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3D Human Posture Capture Algorithm Based on Artificial Intelligence 1

3D Human Posture Capture Algorithm Based on Artificial Intel...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Yang, Liu Hailati, Qiaka Wang, Tao Guo, Jiangtao Sun, Bowen State Grid Xinjiang Electric Power Co. Information and Telecommunication Company Urumqi China

ISBN: (纸本)9798350316469

3D human pose estimation is a research hotspot. The research on this technology can promote the development of other advanced artificial intelligence technologies based on computer vision, such as sports teaching such as music and dance, 3D stereoscopic movies, and human motion pattern recognition, all of which cannot do without the development of this technology. Currently, there are still many researchers in this research field who are researching and developing it. On the basis of current research by researchers, this article explored a convolutional neural network model with semi supervised learning to obtain 3D human body estimation including spatial positions. The proposed method can estimate the corresponding 3D pose skeleton model from the daily motion videos. After experimental testing, the results can confirm that the method proposed in this article is effective and can more accurately estimate the 3D pose of the corresponding video frame (the FPS of the model in this article was 1130 frames/second, which was higher than the model based on the LstM (Long Short Term Memory) algorithm by 950 frames/second, and the MPJPE value was 43.7mm, indicating that the error between the predicted joint position and the actual joint position based on the algorithm model in this article is minimal). © 2023 IEEE.

关键词： 3D Human Pose Capture Convolutional Neural Network image recognition Skeleton Model

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Topological Properties of Mouse Neuronal Populations in Fluorescence Microscopy images 1

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7th international conference on machine learning, Optimization, and Data Science (LOD) / 1st Symposium on Artificial Intelligence and Neuroscience (ACAIN)

作者： Zaleshina, Margarita Zaleshin, Alexander Moscow Inst Phys & Technol Moscow Russia Inst Higher Nervous Act & Neurophysiol Moscow Russia

ISBN: (数字)9783030954673

ISBN: (纸本)9783030954673;9783030954666

In this work, we processed sets of images obtained by the light-sheet fluorescence microscopy method. We selected different cell groups and determined areas occupied by ensembles of cell groups in mouse brain tissue. recognition of mouse neuronal populations was performed on the basis of visual properties of fluorescence-activated cells. Individual elements were selected based on their brightness in grayscale mode. Methods of spatial data processing were applied to identify border areas between ensembles and to calculate topological characteristics of cell groups. By applying cell statistics operations, we obtained the localization of the regions of interest, for subsequent identification of samples with specified topological characteristics. Based on the topological properties of the cell groups, we constructed training samples, and then used these to detect typical sets of ensembles in multi-page TIFF files with optogenetics datasets.

关键词： Mouse brain Optogenetics pattern recognition Brain mapping

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AI-Enable machine learning for Lung Cancer Prediction and Classification 1

AI-Enable Machine Learning for Lung Cancer Prediction and Cl...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Singh, Kamred Udham Kumar, Ankit Sinha, Anurag Kumar, Gaurav Singh, Teekam Pandey, Saroj Kumar School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Apllications UP Mathura India Amity University Department of Computer Science Ranchi India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330915

In comparison to other types of cancer, lung cancer has the highest fatality rate, making it one of the most dangerous illnesses in the world. In India, there are over 70,000 new cases recorded each year, which demonstrates a startlingly rapid growth in the frequency of the disease. Lung cancer's sneaky nature makes early detection difficult. Early lung cancer identification improves survival rates, making it a top priority. Technology is crucial for early detection since it is more reliable. Many scholars have proposed innovative concepts after extensive study. Computer-Aided Diagnosis (CAD) systems, which use computers to diagnose, are a major advancement in this field. These cutting-edge methods employ machine learning, deep learning, and image processing to predict malignant lung cancer risk. This article evaluates and compares technologies for early lung cancer detection and diagnosis. This category includes image segmentation, feature extraction, and classification algorithms. This essay examines these current technological options to help fight this life-threatening sickness. The article's ultimate goal is to improve the outlook for those who are at risk of acquiring lung cancer due to the sickness. © 2023 IEEE.

关键词： CAD Classification CT Scan Lung Cancer MRI PET Pre-processing ReLU

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection machine learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Convolutional Neural Networks for The Detection of Skin Diseases 1

Convolutional Neural Networks for The Detection of Skin Dise...

