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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Anand Singh Rajawat S.B. Goyal Aarti Goyal Kavita Rajawat Maria Simona Raboaca Chaman Verma Traian Candin Mihaltan School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Mind Intellect Technology Indore India ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world.

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A review on efficient EEG pattern recognition using machine learning and deep learning methods and its application

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AIP Conference Proceedings 2022年第1期2555卷

作者： Anson Antony Abir Bhattacharjee Soham Thakur Shreeanant Bharadwaj Shivam Sonawane Mritunjay Tomar 1Computer Engineering JSPM Rajarshi Shahu College of Engineering Puna India 2School of Computer Science and Engineering VIT Vellore India 3Electrical and Electronics Engineering Maharaja Agrasen Institute of Technology Delhi India 4Acropolis Institute of Technology and Research Indore India

With the good development of robotic technology, good communication of robots regarded as the most sought after achievement by researchers these days. If the robot can identify the feelings and intentions of the contact person, which can lead to more robots useful. Electroencephalography (EEG) is considered one of the most effective recording methods the emotions and motives of the brain user. The various types of machine learning are as follows used successfully to separate EEG data accurately. K-closest neighbor, Besesi network, Artificial Neural and Support Vector Machine networks are within the appropriate machine learning methods for classifying EEG data. The purpose of this concept is to explore different machine learning techniques to differentiate EEG data associated with specific emotional / emotional conditions. Different ways based on differences signal-processing techniques are studied Different numbers of EEG data elements are used to identify those that give the best results different classification techniques. Various methods are designed to format the database for EEG data. Formatted data sets were tested in various machine learning techniques in order to find out which process can place EEG data accurately according to it emotional / emotional conditions. On the other hand, this study is to study the electronic learning methods by various EEG tools. Continuous EEG is an exciting approach to cerebral function performance testing in intensive care unit and more. A systematic approach of this work derives various important outcome on EEG.

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Oblique and rotation double random forest

arXiv

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arXiv 2021年

作者： Ganaie, M.A. Tanveer, M. Suganthan, P.N. Snasel, V. Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India School of Electrical & Electronic Engineering Nanyang Technological University Singapore KINDI Center for Computing Research College of Engineering Qatar University Qatar Department of Computer Science VŠB - Technical University of Ostrava Czech Republic

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each nonleaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularisation, axis-parallel split regularisation, Null space regularisation) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembl

关键词： Decision trees

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 579卷 v页

作者： Nagar, Atulya K. Jat, Dharm Singh Mishra, Durgesh Kumar Joshi, Amit School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom Namibia University of Science and Technology Windhoek Namibia Department of Computer Science and Engineering Sri Aurobindo Institute of Technology Madhya Pradesh Indore India Global Knowledge Research Foundation Gujarat Ahmedabad India

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Analysis of the Secure Cloud Framework for Mobile Banking

Analysis of the Secure Cloud Framework for Mobile Banking

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2021 International Conference on Technological Advancements and Innovations, ICTAI 2021

作者： Joseph, Susheel George Jadhav, Swati Subhash Sharma, Smita Poonia, Amarjeet Shrivastava, Anurag Hemavathi, S Kristu Jyoti College of Management and Technology Dept. of Computer Applications Kottayam India D y Patil College of Engineering D y Patil Education Complex E and Tc Dept. Sector-29 Nigdi Pradhikaran Akurdi Pune India U.I.T R.G.P.V Department of Cse Bhopal India Govt. Women Engineering College Department of Information Technology Nasirabad Road Makhupura India Lakshmi Narain College of Technology Ads Science Indore India Institute Chennai Academy of Scientific Innovative Research Central Electrochemical Research Ghaziabad India

ISBN: (纸本)9781665420877

Many service-related tasks can now be completed swiftly and efficiently thanks to advancements in mobile communications. Mobile banking is one such solution that has eliminated the requirement for many ordinary transactions to be completed at a branch. Mobile phones can supplement the reach in a country like India, where the last mile is reached through brick-and-mortar banking facilities. This paper explains how a mobile cloud framework can be used for banking and customer services to improve the customer's banking experience while also assuring data security. This article focuses on cloud-based risk design for banking products to address challenges such as processing speed and storage capacity in mobile banking. The system is evaluated using an improved random forecast technique. This proposed system fulfils 99% of the accuracy. © 2021 IEEE.

关键词： Digital storage

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Experimental Analysis for Position Estimation using Trilateration and RSSI in Industry 4.0

Experimental Analysis for Position Estimation using Trilater...

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International Conference on System Modeling & Advancement in research Trends (SMART)

作者： Robin Singh Chouhan Anand Singh Rajawat S.B. Goyal Pradeep Bedi Neagu Bogdan Constantin Maria Simona Raboaca Chaman Verma Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India School of Computer Science and engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Galgotais University Greater Noida Department of Power Engineering “Gheorghe Asachi” Technical University of Iasi Iasi ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Informatics University of Eötvös Loránd Budapest Hungary

ISBN: (纸本)9781665487351

With the introduction of industry 4.0, the role of robots especially autonomous bots has increased exponentially. The next thing in order to improve the navigation for these bots is the need for a proper indoor navigation system. The paper proposes experimental analysis for estimating the position using trilateration. The experiment includes three wifi nodes operating at 2.4GHz working as wifi beacons. The paper provides an efficient and cost-effective solution to estimate the approximate position of the node in terms of coordinates. The paper also includes a working mobile application to showcase the node's position in real-time in the form of a 2d map.

