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

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Comprehensive Review of AI, IoT, and ML in Enhancing Urban Mobility and Reducing Carbon Footprints 3

Comprehensive Review of AI, IoT, and ML in Enhancing Urban M...

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3rd international conference on Sustainable Mobility Applications, Renewables and Technology

作者： Hossain, Mahmood Khalid, Hamad Rao, Avent Prakasa Lootah, Mohammad Al-Mohammedi, Salah Salim Khalaf Majeed, Salih Rashid Canadian Univ Dubai Sch Engn Appl Sci & Technol City Walk Dubai U Arab Emirates

ISBN: (纸本)9798331532932;9798331532925

The rise in vehicle ownership and urbanization, which exacerbates traffic congestion and carbon emissions, are significant barriers to sustainable urban development. This study investigates how artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) could enhance urban transport systems, reduce traffic, and lower urban regions' carbon footprints. The study examines how urban infrastructures can be made more efficient and flexible through the use of technologies including demand-responsive transport systems, integrated mobility solutions, predictive maintenance, and personalized trip planning. A recent study of educators, students, and other participants at Canadian University Dubai (CUD) found that people are becoming increasingly accepting of AI-driven solutions that reduce fuel consumption and travel time. These technologies have promise even if there are still challenges to be solved, such as the requirement for ongoing validation in dynamic urban contexts and the reliance on historical data. The study concludes with recommendations for integrating AI, IoT, and ML to develop responsive urban mobility solutions and emphasizes the importance of practical implementations to realize these benefits.

关键词： Artificial intelligence (AI) Internet of Things (IoTs) machine Learning (ML) Carbon Emission (CE) Urban Mobility (UM)

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The Use of Artificial intelligence and Blockchain in Healthcare Applications: Introduction for Beginning Researchers 2nd

The Use of Artificial Intelligence and Blockchain in Healthc...

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3rd international conference on Digital Technologies and Applications

作者： Rehali, Majda Elhassani, Merouane Elazami El Jaouhari, Asmae Berrada, Mohammed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Univ Montpellier Sci Res Natl Ctr CNRS Montpellier France Sidi Mohamed Ben Abdellah Univ Higher Sch Technol Technol & Ind Serv Lab Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

The healthcare sector is evolving daily thanks to fast evolution and rapid advancements. It benefits from novel technologies such as Artificial intelligence (AI) and Blockchain. These technologies are causing significant revolutions in various areas of healthcare. This paper addresses the fundamentals and key concepts of AI and Blockchain and focuses on their main applications in healthcare. Then, it presents some research studies that use their integration. This work aims to demystify AI and Blockchain for readers. It will help especially beginning researchers to get started with these technologies and have an overview of their applications in healthcare.

关键词： Artificial intelligence machine Learning Deep Learning Blockchain Healthcare

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Application of an Improved Convolutional Neural Network-Based Method in Network Intrusion Detection 3

Application of an Improved Convolutional Neural Network-Base...

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3rd international conference on Intelligent Communications and Computing, ICC 2023

作者： Chen, Huan You, Guirong Shiue, Yeouren College of Information Engineering Fujian Business University Fuzhou China Fujian Prov. Universities Eng. Research Center of Big Data Analytics for Business Intelligence Fujian Business University Fuzhou China National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu Taiwan

ISBN: (纸本)9798350308327

This study investigates the use of convolutional neural network (CNN) technology to solve the problem of network intrusion detection, specifically studying the impact of convolutional layer count. Experiments revealed that two convolutional layers yield optimal results. An AdaBoost+CNN model was proposed using this CNN with optimization strategies such as ensemble learning. Eight classical learning algorithms were compared with the proposed methods, and the results were evaluated using parameters such as accuracy, precision, and F-measurement. Compared with traditional machine learning, deep learning, and other methods, the method proposed in this study achieved higher classification accuracy and successful results in detecting network attacks. © 2023 IEEE.

关键词： Adaptive boosting

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Predictive Modeling of Dental Health Outcomes Based on Fluoride Concentrations using AI 3

Predictive Modeling of Dental Health Outcomes Based on Fluor...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Swathi Pai, M. Arudra, Annepu Dankan Gowda, V. Vadar, Parashuram Shankar Jadhav, Mahadeo Ramchandra Kaur, Mandeep Nmam Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Nitte574110 India Rajiv Gandhi Institute of Technology Department of Computer Science & Engineering R T Nagar Cholanagar Karnataka Bangalore India Bms Institute of Technology and Management Department of Electronics & Communication Engineering Karnataka Bangalore India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Bharati Vidyapeeth College of Engineering Department of Applied Science Navi Mumbai India Punjabi University Department of Electronics and Communication Engineering Punjab Patiala India

