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4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Rajawat, Anand Singh Barhanpurkar, Kanishk Mukhopadhyay, Debajyoti Ghosh, Ankush SVVV Department of CSE Indore India Sambhram Institute of Technology Department of CSE Bengaluru India Bennett University Dept of Computer Science Engineering Greater Noida India School of Engineering and Applied Sciences The Neotia University West Bengal India

ISBN: (纸本)9781728199511

The classic example of shared hidden data transformation is the Invisible Internet Protocol (I2P), which works with Tor web-net and make incognito relay confidential. The I2P offers an outstanding possibility for people seeking anonymity to circumvent authoritarian regimes. It can also be troublesome in corporate environments where I2P-routers over-use restricted web-net assets or provide a means for malware to exfiltrate information. In this research paper, the authors discussed the IDS possibility to track I2P web-net congestion and associate peers positively is done. Additionally, the authors given context details on the internal mechanisms of I2P, and showed the ways to recognize I2P routers throughout the bootstrapping process. The development and review of the new system is of the current state of the art. We intend to introduce Blockchain techniques for tracking Darkweb User Activities on a trust computational web-net to improve data protection in transparent blockchains (BC). © 2021 IEEE.

关键词： Internet protocols

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Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations

arXiv

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arXiv 2022年

作者： Besginow, Andreas Lange-Hegermann, Markus Department of Electrical Engineering and Computer Science OWL University of Applied Sciences and Arts Lemgo Germany Institute industrial IT Lemgo Germany

Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of Gaussian Processes (GPs) with realizations strictly following a system of linear homogeneous ODEs with constant coefficients, which we call LODE-GPs. Introducing this strong inductive bias into a GP improves modelling of such data. Using smith normal form algorithms, a symbolic technique, we overcome two current restrictions in the state of the art: (1) the need for certain uniqueness conditions in the set of solutions, typically assumed in classical ODE solvers and their probabilistic counterparts, and (2) the restriction to controllable systems, typically assumed when encoding differential equations in covariance functions. We show the effectiveness of LODE-GPs in a number of experiments, for example learning physically interpretable parameters by maximizing the *** Codes 60G15, 62G08, 12H05, 68W30, 13J30, 34-04 Copyright © 2022, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Scalable Exact Output Synchronization of Discrete-Time Multi-Agent Systems in the Presence of Disturbances and Measurement Noise With Known Frequencies

arXiv

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arXiv 2023年

作者： Liu, Zhenwei Zhang, Meirong Saberi, Ali Stoorvogel, Anton A. College of Information Science and Engineering Northeastern University Shenyang110819 China School of Engineering and Applied Science Gonzaga University SpokaneWA99258 United States School of Electrical Engineering and Computer Science Washington State University PullmanWA99164 United States Department of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede Netherlands

This paper aims to achieve scalable exact output and regulated output synchronization for discrete-time multi-agent systems in presence of disturbances and measurement noise with known frequencies. Both homogeneous and heterogeneous multi-agent systems are considered, with parts of agents’ states accessible in the latter case. The key contribution of this paper is on the distributed protocol that only uses the information of agent models, rather than the communication network information and the agent number, so as to achieve the scalable exact synchronization under disturbances and measurement noise. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents. © 2023, CC BY-NC-ND.

关键词： Multi agent systems

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases

Machine Learning Models for Brain Signal Classification: A F...

引用

Cyber Resilience (ICCR), International Conference on

作者： Ali M Ali Shadi Nashwan Ahmad Al-Qerem Ammar Almomani Mahmoud Al Sakhnini Amjad Aldweesh Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities.

关键词： Neurological diseases Machine learning algorithms Scalability Epilepsy Transforms Feature extraction Electroencephalography

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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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Identify High-Priority Barriers to Effective Digital Transformation in Higher Education: A Case Study at Private University in Indonesia 1

Identify High-Priority Barriers to Effective Digital Transfo...

引用

1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Aditya, Bayu Rima Murad, Dina Fitria Ferdiana, Ridi Kusumawardani, Sri Suning Wijanarko, Bambang Dwi Universitas Gadjah Mada Department of Electrical and Information Engineering Indonesia Telkom University School of Applied Science Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Indonesia

ISBN: (纸本)9781728133331

Some barriers negatively affect the implementation of digital transformation in higher education institutions. This research aims to investigate these barriers in a particular context: a private university in Indonesia. The barriers diagnostic framework (BDF) has been applied to identify and prioritize barriers. It is determined that 'Actionable plans based on strategy translation', 'The ability to embed ICT into the education system', and 'Limitations of institutional policies' have high priority barriers and therefore meet critical concerns in the implementation of digital transformation at the case study. The main contribution of this study is providing empirical evidence on barriers to digital transformation in the higher education sector. More understanding of the high-priority barriers will help the management of higher education to find effective and efficient strategies to manage the resources. © 2021 IEEE.

