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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Elantamilan, D. Gnanavel, R. Vinod, D. Nalini, M.K. Dhinakaran, K. Vallal P. T. Lee Chengalvaraya Naicker Arts and Science College Department of Computer Science Chennai112 India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Engineering Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665495295

Growing amounts of information sources are required as the quantity of available information from various sources, both self governing and heterogeneous data maintenance requirement increases. A major component of the increased interest in big data is the heterogeneity of the data itself. In this work, data communications is researched, in which it is the method of constructing a specific case of direct instruction from an illustration of reference conceptual framework, so that when the eventually results entity conforms with target schema configuration. The logic of schema mappings are high-level expressions used to specify relationships across databases and are often used in this process. The experimental results of this paper provide proper proof to the efficiency of research and the results are acquired based on the real-Time working emulations. © 2022 IEEE.

关键词： Electronic data interchange

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Smart Attendance Management using a Self-Supervised Learning Approach

Smart Attendance Management using a Self-Supervised Learning...

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Image Processing and Capsule networks (ICIPCN), International Conference on

作者： D. Vikram H Ajina S Gokilapriya M Malini Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Machine Learning Kalingarkarunanidhi Institute of Technology Coimbatore India Department of Information Technology Sri G.V.G Visalakshi College for Women Udumalpet India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised Learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries.

关键词： Accuracy Soft sensors Self-supervised learning Transforms Medical services Feature extraction Real-time systems

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Unlocking the Potential: A Review of Digital Twins and Integration with Emerging Technologies

Unlocking the Potential: A Review of Digital Twins and Integ...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Dinesh Babu. V Geetha S K Aneeshkumar M Revathi S Arulprakash P. Arulprakash Department of Information Technology Karpagam Institute of Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology (Autonomous) Coimbatore India Department of Artificial Intelligence and Data Science Arjun Collge of Technology Thamaraikulam India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Rathinam Technical Campus (Autonomous) Coimbatore India

Abstract— The Digital Twin (DT) is primarily used to digitally represent a physical object, process, or service. Simply put, DT is a clone representation of a physical object in the real world, such as wind farms, objects like a building or even an entire city. The key uses of technology are to collaborate, train, and simulate processes or products. The results of DT vary greatly depending on the sector and application, but a few key advantages include reducing costs and risks before investing in real-world resources and helping to enhance products or processes over their entire life cycles. The integration of the digital Twin with so many various industries, including manufacturing, healthcare, and the growth of urban and rural areas, has enhanced the prominence of this emerging technology. This paper's major goal is to identify ongoing initiatives by numerous engineering fields to apply digital twin technology for sustainable development.

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Ewald-based Long-Range Message Passing for Molecular Graphs

arXiv

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arXiv 2023年

作者： Kosmala, Arthur Gasteiger, Johannes Gao, Nicholas Günnemann, Stephan Max Planck Institute for the Science of Light & Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Computer Science Munich Data Science Institute Technical University of Munich Germany Department of Physics Ludwig-Maximilians-Universität München Germany Google Research

Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years. A key driver of this success is the Message Passing Neural Network (MPNN) paradigm. Its favorable scaling with system size partly relies upon a spatial distance limit on messages. While this focus on locality is a useful inductive bias, it also impedes the learning of long-range interactions such as electrostatics and van der Waals forces. To address this drawback, we propose Ewald message passing: a nonlocal Fourier space scheme which limits interactions via a cutoff on frequency instead of distance, and is theoretically well-founded in the Ewald summation method. It can serve as an augmentation on top of existing MPNN architectures as it is computationally inexpensive and agnostic to architectural details. We test the approach with four baseline models and two datasets containing diverse periodic (OC20) and aperiodic structures (OE62). We observe robust improvements in energy mean absolute errors across all models and datasets, averaging 10 % on OC20 and 16 % on OE62. Our analysis shows an outsize impact of these improvements on structures with high long-range contributions to the ground truth energy. © 2023, CC BY.

