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International Conference on Computer Research and Development (ICCRD)

作者： Doğu Manalı Hasan Demirel Electrical and Electronic Engineering Eastern Mediterranean University Famagusta Northern Cyprus

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

Brain cancer is one of the most deadly illnesses. It causes abnormal cells to grow in the brain. Planning for treatment and the prognosis of patients with brain tumors depend greatly on early diagnosis. Brain tumors can have different characteristics, treatments, and forms. Consequently, the process of manually detecting brain tumors is difficult, labor-intensive, and error-prone. Doctors use magnetic resonance imaging to detect those abnormal cells in the brain. With the growth of artificial intelligence, it is possible to diagnose the brain tumor from MIR images. For instance, convolutional neural networks and transformers could be used. The self-attention mechanism is implemented by transformers, which are models that give each input data component a distinct weight. Transformers have limited applications in image classification tasks because they were originally designed for use in natural language processing applications. Thus far, the majority of image classification research has employed convolutional neural networks. In this paper, six different pretrained convolutional neural networks and a vision transformer are used to classify four distinct brain tumor classes. The models include ResNet50, AlexNet, VGG16, InceptionV3, MobileNetV2, FractalNet, and the Vision Transformer. The goal of this study is to compare the performance of these pretrained convolutional neural network models with that of the vision transformer, demonstrating that transformers can also be effectively applied to image classification tasks. The performance of a vision transformer model shows 84.39% accuracy in the classification problem, which is better than the other six architectures.

关键词： Computer vision Accuracy Magnetic resonance imaging Computer architecture Transformers Brain modeling Convolutional neural networks image classification Biomedical imaging Residual neural networks

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PPLNs: parametric piecewise linear networks for event-based temporal modeling and beyond 24

PPLNs: parametric piecewise linear networks for event-based ...

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Proceedings of the 38th International Conference on neural Information processing Systems

作者： Chen Song Zhenxiao Liang Bo Sun Qixing Huang Department of Computer Science The University of Texas at Austin Austin TX

ISBN: (纸本)9798331314385

We present Parametric Piecewise Linear networks (PPLNs) for temporal vision inference. Motivated by the neuromorphic principles that regulate biological neural behaviors, PPLNs are ideal for processing data captured by event cameras, which are built to simulate neural activities in the human retina. We discuss how to represent the membrane potential of an artificial neuron by a parametric piecewise linear function with learnable coefficients. This design echoes the idea of building deep models from learnable parametric functions recently popularized by Kolmogorov-Arnold networks (KANs). Experiments demonstrate the state-of-the-art performance of PPLNs in event-based and image-based vision applications, including steering prediction, human pose estimation, and motion deblurring. The source code of our implementation is available at https://***/chensong1995/PPLN.

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Large Scale image Data processing and Content Recognition Based on Deep Learning

Large Scale Image Data Processing and Content Recognition Ba...

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Data Analytics, Computing and artificial Intelligence (ICDACAI), International Conference on

作者： Ruoyan Wan Xiuzhuo Wei Rui Ma Huinan Zhao Feng Wu Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

As the cornerstone of computer vision (CV), image recognition is of great importance. It not only profoundly affects daily life fields such as facial recognition, intelligent security, and medical image analysis, but also plays a key role in breakthroughs in high-tech fields such as autonomous driving technology, intelligent robots, and industrial inspection. With the vigorous rise of artificial intelligence (AI) technology, especially the rapid development of deep learning (DL) technology, image recognition has ushered in unprecedented changes. This article focuses on the latest applications of DL in the field of image recognition and proposes an innovative large-scale image data processing and content recognition algorithm. This algorithm effectively captures complex features in images by constructing deep neural network models, and continuously optimizes model parameters using massive training data, thereby improving recognition accuracy and significantly enhancing the algorithm's ability to understand image content. The experimental results show that the algorithm exhibits excellent performance in processing large-scale image datasets, with fast recognition speed and industry-leading recognition accuracy, laying a solid foundation for the further application of image recognition technology.

