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Interpretable Anomaly Detection: A Hybrid Approach Using Rule-Based and Machine Learning Techniques

Interpretable Anomaly Detection: A Hybrid Approach Using Rul...

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international conference for Convergence of Technology (I2CT)

作者： Rahul Mohite Lahcen Ouarbya Department of Computing Goldsmiths University of London

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Anomaly detection is a critical aspect of ensuring the security and reliability of various systems in diverse domains, including cybersecurity, finance, and industrial processes. Traditional black-box anomaly detection methods often lack interpretability, making it challenging for users to understand the reasoning behind the detection of anomalies. In this research, we propose a novel hybrid approach that combines rule-based and machine learning techniques to enhance the interpretability of anomaly detection systems. Our method integrates a rule-based system that generates interpretable anomaly detection rules with machine learning components that leverage complex pattern recognition and classification capabilities. We evaluate the proposed approach on a diverse set of real-world datasets, demonstrating its effectiveness in identifying anomalies while providing transparent explanations of the detection process. Through comprehensive experimentation and comparative analysis with existing state-of-the-art methods, we showcase the superior interpretability and performance of our hybrid approach. Our findings highlight the significance of interpretability in anomaly detection systems and underscore the potential of the proposed approach for enhancing transparency and trust in critical decision-making processes. This research contributes to the advancement of interpretable anomaly detection techniques and opens avenues for future research in the domain of transparent and reliable anomaly detection systems.

关键词： Decision making Finance Machine learning Robustness Cognition pattern recognition Fraud

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Synaptic Weight Optimization for Oscillatory Neural Networks: A Multi-Agent RL Approach

Synaptic Weight Optimization for Oscillatory Neural Networks...

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IEEE international conference on Agents (ICA)

作者： Shuhao Liao Xuehong Liu Wenjun Wu Rongye Shi Junyu Zhang Haopeng Wang School of Artificial Intelligence Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China

ISBN: (数字)9798331539917

ISBN: (纸本)9798331539924

The Oscillatory Neural Network (ONN) presents itself as a promising architecture model for pattern recognition (PR), based on which advanced neuromorphic computing and integrated circuit designs are implemented. The core of the ONN’s PR capability lies in the synaptic weight design, i.e., how the neurons are connected to each other. Conventional design methods, like the Hebbian rule, are able to store only a limited number of patterns. In this paper, we propose a strategy to leverage the Multi-Agent Reinforcement Learning (MARL) for acquiring the optimal synaptic weights that can efficiently store more patterns into the ONN system as stable quilibria. To obtain the synaptic weights in a more efficient manner and further increase the number of patterns to be stored, we additionally propose a method to leverage Curriculum Learning (CL) to optimize the learning process of the policy. Experimental results demonstrate that the proposed MARL-based method outperforms baseline methods in terms of storing more patterns as stable equilibria in ONN.

关键词： Integrated circuit synthesis Neuromorphic engineering Design methodology Neurons Reinforcement learning Computer architecture pattern recognition Integrated circuit modeling Biological neural networks Optimization

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Optical Observation for Body Angles of Cricket Players’ Motion to Identify the Batting Shots

Optical Observation for Body Angles of Cricket Players’ Mot...

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Advanced Research in computing (ICARC), international conference on

作者： Ishan Jayaweerage Ashan Induranga Kaveendra Maduwantha Samantha Kumara Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Faculty of Technology Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Cricket, one of the world’s most popular sports, has developed considerably during the past few decades. The competition and complexity of this sport increased largely during the past few years, which brought the attention of the researchers to develop technical solutions to improve the quality of the sport by improving both the on-field decision-making and practicing methods. There are several kinds of high-end equipment available in the market that can be used to improve these skills. Even though such equipment can be used at the national level, it is very difficult to use them at school and club levels due to their higher cost. This research focused on enhancing the batting skills of cricket batsmen using a low-cost setup that uses webcams to capture the footage and the MediaPipe library, which was developed by Google to analyze motion data. This research analyzed two cricket shots, the front foot defence, and the pull shot, using four web cameras. The received footage was analyzed using MediaPipe to identify the variations in the angles of the player’s left elbow and left shoulder while playing the shot. These pattern identifications can help successfully understand the angle variation of the forward foot defence and the pull shots by generating an average angle value graph using four web cameras. These graphs show the special patterns for each batting shot. This methodology and the results are valuable for developing a successful player analyzing system by considering the other important cricket shots at a very low cost with affordable 2D web cameras.

