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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

共 7122 条记录，以下是91-100 订阅

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TCPNet: A 3D Point Cloud Classification Model Based on the Advanced CNN and Transformer 3

TCPNet: A 3D Point Cloud Classification Model Based on the A...

引用

3rd international conference on Artificial intelligence and Computer Information Technology, AICIT 2024

作者： Wu, Jinmeng Ma, Jingwei Hong, Hanyu Hao, Yanbin Ma, Lei Wang, Lei Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan China School of Information Science and Technology University of Science and Technology of China Anhui China

ISBN: (纸本)9798350365443

Transformer and Convolutional Neural Network (CNN) are currently two important models in the field of deep learning. Among them, Transformer has strong global perception ability but weak local perception ability, and CNN precisely compensates for this. CNN has strong local perception ability, so in order to combine the advantages of both, we propose a 3D classification model TCPNet based on Transformer and CNN. We first use Transformer to extract the global features of the point cloud, and then concatenate a CNN network to enhance the detail perception ability, so that the network can better understand the 3D point cloud. The experimental results on the ModelNet40 and ScanObjectNN datasets show that our method can effectively achieve 3D classification tasks. On ModelNet40, an accuracy of 93.6% can be achieved. On the most difficult variant PB-T50-RS of ScanObjectNN, an accuracy of 83.93% can be achieved. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing Travel Itineraries: A Multi-Objective machine Learning Model Approach 3

Optimizing Travel Itineraries: A Multi-Objective Machine Lea...

引用

3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Roy, X Samuel Issac, Titus Terrance, J Sebastian Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

A travel itinerary is a complex problem that involves multiple objectives and constraints, such as cost, time, transportation modes, and comfort levels. This research study focuses on creating a real-time eco-friendly itinerary generator by integrating machine learning techniques. This research work explores two machine-learning algorithms: Artificial Neural Networks (ANNs) and Multi-Layer Perceptron (MLP). These algorithms are evaluated for their suitability in generating user-friendly and environment-friendly itineraries. Through experimentation, the accuracy and precision of the proposed models are assessed, leading to the identification of an optimal travel planner model. The ultimate goal of this model is to provide a detailed itinerary that considers prominent modes of transportation while addressing the pressing environmental crisis. By prioritizing eco-friendly options, this approach sets itself apart from conventional travel planning tools. © 2024 IEEE.

关键词： Multilayer neural networks

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Dynamic Face Tracking and Route Prediction Across Distributed Camera Network Using MobileFaceNet and Partial Trajectory Matching 3

Dynamic Face Tracking and Route Prediction Across Distribute...

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3rd IEEE World conference on Applied intelligence and Computing, AIC 2024

作者： Salvi, Sanket Mutha, Rushab Nawghare, Yash Noel, Patrick Reddy, Satya Mit World Peace University Dept. of Computer Engg. Tech. Pune India

ISBN: (纸本)9798350384598

In an era where security and surveillance are paramount, this paper presents a comprehensive framework for real-time face recognition and pedestrian trajectory prediction, leveraging the capabilities of MobileFaceNet and a Partial Trajectory Matching Algorithm. The system is designed to enhance surveillance and monitoring by integrating accurate face recognition with predictive path analysis. Known face images and historical trajectory data are utilized to recognize individuals and forecast their movement patterns. The framework processes live video feeds, detecting and identifying faces in real time. Upon identification, it segments and matches the pedestrian's trajectory against historical data to predict future movements. Performance is evaluated using metrics such as precision, recall, and F1 score. The results demonstrate the efficacy of the proposed approach in improving situational awareness and response times in security applications. © 2024 IEEE.

