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Deep Learning Based Image Analysis for Classification of Foot and Mouth Disease in Cattle

Deep Learning Based Image Analysis for Classification of Foo...

引用

international conference on Inventive Research in computing Applications (ICIRCA)

作者： R. Newlin Shebiah S. Arivazhagan Department of Electronics and Communication Engineering Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Sivakasi Tamilnadu India

Foot and mouth disease (FMD) poses a substantial threat to the global cattle industry, as it is a highly contagious viral infection. Timely detection and precise classification of FMD in cattle are essential for effective disease management and control. The proposed method harnesses the power of convolutional neural networks (CNNs) to automatically extract distinguishing features from cattle images and classify whether the cattle is diseased or not. Furthermore, a pre-trained architecture such as MobileNetV2 is employed and fine-tuned for FMD classification. Experiments were performed to assess the effectiveness of the proposed approach, taking into account metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate the efficacy of proposed deep learning-based classification approach, achieving impressive accuracy rates in differentiating between healthy and FMD-infected cattle.

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User Relationship Discovery Based on Telecom Data

User Relationship Discovery Based on Telecom Data

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yue Wang Wei Gao Xinzhou Cheng Xin Wang Lexi Xu Siwei Wang Yuanguang Wang Fanyu Meng Kunyan Li China Unicom Research Institute China Unicom Beijing China Full Customer Operation Center China Unicom Beijing China Science and Technology Innovation Department China Unicom Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the improvement of the mobile communication market, the smart home business of telecom operators is showing unprecedented development and contribution. With the full-service competition in the domestic telecommunications industry, operators are facing increasing competitive pressure. The home service has become one of the important competitions for operators. In order to solve the user’s relationship recognition problem, this paper compares five commonly used machine learning methods: logistic regression, decision tree, random forest, LightGBM and multi-layer perceptron. Based the experiments on the data set, the results demonstrate the efficient of the LightGBM method. Finally, this paper selects the LightGBM method as the technical method for user relationship recognition in the business.

关键词： Privacy Logistic regression soft sensors Smart homes Data models Telecommunications Decision trees

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Enhancing Weld Quality Assessment Using a Comprehensive Study on Combined Thermal and Residual Stress Through Lenet pattern recognition

Enhancing Weld Quality Assessment Using a Comprehensive Stud...

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international conference on computing and Networking Technology (ICCNT)

作者： G. Deepana V. L. Raja A. Parvathi Priya J. Geetha Priya M Rajendiran M. Dinesh Babu Department of Civil Engineering S.A. Engineering College Thiruverkaadu Chennai Tamilnadu India Department of Mechanical Engineering Loyola Institute of Technology Chennai Tamilnadu India Department of Chemistry R. M. K. Engineering College Kavaraipettai Tamilnadu India Department of CSE R.M.D. Engineering College Kavaraipettai Tamilnadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamilnadu India Department of Mechanical Engineering Raja lakshmi Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study aims to conduct a thorough examination of improving the evaluation of weld quality. This will be achieved by utilizing a mix of thermal and residual stress measurement, along with a pattern recognition approach employing the LeNet neural network architecture. The primary aim of this study is to provide a reliable and precise approach for assessing the quality of welds. In order to enhance the evaluation of weld quality, we propose the utilization of the LeNet convolutional neural network, which is a deep learning framework specifically designed for problems involving image identification. Photographs of weld cross-sections are analyzed using the network, which lets visual patterns of both thermal and residual stress distributions be captured. The findings of our research indicate a notable enhancement in the precision of weld quality evaluation when compared to conventional approaches. We use thermal and residual stress data along with deep learning techniques to make our method much better at finding and classifying defects. The proposed methodology exhibits significant potential for enhancing the overall safety and performance of welded structures across many sectors.

关键词： Measurement Deep learning Accuracy Welding Neural networks Feature extraction Residual stresses Convolutional neural networks Thermal stresses Convergence

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Advanced Student Monitoring System for Physical Classes Using pattern recognition and Behavioral Analysis

Advanced Student Monitoring System for Physical Classes Usin...