关键词： Integrated optics Costs Webcams Ultraviolet sources Lighting Machine learning Optical imaging pattern recognition Sports Foot

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Hidden Emotion Detection from Speech and Text Using Deep Learning Approach

Hidden Emotion Detection from Speech and Text Using Deep Lea...

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Network, Multimedia and Information Technology (NMITCON) international conference on

作者： Ravula Tarun Reddy Suja Palaniswamy Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita VishwaVidyapeetham India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

—The value that comes from accurate detection and understanding human emotions stands across a ton of domains. This paper details a few well-tested models applied in a unique way for discovering hidden emotion through audio and text data with the help of a custom dataset. Every sample in the dataset has an audio snippet and a Q&A pair which tries to evoke a certain emotion. The main goal lies in creating a model that can identify emotional indicators from diverse data sources. To achieve this a custom dataset was developed. The audio samples have been annotated with descriptions of emotions, and their corresponding transcripts have been similarly labeled for enhanced clarity and were able to achieve 81.44% accuracy in identifying hidden emotions.

关键词： Deep learning Emotion recognition Accuracy soft sensors Data models Information technology

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Electric Vehicle Parking Monitoring System Based on Object Detection Algorithm

Electric Vehicle Parking Monitoring System Based on Object D...

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Electronic Technology and Information Science (ICETIS), international conference on

作者： Yixin Xu Heyang Peng Lvzhuoyu Zhou Yudan Yan Jingu Xie Yixin Hou Harbin Economics and Management Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Science Harbin Institute of Technology Harbin China School of Computer Science University of Nottingham Nottingham United Kingdom

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

With the continuous development of society and the increase of urbanization level, electric vehicles have gradually become one of the necessary means of transport for people's daily travel. However, the surge in the number of EVs has also triggered corresponding safety issues, including causing fires and obstructing traffic. The traditional manual supervision method is difficult to meet the needs of detection and alarm, and has low efficiency and high false alarm rate. Therefore, it is important to design a real-time detection system for illegal parking of electric vehicles. In this paper, focusing on the actual scenario of random parking of electric vehicles, we develop an original system for detecting illegal parking of electric vehicles, and in response to the safety problems that may be caused, we use the YOLOv8 algorithm to achieve fast and accurate detection and alarm with the help of computer vision, artificial intelligence, and pattern recognition technology, which realizes the innovative application of the algorithm in the field of electric vehicle monitoring.

关键词： Accuracy Vehicle detection Urban areas Object detection Electric vehicles Real-time systems pattern recognition

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A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

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Smart Devices (ICSD), international conference on

作者： Amit Kumar Mishra Neeraj Kumar Pandey Manoj Diwakar Nilesh Bhaskarrao Bahadure Ankur Dumka Jagdish Chandra Patni Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Dept. of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Computer Science & Engineering Women Institute of Technology Dehradun India Dept. of CSE Alliance School of Advance Computing Alliance University Bengaluru Karnataka India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate.

关键词： Training Accuracy Supervised learning Radial basis function networks Real-time systems pattern recognition Object tracking Object recognition Smart devices Logistics

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international conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Khan, Muhammad Jabir Kumam, Poom King Mongkut’s University of Technology Thonburi Bangkok10140 Thailand Bangkok10140 Thailand Department of Medical Research China Medical University Hospital China Medical University Taichung40402 Taiwan

ISBN: (纸本)9783030511555

The basic idea underneath the generalized intuitionistic fuzzy soft set is very constructive in decision making, since it considers how to exploit an extra intuitionistic fuzzy input from the director to make up for any distortion in the information provided by the evaluation experts, which is redefined and clarified by F. Feng. In this paper, we introduce the similarity measures based on the cosine and cotangent function for and discuss their properties. At the end, a numerical examples of decision making and pattern recognition is discussed based on the proposed similarity measures. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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AI-Infused Research and Development in Universities: Accelerating Scientific Discovery

AI-Infused Research and Development in Universities: Acceler...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Badrus Moch. Abdulloh Salim Ika Novita Sari Septiana Azha Moh. Zulfikri Sutantri Tribakti Islamic University Lirboyo Kediri Indonesia Indonesia

