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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Yagateela Pandu Rangaiah V Revathi Amit Dutt Smita Sharma Taqi Mohammed Khattab Al-Rubaye K. Pushpa Rani Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Applied Sciences New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

As urbanization expands at a rate faster than ever before we experience traffic jams which call for sophisticated predictive methods in order to improve the traffic systems. Hypothesized from the heuristic analysis of big traffic data, the present paper outlines a new traffic flow prediction model. The proposed methodology uses big real-time traffic data and historical big data with a machine learning approach that can detect and analyse temporal and spatial dependencies. The model incorporates DL concepts like Long Short-Term Memory (LSTM) networks which capture the sequential dependencies and the complex correlation in traffic flows. Moreover, we also have Feature Selection mechanism to filter the traffic data indicators and thus decrease the data dimensions and increase the prediction accuracy. The integration of big data analytics is helpful in real-time adjustments to changes in traffic and is more accurate. By testing the method on different traffic datasets, the authors show that the proposed method outperforms the traditional models. The findings of this research are novel and significant because this framework can be easily applied and implemented for the traffic flow prediction across various cities, helping in management of traffic and thereby limit traffic congested areas.

关键词： Analytical models Accuracy Computational modeling Scalability Urban areas Predictive models Big Data Data models Real-time systems Long short term memory

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A Framework for Digital Twin-Based Deterministic Communication in Satellite Time Sensitive Networks

A Framework for Digital Twin-Based Deterministic Communicati...

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IEEE International Conference on Communications (ICC)

作者： Yinzhi Lu Guofeng Zhao Chuan Xu Muhammad Imran Keping Yu Joel J.P.C. Rodrigues School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China College of Applied Computer Science Saud University Riyadh Saudi Arabia Graduate School of Science and Engineering Hosei University Tokyo Japan College of Computer Science and Technology China University of Petroleum Qingdao China Instituto de Telecomunicações Covilhã Portugal

With the explosive growth of real-time applications in satellite systems, Time Sensitive Networking (TSN) is explored to be introduced to provide bounded low latency network services. Nevertheless, some efforts in delay ensuring techniques are ongoing, guaranteeing deterministic low latency communication in satellite networks is still a significant problem. In the article, we first present a Digital Twin-based TSN framework in which digital twin technology is introduced for the purpose of reducing the management cost and optimizing the performance of satellite networks. The virtual model of the scheduling method working in satellite systems is explored and created for the simulation and prediction of the forwarding delay results. Deep convolution generative adversarial network (DCGAN) is adopted to train the scheduling model. The simulation experiments verified that the digital twin could mirror the scheduling behavior and predict the delay in dynamic environments.

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On Approximate and Weak Correlated Equilibria in Constrained Discounted Stochastic Games

arXiv

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arXiv 2022年

作者： Jaśkiewicz, Anna Nowak, Andrzej S. Faculty of Pure and Applied Mathematics Wroclaw University of Science and Technology Wroclaw Poland Faculty of Mathematics Computer Science and Econometrics University of Zielona Góra Zielona Góra Poland

In this paper, we consider constrained discounted stochastic games with a countably generated state space and norm continuous transition probability having a density function. We prove existence of approximate stationary equilibria and stationary weak correlated equilibria. Our results imply the existence of stationary Nash equilibrium in ARAT stochastic games. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability density function

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SHUFFLED LINEAR REGRESSION THROUGH GRADUATED CONVEX RELAXATION

arXiv

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arXiv 2022年

作者： Onaran, Efe Villar, Soledad Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Israel Department of Applied Mathematics & Statistics Mathematical Institute for Data Science Johns Hopkins University United States

The shuffled linear regression problem aims to recover linear relationships in datasets where the correspondence between input and output is unknown. This problem arises in a wide range of applications including survey data, in which one needs to decide whether the anonymity of the responses can be preserved while uncovering significant statistical connections. In this work, we propose a novel optimization algorithm for shuffled linear regression based on a posterior-maximizing objective function assuming Gaussian noise prior. We compare and contrast our approach with existing methods on synthetic and real data. We show that our approach performs competitively while achieving empirical running-time improvements. Furthermore, we demonstrate that our algorithm is able to utilize the side information in the form of seeds, which recently came to prominence in related problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear regression

