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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Human Pose Estimation using Deep learning Techniques 22

Human Pose Estimation using Deep Learning Techniques

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Anant Grover Deepak Arora Anuj Grover Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus. India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus India

ISBN: (纸本)9781450399937

Human Pose Estimation is a method of detecting and classifying all the key human body joints. A 2D Human Pose Estimation method predicts the two-dimensional coordinates from an input image. Classical methods used deformable parts models for detecting body joints. this paper discusses the state-of-the-art Deep learning based approaches for estimating human poses. It can be implemented by either using the top-down or bottom-up technique for pose estimation. Here, the authors have studied the Convolutional Pose machines, Hourglass Network and MoveNet architectures for Pose Estimation. these architectures are compared against each other in terms of their efficiency by measuring the time taken by each of them and their accuracy by evaluating them using the Percentage of Detected Joints metric on the MPII dataset. the architectures were implemented by using TensorFlow and Open-Source libraries built using PyTorch. the inference results for each joint were compared individually for all the body joints. the Hourglass Network showed the best accuracy but is really slow. the MoveNet architecture is the second best according to accuracy but is very fast and can be used in real-time systems.

关键词： Deep learning

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An application of XGBoost algorithm for online transaction fraud detection based on improved sailfish optimizer

An application of XGBoost algorithm for online transaction f...

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international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Hongwei Chen Lun Chen School of Computer Science Hubei University of Technology Wuhan China

Fraud detection is relevant in this context because of the large scale of online transactions, which conceal a small number of fraudulent *** learning (ML) fraud detection models have numerous hyperparameters of the eXtreme Gradient Boosting (XGBoost) algorithm, which are important for the performance impact of fraud detection models. In this paper, the propose an improved version based on Sailfish optimizer(SFO) applied to online transaction fraud detection to optimize the hyperparameters of the XGBoost algorithm. Firstly, the optimization-seeking and local-optimal-skipping capabilities of Sailfish optimizer are improved, and Improved Sailfish optimizer (ISFO) is compared with other classical optimization algorithms, and the results show that the algorithm has outstanding. the Improved Sailfish optimizer is then used to optimize the parameters of XGBoost (ISFO-XGBoost) and compared with several other machine learning classifiers such as: Decision Tree (DT), Random Forest (RF), XGBoost, and several optimization algorithms optimized for the XGBoost model. To validate the performance, a fraud dataset of online transactions is used for evaluation. the experimental results demonstrate that the proposed improved algorithm is highly promising in solving machine learning hyperparameter optimization challenges, and the ISFO-XGBoost algorithm achieves the best results.

关键词： Radio frequency Measurement machine learning algorithms Hyperparameter optimization Fraud Classification algorithms Detection algorithms

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AI-Driven Alert Systems for an Intuitive Animal Monitoring System

AI-Driven Alert Systems for an Intuitive Animal Monitoring S...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： P. Latha Diana Inba Malar Y Hemavarthini C S Manjuparkavi V Sneha S Department of Computer Science and Engineering V.S.B. Engineering College Karur India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

All animals that can sight, including humans, do so in a way that appears instinctive and natural. Creatures use their vision system early in life often even from birth to traverse their surroundings, recognize and communicate with other creatures, and learn about their surroundings. Because of this, an animal's vision system is continuously being educated and modified to enable it to do a variety of activities. In humans, for instance, this system works by way of the optic nerve, which transmits light data from the eye to the brain for interpretation and processing. this sophisticated vision system enables us to focus on objects and see the world in three dimensions, all while being able to autonomously adjust to changing light conditions. It is possible to think of computer vision, digital image processing, and digital image analysis as a combination of phrases that are frequently used to refer to related operations. the main reason for this misconception is that they are related fields that arose with the advancement of digital picture capture. thus, it's important to comprehend how these fields relate to one another, how a digital image is created, and how the various sensors that are available each best suited for a particular application differ. Since the introduction of charge- coupled devices, which signaled the beginning of digital imaging, the field has developed very quickly. In this project, a camera that records the surroundings all day long will be used to periodically monitor the entire farm. We can identify when an animal is entering with the aid of a deep learning model, and then we play the right sounds to eject the animal.

