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pattern recognition Algorithm to Detect Suspicious Activities

Pattern Recognition Algorithm to Detect Suspicious Activitie...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： V C Mahavishnu S G Vikhas T Shyamganesh S Roopakumar S V Kavin Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

Detecting suspicious activities in public places with higher people gathering and interaction has turned out to be an act with growing interest due to the increasing number of crime scenes and causalities happening in these days. Surveying and tracking of human activities are increasingly difficult owing to the random nature of human movements and actions. The reliability is greatly affected due to this randomness. Also a human operator cannot continuously monitor multiple screens efficiently in a consequent manner so an automated surveillance system deployment becomes a necessity. Currently, tracking individuals may be done remotely, and the analysis of the recorded images can be automated using object detection models, with the help of high resolution cameras and the development of machine learning techniques. This proposed system aims in identifying threats that are probable to occur in a public gathering or space which may be an explosion, accident or possession of armoury, etc. This proposed model takes advantage of the information from the image data to learn complex patterns and develop pattern recognition technique to identify the anomalies using high resolution camera and alert the monitoring authority in order to take the necessary actions. This proposed work compares various object detection techniques of machine learning algorithms and suggests the best model based on its performance metrics.

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Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. This work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. This can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. This accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

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Segmentation of Brain Tumors in CT Images: A Comparative Analysis Using Convolutional Neural Network (CNN) Techniques and Support Vector Machine (SVM) 7

Segmentation of Brain Tumors in CT Images: A Comparative Ana...

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7th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2025

作者： Anitha, C. Subha Ramya, V. Ramya, R. Annie Angeline Preethi, S. Annapoorani, C.L. Sofia Bobby, J. Department of CSE Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India Department of Biomedical Engineering Jerusalem College of Engineering Tamil Nadu Chennai India Department of Mathematics S.A. Engineering College Tamil Nadu Chennai India Department of ECE Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Biomedical Engineering Jerusalem College of Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331515683

Brain tumor segmentation using CT scans is essential for diagnosis, therapy planning, and progression tracking. This study compares SVM and CNN methods for segmenting brain tumors in CT images. These methods will be assessed for tumor boundary accuracy. To assure uniformity and quality, this study employed preprocessed CT scans from brain tumor patients. CNN uses deep learning to automatically learn hierarchical features during training, while SVM uses handcrafted image features. SVM uses texture analysis, intensity thresholding, and shape descriptors to train the classifier to discriminate tumor and non-tumor regions. The CNN model learns features directly from raw pixel data, using deep convolutional layers' hierarchical nature to capture complex patterns and spatial correlations in images. Both models are split-sampled, trained, validated, and assessed for robustness and generalizability. Performance indicators including accuracy, sensitivity, specificity, precision, and Dice similarity coefficient quantify segmentation results. To examine segmentation results' clinical relevance, professional radiologists do qualitative assessments. Deep learning techniques can be used in medical image analysis, since the CNN model surpasses the SVM model in segmentation accuracy and resilience. SVM has lesser performance but better computing economy and interpretability, which may be useful in some therapeutic scenarios. This work sheds light on the pros and cons of SVM and CNN methods for brain tumor segmentation in CT images, helping physicians create more accurate and efficient tools. CNNs are excellent tools for this endeavor, but classical machine learning methods have a place, especially in cases where computational resources are restricted or model interpretability is important. © 2025 IEEE.

关键词： Image Processing Machine Learning Neural Networks pattern recognition Radiology Informatics

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MB-ZZLBP: Multiscale Block ZigZag Local Binary pattern for Face recognition 2nd

MB-ZZLBP: Multiscale Block ZigZag Local Binary Pattern for F...

