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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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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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Handwritten Mathematical Expression recognition: An approach on data augmentation 15

Handwritten Mathematical Expression Recognition: An approach...

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15th international conference on Advanced computing and Applications (ACOMP)

作者： Khanh-Ngoc Bui Quoc-Kim-Hoang Nguyen Thanh-Sach Le Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam

ISBN: (纸本)9781665406390

In this paper, we propose an approach for generating Mathematical Expression (ME) images from the CROHME dataset. Our approach employs two methods. The first method transforms original ME images which belong to the CROHME dataset by geometric transformations. The second generates new ME images based on the dictionary of character patterns collected from the CROHME dataset. The generated ME images follow rules of math form. Based on the combination of both two methods, we introduce a much larger dataset for handwritten math expression recognition problem compared to original CROHME. That is the main contribution of this paper. To evaluate, we employ a sequential system containing a module for object detection- Single Shot MultiBox Detector (SSD)- and a module for parsing SSD's outcome into DT EX string- DRACULAE and focus on improving the detector. We trained and evaluated the system on CROHME 2013 training set combined with and without our own generated dataset to point out the impact of our generative approach. The experimental results indicate that the detector achieves 52.57% on mAP instead of 36.98% if we do not use added dataset.

关键词： Handwritten Mathematical Expression recognition pattern Generation SSD DRACULAE

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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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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

2021 2nd International Symposium on Automation, Information ...

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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

The proceedings contain 136 papers. The topics discussed include: a comparative study on part-of-speech taggers' performance on examination questions classification according to bloom's taxonomy;pattern inversion as a pattern recognition method for machine learning;development of a face identification system's prototype;early estimation of daily reference evapotranspiration using machine learning techniques for efficient management of irrigation water;application of algorithm of fuzzy rule conclusions in determination of animal's diseases;using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach;defect detection in the textile industry using image-based machine learning methods: a brief review;offloading decision making for workflow applications using an enhanced genetic algorithm in the edge environment;application of machine learning approaches in health care sector to the diagnosis of breast cancer;anomaly detection and analysis based on deep autoencoder for the storage tank liquid level of oil depot;research on knowledge transfer on multilayer networks based on link prediction algorithm;stock market prediction based on time-frequency analysis and convolutional neural network;and modality-invariant and -specific representations with crossmodal transformer for multimodal sentiment analysis.

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Security Enhanced Smart Hospital Management System achieved through SELSTM Technique

Security Enhanced Smart Hospital Management System achieved ...

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Artificial Intelligence Trends and pattern recognition (ICAITPR), international conference on

作者： S.Faizal Mukthar Hussain R. Karthikeyan S.Ramamoorthi I.Sheik Arafat N.Ahamed Hussain Asif Department of CSE Mohamed Sathak Engineering College India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of EEE Mohamed Sathak Engineering College India

ISBN: (数字)9798350368000

ISBN: (纸本)9798350368017

Environmental changes grow unexpectedly due to that it creates lot of health issues for the human being. Day by day patient’s numbers is getting increased. Many of them affected by the illness like COPD, COVID 19 etc. Situation leads to on demand health care service as it is very risky to carry those patients via hospital vehicle and also, we have the problem of other patients may affected by the COVID 19 patients. Here we are proposing enhanced secure virtual hospital that will handle the patients through online resources. And also, it allows patients to get more clarity about their health from their place itself. SHA-512, ECC and LSTM is suggested for secure virtual hospital as patients has to monitored based on the parameters like heartbeat rate, Blood pressure etc.

关键词： COVID-19 Hospitals Heart beat Market research Chronic obstructive pulmonary disease pattern recognition Security Biomedical monitoring Long short term memory Monitoring

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OCF-Layout2Img: Object-Wise Constraint Free Layout to Image Generation 23

OCF-Layout2Img: Object-Wise Constraint Free Layout to Image ...

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15th international conference on Digital Image Processing, ICDIP 2023

作者： Lin, Xiao Liu, Bin He, Zhifen Wang, Kang Li, Bo School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center China

ISBN: (纸本)9798400708237

Complex scene generation is an important and challenging image synthesis task. Though latent space based conditional generative methods get impressive results, the accurate locating of objects for more detailed situation and editable contiguous synthesis are still a snag. In addition, previous methods with spatial awareness are easy to lose the location information after a series fusion operation. Focusing on such kind of problems, in this paper we propose an object-wise constraint free and location-lossless way of layout embedding by combining VAE and recent high performance VQGAN to achieve a location accurate and stable synthesis for layouts of complex scene. Instead of conditioning on layout, we apply a pretrained VQGAN to define the latent space and project every region of layout to image latent space directly by aligning their distribution with a novel light Cross-VAE structure. Then the quantization and decoder of VQGAN are reused for synthesis. This VAE plugged into VQGAN framework, which is called Layout-DVGAN in this paper. Experimental results demonstrate our method has competitive performance compared with the approaches which applied explicit spatial awareness way. © 2023 ACM.

关键词： Location

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