关键词： Computer vision Deep learning MobileFaceNet Partial Trajectory Matching Algorithm pattern recognition Pedestrian trajectory prediction Real-time face recognition Security monitoring Surveillance systems Video analysis

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Research on Anti-telecom Fraud Based on Artificial intelligence and Edge Computing 3

Research on Anti-telecom Fraud Based on Artificial Intellige...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Fang, Peng Fang, Qinguo Design Institute Co. LTD. Shandong Branch of China Mobile Communication Group Jinan China

ISBN: (纸本)9798350380989

This paper studies the artificial intelligence method based on the collaboration of edge and terminal, integrates the complementary advantages of the local computing and strong computing power of the two, realizes the characteristic recognition of telecom fraud behavior, and realizes the accurate and real-time anti-telecom fraud. By constructing a telecom fraud identification and defense system based on deep learning, the whole life cycle from post-analysis to pre-warning, in-process reminder and even blocking, post-monitoring feedback is realized, and the level of anti-telecom fraud technology is improved. © 2024 IEEE.

关键词： anti-telecom fraud artificial intelligence edge computing

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Proceedings of the 3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

Proceedings of the 3rd International Conference on Intellige...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

ISBN: (纸本)9798331515911

The proceedings contain 260 papers. The topics discussed include: a critical study on hybrid computing and its current influences and future challenges;daily updates on construction sites progress using artificial intelligence;green energy for smart cities: a review of research trends;smart ventilator for affordable health monitoring system;quantum dot-enhanced sodium-air batteries for unprecedented energy storage performance;intelligent question generator system using natural language processing;hybrid product recommendation system using popularity based and content-based filtering;high performance closed-loop control of a phase shifted full bridge converter for electric vehicle application;smart impact mitigation: intelligent speed and safety;face recognition attendance framework using quantum edge processing;and empowering communication: emergency hand recognition for the differently abled.

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Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models 3

Advanced AI Framework for Enhanced Detection and Assessment ...

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3rd international conference on Robotics, Artificial intelligence and Intelligent Control (RAIIC)

作者： Jiang, Liheng Wu, Zhizhong Yang, Xuechun Wang, Yuting Yu, Chang NYU New York NY 10012 USA Google Inc Mountain View CA USA TikTok Inc Milpitas CA USA FASTTEK GLOBAL Livonia MI USA Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350352634;9798350352627

Trauma is a significant cause of mortality and disability, particularly among individuals under forty. Traditional diagnostic methods for traumatic injuries, such as X-rays, CT scans, and MRI, are often time-consuming and dependent on medical expertise, which can delay critical interventions. This study explores the application of artificial intelligence (AI) and machine learning (ML) to improve the speed and accuracy of abdominal trauma diagnosis. We developed an advanced AI-based model combining 3D segmentation, 2D Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic performance. Our model processes abdominal CT scans to provide real-time, precise assessments, thereby improving clinical decision-making and patient outcomes. Comprehensive experiments demonstrated that our approach significantly outperforms traditional diagnostic methods, as evidenced by rigorous evaluation metrics. This research sets a new benchmark for automated trauma detection, leveraging the strengths of AI and ML to revolutionize trauma care.

关键词： Trauma Detection Artificial intelligence machine Learning 3D Segmentation 2D CNN RNN Abdominal Injuries

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Artificial intelligence and its Road to Altering the World Dynamics 3

Artificial Intelligence and its Road to Altering the World D...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Sehaj Batra, Devyansh Sharma, Ashish Chandigarh University Computer Science and Engineering Mohali India Chandigarh University Department of Electrical Engineering Mohali India

ISBN: (纸本)9798350381931

Humans have longed for computers to take over and make the monotonous and tedious tasks obsolete. Artificial intelligence(AI) is just the right answers to this problem. There are not one but many sectors that are being manipulated by this ever- growing technology. It is safe to say that each day machines are getting better at understanding humans which is due to the evolution and innovation in this field. In this paper, we have uncovered various sectors in which artificial intelligence along with its various branches like Natural Language Processing (NLP), machine Learning(ML) and Computer Vision have helped the world curate a better place by personalizing every daily use item. This has enhanced the way we live. However, there is a lot of data that is being used in order to bring these technologies to life. We have highlighted the constraints as well as the pleasures that this technology can bring us. © 2024 IEEE.

关键词： machine learning

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Mobile Forensics in Digital World using Artificial intelligence 3

Mobile Forensics in Digital World using Artificial Intellige...