关键词： Deep learning YOLO computer vision Spirals Animals Reviews Digital images machine vision Search problems Monitoring

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CNN-based Deep learning Approach for MRI-based Brain Tumor Detection

CNN-based Deep Learning Approach for MRI-based Brain Tumor D...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Kanagaraj Narayanasamy Elangovan Sathish Kumar L M Maragatharajan D. Deepa Department of Computer Science Karpagam Academy of Higher Education Coimbatore TamilNadu India Department of DSBS SRM Institute of Science and Technology Kattankulathur TamilNadu India School of Computing Science and Engineering VIT Bhopal University Kotrikalan MP India Department of CSE Kongu Engineering College Perundurai Tamil Nadu India

Conventional brain tumor prediction method is a time-consuming and inaccurate process that involves manually evaluating a large number of MRI (magnetic resonance imaging) scan pictures in search of signs of brain cancer. this might have an effect on the patient's medical care. It may take a long time to analyse a large volume of scan data sets comprising brain pictures. Normal brain tissue and brain cancer tissue seem quite similar under the microscope, making it difficult to distinguish between the two during segmentation. A very accurate automated method for detecting cancer is crucial. this research study has developed a novel method that uses an algorithm to analyse 2D MRI human brain scans and detect brain Tumors. Combining conventional classification algorithms with deep learning methods, the approach makes use of a convolutional neural network (CNN). the model was successfully trained using a dataset of magnetic resonance imaging (MRI) scan images, including Tumors of varied sizes, locations, and intensities. Here, a SVM classifier (Support Vector machine) and several activation methods are also used to check the results, including Softmax, RMSProp, and sigmoid. Our research showed that a CNN could attain an accuracy of 99.87%, which is higher than the present state-of-the-art findings. the CNN -based approach is meant to aid doctors in the precise identification of brain cancers in MRI scans. this innovation might greatly accelerate the pace at which patients are helped.

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Mental Health Analysis using Deep learning of Social Media Data gathered using Chrome Extension

Mental Health Analysis using Deep Learning of Social Media D...

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Emerging Technology (INCET), international conference for

作者： Manvita Joshi Chetashri Mahajan Traividya Korgaonkar Nataasha Raul Meghana Naik Computer Engineering Department Sardar Patel Institute of Technology Mumbai India Applied Sciences Mathematics and Humanities Department Sardar Patel Institute of Technology Mumbai India

Recently the topic of ’mental health’ has gained high awareness, especially among younger generations. the age group that mostly gets affected by mental disorders is between 15-49 years of age. Research conducted on different social media data has found that a person with mental disorders (such as depression, anxiety, etc) tends to use social media more as compared to a mentally healthy person. Most users convey their emotional states through posting on public platforms, tweeting and accordingly watching related videos on YouTube that best describes their current mental state. It is also found that there is a difference in users’ social media activities (on Facebook, Twitter, and YouTube) with a healthy mind and one with an unhealthy mind. A study conducted in 2018 found that 45% of teenagers worldwide are constantly using the internet. According to research, teenagers use the internet on average for about nine hours each day. the internet can prove to be the easiest medium to monitor a person’s online activities (such as posting on social media, writing emails, watching or liking YouTube videos, commenting on YouTube videos, etc). the aforementioned activities and the internet usage time of a person can help to understand his/her mental state. the user’s social media activity can be analysed for finding potential patterns in his emotions/mood. these patterns can be helpful in uncovering underlying mental health issues such as depression, stress disorders, PTSD, schizophrenia etc. these findings can be reported to the user’s therapist for their monitoring record. To predict the mental state of the user by analysing their social media activities, an initiative was taken to develop an application with the objective to forewarn its users of any negative patterns regarding the state of mind that they exhibit. this application makes use of the Bidirectional LSTM algorithm, a deep learning method to identify a user’s emotions (one among the 6 defined emotions) and stores

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Exploring the application of computer Intelligence Algorithms in Logistics and Supply Chain Management 22

Exploring the Application of Computer Intelligence Algorithm...

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proceedings of the 7th international conference on Intelligent Information Processing

作者： Mei-E Xie Lichen Qiao Wuhan Business University China Northeastern University China

ISBN: (纸本)9781450396714

Digital transformation and innovation are the current trend of the times, and computer intelligence algorithms will be an important supporting technology for a new generation of logistics industry. With the continuous development of artificial intelligence technology, the traditional system that originally relied on human beings to make empirical decisions is facing new challenges, and such a decision-making system is undergoing a transformation. the development of a series of technologies such as big data, cloud computing, machine learning and artificial intelligence has improved the ability of logistics data processing, and also helped logistics enterprises to analyze and predict customer behavior, and system equipment will gradually replace manual decision-making, ultimately achieve better cost reduction and efficiency. this paper firstly introduces the concept of artificial intelligence and supply chain management, related research results at home and abroad, the main impact of artificial intelligence on logistics industry, and analyzes the main types of computer intelligence algorithms, and studies the specific application of computer intelligence algorithms in logistics and supply chain management on the basis of the survey of enterprises.