关键词： E-learning

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Cavity-driven ultrafast chiral optical switching 8

Cavity-driven ultrafast chiral optical switching

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2D Photonic Materials and Devices VIII 2025

作者： Zhou, Lingxiao Liu, Bin Liu, Yuze Lu, Yang Li, Qiuyang Xie, Xin Lydick, Nathanial Hao, Ruofan Liu, Chenxi Watanabe, Kenji Taniguchi, Takashi Chou, Yu-Hsun Forrest, Stephen R. Deng, Hui Department of Physics University of Michigan 450 Church Street Ann ArborMI48109-2122 United States Department of Electrical Engineering and Computer Science University of Michigan 1301 Beal Avenue Ann ArborMI48109-2122 United States Applied Physics Program University of Michigan 450 Church Street Ann ArborMI48109-2122 United States Nuclear Engineering and Radiological Science University of Michigan 2355 Bonisteel Blvd Ann ArborMI48109-2122 United States Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Photonics National Cheng Kung University Tainan Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781510684843

Floquet engineering, where an oscillating electric field modifies quantum states, is a promising tool to manipulate quantum systems coherently. For example, the valley-selective A.C. Stark effect can break time-reversal symmetry in monolayer transition metal dichalcogenides by lifting valley degeneracy. However, Floquet engineering has often been limited by the requirement of an ultrashort optical pulse with a high enough pulse energy. Here, based on a cavity structure with monolayer WSe2, we demonstrate that cavity effects can enhance effective Floquet dressing power by orders of magnitude. The strong enhancement also enables us to build an efficient chirality all-optical switch by the Floquet effect. Our study opens up the possibility to exploit Floquet engineering and other nonlinear phenomena for efficient optical devices of low-dimension materials. © 2025 SPIE.

关键词： Stark effect

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Role of AI for Fraud Detection in Banks: A Bibliometric Analysis

Role of AI for Fraud Detection in Banks: A Bibliometric Anal...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Rajesh Tiwari Shivani Rautela Saurabh Sharma Bhasker Pratap Choudhary Rashmi Tripathi Praveen Singh Department of Management Studies Graphic Era (Deemed to be University) Dehradun Bharat Computer Science and Engineering (CSE) Swami Vivekanand Institute of Engineering & Technology Banur Bharat Department of Applied Sciences Chandigarh Engineering College Mohali Bharat Faculty of Business and Commerce ISBAT University Kampala Uganda

The rise of digital banking and online transactions has led to an increase in fraudulent activities within the banking sector. Traditional fraud detection methods are no longer sufficient to detect fraudsters. This paper explores bibliometric analysis on Scopus database. 66 documents were obtained from Scopus Database. India was the prominent country in citation. 64% documents have been published after 2019. There is increase in research interest after the onset of Covid pandemic. It was found that AI-based fraud detection systems outperform traditional methods for fraud detection. The paper provides inputs for further studies to develop framework for AI fraud detection in banks.

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Atomic wave-meter on-chip

Atomic wave-meter on-chip

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Eitan Edrei Elam Gerstel Shani Gamzu-Letova Noa Mazurski Uriel Levy Department of Applied Physics The Benin School of Engineering and Computer Science The Center for Nanoscience and Nanotechnology The Hebrew University of Jerusalem Jerusalem Israel

We demonstrate a wave-meter on chip device with accurate calibration and enhanced robustness against environmental fluctuations enabled by the integration of atomic vapor with a photonic chip and the use of machine le... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Meters Fluctuations Machine learning algorithms Machine learning Robustness Calibration System-on-chip

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MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors

MotionNeXt: A Deep Learning Neural Network for Recognizing H...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Recognition of human activities using wearable sensors is essential for applications related to health and well-being. However, the complex data captured by inertial sensors poses challenges in accurately identifying actions. This research introduces MotionNeXt, a residual deep neural network incorporating aggregated transformation. MotionNeXt utilizes residual and multi-kernel blocks to extract spatial and temporal characteristics from raw IMU data. Subsequently, it employs global average pooling, a fully connected layer, and softmax for classification purposes. An attention layer enables the model to focus on specific segments of the input sequence while categorizing each time step. MotionNeXt is evaluated using a publicly available dataset of activities collected from wearable inertial measurement devices. It achieves state-of-the-art accuracy, boasting an F1-score of 99.37%, surpassing previous deep learning methods by 2- 5%. By integrating accelerometer, gyroscope, and magnetometer modalities, MotionNeXt mitigates the limitations of individual inputs, significantly improving accuracy across various models.

关键词： Deep learning Accuracy Measurement units Inertial sensors Magnetometers Motion segmentation Information and communication technology Gyroscopes Data mining Wearable sensors

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