关键词： Visualization Time-frequency analysis Coordinate measuring machines Service robots Extraterrestrial measurements Chatbots Mathematical models

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Artificial Intelligence Assisted IoT Data Intrusion Detection

Artificial Intelligence Assisted IoT Data Intrusion Detectio...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Amogh Shukla Shahanawaj Ahamad G.Nageswara Rao Avein Jabar Al-Asadi Ankur Gupta Makhan Kumbhkar Research Assistant Vellore Institute of Technology Vellore Tamil Nadu India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Lakireddy Bali Reddy College of Engineering Mylavaram Andhra Pradesh India Computer Institute of Sulaimani Iraq Vaish College of Engineering Rohtak Haryana India Christian Eminent College Indore Madhya Pradesh India

ISBN: (纸本)9781665414487

It has become increasingly important since the introduction of Internet of Things (IoT) technology to ensure the security of IoT networks. Network anomalies and threats can be identified and predicted using a variety of intrusion detection systems (IDS). Despite the fact that the Internet of Things is vulnerable to attacks, early detection of malicious behavior can prevent data from being compromised. In this work, the primary goal is to develop machine artificial intelligence energy efficient models that can be used to detect attacks on the Internet of Things network. Normal and attack data from the IoT environment must be collected in order to construct a model. The Bayesian Network, the Artificial Neural Network, and the Support Vector Machine are considered to have the most promise. Using roundtrip time and power consumption data, a traditional three-layer Artificial Neural Network is put through its paces in the real world.

关键词： Support vector machines Power demand Memory management Intrusion detection Artificial neural networks Energy efficiency Bayes methods

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Analytical Modelling of Y2O3-based Memristive System for Artificial Synapses

Analytical Modelling of Y2O3-based Memristive System for Art...

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International Conference on Emerging Electronics (ICEE)

作者： Sanjay Kumar Mangal Das Kumari Jyoti Amit Shukla Abhishek Kataria Shaibal Mukherjee Electrical Engineering Hybrid Nanodevice Research Group (HNRG) Indian Institute of Technology Indore India Computer Science and Engineering Rustamji Institute of Technology BSF Academy Tekanpur Indore India Hybrid Nanodevice Research Group Rustamji Institute of Technology BSF Academy Tekanpur Indore India Electrical Engineering Hybrid Nanodevice Research Group (HNRG) Centre for Advanced Electronics (CAE) Indore India

ISBN: (纸本)9781728186610

Artificial synapses are the key units for information processing in neuromorphic systems. Memristive systems are frequently used as an artificial synapse because of their simple structures, gradually changing conductance and high-density integration. In this work, a non-linear analytical model for Y 2 O 3 -based memristive system with new parabolic window function has been discussed for artificial synapses applications. Moreover, resistive switching characteristic and synaptic plasticity properties of the memristive systems are modelled by utilizing non-linear analytical model to investigate the performance of artificial synapse. Further, the modelled data is verified by the experimental results of fabricated devices which confirmed that the developed model can be realized the basic functions of spiking neurons and has great potential for neuromorphic computing.

关键词： Performance evaluation Analytical models Neuromorphic engineering Computational modeling Neurons Memristors Switches

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An X-ray and radio study of the Hubble frontier field cluster Abell S1063

arXiv

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arXiv 2021年

作者： Rahaman, Majidul Ram, Raja Datta, Abhirup Burns, Jack O Alden, Brian Rapetti, David Discipline of Astronomy Astrophysics and Space Engineering Indian Institute of Technology Indore Indore India Department of Astrophysical and Planterary Sciences Center for Astrophysics and Space Astronomy University of Colorado BoulderCO80309 United States Nasa Ames Research Center Moffett FieldCA94035 United States Research Institute for Advanced Computer Science Universities Space Research Association Mountain ViewCA94043 United States

We present results from Chandra X-ray observations and 325 MHz Giant Metrewave Radio Telescope (GMRT) observations of the massive and X-ray luminous cluster of galaxies Abell S1063. We report the detection of large-scale "excess brightness" in the residual Chandra X-ray surface brightness map, which extends at least 2.7 Mpc towards the north-east from the center of the cluster. We also present a high fidelity X-ray flux and temperature map using Chandra archival data of 122 ksec, which shows the disturbed morphology in the cluster. The residual flux map shows the first observational confirmation of the merging axis proposed by earlier simulation by Gomez et al. (2012). The average temperature within o?'500 is 11.7 ± 0.56 keV, which makes AS1063 one of the hottest clusters in the nearby Universe.. The integrated radio flux density at 325 MHz is found to be 62.0 ± 6.3 mJy. The integrated spectrum of the radio halo follows a power-law with a spectral index α =-1.43±0.13. The radio halo is found to be significantly under-luminous, which favored for both the hadronic as well as the turbulent re-acceleration mechanism for its origin. Copyright © 2021, The Authors. All rights reserved.

关键词： Luminance

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Preface

引用

CEUR Workshop Proceedings 2022年 3244卷

作者： Chaudhari, Narendra S. Ganesan, Geetha Satti, Srinivasa Rao Department of Computer Science and Engineering IIT Indore India Advanced Computing Research Society Tamilnadu Chennai India Norwegian Institute of Science and Technology Trondheim Norway

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