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1st IEEE international conference on Emerging Research in Computational Science, ICERCS 2023

作者： Sasikala, P. Mohanarathinam, A. Karpagam Academy of Higher Education Department of Ece Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Bme Tamilnadu Coimbatore India

ISBN: (纸本)9798350359756

The human body's largest organ is the skin. It weighs between six and nine pounds and has a size of about two square yards. The skin serves as a partition separating the body's internal organs from external environment. Dermocopy is a technique designed to help diagnose melanoma, a type of cancer that usually starts in melanocytes, the cells that give the skin its color. Skin spots are made more noticeable by photographing and brightening a piece of skin with dermocopy, a non-invasive skin imaging technique. Throughout the world, skin conditions are the most common disorders. Even though it's ubiquitous, recognizing it can be challenging and takes a high level of specialized knowledge. Skin photographs undergo processing to improve their quality and eliminate extraneous noise. The diagnosis report's generation, the photos' classification via the SoftMax classifier algorithm, and the feature extraction procedure utilizing cutting-edge techniques like Convolutional Neural Networks (CNN). The second stage uses algorithms to identify diseases based on the histological features discovered during skin testing. One of the most intricate, unpredictable, and challenging medical specialties to diagnose is dermatology. Comprehensive testing is required in dermatology to determine the potential skin problem a patient may be experiencing on a regular basis. The time may vary depending on the practitioner. This is also based on the experience of that particular person. As a result, a system that can diagnose skin conditions without these restrictions is needed. We propose an automated image-based method that classifies skin disorders using machine learning. Using computational approaches, this system will process, assess, and relegate the picture data based on various features of the photos. Skin photographs are filtered to remove unnecessary noise and processed to enhance the image quality. Create a diagnosis report by extracting features from the image using advanced techniques

关键词： machine learning

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image Sensor—CCD and CMOS 1st

Image Sensor—CCD and CMOS

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1st international conference on Microelectronics, Communication Systems, machine learning, and Internet of Things, MCMI 2020

作者： Tejas, R. Macherla, Pavan Shylashree, N. Department of Electronics and Communication Engineering RV College of Engineering Bengaluru560059 India

ISBN: (纸本)9789811919053

In recent times, the image sensor has become a crucial part of smartphones and have always been critical part of professional cameras, telescopes used in the astronomical observatory and space telescopes. The development of the images sensor in the present day is much particular to the application which it is used for. The image sensor are of two types CCD image sensor and CMOS image sensor. The CMOS image sensor is favoured over the CCD sensor due to the increased speed of processing and comparatively, good pixel size the CMOS sensor is having a fill factor of 30–60% with HDR capabilities and 0.37–0.4 μm technology. The below paper gives the types of an image sensor, the individual architecture of the image sensor and the applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Charge coupled devices

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Improving Ovarian Cancer Diagnosis with Deep learning by Identification of Subtype Using VGG19 CNN Pre-Trained Model 1

Improving Ovarian Cancer Diagnosis with Deep Learning by Ide...

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1st IEEE international conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Jain, Eshika Gill, Kanwarpartap Singh Malhotra, Sonal Devliyal, Swati Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350377002

The construction of a strong machine learning model for accurate categorization of different ovarian cancer subtypes is the main goal, approaches, results, and implications of this research work, which are summarized in the abstract. The main objective was to precisely detect subtypes including endometrioid cancer (EC), clear cell carcinoma (CC), mucinous carcinoma (MC), high-grade serous carcinoma (HGSC), and low-grade serous carcinoma (LGSC) by using the deep convolutional neural network architecture of the VGG19. Using cutting edge feature extraction methods and image processing algorithms, the model was trained on a dataset of histology photos of samples of ovarian tissues. The 75% total accuracy obtained indicates how well the suggested approach works for subtype identification. The model's classification abilities over a wide range of subtypes were evaluated thoroughly, including accuracy, recall, and F1-score. The paper not only summarizes the model's accomplishments but also clarifies possible future directions for research, as well as its advantages and disadvantages. By proving the capacity to distinguish between subgroups of ovarian cancer, the study makes a substantial contribution to the field and provides insightful information that may improve diagnostic accuracy and, in turn, the development of customized treatment plans. The growing tendency in using computational methods, especially machine learning, for improved accuracy in the detection and treatment of ovarian cancer is highlighted in the abstract's conclusion. All things considered, the abstract calls readers to explore the specifics of the study by providing a succinct and educational summary of it. © 2024 IEEE.

