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Research and Application Exploration of Image Processing and Visual Communication Technology Based on Artificial intelligence 3

Research and Application Exploration of Image Processing and...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Wang, Xiaona Shandong Business Institute Shandong China

ISBN: (纸本)9798350318609

In many fields, such as medical imaging, electronic games, and security monitoring, it is very necessary to perform target image recognition and processing on computers. Through image processing and visual communication technology, managers or users can obtain target information more intuitively and clearly, thereby issuing more correct instructions and completing target work. In order to make this technology more mature and efficient, this article proposes to use artificial intelligence technology for assistance, and finally demonstrates it through comparative experiments. In the control experiment of recognition accuracy, the average accuracy of the recognition detection system based on artificial intelligence technology was 93.65%, while the average recognition accuracy of the conventional system was 90.66%. The obvious experimental gap fully demonstrates that artificial intelligence technology can be well used in image processing and visual communication technology. © 2024 IEEE.

关键词： Visual communication

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Interpretation of Electroencephalogram using RNNs to Predict Cognitive Disorders 3

Interpretation of Electroencephalogram using RNNs to Predict...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Moovendran, Manikandan Surendran, S. Midhun, C. Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India SRM Institute of science and technology Chennai603203 India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

EEG signals are very useful in giving an insightful view of what's going on inside the brain. However, such readings are still tricky and challenging to interpret because of the inherent non-linearity and dynamic behavior of the data. The present paper introduces a new approach to the analysis of EEG signals using Recurrent Neural Networks, with a particular focus on recognizing temporal dependencies that exist in sequential data. By exploiting the RNN property of information retention at different time steps, we demonstrate enhanced accuracy in pattern recognition and classification of cognitive states. Our model is trained over a massive dataset of EEG recordings using the LSTM units to get rid of vanishing gradients frequently encountered in deep-learning models. The results from the experiments are given below and illustrate how the proposed method compares with traditional machine learning techniques in terms of accuracy and robustness. It is a promising tool for all applications in neurological diagnosis, cognitive research, and developing a brain-computer interface. © 2024 IEEE.

关键词： Recurrent neural networks

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Brain Tumor Segmentation of MRI images with U-Net and DeepLabV3+ 3

Brain Tumor Segmentation of MRI images with U-Net and DeepLa...

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

作者： Akagic, Amila Kapo, Medina Kandic, Elma Becirovic, Merjem Kadric, Nerma Univ Sarajevo Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9798350372977;9798350372984

In recent years, notable advancements have been made in medical imaging technology, with Magnetic Resonance Imaging (MRI) assuming a pivotal role in the diagnosis of brain tumors. Despite these advancements, medical image segmentation continues to pose a formidable challenge, as highlighted by various factors documented in existing literature. This study delves into the cutting-edge developments in Deep Learning for semantic segmentation, specifically concentrating on the precise identification of brain tumor pixels in 2D images. Employing U-Net and DeepLabV3+ architectures, the research provides experimental evidence that underscores the unparalleled performance of DeepLabV3+ with the Binary Cross Entropy loss function, offering valuable insights for enhancing the accuracy of brain tumor segmentation in medical imaging.

关键词： Biomedical Imaging Medical Image Segmentation MRI Brain tumor Computer Vision Semantic Segmentation

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AI Model and Experimental Study of Drug Effects on Planarians' Regeneration 3

AI Model and Experimental Study of Drug Effects on Planarian...

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

作者： Li, Ella Liu, Yilin Saratoga High Sch Saratoga CA 95070 USA Amador Valley High Sch Pleasanton CA USA

ISBN: (纸本)9798350372977;9798350372984

Due to planarians' remarkable regenerative capabilities, they have been a popular biological model for researching the effects of drugs on tissue regeneration. These unique abilities provide insights into potential applications of regenerative medicine. Despite extensive research, machine-learning models have yet to be integrated into this field of study. This paper proposes the first artificial intelligence machine-learning models used to predict planarian morphology when exposed to different drugs and manipulations, leveraging datasets derived from 1716 experiments documented across 119 publications spanning from 1898 to 2018. Two machine learning models are explored in this paper: a random forest classifier model, and a deep learning neural network multi-output regression model. The initial results indicate that the random forest model achieved over 96% accuracy for predicting lethal, tail, and trunk morphologies, and 64%, 76%, and 80% accuracy for predicting pharynx, head, and eye morphologies. For the neural network regression model, the loss is at 0.18. Additionally, in order to understand drugs' effects on planarian morphology, learning, and memory, experimentation is conducted with different alcohol concentrations. This paper aims to facilitate the analysis of the effects of drugs on planarians using artificial intelligence technologies and to aid future experiments.

