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Research on Image Recognition and Classification Algorithms Based on Artificial Intelligence 2

Research on Image Recognition and Classification Algorithms ...

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2nd IEEE international conference on Control, Electronics and Computer technology, ICCECT 2024

作者： Zhang, Changlu Chang, Ruifeng The Twentieth Research Institute of China Electronics Technology Group Coporation Xi'an China

ISBN: (纸本)9798350380958

In the field of artificial intelligence image recognition and classification, an innovative model named Visual Transformer enhanced by Gull Swarm Optimization (ViGO) has been developed recently. The model cleverly integrates the Vision Transformer (ViT) with the Gull Optimization algorithm (GSO). As an innovative achievement in the field of deep learning, ViT can segment images into a series of patches of fixed size and input them into Transformer architecture in the form of sequence data to effectively capture and utilize the global context information and long-term dependencies of images, thus significantly improving the accuracy and efficiency of image recognition. However, although ViT has demonstrated powerful image recognition capabilities, its need for large-scale annotated data sets and computing resources has become a key factor limiting its widespread application. To solve this problem, ViGO introduces the Seagull optimization algorithm. Seagull optimization is an intelligent optimization algorithm inspired by the foraging behavior of seagulls in nature, which has excellent global searching ability and local fine optimization characteristics. In ViGO model, gull optimization algorithm is used to optimize the key links of ViT such as hyperparameter setting and weight initialization, aiming to improve the learning process and convergence speed of the model, and improve its generalization performance under limited resources. Through the organic integration of ViT and Seagull optimization algorithm, ViGO model not only inherits the powerful function of ViT in image recognition, but also solves the problem of training complexity and resource requirements with the help of Seagull optimization algorithm, so that the model can achieve higher recognition accuracy and faster training speed under small data sets and limited computing power environment. The successful construction and application of this new ViGO model not only promotes the progress of artificial inte

关键词： Image recognition

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Machine Learning Approaches for Efficient Traffic Flow in Smart Cities 3

Machine Learning Approaches for Efficient Traffic Flow in Sm...

引用

3rd Odisha international conference on Electrical Power Engineering, Communication and computing technology, ODICON 2024

作者： Kulkarni, Aniket Anitha, P. Valluri, John Yesudas Sunena Rose, M.V. Hemavathi, U. Hussein, Omar M. Software Qa Manager Drata Inc San DiegoCA United States Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu Srivilliputhur India Faculty of Management Studies Bengaluru India Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Avadi Chennai India Al-Farahidi University Medical Technical College Medical Instrumentation Engineering Baghdad Iraq

ISBN: (纸本)9798350354379

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction;this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible. © 2024 IEEE.

关键词： Traffic congestion

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Hybrid Optimization for Enhanced Retinal Image Classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Anwar Basha, H. Deepak, R. Babu, M. Bhanu, Shaik Thasleem Amutha, B. Madhavan, R. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamilnadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331543617

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO;therefore, it is able to provide an effective solution for the optimization of features within medical image analysis. © 2024 IEEE.

关键词： Ophthalmology

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Application of Improved RPROP Method and BP Algorithm in Music Signal Spectrum Visualization

Application of Improved RPROP Method and BP Algorithm in Mus...

引用

2023 international conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Zeng, Dajun Jiangxi Science and Technology Normal University School of Music Nanchang China

ISBN: (纸本)9798350313413

Music signal spectrum visualization is of great significance in music processing and music information retrieval. In this study, the application effect of the improved RPROP method and BP algorithm in music signal spectrum visualization is discussed, and the performance of the two algorithms is compared. Through the training and evaluation of the music signal dataset, the experimental results show that the improved RPROP method has an accuracy of 0.92, which is better than the BP algorithm's 0.85, and has a lower loss value (0.051 vs 0.078). In addition, the improved RPROP method exhibits faster convergence (2.63s/epoch vs 3.97s/epoch) and shorter training times (16.3s vs 27.8s). This shows that the improved RPROP method has better performance and efficiency in music signal spectrum visualization, which has important practical significance for related applications. © 2023 IEEE.

关键词： BP algorithm improving RPROP method musical signals spectrum visualization

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Comparative analysis of multi-loss functions for enhanced multi-modal speech emotion recognition 14

Comparative analysis of multi-loss functions for enhanced mu...