关键词： Logistics Supply Chain Management computer Intelligent Algorithms

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Research and Design of Active Operation application Platform for Power Supply Stations 23

Research and Design of Active Operation Application Platform...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Yifan Hu Zhihao Shen Xiaojun Shen Jinxia Jiang Jiawen Zhan Information and Communication Branch State Grid Zhejiang Electric Power Co. Ltd. China

ISBN: (纸本)9798400716669

In order to implement the company's strategic deployment, continuously improve the service capacity of the power supply station, implement the national rural revitalization strategy, and solve the problem of incomplete informatization of on-site operations and services, this article proposes an active operation application platform based on the digital intelligent management platform of the power supply station, combined with the actual business needs on site. the paper first studies the overall application of computer applications and mobile operating terminals, and then designs the application architecture. the application functions are mainly aimed at low-voltage and medium voltage customers. Low-voltage includes 8 application functions such as low-voltage inspection, low-voltage defects, and low-voltage leakage, while medium voltage includes 8 application functions such as channel clearing, power supply protection services, and abnormal medium voltage line losses, the Technology roadmap and technical architecture of the platform are studied. the construction and application of the platform will effectively reduce the burden on the grassroots team, integrate and integrate, greatly improve the on-site operation and response capabilities of the station managers, and enhance customer satisfaction with electricity consumption.

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Acute Leukaemia Diagnosis Using Transfer learning on Resnet-50 22

Acute Leukaemia Diagnosis Using Transfer Learning on Resnet-...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Sunita Chand Virendra P. Vishwakarma USICT USICT GGSIPU India

ISBN: (纸本)9781450399937

this paper presents an automated model for leukaemia detection that is based on the computational power of a deep pre-trained model Resnet-50. the conventional manual method to detect the disease from microscopic blood cell images is time driven and the diagnosis is subjective due to the variation of technical expertise of the hematologists and may vary from one pathologist to other. Hence a model is proposed that exploits the transfer learning technique on Resnet-50 to learn the features of microscopic blood cell images from the Acute Lymphoblastic Leukemia Image Database for Image Processing (ALL-IDB1) to classify them into diseased and healthy. As the number of images in the dataset is very less for training on deep-network, the model may overfit. As a precautionary measure, augmentation of images is performed during the training. Apart from image augmentation, L2 regularization is also used to reduce overfitting. the proposed model demonstrates 100% accuracy on unseen test images with Resnet50. the comparison of the obtained results is done with state-of-the-art work performed by contemporary researchers.

关键词： Deep learning Classification Transfer learning Resnet-50 machine learning Acute lymphoblastic leukemia

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Detection of Brain Tumors Using Magnetic Resonance Images through the application of an Innovative Convolution Neural Network Model 22

Detection of Brain Tumors Using Magnetic Resonance Images th...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Sanjay Bhadu Patil Dnyandeo j Pete Department of Electronic Engineering. Datta Meghe College of Engineering India

ISBN: (纸本)9781450399937

According to a report released by the WHO in February 2018, the mortality rate for people with brain or central nervous system cancer is highest in Asia. It is important that cancer screenings are conducted earlier to prevent these deaths. Due to the complexity of brain cancer diagnosis, it is very important that the development of effective and non-invasive tools for analyzing and predicting the grade of the disease is carried out. Currently, there are various imaging modalities that can be used to detect brain tumors, such as CT, MRI, and X-rays. Deep learning is a type of artificial intelligence that imitates the brain's work. It can learn to recognize and interpret the voice, make decisions, and translate languages. It can also detect artifacts in data, and without human intervention, it can understand from unorganized information. A Convolutional Neural Network is a type of deep learning that is commonly used in optical representation analysis. Currently, there are systems that can detect brain tumors using small datasets. However, they only use image processing techniques and require a lot of computational resources. A new system that combines the three components of deep learning, namely image preprocessing, augmentation, and applying, is currently under development.

关键词： Brain tumor

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Caries Level Classification using Combination of Hu and Zernike Invariant Moments Features and machine learning

Caries Level Classification using Combination of Hu and Zern...

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international conference on Information and Communications Technology (ICOIACT)

作者： Yessi Jusman Devie Nur Aini Sartika Puspita Anna Nur Nazilah Chamim Department of Electrical Engineering Faculty of Engineering University of Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Oral Biology Dental School University of Muhammadiyah Yogyakarta Yogyakarta Indonesia

One of the most important parts of the human body is the teeth. In the human body, teeth are used as a digestive process and a beauty in the human body. thus, it is necessary to prevent dental disease so that the teeth can function properly. Dental caries is the most common dental disease in everyday life. With advances in technology, artificial intelligence systems or image processing can be used in the process of diagnosing dental caries images. A total of 4 classes of dental caries imagery were used in this study, namely classes 1, 2, 3, and 4. the dental caries image consisted of 347 which were divided into 314 training images and 33 test images. the purpose of this study is to design a computer-based dental caries level classification system with image processing that uses a combination of the Hu and Zernike Moment Invariant Method used for feature extraction and the machine learning Method (KNN) used for dental caries classification in the Matlab 2020a application. the results of the classification that has been carried out will obtain the KNN accuracy value. the highest accuracy is the KNN method with an accuracy of 94.07% when using the combination of Hu and Zernike Moment Invariant feature extraction algorithms.

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