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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： R. Mohandas D.S.John Deva Prasanna Baiju B V S. Nathiya T. Dhiliphan Rajkumar C. Sivashankar Department of Computational Intelligence SRM Institiute of Science and Technology Chennai India Department of Datascience and Business systems SRM Institiute of Science and Technology Chennai India School of Computer Science and Engineering(SCOPE) VIT University Vellore TamilNadu India Department of Electronics and Communication Engineering Prince shri Venkateshwara Padmavathi Engineering College Chennai Dept. Of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennnai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks; therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities.

关键词： Learning systems Industries Pathology Machine learning algorithms Intrusion detection Machine learning Internet of Things

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Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization

arXiv

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arXiv 2024年

作者： Zheng, Lihao Wang, Hongxuan Li, Xiaocong Ma, Jun Vadakkepat, Prahlad The School of Data Science The Chinese University of Hong Kong Shenzhen China Research Intern The Singapore Institute of Manufacturing Technology A*STAR Singapore The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Singapore Institute of Manufacturing Technology Agency for Science Technology and Research A*STAR 138634 Singapore Guangzhou China

Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimal control systems

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A Rationale-Centric Framework for Human-in-the-loop Machine Learning 60

A Rationale-Centric Framework for Human-in-the-loop Machine ...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Lu, Jinghui Yang, Linyi Namee, Brian Mac Zhang, Yue The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China SenseTime Research

ISBN: (纸本)9781955917216

We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Mental Health Disorder Prediction Using Recurrent Neural Network Algorithm

Mental Health Disorder Prediction Using Recurrent Neural Net...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： Umasree Mariappan D. Balakrishnan G. Merline M. Sandhia Dubba Saitej Reddy Sattineni Gagan Teja Department of Information Technology P.S.R. Engineering College Sivakasi Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Electronics and Communication Engineering Sri Vidya College of Engineering & Technology Virudhunagar Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Mental health disorders pose a significant challenge to healthcare professionals due to their complex and multifactorial nature. Early identification and accurate prediction of mental health disorders play a crucial role in timely intervention and effective treatment planning. The objective of this study is to explore the potential of deep learning algorithms in predicting mental health disorders, specifically focusing on depression, anxiety, and schizophrenia. The study utilizes a dataset containing clinical and demographic information of patients diagnosed with these disorders. The dataset was pre-processed using feature scaling algorithm, and features dimensionality reduction is done using t-Distributed Stochastic Neighbor Embedding (t-SNE) to train and evaluate deep learning models. These models were trained on large-scale datasets, allowing for the identification of complex patterns and relationships within the data. The training on dataset is done by using the Recurrent Neural Networks. The performance of the models was evaluated using standard evaluation metrics, such as accuracy, precision, recall, and F1-score. The results indicate that deep learning algorithms have shown promising results in predicting mental health disorders. The models achieved high accuracy rates, with some models surpassing the performance of traditional machine learning algorithms. Performance evaluation of the model is conducted using relevant metrics such as precision, recall, and F1-score, on a separate validation or test dataset from which it is proved that the proposed methodology attains 6% performance improvement than the existing methodology.

关键词： Deep learning Training Recurrent neural networks Accuracy Machine learning algorithms Mental health Medical services

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Medical Image Classification for Disease Prediction with the Aid of Deep Learning Approaches 2

Medical Image Classification for Disease Prediction with the...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Pandiyarajan, M. Thimmiaraja, J. Ramasamy, Jayaraj Tiwari, Mohit Shinde, Seira Chakravarthi, M. Kalyan Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India School of Computing Kalasalingam Academy of Research and Academy Tamil Nadu Krishnankovil India Botho University Department of It Botswana Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Computer Science & Engineering BITR India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9781665437899

Machine learning as well as deep learning algorithms is recently in huge demand in the field of image classification. This research paper illustrates the relevance of these two prospects for making further improvement in the treatment system in the upcoming days. In this context the evolution of the machine learning from its invention to the modern stage has been discussed. The limitations of machine learning are elaborated and as a consequence the emergence of deep learning have been depicted. The study also provides an insight to the different leaning process and their relevance in the field of image classification and image analysis. Opinion of people are gathered about this aspect and presented in a survey format. The overall presentation illustrates a precise discussion about the relevance and evaluation of artificial learning system in the context of image classification which can be used in diagnostic process. The different fields of medical treatment which are seeing benefits of introduction of deep learning network and convolutional neural network are also discussed here. At the end a conclusive summary about the machine learning process and its pros and cons are elaborated to get an idea about the present standing point of machine learning on the field of medical science. © 2022 IEEE.

关键词： Image classification

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Fuzzy-Based Kernelized Clustering Algorithms for Handling Big Data Using Apache Spark 6th

Fuzzy-Based Kernelized Clustering Algorithms for Handling Bi...

