Kanchipuram Department of Biomedical Engineering Kongunadu College of Engineering and Technology Trichy Department of Instrumentation and Control Engineering Sri Manakula Vinayagar Engineering College Puducherry Department of Computer science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering J.P. College of Engineering Ayikudi Tenkasi

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

degenerative illnesses that lead to the eventual death of brain neurons afflict a large number of people. Compared to the traditional Machine Learning (ML) approach, the recent success in the medical community of classifying Alzheimer's disease (Ad) utilized a deep Learning (dL) technique eliminates the necessity for manual feature extraction techniques. Although dL methods are widely used, there is currently a dearth of trustworthy and comprehensive research on the quantitative analysis of brain Magnetic resonance Image (MrI) data. In this paper, an effective Ad classification approach is proposed that use MrI to classify brain structure and diagnose Ad. Gaussian filter is used to eliminate noises at different degrees of complexity, noisy backgrounds, low contrast, and partial volume effect in the original input image. Next, K-Means clustering segmentation is useful for dividing image into different regions based on pixel intensity values. Key features for categorization are extracted using the Gray Level Co-Occurrence Matrix (GLCM) method. A Convolutional Neural Network (CNN), enhanced by Falcon Optimization Algorithm (FOA), is employed for Ad categorization. The proposed system, implemented on the Python platform using the best Alzheimer's MrI dataset, demonstrates an enhanced accuracy of 93.21 %. Additionally, this model-based approach provides a capable avenue for advancing research in the early detection of Ad and holds substantial potential in differentiating Ad patients from healthy individuals.

关键词： Training Accuracy Magnetic resonance imaging Neurons Magnetic resonance Feature extraction Convolutional neural networks Alzheimer's disease Optimization Testing

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Enhancing Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication 5

Enhancing Natural Language Processing in Multilingual Chatbo...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Orosoo, Myagmarsuren Goswami, Indrajit Alphonse, Fredrick ruban Fatma, Gulnaz rengarajan, Manikandan Kiran Bala, B. Mongolian National University of Education Alliance University General Management Strategy and Hrm Alliance Bengaluru562106 India School of Business Alliance University General Management Strategy and Hrm Alliance Bengaluru562106 India Institute of Finance and International Management Department of Languages Bengaluru India Jazan University Dept. of English Jazan Saudi Arabia R&d Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Tamil Nadu Chennai6000627 India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350385649

In order to promote smooth cross-cultural communication, this research focuses on improving Natural Language Processing (NLP) features within multilingual Chabots. The need for efficient communication across various linguistic and cultural barriers has grown as the world becomes more interconnected. In this context, multilingual Chabot's are essential tools that facilitate easy communication between users of various cultural backgrounds. In order to better understand and respond to a wide range of linguistic details, idioms, and cultural allusions, the research explores ways to improve NLP algorithms. This research investigates how machine learning techniques can be integrated to improve the Chabot's ability to adapt to various linguistic patterns, enabling it to acquire new language skills over time. By implementing an extensive and flexible approach, the proposed approach seeks to transform Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication. In order to identify language-specific patterns and specifications, the methodology starts with the collection of a diverse dataset that is representative of linguistic variations and cultural contexts. It then uses advanced linguistic analysis techniques like phonetic analysis. The next step is cultural context modelling, which entails building a database enhanced with cultural allusions and language variations unique to a given context that are obtained from various sources. Transfer learning, reinforcement learning for dialogue, and modern algorithms such as Multilingual Unsupervised and Supervised Embeddings (MUSE) for cross-lingual embeddings are all included in machine learning integration. In order to obtain information about language precision and cultural sensitivity, the methodology incorporates interactive surveys and sentiment analysis into a strong user feedback mechanism. The proposed model achieved 98% performance. © 2024 IEEE.

关键词： Embeddings

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detection Of Counterfeit Images Through deep Learning based Yolov3 Model