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2nd international conference on Machine Learning, Advances in computing,Renewable Energy and communication, MARC 2020

作者： Karanwal, Shekhar Diwakar, Manoj Dehradun Uttarakhand India

ISBN: (纸本)9789811623530

This work presents the LBP variant so-called MB-ZZLBP under illumination and expression variations. In MB-ZZLBP, initially mean is computed for all the square sub-blocks (size 2 × 2) of the 6 × 6 pixel window. After the mean computation, 3 × 3 window (pixel) is produced. Then ZigZag pixels are compared with each other, or in other words, the higher-order pixels are subtracted from the lower-order pixels. Differences of those which produce value ≥0 are allocated the label 1, else 0. After encoding each pixel position (binary pattern) eventually results in MB-ZZLBP transformed image. The MB-ZZLBP image is further divided into 3 × 3 sub-regions for extraction of histograms. The fused histogram is the feature size of MB-ZZLBP. The proposed FR approach attains remarkable results on EYB and Faces94 databases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

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2nd international conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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The Conglomerate Compression Approach for Visual pattern recognition Networks in Edge AI Applications

The Conglomerate Compression Approach for Visual Pattern Rec...

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IEEE Punecon

作者： Seema Bhalgaonkar Mousami Munot Alwin Anuse Dept of E&TC SCTR's Pune Institute of Computer Technology & PES's MCOE SPPU Pune India Dept of E&TC SCTR's Pune Institute of Computer Technology SPPU Pune India School of ECE Dr. Vishwanath Karad MIT-WPU Pune India

Deep Neural Networks have shown remarkable performance in various applications of Visual pattern recognition (VPR). This field continues to grow due to emergence of new architectures, availability of huge data, and powerful computing resources. However, because of their high computational complexity that subsequently demands higher memory requirements, and power consumption, the deployment of such DNN-based Visual pattern recognition Networks (VPRN) models in edgeAI applications is hindered. Such overheads necessitate the compression of VPRN models while retaining their performance. This paper explores multiple compression techniques which combines Knowledge Distillation (KD), pruning and quantization that are applied to DNNs to achieve higher compression ratio.

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Identification of AI-Related Topic Evolution Based on Extraction of international conference Proceedings Titles 11

Identification of AI-Related Topic Evolution Based on Extrac...

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11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022

作者： Wibawa, Adhi Dharma Ramadhani, Prio Adi Utami, Ratna Purnama Technology Institute of Technology Sepuluh Nopember Deptartement of Medical Surabaya Indonesia

ISBN: (数字)9781665406482

ISBN: (纸本)9781665406482

international conference proceedings are a forum for researchers in various parts of the world to communicate their research findings to the scientific community and to the public in general. international conference proceedings published by globally recognized institutions are of unquestionable quality. Every conference must have a title that describes the theme or topic being discussed. This study wants to reveal the topic evolution by extracting conference titles. This study collects conference titles that contain terms related to artificial intelligence (AI) in them. Then the titles that have been collected are extracted to obtain a base noun phrase and filtered to eliminate uninformative terms. Of the 139,495 conference titles, it was obtained 8,133 titles containing topics related to AI. The results of the title extraction are 4,601 terms which are dominated by the exact term 'artificial intelligence', followed by the terms 'machine learning', 'applications', 'intelligent systems', 'semantic', 'pattern recognition' and other important related terms. Based on the glossary, frequency of occurrence, and year of publication, further identification of the evolution of a more specific topic is presented in this study. This study identified the early emergence and evolutionary phases of topics for specific terms such as natural language processing, cloud computing, and cybernetics or cyber security. Using the model proposed in this study, the process of identifying the evolution of specific topic can be extracted to get new insights. © 2022 IEEE.

关键词： Intelligent systems

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Utilizing Capsule Networks in Deep Learning for Traffic Sign Detection

Utilizing Capsule Networks in Deep Learning for Traffic Sign...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： R Giridharan P Mariush Rufin R A Navaneethan C Pranesh T Rahul Department of CSE Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Convolutional neural networks (CNNs) have been the predominant Current Advanced neural network techniques and Methods for Traffic Signal Classification. Despite their capabilities, image recognition models are restricted in their ability to capture aspects like pose, view, and orientation because of the limitations of the max pooling layer. A new approach has been introduced in this paper to identify German traffic signs utilizing capsule networks, which have displayed exceptional performance. Caps nets consist of capsules, which are clusters of neurons that encode object instantiation parameters such as position and alignment. This is achieved through algorithms for dynamic routing and agreement-based routing. In contrast to prior techniques that depended on manual feature extraction and the utilization of several complex neural networks with numerous parameters, our approach minimizes the necessity for manual parameter configuration and bolsters resilience against spatial variations. Moreover, CNNs are susceptible to various adversarial attacks, whereas capsule networks can effectively counter such intrusions, enhancing the dependability of traffic sign detection in autonomous vehicles.