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The past decade has witnessed the role that artificial intelligence (AI) has in university research and development (R&D) processes emerge as a very potent catalyst for accelerating scientific discovery. It thus seeks to explore the transformative impacts that AI-infused R&D is likely to bring in universities, focusing mostly on key mechanisms, challenges, promising avenues for future exploration, and technological breakthroughs in the field. The traditional methodologies of R&D have seen a massive revolution through the entry of AI, which now poses uncharted capabilities in data analysis, pattern recognition, and predictive modeling. The application is able to thoroughly search vast sets of data in an unrealistically short period, so as to reveal latent structures, patterns, and other actionable insights, with the help of machine learning algorithms, neural networks, and natural language processing techniques. This paradigm shift in R&D supports a more agile, data-driven approach to scientific inquiry that allows researchers to better focus their efforts with precision and alacrity on the most vexing, complex problems. The biggest gain to be reaped from infusing $A I$ in R&D is the hastening of the pace of experimentation and hypothesis testing. AI systems thus enable researchers to iterate very quickly by automating repetitive tasks and offering the best designs for experimentation. This will, in effect, bring down the time-todiscovery cycle. Equally, AI simulations allow for the possibility of in silico virtual experiments, thus reducing overreliance on capital-intensive and unwieldy laboratory experiments.

关键词： Training Watersheds Predictive models Trajectory pattern recognition Artificial intelligence Task analysis

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Design and Implementation of Visually Impaired Assistant system

Design and Implementation of Visually Impaired Assistant sys...

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2021 international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2021

作者： Osama, Mayar Yehia, Alaa Mohamed, Salma Sherief, Rawan Elmasry, Noha Adel, Verina Hamdy, Alaa Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9781665412438

The need for independent living is recognised in the case of visually impaired people who face a variety of issues, including social *** are forced to live in strange environments with no means of assistance. Today's tech world should supply them with this assistance for easier socialization. Our aim is to facilitate the life of visually impaired people who could lack the technology to help them in their lives. This project is proposed to focus on the development of a mobile application that uses voice commands and text to voice technology to enable users to interact with the mobile application. The user captures the item they want to identify using the smartphone's camera and pick the image they want to upload for processing. The image chosen then will undergo through pre-processing techniques depending on the feature the user chose, the features include Money recognition CNN MobileNet architecture was used to analyze the Egyptian banknotes and Color and patterns recognition CNN resnet50 architecture was used to analyze the color and pattern of the clothing. As well, the application allows the user to register or login using facial recognition to further facilitate the experience for users. The application is designed to be effective and can be easily navigated by visually impaired users. ResNet50 architecture produce the highest accuracy in patterns and colors cloth recognition, Mobilenet architecture produce the highest accuracy in currency recognition. © 2021 IEEE.

关键词： Architecture

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Outlier Detection based Model for Event pattern recognition in Vehicular Networks

Outlier Detection based Model for Event Pattern Recognition ...

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IEEE international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Kawthar Zaraket Ismail Bennis Hassan Harb Ali Jaber Abdelhafid Abouaissa MERCEL Lab Doctoral School of Science and Technology Lebanese University Beirut Lebanon IRIMAS University of Haute-Alsace 2 Rue des Frères Lumière Mulhouse France College of Engineering and Technology American University of the Middle East Kuwait

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

Today, detecting outliers plays a crucial role in modern transportation systems, improving traffic management and road safety. This paper introduces a new outlier detection-based model for recognizing event patterns in Vehicular Ad hoc NETworks (VANETs). Our solution utilizes advanced techniques, combining outlier detection and multiclassification capabilities to enhance the resilience and reliability of transportation systems in dynamic and complex traffic scenarios. The approach involves three stages: data preprocessing and feature extraction, outlier detection, and multiclassification. In the first stage, an image-based dataset is transformed into a feature-based dataset after essential preprocessing operations, such as feature extraction by analyzing local patterns in the image pixels using the Local Binary patterns (LBP) method. In the second stage, a hybrid classification model based on a neural network is proposed to identify outlier events in real-time vehicle data, followed by the employment of machine learning models to classify them as normal or abnormal. When an abnormal traffic situation is detected, the final stage utilizes multiclassification deep neural networks, specifically ResNet and Inception, to categorize events into predefined classes. Through extensive simulations using real VANET data, we have demonstrated the relevance and accuracy of our model in recognizing event patterns in traffic systems compared to other existing techniques.

关键词： Wireless communication Computational modeling Vehicular ad hoc networks Transportation Feature extraction Data models Road safety pattern recognition Vehicle dynamics Anomaly detection

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