关键词： Message passing

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IPL data Analysis and Visualization for Team Selection and Profit Strategy

IPL Data Analysis and Visualization for Team Selection and P...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Saranya G Aravind Swaminathan Joel Benjamin J Surendran R Leema Nelson Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India MSc student Data Science Middlesex University Dubai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & technology Chitkara University Punjab India

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) data Analysis 2022 and data Visualization for the duration (2008–2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for Scientific Computing, Pandas used to perform data Analysis, and finally Matplotlib and Seaborn to enable data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report.

关键词： Analytical models Scientific computing Computational modeling data visualization Machine learning data science Probability

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Federated Learning in Big data with IoT for Intrusion Detection

Federated Learning in Big Data with IoT for Intrusion Detect...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Anitha Govindaram J. S. Prasath A. Suganya K. Jayasakthi N. Rajkumar Jose Anand A. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Bengaluru Karnataka Department of Artificial Intelligence and Data Science Anand Institute of Higher Technology Chengalpattu Tamil Nadu Department of ECE KCG College of Technology Chennai Tamil Nadu Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Artificial Intelligence (AI) part of Federated Learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has not yet gained mainstream adoption, mostly because of concerns about privacy and security. Security and privacy issues with the FL technique need to be found, examined, and recorded before at this point, we can expect to see significant progress in this field of study or widespread adoption of the FL approach. This article aims to afford a complete review of the methods discussed with FL for huge data by introducing studies from six angles: blockchain, privacy, hybrid approach, VFL, FL, and foundation design and device heterogeneity. This survey can address the challenges of FL, offer their predictions for the future of research on the subject, outline how FL is now being put to use in the real world, and with most salient features.

关键词： Surveys Privacy data privacy Federated learning Reviews Intrusion detection Learning (artificial intelligence) Security Informatics Mobile computing

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Anomaly Detection in Social networks using Improved Local Outlier Factor

Anomaly Detection in Social Networks using Improved Local Ou...

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Intelligent Systems and Computational networks (ICISCN), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Muntader Mhsnhasan Sowmya M Prabhakaran M V K. Ghamya Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering (Cyber Security) Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively.

关键词： Measurement Accuracy Social networking (online) High dimensional data Transfer learning Feature extraction Real-time systems Intelligent systems Anomaly detection Principal component analysis

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Computation Analysis for Identifying the Protagonist and Antagonist and their Sentiments in Harry Potter Books

Computation Analysis for Identifying the Protagonist and Ant...

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Artificial Intelligence and Speech Technology (AIST), International Conference on

作者： D. K Tayal Sonakshi Vij Divya Arora Bhavna Meena Prakhar Jain Kritik Sharma Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Science Vivekananda Institute of Professional Studies-Technical Campus Delhi India

The main goal of sentiment analysis is to find the text polarity (positive or negative). Sentiment analysis can be Natural language processing and computational literary studies have long been interested in sentiment analysis, which can be used to infer relationships between fictional characters. This study uses sentence-based semantic analysis to analyze sentiment in the Harry Potter series. The findings showed that among the primary characters with heroic attributes, Harry Potter scored the most positive polarity; Voldemort scored the most negative polarity, and Hermoine scored the neutral polarity.

关键词： Sentiment analysis Semantics Speech processing Artificial intelligence

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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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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Cybernation and Computation (CYBERCOM), International Conference on

作者： Gaurav Kumar Pankaj Gupta Ganesh Kumar Yadav Richa Verma Jai Prakash Bhati Vihan Singh Bhakuni Department of CSE IIMT College of Engineering Greater Noida Uttar Pradesh India Manager Data Engineer Discover Financial Services USA Department of Computer Science and Engineering JSS Academy of Technical Education Noida UP India Computer Science and Engineering BBDITM LUCKNOW Department of CSE (IoT) GNIOT (Engineering Institute) Greater Noida Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

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.

关键词： Deep learning Measurement Support vector machines Accuracy Network intrusion detection Artificial neural networks Network security Robustness Decision trees Random forests

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