关键词： Image recognition Heuristic algorithms Neural networks Neurons pattern classification Manuals Routing Vehicle dynamics Resilience Image classification

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Physical Reservoir computing for Interactive Estimation of Weight from Food Texture in 3D-Printed soft Matter in Picking Operations

Physical Reservoir Computing for Interactive Estimation of W...

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international conference on Information Fusion

作者： Rikuto Kawakami Moe Kakegawa Gianluca Inagawa Yorito Igeta Yuto Suzuki Jun Ogawa Hidemitsu Furukawa Dept. of Eng. Yamagata Univ. Yonezawa Japan Grad. Sch. of Sci. and Eng. Yamagata Univ. Yonezawa Japan

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper aims to improve the “Gel Biter,” a device that can simultaneously acquire chewing texture data from three different parts (the upper jaw, tongue, and lower jaw) composed of oral mimic end effectors with different softness (Young’s modulus), created using 3D printer technology, for use as a picking device. The Gel Biter utilizes physical reservoir computing, exploiting the deformation of the soft material in the oral model during chewing, to classify the texture of food materials with high accuracy. To apply this principle as a picking system, we examine whether it is possible to determine whether fried foods and fish roe have been cooked correctly and the extent of the weight being gripped based on texture. The results of the gripping object estimation experiments demonstrated that it is possible to distinguish between overcooked fried chicken and properly cooked fried chicken with $\mathbf{9 4 . 7 \%}$ accuracy, and to identify the weight difference of artificial salmon roe in $5 \mathrm{~g}, 10 \mathrm{~g}$, and 20 g increments with $\mathbf{9 4 . 5 \%}$ accuracy. These results suggest that the Gel Biter’s piezoelectric sensors through a physical reservoir computing system can determine information on weight and cooking status based on texture information.

关键词： Accuracy Tongue Three-dimensional displays Reservoir computing Estimation Sensor systems Safety Sensors Object recognition Standards

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Measurement of Imaging Indexes of Distal Radius based on deep Learning Key Point Detection 23

Measurement of Imaging Indexes of Distal Radius based on dee...

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9th international conference on computing and Artificial Intelligence, ICCAI 2023

作者： Jiachao, Niu Xin, Zhang Xufeng, Ling Yang, Jie Yin, Yong Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road China Department of Orthopedics Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences No.1 Chengbei Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

The measurement of imaging indexes of distal radius is the basic work of diagnosis and recovery evaluation. Due to the problems of large morphological differences and insufficient clarity in radial images, the accurate measurement of indexes relies heavily on the clinical experience of doctors, with defects such as low accuracy and poor consistency. In order to solve the above problems, this paper proposes an intelligent technology to achieve accurate measurement of imaging indicators of the distal radius. Firstly, 12 key points needed for distal imaging indexes were determined: radius left vertex, radius right vertex, radius left neck point, radius right neck point, radius left lower point, radius right lower point, ulna left vertex, ulna right vertex, ulna left neck point, right neck point, ulna left lower point, ulna right lower point. Then, the deep convolutional neural network was used to estimate the geometric transformation features and regress the thermal map of the feature points, so as to achieve accurate positioning of the 12 feature points. Finally, each imaging index was calculated accurately by the preset computational method. The experimental results show that the proposed measurement method has advantages of high accuracy, good consistency and fast speed. The test accuracy is higher than the manual measurement results of inexperienced doctors, and the network model has good robustness and generalization ability. This method has practical value and can be applied in the field of medical imaging to help doctors improve the efficiency of diagnosis. © 2023 Copyright held by the owner/author(s).

关键词： Diagnosis

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