引用

14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Tran, Phuong-Nam Vu, Thuy-Duong Thi Truong Pham, Nhat Dang-Ngoc, Hanh Minh Dang, Duc Ngoc Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam Sungkyunkwan University Dept. of Integrative Biotechnology Suwon Korea Republic of University of Technology Sydney School of Electrical and Data Engineering SydneyNSW Australia

ISBN: (纸本)9798350313277

In recent years, multi-modal analysis has gained significant prominence across domains such as audio/speech processing, natural language processing, and affective computing, with a particular focus on speech emotion recognition (SER). The integration of data from diverse sources, encompassing text, audio, and images, in conjunction with classifier algorithms has led to the realization of enhanced performance in SER tasks. Traditionally, the cross-entropy loss function has been employed for the classification problem. However, it is challenging to discriminate the feature representations among classes for multi-modal classification tasks. In this study, we focus on the impact of the loss functions on multi-modal SER rather than designing the model architecture. Mainly, we evaluate the performance of multi-modal SER with different loss functions, such as cross-entropy loss, center loss, contrastive-center loss, and their combinations. Based on extensive comparative analysis, it is proven that the combination of cross-entropy loss and contrastive-center loss achieves the best performance for multi-modal SER. This combination reaches the highest accuracy of 80.27% and the highest balanced accuracy of 81.44% on the IEMOCAP dataset. © 2023 IEEE.

关键词： center loss contrastive-center loss cross-entropy loss multi-modal analysis multi-modal model speech emotion recognition

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Smart Contract Vulnerability Detection Techniques for Hyperledger Fabric 8

Smart Contract Vulnerability Detection Techniques for Hyperl...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Shah, Neelkumar K. Nandurkar, Sachin Bilapate, Mayur Maalik, Mohammad Aziz Harne, Niteshkumar Shaik, Karimullah Kumar, Ajai Centre for Development of Advanced Computing Applied Artificial Intelligence Group Maharashtra Pune India

ISBN: (纸本)9798350334012

Hyperledger Fabric is becoming the most popular blockchain framework used by enterprises and medium-sized companies. Blockchain technology is used for many different domains like finance, voting service, land record etc. Blockchain innovation is a self-executing program called Smart Contract. Hyperledger fabric uses programming languages like ***, Java, and GO to implement smart contracts also called chaincode. Smart contracts are programmed to moderate critical transactions between individuals or organizations because terms and conditions embedded in the contract will be executed when certain conditions are met. So smart contract needs to be analyzed before deployment to ensure zero vulnerabilities and flaws on the blockchain application, their execution is critical against attacks which aim at stealing assets. Hence security auditing is an essential part of smart contract development. This paper proposes a static code analysis tool for smart contracts named as UBF-ChaincodeScan which helps to identify security risks in the smart contract code written in *** and GO language. © 2023 IEEE.

关键词： Smart contract

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A Comparative Study of Image Segmentation Techniques in Mammograms (Otsu Thresholding, FCM, and U-Net)

A Comparative Study of Image Segmentation Techniques in Mamm...

引用

2024 international conference on Information technology and computing, ICITCOM 2024

作者： Rahmi Fajrin, Hanifah Kim, Young Dong Min, Se Universitas Muhammadiyah Yogyakarta Department of Medical Electronic Technology Yogyakarta Indonesia Soonchunhyang University Department of Software Convergence Asan Korea Republic of Soonchunhyang University Department of Medical IT Engineering Asan Korea Republic of

ISBN: (纸本)9798350379839

This study aims to employ three image segmentation techniques - Thresholding (Otsu's method), Fuzzy C-Means (FCM) clustering, and the U-Net deep learning model-on mammographic images. By assessing these methods, we seek to identify the most effective approach for different types of mammographic data and provide insights into their practical applications in clinical settings. Pre-processing steps included image conversion, grayscale transformation, median filtering, histogram equalization, and region of interest (RoI) cropping based on given coordinates. Each method's performance was evaluated using the Dice Similarity Coefficient (DSC). The results showed that U-Net achieved the highest accuracy, with DSC scores of 0.84 for benign and 0.81 for malignant images. These findings suggest that U-Net provides the most accurate segmentation, closely matching the ground truth annotations. Additionally, U-Net's ability to learn and represent complex features, its robustness to variations in image quality. However, U-Net is computationally intensive and requires substantial computational resources for training and inference. Followed by Otsu's method, while simpler and effective for images with clear intensity separations, is limited by global thresholding approach, which may not perform well on images with subtle variations despite noise reduction by median filtering. FCM, although capable of handling overlapping intensities, requires careful parameter tuning to achieve optimal results. This study underscores the importance of selecting and optimizing segmentation methods based on specific imaging data characteristics and preprocessing techniques, ultimately contributing to more reliable and accurate breast cancer detection (benign and malignant). © 2024 IEEE.

