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Data, Decision and Systems (ICDDS), IEEE International Conference on

作者： Shobhan Banerjee Geetanjali Hota Ribhu Sanyal Manas Kumar Rath Department of Engineering Technology BITS Pilani Rajasthan India PGT Computer Science DA V Public School - Pokhariput Bhubaneswar India School of Computer Application Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9781665491631

In the Indian Classical system of music, we have various raags which differ from one another based on the notes being used in them, their frequency bandwidth, the subtle intricacies of the progression of notes, etc. In [1], we have worked upon the classification of a sample of music into a thaat which is an upper-level classification simply based on the frequencies present in it. In this paper, we take the work one step ahead to recognize the raag after successful classification of the thaat. In this way, it forms a two-step process where we first identify the thaat under which the music falls, followed by which we recognize the raag which corresponds to the music in consideration.

关键词： Industries Deep learning Plagiarism Detectors Bandwidth

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Mapping Research Trends and Visualization of E-learning in Entrepreneurship in the Last Ten Years 1

Mapping Research Trends and Visualization of E-learning in E...

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1st International Conference on Electrical and Information technology, IEIT 2021

作者： Maulana, Fairuz Iqbal Candra Pratama, Febby Permana, Frihandhika Khaeruddin Herasmara, Ridho School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

ISBN: (纸本)9781665417860

E-learning is a system or concept of education or learning process that utilizes information technology in the teaching and learning process anywhere and anytime. E-learning is widely applied in various fields of science. In this study, we draw scientific efforts on e-learning and entrepreneurship from publications in the Scopus database. We reviewed 218 academic documents published over the last 10 years, from 2011 to 2020. Our method is a global bibliometric analysis by visualizing connected networks using Vosviewer. The method used consists of five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. We researched several countries, affiliations, authors, subject areas, to types of publications that have relations in this field. Among the most published and indexed articles by Scopus, papers published by researchers in the China have the highest number of publications is 36 academic documents, followed by United States 18 academic publications, and Italy with 16 academic publications. The results showed that it had a limited impact, and through visualization, the network presented research gaps that future researchers should fill. The processed data shows the pattern and trend of increasing the number of international publications on E-learning in the field of entrepreneurship indexed by Scopus. Finally, we address the limitations of this study. © 2021 IEEE.

关键词： Visualization

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Privacy-Preserving Techniques for IoT Data Sharing

Privacy-Preserving Techniques for IoT Data Sharing

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International Conference on science technology Engineering and Management (ICONSTEM)

作者： P Karthikeyan Shashiraj Teotia Toralkar Pawan B Ardly Melba Reena Chirag Vibhakar Kalai Priya V Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu Department of Computer Application Swami Vivekanand Subharti University Meerut Department of Artificial Intelligence Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electrical Engineering Gujarat Power Engineering &. Research Institute Mevad Mehsana Gujarat Technological University Ahmedabad Gujarat India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In aperiod dominated by the Internet of Effects (IoT), the sharing of data has become an integral aspect of technological advancements. still, the ubiquitous nature of IoT raises significant enterprises regarding stonerse questration. Thisexploration paper delves into innovatives equestration conservingwaysacclimatized for securing the sharing of IoT-generated data. The explores cryptographic protocols, anonymisation strategies, and decentralized fabrics toguard sensitive information while easing meaningful data exchange. Addressing the contradiction between datamileage and individuals equestration, the paper proposes new methodologies that intricately balance the need for dataparticipation with the imperative tocoverstoner confidentiality. By introducing a comprehensive analysis of sequestration-conserving mechanisms with in the IoT eco system, this exploration contribute sprecious perceptivity to the ongoingconverse on securing the digitalgeography. The findings offer a foundation for developing robustfabrics that harmonize the imperatives of dataparticipating with the consummatesignificance of securing stoner sequestration in the expanding realm of Io'Toperations.

