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Deep Convolutional Neural Network for Anomaly Detection in Power System Networks

Deep Convolutional Neural Network for Anomaly Detection in P...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Manimegalai R Muhamed Fallahhusein S. Devi Piyush Kumar Pareek M. Ramya Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Techniques Engineering College of Technical Engineering Islamic University of Najaf Najaf Iraq Department of Computer Applications Nandha Engineering College Erode India Department of AIML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, the increasing complexity of power system networks has made them valuable to various types of anomalies. Also, the self-organizing neural networks learn patterns to recognize in data without prior knowledge of the underlying system as a solution for anomaly detection in power system networks. However, the existing One-Class Support Vector machine (OC-SVM) faced challenges in detecting anomalies due to the high-dimensional and non-linear nature of data. Hence, this research proposes Deep Convolutional Neural Network (DCNN) to detect anomalies in traffic real-time data. Initially, the input data is collected from KDDcup99 dataset which consists of various Daniel of Service (DoS) attack data points. then, the data is preprocessed with Regression Imputation (RI) to handle the missing and duplicate data. After that, Fisher Discriminant Analysis (FDA) is used to extract the features by finding the optimal linear combination of features that maximizes the separation between normal and attack classes. Finally, the proposed DCNN is introduced to detect the anomalies by learning hierarchical representations of temporal and spatial patterns. From the results, the proposed DCNN established better results when compared to Long Short-Term Memory with Auto Encoders (LSTM-AE) in terms of precision (96.72%) and recall (98.76%) respectively.

关键词： Support vector machines Feature extraction Real-time systems Phasor measurement units Imputation Smart meters Power systems pattern recognition Convolutional neural networks Anomaly detection

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Vision Assist: Real-Time Guidance System

Vision Assist: Real-Time Guidance System

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： E. Mohanraj D. Devadarshini M. Manonavaneethan S. Aravind Department of Artificial Intelligence and Data Science K.S.Rangasamy College of Technology Tamil Nadu India Department of Artificial Intelligence and Data Science K S Rangasamy College of Technology Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In order to help visually impaired people, this study proposes a machine learning-based system that provides audio feedback, distance calculation, and real-time object detection. the system uses Faster Region-based Convolutional Neural Network (Faster R-CNN), optimized with Compute Unified Device Architecture (CUDA) and CUDA Deep Neural Network library (cuDNN) for GPU-accelerated performance with low latency. Images captured by the rear camera of a smartphone are processed to identify objects and compute their proximity. the system offers audio feedback using the Pyttsx3 text-to-speech engine, alerting users to nearby objects and potential hazards. the system achieved over 99% accuracy in recognizing everyday objects. Parallelized GPU operations ensure efficient real-time object recognition and distance estimation, improving users' safety and mobility. Experimental results demonstrate the system's stability and reliability in assisting visually impaired individuals during navigation.

关键词： Accuracy Navigation Graphics processing units Estimation Object detection Ubiquitous computing Real-time systems Text to speech Object recognition Low latency communication

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UDON: Unsupervised data SelectiON for Biomedical Entity recognition 4

UDON: Unsupervised Data SelectiON for Biomedical Entity Reco...

引用

4th international conference on Computing and Big data, ICCBD 2021

作者： Akdemir, Arda Shibuya, Tetsuo University of Tokyo Bunkyo City Tokyo Japan

ISBN: (纸本)9781450387194

High-quality training datasets are critical for building successful machine learning (ML) based NLP systems. However, these datasets are not always available in low-resource contexts such as the biomedical domain. Here, selecting relevant training data is as important as the choice of the ML model. In this study we propose UDON: Unsupervised data selectiON for biomedical entity recognition using domain-specific pretrained Language Models (LMs). We first show that pretrained LMs succeed at implicitly learning the differences between datasets without any supervision, and then use these models to select relevant data instances. Next, we evaluate the proposed methods for entity recognition on seven biomedical datasets and one news domain dataset using four LMs and three selection methods. Our results show that using pretrained domain-specific LMs for data selection outperforms all other approaches. Finally, we use domain classification as an auxiliary task for pretraining the neural network on the in-domain dataset and show this yields further improvements. © 2021 ACM.

关键词： Classification (of information)

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HAR data Analysis: Unveiling the Potential of Federated CNN Models

HAR Data Analysis: Unveiling the Potential of Federated CNN ...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Chaimae Zaoui Faouzia Benabbou Laboratory of Modeling and Information Technology Faculty of Sciences Ben M'Sick University Hassan II Casablanca Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

the Internet of things has seamlessly integrated into various facets of our daily lives, playing a pivotal role in sectors like security, transportation, smart homes, and healthcare. Across these domains, IoT employs numerous sensors that generate substantial data, traditionally stored in centralized infrastructures like Cloud Computing. However, this approach introduces challenges such as high latency, extended response times, and security concerns during data processing and analysis. In response to these challenges, federated learning, a revolutionary branch of artificial intelligence, has emerged. this paradigm shift allows machine learning techniques to benefit from both decentralized data and computing power, predominantly found in edge devices. this approach ensures a personalized user experience without compromising privacy. this paper introduces a federated Convolutional Neural Network (FedCNN) model and conducts a comparison between classical and federated learning for Human activities’ recognition. After a state of art, different aggregation approaches were evaluated including Avg-CNN, Sgd-CNN, Prox- CNN, Med-CNN, and KRUM-CNN. Avg-CNN model outperformed with an accuracy of 93.87% on the HAR dataset, while Prox- CNN Med-CNN, Sgd-CNN, and KRUM-CNN achieved respectively 93.77%, 93.38%, 93.38% and 85.40%.