关键词： drug effects planarian regeneration artificial intelligence machine learning planarian morphology

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Review of Cybercrime Detection Approaches using machine Learning and Deep Learning Techniques 3

Review of Cybercrime Detection Approaches using Machine Lear...

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

作者： Gumma, Yalamandeswara Rao Peram, Subbarao Vignan Foundation for Science Technology and Research Department of Computer Science and Engineering Guntur - Tenali Rd Andhra Pradesh Vadlamudi522213 India Department of Information Technology Guntur -Tenali Rd Andhra Pradesh Vadlamudi522213 India

ISBN: (纸本)9798350375190

This research explores the use of deep learning and machine Learning techniques in cybersecurity to detect and mitigate cyber threats. Specifically, this study focuses on Long Short-Term Memory (LSTM) and Graph Neural Networks (GNN) to analyse network traffic and system logs for patterns, anomalies, and malicious activities. The goal of the proposed research work is to enhance cyber-defining mechanisms and network security by leveraging Artificial intelligence (AI) to combat the evolving cyber threats. © 2024 IEEE.

关键词： Network security

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Metal Surface Damages Detection Based-on Artificial intelligence 3

Metal Surface Damages Detection Based-on Artificial Intellig...

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

作者： Hu, Zetong Wuhan Univ Technol Wuhan Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Based on the shortcomings of existing metal surface damages identification methods such as multiple limitations, and requiring a lot of manpower and resources, this paper intends to introduce deep learning algorithms into the field of steel surface damage identification, in order to find an efficient, simple, low-threshold and high-precision identification method for surface damages. After comparing existing convolutional neural network models such as VGG16, Resnet50, InceptionV3, DenseNet121, we optimized Resnet50 from the aspects of batchsize and optimizers. Based on the best settings and drawing on the structure of residual blocks in Resnet50, a new convolutional neural network model "Cbam Resnet" specifically designed for surface damage problem recognition was constructed. Finally, Cbam Resnet achieved an accuracy of 99.43% on the test set.

关键词： deep learning neural network models residual network attention mechanism

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CIBIP-AI: Development of California Invasive Bird Identification and Prediction Artificial intelligence System 3

CIBIP-AI: Development of California Invasive Bird Identifica...

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

作者： Chen, Kayla Shao, Claire Harker Sch San Jose CA 95129 USA Lynbrook High Sch San Jose CA USA

ISBN: (纸本)9798350372977;9798350372984

Invasive species are detrimental to native wildlife and pose a major threat to biodiversity. In California alone, municipalities and conservation authorities are estimated to spend approximately $50.8 million per year on invasive species. Effective management of bio-invasions depends on reliable identifying the invasive threats and predicting areas at risk of invasion. Yet, there is no efficient solution to address the issue. This paper proposes the first California Invasive Bird Identification and Prediction AI System (CIBIP-AI). The CIBIP-AI system utilizes a deep learning solution to automatically identify and predict two California invasive birds, the mute swan and brown-headed cowbird. The CIBIP-AI system uses a hybrid deep learning architecture based on VGG16 and ResNet50 for identification. The CIBIP-AI system can also predict the geographical areas at risk of invasion based on a set of the birds' physiology features and microclimate conditions. The CIBIP-AI database is created for storing data and continuous training. The initial experimental results show that the classification accuracy for the image input reached 96%. The audio input was more challenging to classify, with 73% accuracy. After applying knowledge-based filtering and common pattern extraction, the accuracy was improved to 77%. The video was split into multiple images and audio and input into the hybrid models. The classification accuracy of the hybrid models reached 97%. The work shows that the proposed CIBIP-AI system provides significant aid to the early detection and prevention of invasive bird species.