关键词： Mathematics Fabrics Internet of Things STEM Cryptographic protocols

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Region-aware Image-based Human Action Retrieval with Transformers

arXiv

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arXiv 2024年

作者： Wang, Hongsong Gui, Jie Department of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Engineering Research Center of Blockchain Application Supervision and Management Nanjing China

Human action understanding is a fundamental and challenging task in computer vision. Although there exists tremendous research on this area, most works focus on action recognition, while action retrieval has received less attention. In this paper, we focus on the neglected but important task of image-based action retrieval which aims to find images that depict the same action as a query image. We establish benchmarks for this task and set up important baseline methods for fair comparison. We present an end-to-end model that learns rich action representations from three aspects: the anchored person, contextual regions, and the global image. A novel fusion transformer module is designed to model the relationships among different features and effectively fuses them into an action representation. Experiments on the Stanford-40 and PASCAL VOC 2012 Action datasets show that the proposed method significantly outperforms previous approaches for image-based action retrieval. © 2024, CC BY.

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Modeling of Measurement Error in Financial Returns Data

arXiv

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arXiv 2024年

作者： Jasra, Ajay Maama, Mohamed Mijatović, Aleksandar School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistics University of Warwick United Kingdom

In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period, we develop a stochastic model which models the true log-returns and maximum via a Lévy process and the data as a measurement error there-of. The main technical difficulty of trying to infer this model, for instance Bayesian parameter estimation, is that the joint transition density of the return and maximum is seldom known, nor can it be simulated exactly. Based upon the novel stick breaking representation of [12] we provide an approximation of the model. We develop a Markov chain Monte Carlo (MCMC) algorithm to sample from the Bayesian posterior of the approximated posterior and then extend this to a multilevel MCMC method which can reduce the computational cost to approximate posterior expectations, relative to ordinary MCMC. We implement our methodology on several applications including for real data. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Yanfi Yanfi Yaya Heryadi Lukas Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Food industry Companies Classification algorithms

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Adaptive Beamforming in MIMO Systems Using Deep Learning

Adaptive Beamforming in MIMO Systems Using Deep Learning

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Vinithra Banu, T. Hussein, Layth Grover, Varsha Kumar, T. Arun Sharma, Rupesh Sudha, I. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Kongu Engineering College Department of Cse India Ies University Ies Institute of Technology and Management Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

One of the most significant procedures in MIMO system is the adaptive beamforming which is the technique of adjusting the direction of signal transmission for better wireless communication. This paper aims to investigate the application of deep learning in combination with adaptive beamforming for the improvement of the existing technologies and techniques having some limitations which prove efficient when working in certain applications and scenarios only. Utilizing the CNNs and RNNs, the proposed approach then determines ideal beamforming patterns from various datasets, synthetic simulations, as well as measured ones. In this paper, various approaches are employed which include the Super & Reinforcement learning techniques, which makes the deep learning-based models capable of learning in real-time the change of channel characteristics. Simulations and practical adaptations of changing protocols have illustrated substantial enhancements on SINR, BER, and system capacity. The conclusion indicates that the proposed DL based technique is better than the existing standards providing more considerably higher data rates with reasonable less error rates and possessing highly adaptive capability. From the literature, it is clear that deep learning has a significant use in adaptive beam forming;although it has significantly increased the computational load. For future wireless communication technologies such as 5G and beyond MIMO systems this work has demonstrated the promises of deep learning to improve such systems. © 2024 IEEE.

关键词： Convolutional neural networks

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A Linear Feedback Coding Scheme for Computation Over Gaussian Multiple-Access Channels

A Linear Feedback Coding Scheme for Computation Over Gaussia...

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IEEE International Symposium on Information Theory

作者： Bin Dai Fan Cheng Yuhan Yang Dengfeng Xia School of Information Science and Technology Southwest Jiaotong University Chengdu China Chongqing Key Laboratory of Mobile Communications Technology Chongqing China Provincial Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The problem of reliably reconstructing a function of sources over a multiple-access channel (MAC) is revisited by considering channel feedback. First, a linear feedback scheme is proposed to compute the average of the Gaussian sources transmitted over the Gaussian MAC. Next, numerical results show that in some cases, our proposed scheme performs better than the existing ones without using channel feedback. Finally, we show that with a slight modification, our scheme can be directly extended to compute any linear function of the Gaussian sources transmitted over the Gaussian MAC.