关键词： Convolutional neural networks

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AI and Big Data for the management of COVID-19 1

AI and Big Data for the management of COVID-19

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1st international conference on Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition, ICRSEtoSET 2023

作者： El Kayaly, Dina Elhady, Sherifa Hazem, Nahla Ismail, Tawfik Fahim, Irene Samy New Giza University International Business Cairo Egypt Nile University Sesc Research Center Sheikh Zayed Giza Egypt Nile University Management of Technology Sheikh Zayed Giza Egypt Giza Egypt

ISBN: (纸本)9798350346336

In March 2020, World Health Organization (WHO) recognized COVID-19 as a pandemic and urged governments to exert maximum efforts to prevent its spreading through political decisions together with public awareness campaigns positively impacting personal behaviors. Moreover, the WHO recommends collecting facts and data from reliable sources to help accurately determine the risks, accordingly governments take reasonable precautions. There are several ways to fight against corona such as accelerating research for the doctors, scientists and organizations working to find a vaccine or a medicine to defeat the COVID-19 virus, cleaning and sterilizing facilities, ensure health and productivity of people while changing their workplace, provide supercomputers to fight the virus. Artificial Intelligence (AI) possesses remarkable potency in its capacity to assist in combating the COVID-19 pandemic. This is achieved through a diverse range of methodologies such as machine learning, Natural Language processing, and Computer Vision applications. By instructing computers to effectively employ models based on extensive data sets, the objective of pattern recognition, explication, and prognostication is pursued [1]. These techniques will generate knowledge that can be useful in diagnosing, predicting, and treating COVID-19. [2]-[3] AI can also help in detecting patterns that help us to manage COVID 19 socio-economic impacts [4]. Since the outbreak of the pandemic, there has been a scramble to use AI. This article aims to overview the possible applications of AI and Big Data in facing COVID 19 pandemic. Four possible applications are identified, namely effective alert helping in monitoring the outbreaks instantaneously;diagnostic cases of COVID 19 and tailor medication;facilitating the implementation of Public Health interventions and resource optimizations;and Building cities with smart healthcare services. © 2023 IEEE.

关键词： COVID-19

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2022 1st international conference on Computational Science and Technology, ICCst 2022 - Proceedings

2022 1st International Conference on Computational Science a...

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1st international conference on Computational Science and Technology, ICCst 2022

ISBN: (纸本)9781665476553

The proceedings contain 214 papers. The topics discussed include: disease detection based on nail color analysis using image processing;various algorithms and techniques for traffic density estimation;determination of gout disease using machine learning;detection of emotions using a boosted machine learning approach;predicting Parkinson’s disease progression using machine learning ensemble methods;quantified symptomatic analysis of psychological disorders;sentiment analysis for headlines categorization in newspaper industry Malayala Manorama company limited;flood prediction in the area of Tamil Nadu and Andhra Pradesh using machine learning technique;hippocampus region's volume-based Alzheimer’s stages detection using a deep learning model;a IoT based residential smart energy meter with power saving methodology;adoption of extended reality as a teaching tool;a comprehensive analysis of techniques used for dimensional reduction of HSI images;and an analysis comparing traditional and digital marketing (advertising) using chi square test and linear regression model.

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A Comprehensive study of machine learning Algorithms for Date Fruit Genotype Classification 1

A Comprehensive Study of Machine Learning Algorithms for Dat...

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1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Subbarao, M Venkata Rani, U Leela Soujanya Sindhu, J.T.S. Kumar, G Prasanna Ravuri, Viswanadham Silpa, N. Shri Vishnu Engineering College for Women Department of ECE Bhimavaram India Vishnu Institute of Technology Department of ECE Bhimavaram India BVRIT Hyderabad College of Engineering for Women Department of ECE Hyderabad India

ISBN: (纸本)9798350323795

There are over 200 distinct date fruit varieties around the globe. Physical characteristics such as size and structure (collectively known as morphological attributes), colour, and other shape characteristics are used to manually determine their class of genome type. Date fruit processing industries require expert systems to determine the type of a fruit based on its exterior without a great deal of effort and time-consuming information. This study aims to identify the type of date fruit among the seven varieties Sukkary, Ruthana, Barhee, Safawi, Deglet Nour, Sagai, and Rotab Mozafati, which are popularly cultivated in Turkey and other Middle Eastern countries. Using image processing techniques, a total of 34 attributes were extracted from each date fruit, including morphological (12), shape (4), and colour (18). Performance analysis of machine learning models is investigated with the different sets of extracted features to know the superior classifier. Analysis is carried out with individual sets of features and combinations of features. © 2023 IEEE.

关键词： Color

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