关键词： Deep learning image recognition Accuracy Face recognition Training data artificial neural networks Solids Data models Security artificial intelligence

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Black Fungus Infection Detection using AI-based Early Warning System for Patients through Multi-Modal Medical Imaging 2

Black Fungus Infection Detection using AI-based Early Warnin...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Annie Grace Vimala, G.S. Kesavan, R. Manigandan, E. Pushpa Latha, S. Kumar, Bura Vijay Padmakala, S. Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of CSE Saveetha School of Engineering SIMATS Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350340235

The COVID-19 pandemic has inflicted widespread devastation worldwide, causing a significant loss of human lives and impacting economies. Amid the ongoing pandemic, a perilous fungal infection known as mucormycosis, or "black fungus," emerged as a serious concern during the second wave of COVID-19 in 2021. This disease primarily affects individuals already battling other illnesses and undergoing intensive medication, leading to a weakened immune response against fungal infections. Mucormycosis predominantly manifests on the skin but can also affect vital organs such as the eyes and brain. This research paper presents a novel approach to detect and diagnose mucormycosis using artificial intelligence (AI) techniques, specifically a modified neural network logic integrating Regional Convolutional neural networks (RCNN) and S upport Vector Machine (S VM). The study utilizes a dataset containing a collection of eye photographs obtained from COVID-19 patients, some of whom developed mucormycosis during their illness. The research methodology encompasses key steps, including image acquisition, preprocessing, feature extraction, and classification, all aligned with established principles of dataset training and testing. The results of this approach are presented graphically, providing precise specifications, and highlighting the effectiveness of the proposed AI-based method. S igns and symptoms of mucormycosis are detailed, emphasizing the importance of early diagnosis and specialized tests to combat this potentially life-threatening infection. The clinical pathogenesis of mucormycosis, its risk factors, and treatment strategies are explored in-depth. Additionally, the paper underscores the increased susceptibility to mucormycosis among COVID-19 patients with certain comorbidities. Furthermore, the study discusses the challenges in diagnosing mucormycosis and highlights the significance of advanced techniques such as computed tomography (CT) scans, magnetic resonance imaging

关键词： artificial Intelligence Black Fungus Computed Tomography COVID-19 Diagnosis Early Detection image processing Medical Imaging Mucormycosis neural Network

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image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro-fuzzy inference system, and regression methods

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CROP SCIENCE 2021年第2期61卷 1013-1029页

作者： Sabouri, Hossein Sajadi, Sayed Javad Jafarzadeh, Mohammad Reza Rezaei, Mohsen Ghaffari, Sanaz Bakhtiari, Samira Gonbad Kavous Univ Coll Agr Sci & Nat Resources Dept Plant Prod Gonbad Golestan Iran

In this study, the leaf area was estimated by different regression models and new methods of image processing by using artificial intelligence (AI) in bread (Triticum aestivum L.) and durum wheat (Triticum durum L.) and triticale (xTritosecale Wittm. ex A. Camus) at seedling, booting, and milk development stages. Data on leaf traits in 1,000 plants of breed wheat, triticale, and durum wheat were studied. Among regression models using general data, LA=a+bLW, LA=a+b(W), and LA=a+b(W/L) models had a R-2 > 90% for all three cultivars (L and W represented length and width of leaves). For wheat bread, models W+(LW) and Lx(LW) exhibited a good estimate of the leaf area at all stages of growth, whereas in triticale, the linear models that used the product of WxL and LW were more suitable to estimate leaf areas in booting and milk development. The multilayer perceptron (MLP) neural network modeling indicated that the 'trainlm,' 'trainlm,' and 'traincgb' algorithms with an optimal structure of 2-10-1, 2-3-1, and 2-10-1 had the least amount of the mean square error for bread wheat, triticale, and durum wheat, respectively, for each of the growth stages. Furthermore, based on the adaptive neuro-fuzzy inference system (ANFIS) method, a very accurate estimate of the leaf area was obtained for each of the growth stages. The comparison of different models in the leaf area estimation showed that unlike estimating leaf area by regression, AI-based methods did not depend on plant growth stages. In this study, we suggested the utilization of image processing and artificial intelligence in leaf area estimation. It will accelerate leaf area measurement in the field, by extending these methods and transferring it into smartphone applications.