关键词： Encoding Reliability

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Cluster-Driven CA Selection Space Optimization in Blockchain with POA for Trust Calculation

Cluster-Driven CA Selection Space Optimization in Blockchain...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Amrutanshu Panigrahi Abhilash Pati Bibhuprasad Sahu Bibhuti Bhusan Dash Subrata Chowdhury Sudhansu Shekhar Patra Dept of CSE Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar India Dept of Information Technology Vardhaman College of Engineering (Autonomous) Hydrabad Telengana India School of Computer Application Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Blockchain technology has gained prominence because of its features and characteristics, such as transparency, immutability, and decentralization. However, choosing an appropriate CA for authenticating these transactions is one of the major issues, especially in a larger network. Mechanisms like PoW and PoS suffer from very high computational costs, significant energy wastage, and scalability problems. While simple, centralized CA systems contradict the decentralized principles of blockchain and present with single points of failure. In large networks, this selection process is very long when working with such a large number of nodes, thereby increasing the transaction processing times and the overall inefficiency of the systems. To address the above challenges, the current research aims to implement the K-Means++ clustering algorithm with the Elbow and Silhouette methods as internal validation methods to define the number of clusters in the network. In addition, the current work also aims to define a new trust calculation mechanism using a Proof of Authority (PoA) consensus mechanism, depending on which node will become the CA. Gas utilization becomes an effective measure of efficacy.

关键词： Scalability Current measurement Clustering algorithms Process control Consensus protocol Computational efficiency Time factors Intelligent systems Elbow Optimization

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Optimized Siamese Humboldt Squid Attention Network Architecture for Cyber-Attack Detection in IoT 4.0

Optimized Siamese Humboldt Squid Attention Network Architect...

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Sustainable Expert Systems (ICSES), International Conference on

作者： M. Varusai Mohamed Ankita Nainwal Ayshwarya B H S Kumar Dhaval Rabadiya Syed Noeman Taqui Department of Computer Application B. S. Abdur Rahman Crescent Institute Of Science And Technology (BSACIST) Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer science Kristu Jayanti College Bengaluru Karnataka India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Faculty of Management studies Parul University Vadodara Gujarat India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The rapid expansion of IoT within Industry 4.0 has heightened the risk of cyber-attacks, underscoring the need for advanced detection mechanisms. Existing cyber-attack detection systems in this context often struggle with reduced accuracy, high computational time, complexity, and elevated error rates. This study addresses these challenges by proposing an Optimized Siamese Humboldt Squid Attention Network (OSH-SqAtNet) architecture for cyber-attack detection in IoT Industry 4.0 environments. The input data, sourced from datasets like CSE-CIC-IDS2018, undergoes preprocessing via a Mixture of Conventional Fast Bilateral Filter (MC2FB) to enhance data quality. A Gold Rush Optimizer (GRO) is then employed for feature selection, isolating the most significant features to optimize classification performance. The preprocessed and optimized data are classified using the OSH-SqAtN et architecture for accurate cyber-attack detection. For secure data transmission, Rabin Fuzzy Cryptosystem assisted Elliptic Curve Cryptography (RFC-ECC) is utilized. Experimental simulations conducted on the Python platform reveal that the proposed method outperforms existing techniques, achieving higher accuracy (98%) and recall (0.993%). These results demonstrate the approach's superior efficacy and potential for advancing cybersecurity in IoT Industry 4.0.

关键词： Gold Accuracy Data preprocessing Urban areas computer architecture Feature extraction Elliptic curve cryptography Fourth Industrial Revolution Time complexity Cyberattack

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