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Image vision-Based Pull-Up Recognition 3

Image Vision-Based Pull-Up Recognition

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3rd international conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Ke, Mingyan Tang, Haoran Zhu, Huanan Li, Mo Beijing University of Information Science and Technology Changping Beijing China

ISBN: (纸本)9798350316674

With the accelerated pace of modern life and increased work pressure, people tend to live in an extremely depressing and lack of exercise environment, and diseases and various physical health conditions come one after another. Therefore, this project team plans to use the Kinect sensor launched by Microsoft to achieve the pull-up test automatically, and apply it to the general public. This project is based on image vision to identify and analyse pull-ups, using Kinect V2 to interact with LabVIEW, and extracting and identifying human bones through Kinect and analysing the data information. When LabVIEW receives the coordinate data of each bone point of the user, it uses the coordinate height comparison between the head and the crossbar and the size of the bending degree of the arm to judge the counting. And levels have been added so that users get a workout while experiencing the fun of getting through the levels. © 2023 IEEE.

关键词： Kinect V2 LabVIEW pull-up recognition skeleton recognition

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Analysis of Moroccan Arabic Speech Recognition for Integration in Voice Command Systems 3

Analysis of Moroccan Arabic Speech Recognition for Integrati...

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3rd international conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Ezzine, Abderrahim Laaidi, Naouar Satori, Hassan USMBA University FSDM LISAC Laboratory Departement of Computer Sciences Fez Morocco

ISBN: (纸本)9798331522056

Automatic speech recognition (ASR) systems transform acoustic waveforms into corresponding text representations, effectively capturing the semantic content conveyed by speakers. This paper presents the development of an ASR system tailored to Moroccan Arabic (Darija) for recognizing connected words in command-And-control scenarios. The system was trained on a dataset consisting of 2,394 words, each repeated 10 times, articulated by 19 speakers (both male and female) in Moroccan Arabic. It leverages the CMU Sphinx toolkit and employs Hidden Markov Models (HMMs) integrated with Gaussian Mixture Models (GMMs) for acoustic modeling. Mel-Frequency Cepstral Coefficients (MFCCs) were used for feature extraction. Experimental results demonstrate a recognition accuracy of9 3. 3 3% for connected words, achieved using a configuration with four Gaussian mixtures. © 2024 IEEE.

关键词： Stochastic models

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Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software Defined Internet of Things (SD-IoT) 3

Regularized Hybrid Deep Learning for DDoS Attack Prediction ...

引用

3rd international conference on Power, control and Computing Technologies, ICPC2T 2024

作者： Deepthy, J. Radhika, N. Denny, Alosh Department of Electronics & Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Ernakulam Cochin University of Science and Technology Division of Computer Science and Engineering India

ISBN: (纸本)9798350349207

The availability of online resource is seriously threatened by the fatal DDoS (Distributed Denial of Service) attacks, which can earnestly break up a targeted network or website by flooding it with traffic. DDoS attacks can use a variety of tactics and can be launched from different points around the world, making it difficult to predict when they will occur. In order to improve accuracy by minimizing overfitting of data in CNN, this work proposes an efficient method by combining CNN with Bi-LSTM for predicting DDoS attacks based on a modified loss function with regularization. We test our method on the CICDDOS2019 dataset of network traffic and demonstrate that it can accurately and successfully predict DDoS attacks with an accuracy of 99.8% and an F-score of 97.29%. © 2024 IEEE.

关键词： Denial-of-service attack

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Accident Emergency Alert System using Deep Learning 3

Accident Emergency Alert System using Deep Learning

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Bhatia, Nidhi Dixit, Yash Balamurugan, K.M. Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665462167