ISBN: (纸本)9798350319125

Oral health is a crucial aspect of general health, and the presence of fluoride in drinking water has been consistently linked to its improvement. This work utilizes deep learning and machine learning approaches to develop prediction models that can estimate oral health consequences based on fluoride concentrations. Our analysis is based on a comprehensive dataset that includes fluoride levels and oral health indicators from several geographic locations. The dataset covers a broad range of demographic and environmental factors. The study involves a thorough data pretreatment procedure, which includes activities such as data cleaning, standardization, and feature engineering. All of these processes contribute to improving and making the input variables more relevant. The approach used encompasses a range of machine learning algorithms, including as neural networks, decision trees, and ensemble approaches, which are used to create prediction models. Thoroughly adjusting hyperparameters and using cross-validation methods are used to maximize the effectiveness of the model. © 2023 IEEE.

关键词： Artificial intelligence Data Preprocessing Feature Engineering Healthcare Analytics machine Learning Oral Health Predictive Modeling

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End to End Scalable Image Coding for machines 3

End to End Scalable Image Coding for Machines

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3rd international conference on Intelligent Communications and Computing, ICC 2023

作者： Wu, Zijun Wang, Hanming Wang, Huifen Zhang, Yuan China Telecom Big Data and Artificial Intelligence Shanghai China

ISBN: (纸本)9798350308327

With the rapid advancements in image data acquisition technology, there has been a significant improvement in the resolution of captured images. This progress enables clear display of intricate details. However, to support downstream tasks such as object detection and semantic segmentation, image encoding in machine vision requires compression and transmission of source images for machine analysis. Additionally, it involves allocating transmitted bitstreams to different task networks and controlling bit rates. In this article, we propose a deep progressive image codec framework specifically designed for machine vision tasks. Our approach consists of several key steps. Firstly, we convert the image into a potential tensor representation. Then, utilizing a depth-constrained image encoder, we encode each element of the tensor into a marching plane. Subsequently, we decode the marching planes into images of varying quality based on the decoding points' priority. To enhance the framework's suitability for machine vision tasks, we perform joint training with downstream task networks. This collaborative training ensures that the codec aligns effectively with the requirements of these tasks. By integrating the feedback from the downstream networks, we optimize the codec's performance in relation to machine vision objectives. Experimental results demonstrate that the proposed framework not only effectively guarantees the accuracy of downstream tasks but also enables substantial savings in transmission bit rates. Additionally, the framework meets the demands of reconstructing images of varying quality without requiring further training. © 2023 IEEE.

关键词： Deep neural networks

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3rd international conference on Artificial intelligence and Speech Technology, AIST 2021

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial intelligence and Speech Technology, AIST 2021

ISBN: (纸本)9783030957100

The proceedings contain 54 papers. The special focus in this conference is on Artificial intelligence and Speech Technology. The topics include: Feature Extraction and Sentiment Analysis Using machine Learning;a Neural Network Based Approach for Text-Level Sentiment Analysis Using Sentiment Lexicons;cross Linguistic Acoustic Phonetic Study of Punjabi and Indian English;prashn: University Voice Assistant;spectrogram Analysis and Text Conversion of Sound Signal for Query Generation to Give Input to Audio Input Device;a Contrastive View of Vowel Phoneme Assessment of Hindi, Indian English and American English Speakers;Text-Based Analysis of COVID-19 Comments Using Natural Language Processing;indian Languages Requirements for String Search/comparison on Web;dictionary Vectorized Hashing of Emotional recognition of Text in Mutual Conversation;Speaker Independent Accent Based Speech recognition for Malayalam Isolated Words: An LSTM-RNN Approach;Comparative Analysis of NLP Text Embedding Techniques with Neural Network Layered Architecture on Online Movie Reviews;current State of Speech Emotion Dataset-National and international Level;context-Aware Emoji Prediction Using Deep Learning;development of ManiTo: A Manipuri Tonal Contrast Dataset;deep Neural Networks for Spoken Language Identification in Short Utterances;a Lightweight Deep Learning Approach for Diabetic Retinopathy Classification;machine Hearing a Cognitive Service for Aiding Clinical Diagnosis;critical Insights on Cancer Detection Using Deep Learning;OHF: An Ontology Based Framework for Healthcare;enhancing the Deep Learning-Based Breast Tumor Classification Using Multiple Imaging Modalities: A Conceptual Model;a Review on Speech Synthesis Based on machine Learning;EEG Based Stress Classification in Response to Stress Stimulus;hindi Phoneme recognition - A Review.

