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Deep Dynamic Feature Alignment Based Few-shot learning for Figure Skating Action Localization 4

Deep Dynamic Feature Alignment Based Few-shot Learning for F...

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4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Wang, Mingyang Pang, Yiqun Wang, Qiurui Chen, Dan Institute of Artificial Intelligence in Sports Capital University of Physical Education and Sports Beijing China School of Winter Sports Capital University of Physical Education and Sports Beijing China

ISBN: (纸本)9781510666412

Temporal Action localization is a challenging and important task in computer vision. Although great progress has been made in recent years in this area with the help of deep learning, few jobs focus on applications on competitive sports, such as figure skating. To better analyze the players’ actions in figure skating, action localization is needed. Hence we collect 82 videos from worldwide figure skating contests, including championship and grand prix, etc. these videos are annotated by experts for action start timestamps, action end timestamps, and action names. We further analyze these videos by our proposed Deep Dynamic Feature Alignment (DDFA) method, which employs deep convolutional features and learns the action pattern between the target action and reference action. Experiments on models’ performances and results based on these datasets are presented, showing the proposed methods can predict proper action candidates for raw figure skating videos. © 2023 SPIE.

关键词： computer vision

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Lie Detection with the SMOTE Technique and Supervised machine learning Algorithms 4th

Lie Detection with the SMOTE Technique and Supervised Mac...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Ramesh, M. Edla, Damodar Reddy Department of Computer Science and Engineering National Institute of Technology Goa Farmagudi Goa Ponda403401 India

ISBN: (纸本)9789819900466

Lie detection identification has become a significant concern with the rising crime rate. It is a growing research field that is critical in real-time applications. Recognizing lies is an essential technique in human-computer interaction. the primary job is to determine whether the EEG data gathered for the lie detection is guilty or innocent. this paper proposes that EEG channels data for the human brain learn from EEG signals. the 16-channel electrode for the human brain to capture EEG channels data from the concealed information test. EEG channel imbalanced data handled with the SMOTE technique and machine learning methods will aid in the analysis of large amounts of data to lie detection. this system used the SMOTE method to remove the imbalanced EEG channel data set. these balanced data sets are trained to lie detection and increase system performance using several classifiers such as the KKN, DT, LR, RM, and SVM approaches. this algorithm compares performance parameters such as accuracy, F1 score, specificity, precision, and sensitivity. According to the trial outcomes, the SVM technique achieves the most incredible accuracy of 98.3% on the EEG data set. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Protecting the IoT Devices from DDoS Attack Using Blockchain-Based Multi-code-Driven Trust Mechanism 1

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4th international conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Jayanarayana Reddy, D. Jahnavi, Addula Indhu, Pothula Latha, Mekala Hema Department of CSE G. Pullaiah College of Engineering and Technology Kurnool India Kurnool India

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

the Internet of things (IoT) offers a promising platform for developing mission-critical programs. However, these programs often depend on a centralised storage architecture, which poses significant challenges such as security, privacy, and a single point of failure. Recently, blockchain technology has emerged as a key component in IoT application development. Blockchain can address issues related to data privacy, security, and third-party dependence in IoT applications. Both individuals and communities can benefit from integrating blockchain with IoT. Nonetheless, a DDoS attack on a mining pool in 2017 exposed significant vulnerabilities in blockchain-enabled IoT systems. Additionally, the data generated by these programs is vast. machine learning (ML) is an effective analytical tool that facilitates fully autonomous analysis and decision-making with large datasets. this research proposes a novel distributed intrusion detection system (IDS) utilising fog computing to detect DDoS attacks on mining pools in blockchain-enabled IoT networks, addressing the aforementioned challenges. It introduces an energy-efficient, decentralised, blockchain-based multi-mobile code-driven method for detecting internal threats in sensor node-powered IoT. the results indicate that the proposed approach outperforms existing alternatives. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain-based multi-mobile code driven solution Distributed Denial of Service Internet of things machine learning

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Research on intelligent elderly care service information system based on machine learning 5

Research on intelligent elderly care service information sys...