Detection Of Counterfeit Images Through Deep Learning based ...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Jobez J. C Golden Nancy r Syed rabiya Mohamed Ali Muthukumaran N raghavan Sheeja Jasmine Gnana Malar A Department of AIML Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. Engineering College Gummidipoondi Tamil Nadu India Department of ECE PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Image forgery is the procedure of analysing and detecting any fraudulent or unauthorised alterations made to digital images. The detection in images is difficult for achieving a high accuracy rate and the process is time-consuming. researchers and forensic experts are developing tools for detecting fake images. The identification of computer-generated picture forgeries is a notable area of study, aiming to provide a comprehensive review of detection tools and techniques. In this work, a novel deep-learning model is proposed for detecting forgery in images. The images are pre-processed using an adaptive Gaussian bilateral (AGB) filter to enhance the quality of the images. The enhanced photos are fed into the Mobile Net for extracting the relevant features in the pictures. The modified Yolov3 is utilized for object detection in images and detecting the real and forgery images. According to the result, the proposed model attains 99.55% for forgery detection in images. The modified Yolov3 yields 99.55% accuracy as opposed to Mask rCNN, rCNN, and SSd's 0.68%, 0.65%, and 1.16% accuracy. CNN, resNetV2 and dCNN are all outperformed the proposed model in terms of overall accuracy by 0.11%, 1.10%, and 5.55%, respectively.

关键词： YOLO deep learning Adaptation models Accuracy reviews Computational modeling Forensics digital images Feature extraction Forgery

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Fish Farm Monitoring System Using IoT

Fish Farm Monitoring System Using IoT

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： K.V. Kishore d. dhinakaran N. Jagadish kumar S. M. Udhaya Sankar K. Chandu L. Maria Michael Visuwasam Department of Information Technology Velammal Institute of Technology Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSBS Sri Sairam Engineering College Chennai India Department of CSE(Cyber Security) R.M.K College of Engineering and Technology Chennai India Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai India

Fish aquaculture is an essential sector of the economy; it has seen rapid expansion in recent years. It can be challenging to manage a fish farm, though, as it demands continual attention to environmental parameters like water quality and temperature. In this study, we offer a monitoring system for fish farms that leverages IoT technology to gather and process data from sensors placed inside a fish farm. When any parameters are outside the specified range, the system notifies the farmer and provides real-time monitoring of water quality, temperature, and other environmental conditions. The method comprises a network of wireless sensors connected to a central computer and installed across the fish farm. The server gathers and analyses the sensor data before giving the farmer real-time access via a web-based interface. The fish farmer can utilize this interface to keep an eye on the fish farm's water quality and temperature as well as to change the feeding schedule and other environmental factors as needed. The system has undergone testing in a commercial fish farm, and the outcomes indicate that it successfully keeps track of the environmental conditions there. The technology can increase fish farming's productivity and profitability while minimizing its adverse environmental effects.

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Artificial Intelligence based Facial Emotion recognition with deep Neural GAN Augmentation

Artificial Intelligence based Facial Emotion Recognition wit...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： d. Menaka d. Karthika Priya C. reeda Lenus A Ashwini P. Josephin Shermila Banu Priya Prathaban Department of Electronics and Instrumentation Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Artificial Intelligence and Data Science Easwari Engineering College Ramapuram Chennai India Department of Physics S.A.Engineering College Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science R.M.K.College of Engineering and Technology Chennai India Department of Networking and Communication School of computing SRM institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. The findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. The proposed strategy is based on the generation of artificial ‘emotional’ images on the face using deep Neural Self Attention GANs (dNSAGANs). This way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. This larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FEr2013 dataset is used. The training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. The percentage of accuracy is about $88.64 \%$ for 100 epochs using the proposed optimizer techniques.

关键词： Training Emotion recognition Adaptation models Accuracy Face recognition Computational modeling Generative adversarial networks data models robustness Time complexity

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Exploring the Efficacy of U-Net-CNN Architecture for Automated detection of Psoriatic Arthritis from Medical Imaging data

Exploring the Efficacy of U-Net-CNN Architecture for Automat...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： divya Muralitharan S Tamilselvi J daphney Joann Sajeev ram Arumugam Sankar Ganesh Karuppasamy C S Shylaja Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of AI&DS Panimalar Engineering college Chennai India Department of CSE(Data Science) Sri Venkateswara College Of Engineering & Technology(Autonomous) Chittoor India Department of AI&DS Sri Krishna College of Engineering and Technology Coimbatore India Independent Researcher Coimbatore India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Psoriatic arthritis (PsA) is a chronic inflammatory joint disease that often exists with psoriasis, causing substantial morbidity and disability if left untreated. Timely intervention and better patient outcomes depend on the early and precise identification of PsA. This work proposes an innovative method for PsA detection from medical imaging data that uses the U-Net-CNN architecture. Precise localization and characterization of arthritis-associated features are made possible by the U-Net-CNN model, which combines the U-Net architecture for precise segmentation with convolutional neural networks (CNNs) for feature extraction and classification. Using a dataset of medical images from radiopaedia, the effectiveness of the proposed method is evaluated. It shows that, compared with traditional architectures, it achieves a high accuracy of $\mathbf{0. 9 7 4 6}$, precision of 0.9335, recall of 0.9035, dice coefficient of $\mathbf{0. 8 9 0 5}$, and Jaccard Index of 0.9092.