An accident is an unexpected and unwanted event. One of the primary causes of car accidents is speed. Many lives could be saved if rescue services received accident information and responds to it quickly. Human lives became simpler with the emergence of technology and infrastructure. Given the concerning growth in the number of accidents occurring in India, this method ensures that authorities are notified prior to an accident or at the moment of the event. The advancement of technology has also resulted in an increase in traffic dangers and road accidents which result in huge loss of life and property due to inadequate emergency services. The failure to receive timely care is the leading cause of mortality in traffic accidents accounting for half of all fatalities. Since, after an accident every second counts and immediate action is required to avoid the death of a person, intelligent transportation systems have lately evolved as a powerful means of improving the interpretation of transportation systems and promoting travel safety. One of the most effective technologies is accident detection systems so that the rate of death in road accidents can be decreased by treating the victims as soon as possible. This article is based on computer vision which is an emergency alert system built on python which uses object tracking techniques to detect the occurrence of an accident. An efficient automatic accident detection system featuring automated reporting of the accident site to emergency responders is critical for saving valuable human life. This system intends to identify accidents beforehand and communicate the information to emergency responders in order to give rapid aid to the wounded individual. The research work purpose is to determine the severity of an accident and to warn the rescue crew in time. © 2023 IEEE.

关键词： computer vision

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A Simple and Efficient Deep Learning-Based Framework for Vegetable Recognition 3

A Simple and Efficient Deep Learning-Based Framework for Veg...

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3rd international conference on Image Processing and Intelligent control, IPIC 2023

作者： Gong, Xian The University of Adelaide Computer Science Adelaide Australia

ISBN: (纸本)9781510668140

Since the 21st century, artificial intelligence has been continuously evolving in various fields, particularly in agriculture. Vegetables, as a critical component of agriculture and human diets, have always been a focal point in terms of cultivation, production, and sales. Compared to traditional vegetable classification that requires professional knowledge and experience, AI technology utilizes computer vision to achieve automated classification. This study presents a deep learning-based vegetable recognition system aimed at automating the identification and classification of vegetables. The system utilizes a convolutional neural network (CNN) as its fundamental algorithm, integrating the traditional CNN architecture, which comprises convolutional layers, pooling layers, and fully connected layers. In comparison to other vegetable recognition systems on the market, this system utilizes a simpler architecture for processing and classifying vegetable images, significantly improving the accuracy and compatibility of identification. The research steps comprise data collection, image preprocessing, model training, and model testing. Experimental results demonstrate that the system can rapidly and accurately identify and classify various vegetables, with an average accuracy rate exceeding 95% on the test dataset, showcasing high practical value. © 2023 SPIE.

关键词： Vegetables

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Identification of Different Types of Buffalo Using YOLO Algorithm Based on computer vision 3

Identification of Different Types of Buffalo Using YOLO Algo...

引用

3rd international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Butu, Joshua Reska Nurtanio, Ingrid Paundu, Ady Wahyudi Hasanuddin University Departement of Informatics Makassar Indonesia

ISBN: (纸本)9798350394559

The purpose of this research is to create a system that can distinguish and correctly label different types of black and white buffaloes, such as Bonga, Saleko, Lotongboko, and buffaloes that are not black and white or other than their species. This research uses classification methods in pattern recognition, training a model to distinguish the different characteristics of each type of buffalo. This model was then used to classify the buffalo species in the images using the YOLO (You Only Look Once) algorithm. The training involved 40 learning iterations. The dataset consisted of 497 buffalo images, each measuring 640 x 640 pixels, which were divided into 442 images for training and 55 images for validation. By the 40th epoch, the model had achieved an overall mAP50 value of 0.959 and mAP50-95 of 0.825. The research shows that the buffalo species detection and classification system achieved optimal performance, demonstrating the effectiveness of the YOLO algorithm in computer vision, achieving 98% accuracy in the identification and labeling of different buffalo species using image analysis and pattern recognition techniques, while the increasing mAP value indicates that the model is still learning and improving its performance with each additional epoch. This study demonstrates the very high accuracy and potential convergence of the model to optimal performance. © 2023 IEEE.

关键词： computer vision

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Effects of Face Image Degradation on Recognition with vision Transformers: Review and Case Study 3

Effects of Face Image Degradation on Recognition with Vision...