关键词： Invasive species artificial intelligence deep learning ecology mute swan brown-headed cowbird

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Determining Whether a Turkish Text is Produced by Artificial intelligence or Human 3

Determining Whether a Turkish Text is Produced by Artificial...

引用

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

作者： Er, Rezzan Nisa Cogalmis, Kevser Nur Istanbul Sabahattin Zaim Univ Comp Engn Istanbul Turkiye

ISBN: (纸本)9798350372977;9798350372984

Nowadays, with the acceleration of the flow of information over the internet, text content such as articles, blog posts and news are now produced in digital environments. Some of this content is produced by artificial intelligence rather than traditional authorship. This situation raises important questions about the reliability of text contents as well as the ease of access to information. The aim of this study is to determine whether an academic text was written by the ChatGPT artificial intelligence content creation tool or by a human. We received our data from the Dergipark platform, which contains Turkish academic articles. The data consists of summaries of Turkish articles. The dates of the articles taken from Dergipark are before the launch of Chatgpt (November 2022). In order to make a real distinction, we had to make sure that the data was actually written by a human. Distinguishing information such as the name and keywords of each article was given to Chatgpt 3.5 and the article summary was requested (tagged as ai). Abstracts of articles with the same distinctive information are also labeled human. The accuracy rate obtained in the study using LSTM is 0.8869 for the test set. In this way, it is aimed to make it easier to determine whether a written text was written by a human or an artificial intelligence text creator.

关键词： NLP LSTM Deep Learning machine Learning ChatGPT

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Enhanced Human Interaction recognition Framework Using Pyramid Matching and Deep Neural Network 3

Enhanced Human Interaction Recognition Framework Using Pyram...

引用

3rd international conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Bukht, Tanvir Fatima Naik Jalal, Ahmad Rahman, Hameedur Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

The interaction of humans on images has been of interest in computer vision and pattern recognition. In fact, interaction recognition has become fertile ground for research in virtually all areas of research. Thus, proposing to use a Deep Neural Network to design an activity recognition system in that case. However, at the initial stages of the system, the integration of hue, saturation, intensity (HSI) color transformation brings clarity of the video frames. Additionally, filtering for noise interferences are used to minimize noise. Multiple Object Tracking (MOT) and statistical methods are used to extract silhouette. In the extraction procedure, we use the Texton maps, ORB (Oriented FAST and Rotated BRIEF), and Spatial pyramid matching (SPM) Method. Using Gray Wolf algorithm, we discriminate features and then identify the most meaningful independent content that describes the data structure. Finally, the DNN takes the last feature and dumps it into the human interaction zone. This approach is tested using the SBU Interaction dataset and achieves a recognition rate of 87% using the experimental method. ©2024 IEEE.

关键词： Feature Selection

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Progress and Research Trends of Artificial intelligence Incorporation in Protein Structure Prediction 3

Progress and Research Trends of Artificial Intelligence Inco...

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

作者： Lau, Maggie Santa Clara Univ Sch Engn Santa Clara CA 95053 USA

ISBN: (纸本)9798350372977;9798350372984

Protein synthesis is closely regulated in nature, and in the field of protein design, scientists have attempted to understand and replicate those processes due to the importance of proteins. Existing protein structure prediction methods include physics-based approaches like molecular dynamics or knowledge-based methods such as energy functions, but these methods are computationally costly and time-consuming. Artificial intelligence (AI) technology has been applied to protein structure prediction due to its ability to address current challenges more effectively. This paper will review the significant progress made recently in protein structure prediction using both classical machine learning and deep learning methods. Advancements in protein structure prediction using computational methods have been made possible due to the increase of available data, and representative examples of six databases used in protein structure prediction will be provided in this review. These public databases have diverse entries such as protein sequences, protein structures, protein interactions, enzyme nomenclature, and proteomes, which will facilitate more research in the protein design field. Despite the advancements with the incorporation of AI, several challenges remain, and the proposed solutions and research trends will be discussed in this paper.

关键词： Protein Design Protein Structure Prediction Deep Learning machine Learning

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