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Advancements in computing (ICAC), international conference on

作者： R.K.A.R Rathnayaka Mallawaraarachchi S. M. A. H.G.N.D. Wijesiriwardana S.S.A Perera Jeewaka Perera Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research aims to develop an intelligent classroom system that utilizes Computer Vision and Deep Learning to measure students' concentration levels and performance, enabling teachers to gain insight into overall performance and adjust their teaching accordingly. The main goal of this research is to develop a precise way of tracking student concentration level and automate attendance marking in physical classroom. The proposed system consists of four components. To take attendance, it utilizes OpenCV and dlib for precise face recognition and eye gaze tracking to measure student focus levels. In the emotion analysis component, it employs DeepFace, dlib, and MediaPipe to identify students' emotional states and behaviors. For improving low-resolution video footage, the low-resolution enhancement component combines dlib and ESRGANG. In the concentration level component, Convolutional Neural Networks (CNN) are used to build a precise model for assessing students' concentration levels. The implemented system offers real-time attendance tracking, emotional and behavioral insights, and updates on students' concentration levels. It improves the detection of students at the back of the classroom using low-resolution footage. These features support educators in identifying students who need assistance, adjusting teaching methods, and creating a more engaging learning environment, ultimately enhancing classroom performance.

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Local Neighborhood Average pattern: A Handcrafted Feature Descriptor for Hand Gesture recognition

Local Neighborhood Average Pattern: A Handcrafted Feature De...

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international conference on Secure Cyber computing and Communication (ICSCCC)

作者： Arti Bahuguna Shalom Ben Tenzing Namchyo Deepak Kumar Chaudhary Gopa Bhaumik Mahesh Chandra Govil Computer Science and Engineering National Institute of Technology Sikkim Sikkim India Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India

This paper proposes a handcrafted feature-based descriptor namely Local neighborhood average pattern (LNAP) for static hand gesture recognition. The fact, that the local descriptors are important in numerous computer vision applications and show great performance cannot be overstated. This is accentuated further in difficult environments. This is the major driving force behind continued research in this field. We developed a feature descriptor LNAP in this paper, with the aim of extracting complete microstructural features achieved by evaluating excitation in different ways and directional relevant data based on connections between pixels taken at various spatial configurations inside each 3 × 3 neighborhood. An LNAP descriptor represents the structure of hand textures in a simple and compact coding manner, resulting in its unique code in less time and memory than existing approaches. The proposed LNAP descriptor extracts the dominant features from the hand region which are further classified using SVM. The proposed LNAP's performance is assessed using three benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, and Ouhands Dataset in respect of accuracy and F1-score and found to achieve an accuracy of 93% (MUGD Set1), 96% (MUGD Set2), 92 % (MUGD Set 3), 89% (MUGD Set4), 71% (MUGD Set5), 99%, and 47% respectively. The experimental findings show that the suggested LNAP produces better outcomes than existing techniques.

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

20th International Conference on Artificial Intelligence and...

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

ISBN: (纸本)9783030878962

The proceedings contain 90 papers. The special focus in this conference is on Artificial Intelligence and soft computing. The topics include: Efficient Recurrent Neural Network Architecture for Musical Style Transfer;Impact of ELM Parameters and Investment Horizon for Currency Exchange Prediction;spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks;possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks;convolutional Autoencoder Based Textile Defect Detection Under Unconstrained Setting;clustering-Based Adaptive Self-Organizing Map;applying Machine Learning Techniques to Identify Damaged Potatoes;Quantifying the Severity of Common Rust in Maize Using Mask R-CNN;federated Learning Model with Augmentation and Samples Exchange Mechanism;factor Augmented Artificial Neural Network vs Deep Learning for Forecasting Global Liquidity Dynamics;flexible Data Augmentation in Off-Policy Reinforcement Learning;polynomial Neural Forms Using Feedforward Neural Networks for Solving Differential Equations;quantum-Hybrid Neural Vector Quantization – A Mathematical Approach;A Graphic CNN-LSTM Model for Stock Price Predication;applying Convolutional Neural Networks for Stock Market Trends Identification;the Extreme Value Evolving Predictor in Multiple Time Series Learning;towards Synthetic Multivariate Time Series Generation for Flare Forecasting;the Streaming Approach to Training Restricted Boltzmann Machines;abrupt Change Detection by the Nonparametric Approach Based on Orthogonal Series Estimates;Recommendation System for Signal Processing in SHM;integrate-and-Fire Neurons for Low-Powered pattern recognition;monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines;a Proposal for Hybrid Memories Management Exploring Fuzzy-Based Page Migration Policy;a Learning Automata-Based Approach to Lifetime Optimization in Wireless Sensor Networks.