关键词： Image segmentation Accuracy Sensitivity Psoriasis Feature extraction Arthritis Vectors Planning Indexes Convolutional neural networks

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Performance Analysis for Cloud-Based Olap over big data

Performance Analysis for Cloud-Based Olap over Big Data

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Jagan, S. Mishra, Pavan Turai, Anjaneya Krishna Joan, Nithesh derin Anuradha, Nandula Gangodkar, durgaprasad Perwej, Yusuf Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Ambalika Institute of Management & Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Symbiosis Skills and Professional University Department of Data Science Maharashtra Mumbai India St.Joseph's Institute of Technology Department of Data Science Tamil Nadu Chennai India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Department of Computer Science & Engineering Uttar Pradesh Lucknow India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

OLAP-based middleware for parallel OLAP query processing is presented in this study, which builds on previous research results to provide two new areas of authority application built on top of OLAP. This multi-faceted investigation of several business viewpoints is referred to as "online analytical processing"(OLAP).The fundamental purpose of OLAP systems is to allow users to conduct manual interpretation and analysis of corporate summary and extensive statistics. It's critical to realise that OLAP require users to know what data they are looking for OLAP merely analyses past facts, whereas "data-mining techniques"allow organisations to make predictions about future events. Clustering, identifying, and predictive analysis approaches are the three types of data-mining approaches. This research has considered secondary data collection method to gather informative and relevant "cloud based OLAP over big data"related information from different secondary sources such as journals, articles. © 2022 IEEE.

关键词： big data

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Learnable Convolutional Attention Network for Knowledge Graph Completion

SSRN

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SSrN 2023年

作者： Shang, Bin Zhao, Yinliang Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Shaanxi Xi’an710049 China

recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been widely used in knowledge graph completion (KGC), which aims to solve the incompleteness of knowledge graphs (KGs). However, GCNs based KGC models tend to stack redundant information when one entity have several noisy neighbors. GATs based KGC models may focus on certain neighbors and lead to weakening of structural information. Furthermore, relations in KGs have their specific semantics and should be considered when aggregating neighbor information (message passing). To address the above limitations, we propose a learnable convolutional attention network for knowledge graph completion named LCA-KGC. LCA-KGC introduces a knowledge graph convolutional attention network by using the convolution operation before the attention mechanism to ensure the structural information acquisition and avoid the redundant information stacking. Furthermore, to complete the autonomous switching of GNNs types and eliminate the necessity of pre-validating the local structure of KGs, LCA-KGC designs a learnable knowledge graph convolutional attention network by comprising three types of GNNs in one learnable formulation. Moreover, a learnable message function is proposed to emphasize relational semantics when aggregating neighbor information. Extensive experiments on standard KG datasets validate the effectiveness of the proposed innovations, and LCA-KGC achieves state-of-the-art (SOTA) performance compared to existing approaches (e.g., compared to SOTA approaches, LCA-KGC improves Mrr from 0.360 to 0.372 on FB15k-237 dataset, and Hits@3 from 0.561 to 0.581 on YAGO3-10 dataset). © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Industrial Internet of Things (IIoT): Transforming Industrial Operations through Advanced Connectivity

Industrial Internet of Things (IIoT): Transforming Industria...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S. Viveka Vivek Justus Sangeetha J desidi Narsimha reddy V. Samuthira Pandi S. Banumathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE S. A. Engineering College Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The Industrial Internet of Things (IIoT) seamlessly integrates operational technology with modern connectivity technologies, ushering in a new age in industrial operations. This paper examines IIoT's revolutionary potential in changing industrial landscapes through smart sensors, actuators, and intelligent devices that enable real-time data sharing and process automation. This paper uses case studies from numerous sectors to demonstrate how IIoT may improve operational efficiency, downtime, and decision-making. Manufacturing, energy, and logistics are discussed. The report also examines IIoT technologies like cloud, edge, and machine learning techniques. These help enterprises exploit enormous data sets for predictive maintenance, supply chain optimization, and product quality. The article discusses IIoT's risks and challenges, including data privacy, cybersecurity, and the need for skilled personnel. A detailed research shows IIoT's strategic importance in the fourth industrial revolution. It also discusses how industrial networking will enable smart and sustainable operations.

关键词： Actuators Supply chains real-time systems Product design Telecommunication computing Quality assessment Personnel Intelligent sensors Industrial Internet of Things Predictive maintenance

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