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Optimization of Convolution Operators' Backpropagation for Domestic Accelerators: Convolution Operators: Convolution kernel operation Backpropagation optimization to Enhance Performance and Efficiency in Convolutional Neural Network 24

Optimization of Convolution Operators' Backpropagation for D...

引用

3rd international conference on Computer, Artificial intelligence and Control Engineering, CAICE 2024

作者： Li, Wencong Chen, Yongqing Li, Bin School of Computing and Artificial Intelligence Zhengzhou University Zhengzhou China Co. Ltd Zhengzhou China

ISBN: (纸本)9798400716942

Since the emergence of Convolutional Neural Networks (CNNs), their special structure of weight sharing and high efficiency have found widespread applications in various fields such as speech recognition and image processing. To enhance computational efficiency and performance, convolutional operations are computed in parallel on domestic GPU-like accelerators, while CPUs are utilized for intricate logical decision-making or task scheduling. The paper focuses on an optimization method for backpropagation operators based on domestic GPU-like accelerators. By employing domestic GPU-like accelerators combined with the Implicit GEMM algorithm, it achieves universal operators, significantly reducing compilation time while maintaining performance without a substantial gap, thereby enhancing the efficiency and performance of CNN model training. The generic version of operators can substantially simplify the development and implementation process of CNN models. It possesses scalability and flexibility, catering to various application scenarios and requirements, enabling simultaneous processing of multiple data samples and computational tasks. Experimental results indicate that the efficiency of the generic version of operators meets expectations and can substitute for most operators in network training. © 2024 ACM.

关键词： Convolution

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Efficient FPGA Implementation of Feedback Perceptron for Hardware Acceleration

Efficient FPGA Implementation of Feedback Perceptron for Har...

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Computing and machine intelligence (ICMI), international conference on

作者： Tamador Mohaidat Azeemuddin Syed Mohammed Alqodah Kasem Khalil Department of Electrical and Computer Engineering University of Mississippi Mississippi USA CVEST International Institute of Information Technology Hyderabad India

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Artificial Neural Networks (ANNs) have revolutionized machine learning, mimicking human cognitive abilities to recognize patterns and make decisions. The perceptron, a fundamental unit in ANNs, forms the basis for complex network structures. This paper introduces a novel approach to perceptrons by incorporating a feedback mechanism using a gain factor, replacing conventional learning rates. The proposed method aims to optimize network performance while adapting to hardware constraints. Implementation of MNIST and Heart Attack datasets showcase the superiority of the proposed approach over tra-ditional methods, revealing substantial accuracy improvements across various activation functions (Sign, Step, and Sigmoid) in both single perceptron and multilayer perceptron (MLP) architectures. The proposed method achieves significant accuracy of 96.80% with the MNIST dataset, and 88.52% with the Heart Attack dataset compared to other methods. It has a small overhead in power consumption. The proposed approach is implemented in Verilog HDL on Xilinx Virtex-7 FPGA XC7A35T-IFGG484C. The proposed method demonstrates remarkable accuracy enhancements in pattern recognition tasks, promising advancements in real-world applications.

关键词： Accuracy Power demand Cardiac arrest machine learning Computer architecture Artificial neural networks pattern recognition

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A study on license plate recognition based on convolutional neural network 3

A study on license plate recognition based on convolutional ...

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3rd international conference on Computer Vision and Data Mining, ICCVDM 2022

作者： Shi, Xiangui Wang, Xiaole Chen, Tao Qian, Cong Liu, Yiming School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9781510661363

With the rapid development of advanced technologies such as artificial intelligence and Internet of Things, intelligent traffic management has been widely used. License plate recognition technology is particularly important in intelligent traffic management as the unique identification of vehicles, but due to the influence of natural environment, recognition angle, image clarity, size of license plate and other factors, the traditional license plate recognition technology is less robust and difficult to accurately identify the number plate number. In this paper, we describe a license plate recognition technique implemented by constructing Convolutional Neural Networks (CNN), which is able to recognize license plate numbers more accurately and has certain significance for further research on deep learning in the field of intelligent traffic management. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image processing

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3rd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Liu, Zhe School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia

ISBN: (纸本)9781643684581

Pythagorean fuzzy sets (PFSs) are a versatile tool for handling uncertain problems and have proven effective in practical applications. However, many existing Pythagorean fuzzy similarity measures have counter-intuitive situations, making it challenging to measure the similarity or difference between PFSs accurately. To address this issue, we propose two similarity measures for PFSs inspired by the Tanimoto similarity measure. We also explore the properties of the proposed measures and provide several numerical examples to illustrate their superior performance in processing fuzzy information from PFSs. Finally, we demonstrate the applicability of the proposed measures in solving pattern recognition problem. © 2023 The Authors.

关键词： Fuzzy sets

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