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The Terms Estimation of S Robotics using MTRS as well as FEM

The Terms Estimation of S Robotics using MTRS as well as FEM

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R. Gopal Mustafa Mudhafar Amjad Hashem Venkadeshan Ramalingam Dibyahash Bordoloi Muhammad Numan Ali Khan Myasar Mundher Adnan Zahraa N. Abdulhussain Assistant Professor Information and communication Engineering College of Engineering University of Buraimi Sultanate of Oman Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences– Shinas Sultanate of Oman Dept of CSE Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand Faculty – Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences – Shinas Sultanate of Oman Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and TechnologyDhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot’s form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics’ frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot’s form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation.

关键词： Deformable models Deformation Force Estimation Soft robotics Robot sensing systems Software

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Inventing the Potential of a High-Frequency EEG, Namely Dodecanogram (DDG): Human Subjects’ Study 5th

Inventing the Potential of a High-Frequency EEG, Namely Dode...

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5th International Conference on Trends in Computational and Cognitive Engineering, TCCE 2023

作者： Singh, Pushpendra Sarkar, Jhimli Dey, Parama Sarkar, Sounak Pattanaya, Anindya Nag, Sudipa Pramanik, Sudeshna Sahoo, Pathik Saxena, Komal Krishnanda, Soami Daya Dutta, Tanusree Ghosh, Subrata Bandyopadhyay, Anirban NIMS 1-2-1 Sengen Ibaraki Tsukuba3050047 Japan Department Electronics and Electrical Communication Engineering IIT Kharagpur West Bengal Kharagpur721302 India Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Assam781039 India Organizational Behavior and Human Resource Management Indian Institute of Management Ranchi834008 India Amity School of Applied Science Amity University Rajasthan Kant Kalwar NH-11C Jaipur Delhi Highway Rajasthan Jaipur303007 India Microwave Physics Laboratory Department of Physics and Computer Science Dayalbag Educational Institute Uttar Pradesh Agra282005 India Chemical Science and Technology Division CSIR-North East Institute of Science and Technology NEIST Assam Jorhat785006 India Uttar Pradesh Ghaziabad201002 India

ISBN: (纸本)9789819719228

The conventional EEG machine measures brain signal frequencies between 1 and 300 Hz on the scalp. By utilizing Fourier transform (FFT), it generates a 2D frequency profile. Our innovative EEG, known as Dodecanogram (DDG), operates in two modes. In the first mode, it detects a wide range of frequencies from 6 THz to 1 milliHz, creating a brain scan based on resonance. In the second mode, it uses picosecond pulses to capture potential surges, measuring their duration, intensity, and phase variation, revealing brain activity patterns. Both modes work simultaneously in the DDG device. To minimize environmental interference, we use interconnected DDG devices on eight human brains. The detected results from DDG show that DDG is a fundamental technology for understanding various mental states. Potential applications include neuro-disease diagnosis and exploring treatments involving electrical and electromagnetic pulses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electromagnetic pulse

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Comparative Analysis of Machine Learning Models in Online Payment Fraud Prediction

Comparative Analysis of Machine Learning Models in Online Pa...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Ahmed Younes Shdefat Mennatallah Mohamed Shahd Khaled Farrah Hany Hanaa Fathi Diaa Salama AbdElminaam College of Engineering and Technology American University of the Middle East Egaila Kuwait Faculty of Computer Science Misr International University Cairo Egypt Applied Science Research Center Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid Jordan Information Systems department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt MEU Research Unit Middle East University Amman Jordan