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3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Dwivedi, Avani Singh, Ujjwal Kumar Kathuria, Samta Lande, Jayapal Negi, Prafful Law College Dehradun Uttaranchal University Dehradun India Uttaranchal University LCD Division of Research and Innovation Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (纸本)9798350396157

After the advent of industry 4.0, technologies are comprehensively unveiled to the public. Because of this, the proliferation in demand for technology in every field has begun to soar. Today, in the modern society, the pattern of crimes against women is increasing unprecedentedly. Cybercrimes are more frequent than traditional crimes. Therefore, Mobile Forensics (MF), a subset of digital forensics plays a crucial role in extracting the digital evidence. The technologies such as Artificial intelligence (AI) is then incorporated to improve the efficiency and accuracy in mobile forensics and digital investigation. This study addresses the significance, challenges, and effects of integrating AI in Mobile Forensics and Digital Investigation (DI). In addition, this research study also emphasizes on the stages or directives of mobile forensics, which are considered as essential components in gathering the evidence. It fills the void by analyzing how various technologies and techniques such as machine learning, deep learning, and data mining can be used in mobile forensics. Eventually, this study is concluded by providing the suggestions for its advancement and avoidance while illustrating the challenges to negate the same in the future. © 2024 IEEE.

关键词： Crime

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ML Model For Detection of Depression in Menopause Women 3

ML Model For Detection of Depression in Menopause Women

引用

3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Britto, Kasey Ann Varnikaa, D.T. Sathishkumar, R. Anita Dolorosa, E. St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Jerusalem College of Engineering Department of Science and Humanities Chennai India

ISBN: (纸本)9798350372120

Menopause, an inevitable milestone in a woman's journey, signifies not only the onset of physical transformations but also the potential emergence of mental health complexities. This study investigates the potential of machine learning to predict depression in menopausal women, aiming to improve their well-being. Depression is a prevalent concern during menopause, yet limited specialist access and knowledge gaps hinder effective intervention. We have developed a supervised multi-classifier machine learning system to predict depression and its severity, evaluating various classification algorithms. Our findings demonstrate high accuracy (97%) using a Random Forest model, suggesting promise as an AI-powered tool in this domain. © 2024 IEEE.

关键词： Forecasting

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Artificial intelligence-Based Detection of Autism Spectrum Disorder Using Linguistic Features 3

Artificial Intelligence-Based Detection of Autism Spectrum D...

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3rd international conference on Computing and machine intelligence (ICMI)

作者： Li, Juliana Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

Artificial intelligence (AI) technology has recently shown promising results in detecting Autism Spectrum Disorder (ASD), but it faces significant challenges because it is still in early stages of development. This study proposes to use linguistic features in the detection of ASD using artificial intelligence. In order to systematically understand different AI model performances, two types of AI models have been explored and evaluated. Five classical ML models were used: logistic regression, Gaussian naive Bayes, random forests, k-nearest neighbors (K-NN), and support vector machines (SVMs), and two deep learning models were used: multilayer perceptron (MLP) and a convolutional neural network (CNN). All model development work is based on analyzing speech transcripts of 64 children total aged 3 to 6 from two data banks, CHILDES and ASDBank, including 30 children with ASD and 34 typically developing (TD) controls. The first step was to examine the annotations of the transcriptions, then extract various linguistic features from the texts. AI models were trained to determine whether a child has ASD based on the characteristics of the transcripts analyzed. There are three major findings of this study. First, neural network models will outperform classical models in ASD detection by around 5%-10%. The models multilayer perceptron and convolutional neural networks achieved 83% and 84% accuracy, respectively. Second, among the classical machine learning models, logistic regression, SVMs, K-NN, and random forests all achieved accuracies in the range 77%-80%, while Gaussian naive Bayes performed the worst by around 10%. Third, the linguistic feature with the highest importance in ASD detection is MLU, and the features MLU, MLT, rUtts, and rTNW show significant distinctions between age and gender demographics in addition to having high importance in ASD detection. Overall, this research recommends using deep learning for clinical applications of ASD detection.

关键词： Autism Spectrum Disorder Natural Language Processing Artificial intelligence machine Learning Deep Learning Linguistics

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