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5th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Chen, Qi Sheng, Nan Guangzhou Academy of Fine Arts Guangdong Guangzhou510006 China Lancaster University Lancashire LancasterLA1 4YW United Kingdom

ISBN: (纸本)9798400716485

the aging of the population is becoming more and more serious, and intelligent pension is becoming more and more important. Today, machine learning and artificial intelligence are widely used in all walks of life, and facial recognition based on machine learning is applied to the pension service system, which will make the pension system intelligent. the elderly identity verification through face recognition has the characteristics of direct and convenient, not easy to lose. In recent years, with the research and application of machine learning in the field of artificial intelligence such as computer vision, face recognition as an important part of computer vision technology has also been greatly developed, all kinds of face recognition algorithms based on machine learning emerge endlessly, and the effect is far higher than the traditional method. this paper designs and implements an artificial intelligence elderly care service information system based on machine learning, and studies the face recognition technology of intelligent elderly care system. the paper carried out feasibility analysis and demand analysis of the system research, discussed the functions to be designed and implemented in the research, analyzed the work flow of the research, and according to the work flow, the research was divided into five functional modules: image acquisition, face detection, feature extraction, identity recognition and result output. the machine learning model YOLO is used for face detection, ERT algorithm is used for face alignment, and 128-dimensional feature vector is extracted by residual neural network to realize face recognition. the input and output of real-time images are realized by OpenCV. the function of the algorithm is tested on the flw data set, and the accuracy of the algorithm can reach more than 98%. It realizes the research of face recognition system in artificial intelligence elderly care service information system based on machine learning, which can achi

关键词： Face recognition

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Real-Time Voice Cloning System Using machine learning Algorithms 4th

Real-Time Voice Cloning System Using Machine Learning Algori...

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4th international conference on Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2022

作者： Rajesh Chandra, G. Tata, Venkateswarlu Anand, D. Department of Computer Science and Engineering KKR & KSR Institute of Technology & Sciences AP Guntur India Department of CSE Guntur Engineering College AP Guntur India Annamalai University AP Chidambaram India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India

ISBN: (纸本)9789819916641

this real-time voice cloning system uses machine learning algorithms and has a lot of ease of use applications like bringing back the capacity to speak naturally users, who have missing their voices and unable to provide a lot of novel training instances. Using a neural technique, from speaker confirmation to multi-speaker text-to-speech synthesis, transfer learning is possible. In this paper, the model will strive to provide output that is completely independent of any pre-existing record while dealing with pre-existing information in the datasets. the model is built on the dataset’s base, but it is not confined to it. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloning

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DRL Based Multi-objective Resource Optimization Technique in a Multi-cloud Environment 4th

DRL Based Multi-objective Resource Optimization Technique in...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Kaur, Ramanpreet Anand, Divya Kaur, Upinder Department of Computer Application Lovely Professional University Punjab Phagwara India Department of Computer Science Baba Farid College Punjab Bathinda India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Akal University Talwandi Sabo Punjab India

ISBN: (纸本)9783031488870

the concept of multi-cloud becomes interesting progressively to cloud users because of its high response time, flexibility, high throughput, and reliability. But at the ground level, the concept of multi-cloud creates many challenges for researchers. the request of users and the multi-cloud environment is heterogeneous now a day. To work in this kind of environment required an intelligent system. Researchers are doing well in this field to make the whole process very flexible by providing an intelligent environment. the proposed multi-objective resource optimization deep reinforcement learning (MOROT-DRL) model uses the Q-learning technique of Deep Reinforcement learning (DRL) to allocate resources in a multi-cloud environment. It includes a service analyzer for analyzing the requests and MET(Minimum Execution Time)algorithm used for scheduling the task according to execution time and then enhanced flower pollination allocate the optimized resources for the demanded request. the comparison of the proposed model is done with simulation results of MOROT and neural network model and also implemented on GoCS real dataset of google. the proposed model gives better results when compared based on energy, CO2, and cost. © 2024, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reinforcement learning

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Comparison of Attention Mechanisms in machine learning Models for Vehicle Routing Problems 4th

Comparison of Attention Mechanisms in Machine Learning Model...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Krishna Munjuluri Vamsi, V.S. Telukuntla, Yashwanth Reddy Kumar, Parimi Sanath Gutjahr, Georg Department of Computer Science and Engineering Amritapuri India Department of Electronics and Communication Amritapuri India Center for Research in Analytics & Technologies for Education Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819900466

the Capacitated Vehicle Routing Problem (CVRP) is an important combinatorial optimization problem in which a fleet of vehicles must be routed to a set of customers. Many exact and heuristic methods have been proposed to solve this problem. Recently, efficient machine learning algorithms have been developed for solving the VRP. A major component of these algorithms is the Attention Mechanism which enables the vehicle to make more informed decisions on which customer to serve next. this paper compares various topologies for implementing such attention mechanisms when solving VRPs. Using simulations, it is found that Single-Head Attention works better with larger node instances, whereas Multi-Head Attention is superior for smaller node instances. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic methods

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MUCS@DravidianLangTech2023: Malayalam Fake News Detection Using machine learning Approach 3

MUCS@DravidianLangTech2023: Malayalam Fake News Detection Us...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Coelho, Sharal Hegde, Asha Kavya, G. Shashirekha, Hosahalli Lakshmaiah Department of Computer Science Mangalore University Mangalore India