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6th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2020

作者： Jha, Preeti Tiwari, Aruna Bharill, Neha Ratnaparkhe, Milind Nagendra, Neha Mounika, Mukkamalla Department of Computer Science and Engineering Indian Institute of Technology Indore Indore India Department of Computer Science and Engineering Mahindra University Ecole Centrale School of Engineering Hyderabad India Biotechnology ICAR-Indian Institute of Soybean Research Indore Indore India

ISBN: (纸本)9789811586026

In this paper, we propose a novel Kernelized Scalable Random Sampling with Iterative Optimization Fuzzy c-Means (KSRSIO-FCM) and a Kernelized Scalable Literal Fuzzy c-Means (KSLFCM) clustering algorithms for big data framework. The evolution of kernelized clustering algorithms led us to deal with the nonlinear separable problems by applying kernel Radial Basis Functions (RBF) which map the input data space nonlinearly into a high-dimensional feature space. The experimental result shows that the KSRSIO-FCM algorithm achieves significant improvement in terms of F-score, Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI) for Big Data. Experimentation is performed on well-known IRIS datasets to show the effectiveness of proposed KSRSIO-FCM in comparison with KSLFCM. The KSRSIO-FCM implemented on Apache Spark shows better potential for Big Data clustering. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Optimizing Smart Grid Energy and Load Prediction with Namib Beetle Algorithm and Self-Adaptive Physics-Informed Neural Networks

Optimizing Smart Grid Energy and Load Prediction with Namib ...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： D. Santhakumar Dilip Mishra Balasubbareddy Mallala Sridhar. S Trupti Dhanadhya Department of Computer Science and Engineering Saveetha School of Engineering Thandalam Chennai Tamil Nadu Department of Science and Technology ICFAI University Raipur Chhattisgarh India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Research Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electrical Engineering Dr. D Y Patil Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

For smart grid (SG) operations to be effective and improve resource allocation and grid stability, it is essential to forecast electricity load and renewable energy generation. However, because renewable energy sources are very variable and load demand patterns in smart grid environments are complicated, it is still difficult to estimate renewable energy output and electrical load with any degree of accuracy. This paper proposes a hybrid approach for predicting wind energy generation. The proposed method is combined execution of Namib Beetle Optimization Algorithm (NBOA) and Self-Adaptive Physics-Informed Neural Networks (SAPINN). Hence, it is named as NBOA-SAPINN Approach. The input data is collected from the Germany Wind Energy and Weather Dataset. Then, the input data is fed to pre-processing using Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). The GMKMCKF is used to normalize the data. The pre-processed data is then fed to SAPINN for wind energy generation forecast. To achieve this prediction, the SAPINN is fine-tuned using the NBOA. The proposed method is implemented in MATLAB. It is therefore feasible to use several performance criteria to measure the efficiency of the proposed NBOA-SAPINN model such as Accuracy, Root Mean Square Error (MSE), Mean Squared Error (MSE). The NBOA-SAPINN model achieves an accuracy of 98%, with MSE and RMSE values converging to 0.23 and 0.22 by epoch 100 compared with existing methods such as Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Feed-Forward Neural Network (FFNN), Deep Reinforcement Learning (DRL), and Recurrent Trend Predictive Neural Network (RTNN).

关键词： Wind energy generation Renewable energy sources Accuracy Prediction algorithms Mathematical models Real-time systems Smart grids Resource management Reliability Wind forecasting

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Study on ESS maximum capacity calculation and operation algorithm based on load pattern of substations in urban railway

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Transactions of the Korean institute of Electrical Engineers 2021年第9期70卷 1405-1412页

作者： Yun, Chi-Myeong Cho, Gyu-Jung Kim, Hyungchul Kim, Chul-Hwan Jung, Hosung Smart Electrical & Signaling Division Korea Railroad Research Institute Korea Republic of Dept. of Transportation Engineering Korea University of Science and Technology Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In this paper, we introduce the method of reducing only the morning peak of the ESS and the method of calculating the maximum ESS capacity according to the method of reducing the entire day by using the characteristic that the load of the urban railway substation shows a different load pattern depending on weekdays, weekends, and holidays. Furthermore, an operation method suitable for urban railroad is introduced using the operation algorithm for each of the five points of time of the ESS according to the load pattern. To verify the introduced maximum capacity calculation method and ESS operation algorithm, power analysis program, PSCAD/EMTDC was used. Copyright © The Korean institute of Electrical Engineers.

关键词： Cost reduction

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Investigation of High Performance Conducting Polymer Parameters for Supercapacitor Electrode

Investigation of High Performance Conducting Polymer Paramet...

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2021 International Telecommunications Conference, ITC-Egypt 2021

作者： Hassan, A.M. Ebrahim, Sh. Elshaer, A.M. Institute of Graduate Studies and Research Dept. of Materials Science Alexandria Egypt Higher Institute of Engineering and Technology Dept. of Computer Engineering El-Boheira Egypt

ISBN: (纸本)9781665445733

Polypyrrole film was electrodeposited onto nickel alloy and stainless-steel substrates to be assessed as a supercapacitor electrode. Double layer supercapacitor electrodes were electrochemically characterized by electrochemical impedance spectroscopy, cyclic voltammetry, and charge/discharge. It was observed that the polypyrrole electrodeposited onto stainless steel and nickel alloy electrodes provided an outstanding specific capacitance of 1400 F/g and 1030 F/g and energy density of 583.33 Wh/kg and 429.16 Wh/kg, respectively. The electrode structure and morphological properties of polypyrrole films was studied by the scanning electron microscope. © 2021 IEEE.

关键词： Polypyrroles

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Counting on AR: EEG responses to incongruent information with real-world context

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computers in Biology and Medicine 2025年 185卷 109483-109483页

作者： Wimmer, Michael Pepicelli, Alex Volmer, Ben ElSayed, Neven Cunningham, Andrew Thomas, Bruce H. Müller-Putz, Gernot R. Veas, Eduardo E. Know Center Research GmbH Graz Austria Institute of Neural Engineering Graz University of Technology Graz Austria Wearable Computer Lab University of South Australia AdelaideSA Australia BioTechMed Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions. © 2024

关键词： Interfaces (computer)

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