关键词： Privacy Federated learning Computational modeling Transportation Smart homes Medical services User experience

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Research on water quality classification of unbalanced river image data under deep learning framework

Research on water quality classification of unbalanced river...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： ShouYi Mo YuZe Wang JiaQi Zhi JiaQi He Lin Guo Hohai University Business School Changzhou Jiangsu China

Water quality is an important indicator of current river ecological monitoring. Water quality monitoring based on image recognition can greatly save monitoring costs and time. But the river image data are mostly unbalanced data, which will affect the accuracy of image recognition. Aiming at this problem, this paper constructs a deep learning framework for water quality classification of unbalanced river image data. According to the imbalance degree of samples, adaptive unbalanced sampling processing is carried out, and then a convolutional neural network model is constructed to classify water quality of balanced data sets. through comparison experiments with a variety of deep learning models, this framework has achieved good results, indicating that the method can effectively classify water quality based on images.

关键词： Deep learning machine learning algorithms Image recognition Costs Adaptive systems Biological system modeling Water quality

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

引用

4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

the automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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Predictive Modelling of Osteoporosis using machine learning Algorithms

Predictive Modelling of Osteoporosis using Machine Learning ...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： R. Dhivya Sreenivas Reddy Sagili Praveen RVS Purushothapatnapu Naga Venkata VamsiLala A Sangeetha Suchithra B Department of Information Technology M.Kumarasamy College of Engineering Karur Tamilnadu India Department of Computer Science Rochester Institute of Technology Rochester New York Digital Engineering LTIMindtree (of Affiliation) M/s. Divija Commercial Properties Hyderabad Telangana Vignan's Lara Institute of Technology and Science Vadlamudi Andhra Pradesh Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology(Autonomous) Thottiam Trichy Department of CSE Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Osteoporosis implies the deterioration of bones or their denser tissue and the subsequent elevated risk of skeletal fractures constitutes a major health problem today. therefore, early recognition and initiation of treatment should be enacted to enhance the status of patients. this paper aims at presenting an understanding and analysis of the use of these particular methods, and their specificity in the context of osteoporosis modeling using Support Vector machines. With the help of the large number of features, which are clinical and demographic factors, we are to create a stable model that will allow defining the probability of osteoporosis. the SVM algorithm for dealing with high-dimensional data and classification is used for the investigation of the connection between input characteristics and the advance of the condition. It also makes it easier to determine the most relevant factors that could be used in the creation of a predictive instrument that might help in the early detection of the disease and the subsequent improvement of the available treatment plans. It is with this goal in mind that this research aims at contributing to development of further implementation of machine learning approaches in osteological field, thus laying basis for enhancing diagnostic approaches and patients' management.

关键词： Support vector machines Osteoporosis Analytical models High dimensional data Calcium Predictive models Ubiquitous computing Vectors Reliability Diseases

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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

引用

Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： K. Ananthi M. Balakrishnan M. Pandi N. Senthil Madasamy Artificial Intelligence &Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Computer Science and Engineering Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. the tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. the problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. the experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library.

关键词： Measurement Fuzzy logic Weight measurement Training Computer bugs Support vector machine classification machine learning

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Summary of MIMO System Signal Detection Algorithms under Deep learning 4

Summary of MIMO System Signal Detection Algorithms under Dee...

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4th international conference on Artificial Intelligence and pattern recognition, AIPR 2021

作者： Xu, Dongming Ding, Zhijie Cheng, Yu Xi'an University of PostsandTelecommunications China

ISBN: (纸本)9781450384087

Massive Multiple Input Multiple Output (MIMO) is one of the key technologies of multi-user cellular systems. We must do our best to find a suitable detection algorithm to ensure high-throughput data signal detection. In recent years, deep learning technology has made great progress in the field of wireless communication systems, and signal detection algorithms based on deep learning have gradually become the focus and hotspot of research in this field. this article describes the signal detection task and its difficulties under computer vision, focusing on the signal detection algorithm in the MIMO system based on deep learning, summarizes the current representative related methods, and summarizes the development of the technology and outlook. © 2021 ACM.

关键词： Signal detection

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Deep learning Based Intrusion Detection for IoT Networks

Deep Learning Based Intrusion Detection for IoT Networks

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： P. Ananthi K. Nirmaladevi Mothika G Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Computer Applications Madanapalle Institute of Technology &Science MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. this study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. the project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. the novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. the TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. the ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance.

关键词： Deep learning Accuracy Intrusion detection Telecommunication traffic Prediction algorithms Ubiquitous computing Convolutional neural networks Internet of things Transient analysis Optimization

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