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3rd international conference on Computing and Information Technology, ICCIT 2023

作者： Al-Otaiby, Nehal El-Alfy, El-Sayed M. Information and Computer Science Department Dhahran31261 Saudi Arabia Information and Computer Science Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350321487

vision Transformers have been recently introduced and achieved great success in the last two years beating convolutional neural networks (CNN) in many computer vision applications including Face Transformer for face recognition. However, one of the rising concerns is how sensitive these models are to various types of image degradations, especially when used for biometric authentication and person identification. This research aims at reviewing related work and presenting a case study to investigate the robustness of a fine-tuned version of Face Transformer under different pose, age, and gender variations for several degradations such as noise, blur, and resolution reduction. Several experiments have been conducted to train and test the model using two benchmark datasets (VGGFace2 and LFW) and benchmark its performance with FaceNet. © 2023 IEEE.

关键词： Face recognition

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Facial Skin Analysis for Detection of Dark Circles and Acne 3

Facial Skin Analysis for Detection of Dark Circles and Acne

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Kiran, M.K. Meda Ravi, Kusum Umadevi, V. B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665462167

The aim of this research is to analyze and detect the most commonly found facial skin conditions like acne vulgaris and dark circles. In the skin analysis algorithm, the occurrences of facial acne and dark circles are detected by using existing computer vision algorithms such as Otsu's thresholding algorithm and Deep Learning (DL) techniques. These techniques are further modified to suit the prepared dataset and achieve greater value of evaluation metrics. This article proposes two techniques for the detection of dark circles, which are the difference in Skin Tone Pixel Values and the Thresholding Technique and compare their performance. In the Thresholding Technique, the IoU obtained was 0.737, which provided better visualization of the affected region. Further, acne detection was carried out using two deep learning backbones viz, Inception ResNet 50 and MobileNet. The accuracy obtained for both the methods was 99%. © 2023 IEEE.

关键词： Deep learning

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Crossroad accident prevention system using real-time depth sensing 3

Crossroad accident prevention system using real-time depth s...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Veera, Dhairya Shekokar, Narendra Dwarkadas J. Sanghvi College of Engineering Computer Engineering Department Mumbai India

ISBN: (纸本)9798350348347

Road accidents have reached an all-time high in today's fast-paced world. Side-impact collisions and side-swipes are the most common types of road accidents. These accidents usually happen at intersections with little traffic and no traffic lights. Cars approaching the crossroads from different directions cannot see each other and may approach at high speeds. As a result, when the drivers notice the car at the crossroads, it is too late to apply the brakes, and an accident is unavoidable. As a result, to address such a life-threatening issue, we devised a strong solution. The process begins with the installation of a traffic signal-like pole that will support the main component. The main component consists of four signal lights and four cameras, each pointing the four crossroads directions. Real-time vehicle detection will take place using Machine Learning and computer vision, which will calculate both the distance of the vehicle from the crossroad and the speed with which it is approaching. When a car approaches the crossroads speedily, the lights on the other roads turn red, alerting vehicles on the other roads about the approaching vehicle. The signal will remain green if all roads are empty. © 2023 IEEE.

关键词： Object detection

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Analysis and research on acceleration measurement methods for indoor large length standard devices 3

Analysis and research on acceleration measurement methods fo...

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3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Hu, Changan Zhou, Chengyi Li, Xingxing Li, Li Lv, Fei Hu, Baoquan National Institute of Measurement and Testing Technology Chengdu610021 China School of Computer Science Chengdu Normal University Chengdu611130 China School of Engineering Xi’an International University Xi’an710077 China

ISBN: (纸本)9781510681880

The indoor long length standard device is an important large-size measuring standard device, which has an acceleration and deceleration process during the starting and stopping stages of the slide. Studying the acceleration of the indoor long length standard device is of great physical significance for understanding its operating rules. In the article, a laser tracker was used to study the acceleration of indoor large length standards. Through data analysis using a laser tracker at equal sampling frequencies of 50Hz and 100Hz, some conclusions were drawn as follows: the acceleration value significantly increases when the measurement frequency of the laser tracker is increased. The maximum acceleration at 50Hz is 579.75 mm/s2, and the maximum acceleration at 100Hz is 920 mm/s2. After the acceleration phase is completed, the sliding table enters a constant speed phase, and the data oscillates in a zigzag pattern with zero values. After the deceleration phase is completed, the acceleration value of the sliding table steadily approaches zero. © 2024 SPIE.

关键词： Deceleration

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