ISBN: (纸本)9789544520854

Social media is widely used to spread fake news which can lead to individuals or group making wrong judgments based on fake news. the fake news can even create confusion, panic, anxiety, etc., leading to taking inappropriate actions by the individuals. Fake news creators with their tactics may use legitimate sources and mimic the style of reputed publications to create fake news, making it difficult and challenging to identify such content. To address the challenges of detecting fake news in this paper, we - team MUCS, describe the machine learning (ML) models submitted to"Fake News Detection in Dravidian Languages" at DravidianLangTech@RANLP 2023 shared task. three different models, namely: Multinomial Naive Bayes (MNB), Logistic Regression (LR), and an Ensemble model (MNB, LR, and Support Vector machine (SVM)) with hard voting, are trained using Term Frequency - Inverse Document Frequency (TF-IDF) of word unigrams, to detect fake news in code-mixed Malayalam text. Among the three models ensemble model performed better with a macro F1-score of 0.831 and placed 3rd rank in the shared task. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - proceedings.

关键词： Support vector machines

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Determination of Fatigue Strength of Steel Using Deep learning Techniques 4th

Determination of Fatigue Strength of Steel Using Deep Learni...

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4th international conference on Advanced Engineering Optimization through Intelligent Techniques

作者： Kumar, Punit Anand, Ayush Kavitha, B. Natl Inst Technol Warangal Dept Civil Engn Hanamkonda Telangana India Natl Inst Technol Warangal Fac Civil Engn Hanamkonda Telangana India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Steel is one of the primary components of a structure from a strength perspective. Since the load applied to some structures is cyclic, fatigue strength becomes a crucial factor for durability. However, due to the complex nature of fatigue strength formation in steel, it is not yet completely understood. Fortunately, the evolution of new technologies like artificial intelligence and machine learning has led to the development of regression models that can accurately predict the fatigue strength of steel. Although machine learning models have a defined structure, their performance improvement is limited after reaching a certain threshold. However, this limitation can be overcome with neural networks (NN), which are highly customizable and improve with the addition of data. In this study, seven different NN models were constructed with successive enhancements to predict the fatigue strength of steel with high accuracy. Various performance metrics were utilized to evaluate the models. the dataset used in this project is the fatigue dataset for steel from Japan NIMS (National Institute for Materials Science), which is an open-source and the world's largest dataset on steel fatigue strength. It comprises 437 instances with 25 different factors influencing the property, such as chemical composition (9), heat treatment conditions (12), and upstream processing details (4). these instances include 371 carbon and low-alloy steels, 48 carburizing steels, and 18 spring steels. the data covers various heats of each steel grade and different processing conditions. After testing various neural networks and determining the best train-test split, a maximum accuracy of 99.7% was achieved on the test data. Furthermore, the model was deployed as a web application, making it accessible to everyone.

关键词： Fatigue strength of steel Material science Deep learning Artificial neural network Model deployment

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On Comparing Mutation Testing Tools through learning-based Mutant Selection 4

On Comparing Mutation Testing Tools through Learning-based M...

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4th IEEE/ACM international conference on Automation of Software Test (AST)

作者： Ojdanic, Milos Khanfir, Ahmed Garg, Aayush Degiovanni, Renzo Papadakis, Mike Le Traon, Yves Univ Luxembourg Luxembourg Luxembourg

ISBN: (纸本)9798350324020

Recently many mutation testing tools have been proposed that rely on bug-fix patterns and natural language models trained on large code corpus. As these tools operate fundamentally differently from the grammar-based traditional approaches, a question arises of how these tools compare in terms of 1) fault detection and 2) cost-effectiveness. Simultaneously, mutation testing research proposes mutant selection approaches based on machine learning to mitigate its application cost. this raises another question: How do the existing mutation testing tools compare when guided by mutant selection approaches? To answer these questions, we compare four existing tools mu BERT (uses pre-trained language model for fault seeding), IBIR (relies on inverted fix-patterns), DeepMutation (generates mutants by employing Neural machine Translation) and PIT (applies standard grammar-based rules) in terms of fault detection capability and cost-effectiveness, in conjunction with standard and deep learning based mutant selection strategies. Our results show that IBIR has the highest fault detection capability among the four tools;however, it is not the most cost-effective when considering different selection strategies. On the other hand, mu BERT having a relatively lower fault detection capability, is the most cost-effective among the four tools. Our results also indicate that comparing mutation testing tools when using deep learning-based mutant selection strategies can lead to different conclusions than the standard mutant selection. For instance, our results demonstrate that combining mu BERT with deep learningbased mutant selection yields 12% higher fault detection than the considered tools.

关键词： Fault Seeding, Mutation Testing, Empirical Study, Empirical Comparison

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