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

With the substantial increase in Cybercrime, the pressing need for effective solutions in the light of the rising amount of corporate financial losses, is a concern f or many researchers. The widespread adoption of ecommerce and diverse online payment methods has contributed to the surge in a major form of cybercrime—online payment fraud. This further highlights the critical need to control and reduce the widespread danger of fraudulent behaviour on the internet. However, predicting online payment fraud is difficult as fraudsters are constantly changing their tactics, transactions happen quickly and in large volumes, transactions involve different regions and countries, stolen data is often used and other factors. Our goal is to improve the accuracy and usability of online payment fraud prediction through the use of machine learning. A well-established and widely recognized dataset specifically designed for studying online payment fraud was used. The six proposed algorithms trained and tested were (SVM), Decision Tree, Naïve Bayes, Random Forest, (KNN) and Logistic Regression. After rigorous testing, Decision Tree presented the highest accuracy in performance of 98.58%. However, as ten-fold cross validation was implemented on the dataset, Decision Tree suffered a loss while Random Forest had an increase in accuracy making it the best model with accuracy of 98.47%. As shown in this paper, machine learning has proven to be a reliable and ever-evolving approach for the prediction of Online Payment Fraud and results can be further improved in collaboration with financial and cybercrime specialists.

关键词： Support vector machines Accuracy Machine learning algorithms Predictive models Prediction algorithms Fraud Bayes methods

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Jin Xiao Hui Cui Ping Xuan Xikang Feng Leyi Wei Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University China School of Software Northwestern Polytechnical University China AIDD Faculty of Applied Science Macao Polytechnic University Macao SAR China School of Information Xiamen University Xiamen China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGA mm , which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer.

关键词： Knowledge engineering Representation learning Pathology Genomics Prediction methods Data models Gene expression Bioinformatics Prognostics and health management Cancer

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Context based ranking strategies for renowned instructional methodologies

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Intelligence-Based Medicine 2024年 10卷 100186-100186页

作者： V, Saranya Abdullah, Azween Ramadass, Parthasarathy Srinivasan, Saravanan Shivahare, Basu Dev Mathivanan, Sandeep Kumar P, Karthik Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai 600119 India Faculty of Applied Science and Technology Perdana University Kuala Lumpur Malaysia Spectrum International University College Selangor Malaysia Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 600062 India School of Computer Science and Engineering Galgotias University Greater Noida 203201 India Department of Information Science Engineering Cambridge Institute of Technology Bangalore India Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai 600062 India

The main objective of this work is to validate the decisions made towards adoption of appropriate instructional methodologies based on the context of a specific region considering the quality of education, the cost of education and the learning outcomes as predominant parameters. The non-deterministic events and uncertain situations that may arise over a long-range period impose a vague and fuzzy environment in the educational system. Investigations have been made to identify suitable educational framework for implementation in the institutions of a specific region in view of these unpredictable events and non-deterministic conditions. Fuzzy decision analysis and rough set theory have been applied to rank the prominent instructional methodologies which are encompassed within each educational framework. Hurwicz Rule is adopted to balance the pessimistic and optimistic opinions about the non-deterministic events while validating the merits of the instructional methodologies. Grey relational analysis is carried out while ranking instructional methodologies in a vague environment. In this work, the instructional methodologies are ranked using fuzzy entropy as well as crisp entropy measures and the outcomes of the fuzzy and rough sets-based decision analysis have been validated. © 2024 The Authors

关键词： Crisp entropy measure Fuzzy decision analysis Fuzzy entropy measure Grey relational analysis Hurwicz rule Instructional methodologies Rough sets

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Investigating of Classification Algorithms for Heart Disease Risk Prediction

Journal of Intelligent Medicine and Healthcare

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Journal of Intelligent Medicine and Healthcare 2022年第1期1卷 11-31页

作者： Nisha Mary Bilal Khan Abdullah A Asiri Fazal Muhammad Samar Alqhtani Khlood M Mehdar Hanan Talal Halwani Turki Aleyani Khalaf A Alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied Medical Science Najran UniversityKingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Computer Science Department College of Computer Science and Information SystemNajran UniversitySaudi Arabia Anatomy Department Medicine CollegeNajran UniversitySaudi Arabi

Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVMare evaluated using MAE, RAE, precision, recall, f-measure, and *** overall finding indicates that for the dataset taken from UCI, the SVMclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 forMAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for MAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.

关键词： Heart disease classification techniques evaluation measures

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