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Comparative Analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer Models in Predicting Stock Prices

Comparative Analysis of Long Short-Term Memory (LSTM), Gated...

引用

IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Chinecherem Umezuruike Deborah Olaniyan Julius Olaniyan Abidemi Emmanuel Adeniyi Adedoyin Oyebade David Abaneme Department of Software Engineering Bowen University Iwo Department of Computer Science Bowen University Iwo Department of Computer Science University of Ilorin Ilorin Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

This research explores the application of LSTM, GRU and Transformer models for predicting stock prices, aiming to enhance accuracy in financial forecasting. Stock price prediction is crucial for investment decision-making, yet challenging due to market volatility and complex patterns. The objectives are to evaluate the performance of LSTM, GRU and Transformer models using key metrics such as test loss, MAE, and MSE, and to compare their predictive capabilities. The LSTM model demonstrates robust performance with low test loss and MAE, indicating precise predictions and effective pattern recognition in financial data. In contrast, the Transformer model also shows promising results with relatively low test loss and MAE, albeit with larger errors in MSE and MAE metrics. Both models highlight the potential for accurate stock price prediction, suggesting avenues for future research to optimize model performance and reliability in financial forecasting applications. The experimental results show that GRU Outperformed LSTM and Transformer with an MSE of 0.0008, MAE of 0.0023, and high test accuracy of 0.9833.

关键词： Measurement Analytical models Accuracy Refining Predictive models Transformers pattern recognition Reliability Forecasting Long short term memory

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

20th International Conference on Artificial Intelligence and...

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

ISBN: (纸本)9783030879853

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Mobile Robot Control Based on Virtual Impedance Force Feedback

Mobile Robot Control Based on Virtual Impedance Force Feedba...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Masaru Sasaki Taro Shibanoki Hideyuki Tonooka Graduate School of Science and Engineering Ibaraki University Ibaraki Japan Graduate School of Natural Science and Technology Okayama University Okayama Japan

In this study, a tactile feedback method is proposed for virtual repulsive force based on non-contact impedance and applied to collision avoidance for a biological signal-controlled mobile robot. Virtual walls based on mechanical impedance are placed around the robot, it can avoid obstacles using virtual repulsive forces when obstacles meet the virtual walls. The proposed method provides tactile feedback about the force to the operator, which enables the operator to recognize the environment around the robot. In the experiments, a blindfolded participant controlled a mobile robot using myoelectric signals. The results demonstrated that the robot could be operated stably.

关键词： Vibrations Computational modeling Force Force feedback Tactile sensors pattern recognition Impedance

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Terrain environment classification and recognition for soft lower-limb exosuit

Terrain environment classification and recognition for soft ...

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2021 IEEE international conference on Real-Time computing and Robotics, RCAR 2021

作者： Ni, Jiangpeng Wang, Zhuo Li, Feng Su, Zhilong Cao, Wujing Chen, Chunjie Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Advanced Technology Chinese Academy of Sci-ences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Reliable environmental context prediction is critical for wearable robots (exoskeletons or prostheses) to assist terrain-adaptive locomotion. Inspired by the mechanism of human perception of the environment, the vision sensor can be introduced into the soft lower limb exosuit robot so that future environmental information can be used to improve the control. In this paper, a stable terrain classification and recognition system (TCRS) is designed for lower-limb soft exosuit robots. When the exosuit robot perceives the environment, it uses the residual network to train the terrain classifier to classify the terrain. During walking, the wearable soft exosuit robot collects the indoor environment, preprocesses, and marks the sampled images separately. The experimental results show that the classification accuracy is more than 90%, and the highest is 97.5%. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. © 2021 IEEE.

关键词： Landforms

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