关键词： Median filters

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Heterogeneous Wireless Device Management in Edge computing Systems for IoT Services 14

Heterogeneous Wireless Device Management in Edge Computing S...

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14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Vitumbiko, Mafeni Kim, Younghan School of Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

In a heterogeneous Internet of Things (IoT) setup, it is impractical and requires human intervention to deploy workloads for every device after enrolment, especially considering the diverse range of wireless technologies involved. Furthermore, it is extremely complex to design and develop a large-scale system that can accommodate and integrate heterogeneous wireless technologies to achieve interoperability between the devices. As the number of devices increases, it is necessary to dynamically adjust workloads to accommodate the growing demand and maintain quality of service. In this paper we employ a state-of-art solution called Project-Flotta, which is utilized for managing edge devices and edge workloads. However, Project-Flotta does not provide a way of automating and managing heterogeneous wireless technologies for an IoT service. Therefore, we propose a system based on Project-Flotta leveraging its kubernetes custom resources by extending its functionality to adapt diverse wireless technologies. © 2023 IEEE.

关键词： automation edge compuning heterogenous horizontal pod auto scaling kubernetes Project-Flotta wireless sensor

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Wind Power Prediction Based on GA-optimized BP Neural Network 2

Wind Power Prediction Based on GA-optimized BP Neural Networ...

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2nd international conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

作者： Zeng, Yanrui Li, Jiahao Gao, Yiwen Qian, Boyu Wuhan University of Science and Technology Wuhan China Wuchang University of Technology Wuhan China

ISBN: (纸本)9798350322866

With the gradual warming of the global climate, wind energy, as one of the representatives of clean energy, has been rapidly developed in countries worldwide. However, wind power generation has strong intermittency and variability, constantly impacting the stability of the power grid. In order to improve the accuracy of wind power prediction, this paper proposes a BP neural network model optimized by genetic algorithm (GA) for wind power prediction. This model uses GA to optimize the initial weights and thresholds of the BP neural network to improve the computational accuracy and convergence speed of the model, effectively avoiding the problem of a single BP neural network model easily falling into local minima and the slow training speed of GA algorithm. By preprocessing and normalizing meteorological data such as weather, temperature, and wind direction, a wind power prediction model is constructed, and the prediction results show that the model effectively improves the computational accuracy and convergence speed compared to the traditional BP neural network model. This result proves the feasibility of using GA to optimize BP neural network for wind power prediction in this paper. © 2023 IEEE.

关键词： Wind power

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K-means++ Algorithm Based on Spark Platform in Large-scale Tourism Data Mining in Cloud computing Environment 3

K-means++ Algorithm Based on Spark Platform in Large-scale T...

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3rd international conference on Robotics, Artificial Intelligence and Intelligent Control, RAIIC 2024

作者： Nan, Xiang Parthasarathy, Rajamohan Wang, Chu NanTong Normal College School of Information Technology Jiangsu Nantong226000 China SeGi University Faculty of Engineering Built Environment and Information Technology Kota Damansara Malaysia Xidian University School of Microelectronics Shaanxi Xi'an710000 China

ISBN: (纸本)9798350352627

With the acceleration of global economic integration and the rapid growth of tourism data, the processing and analysis of tourism data are facing severe challenges. In order to manage and analyze massive data effectively, it must use effective data mining technology. In order to solve this problem, this project plans to build a set of K-means++ algorithm for big data based on cloud computing and Apache Spark platform, aiming at improving the efficiency of big data clustering analysis. Spark is chosen as the development platform because it has strong storage capacity and scalability, and can effectively handle massive data. On this basis, a clustering method based on Kmeans is proposed, and the initial value is optimized, thus shortening the convergence speed and improving the stability and accuracy of clustering results. The implementation method includes first preprocessing large-scale tourism data, such as data cleaning and feature selection, then applying an improved K-means++ algorithm for data clustering, and finally analyzing the clustering results to identify potential trends and consumer behavior patterns in the tourism market. As the maximum number of iterations increases, the average intra cluster distance gradually decreases from 10.5 to 9.8, indicating that the clustering effect is gradually improving, that is, data points are more tightly concentrated within their respective clusters. This study provides an effective big data analysis tool for the tourism industry, helping it better understand customer needs, optimize service supply, and promote the development of personalized tourism products. © 2024 IEEE.

关键词： Cloud platforms

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