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Deep learning models for digital image processing: a review

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artificial INTELLIGENCE REVIEW 2024年第1期57卷 11-11页

作者： Archana, R. Jeevaraj, P. S. Eliahim Bharathidasan Univ Bishop Heber Coll Dept Comp Sci Tiruchirappalli 620017 Tamil Nadu India

Within the domain of image processing, a wide array of methodologies is dedicated to tasks including denoising, enhancement, segmentation, feature extraction, and classification. These techniques collectively address the challenges and opportunities posed by different aspects of image analysis and manipulation, enabling applications across various fields. Each of these methodologies contributes to refining our understanding of images, extracting essential information, and making informed decisions based on visual data. Traditional image processing methods and Deep Learning (DL) models represent two distinct approaches to tackling image analysis tasks. Traditional methods often rely on handcrafted algorithms and heuristics, involving a series of predefined steps to process images. DL models learn feature representations directly from data, allowing them to automatically extract intricate features that traditional methods might miss. In denoising, techniques like Self2Self NN, Denoising CNNs, DFT-Net, and MPR-CNN stand out, offering reduced noise while grappling with challenges of data augmentation and parameter tuning. image enhancement, facilitated by approaches such as R2R and LE-net, showcases potential for refining visual quality, though complexities in real-world scenes and authenticity persist. Segmentation techniques, including PSPNet and Mask-RCNN, exhibit precision in object isolation, while handling complexities like overlapping objects and robustness concerns. For feature extraction, methods like CNN and HLF-DIP showcase the role of automated recognition in uncovering image attributes, with trade-offs in interpretability and complexity. Classification techniques span from Residual networks to CNN-LSTM, spotlighting their potential in precise categorization despite challenges in computational demands and interpretability. This review offers a comprehensive understanding of the strengths and limitations across methodologies, paving the way for informed decis

关键词： image processing Deep learning models Convolutional neural networks (CNN)

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The Application of AI Video Generation Technology in Virtual Reality (VR) and Augmented Reality (AR)

The Application of AI Video Generation Technology in Virtual...

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artificial Intelligence, Deep Learning and neural networks (AIDLNN), International Conference on

作者： Biqin Deng Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (数字)9798331520816

ISBN: (纸本)9798331520823

AI has become one of the key forces driving social progress, particularly in the fields of VR and AR, where the application of AI video generation technology is spearheading a technological revolution. This article delves into the specific applications, potential impacts, and future development trends of AI video generation technology in VR and AR. VR technology enables users to fully immerse themselves and interact with virtual environments. AR technology, on the other hand, overlays virtual information onto the real world, integrating virtual objects and information into the user's actual surroundings. AI video generation technology plays a crucial role in these two domains. In the VR sector, AI video generation technology, leveraging deep learning techniques, can learn and mimic human creative styles, automatically generating video content. Through image processing and machine learning technologies, this article explores how AI can generate high-quality virtual objects and scenes, making them more realistic and seamlessly integrated with the real world. Additionally, this article analyzes the challenges and opportunities faced by AI video generation technology in VR and AR applications, such as technological bottlenecks, data privacy, and security issues. AI video generation technology is poised to play an increasingly significant role in the VR and AR sectors.

关键词： Deep learning Hands image processing neural networks Fitting Virtual environments Market research User experience Security artificial intelligence

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A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN Training 41

A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN...

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41st IEEE International Conference on Computer Design (ICCD)

作者： Chen, Menglei Hua, Yu Bai, Rong Huang, Jianming Huazhong Univ Sci & Technol WNLO Wuhan Hubei Peoples R China

ISBN: (纸本)9798350342918

Distributed deep neural network (DNN) training is important to support artificial intelligence (AI) applications, such as image classification, natural language processing, and autonomous driving. Unfortunately, the distributed property makes the DNN training vulnerable to system failures. Check-pointing is generally used to support failure tolerance, which however suffers from high runtime overheads. In order to enable high-performance and low-latency checkpointing, we propose a lightweight checkpointing system for distributed DNN training, called LightCheck. To reduce the checkpointing overheads, we leverage fine-grained asynchronous checkpointing by pipelining checkpointing in a layer-wise way. To further decrease the checkpointing latency, we leverage the software-hardware codesign methodology by coalescing new hardware devices into our checkpointing system via a persistent memory (PM) manager. Experimental results on six representative real-world DNN models demonstrate that LightCheck offers more than 10x higher checkpointing frequency with lower runtime overheads than stateof-the-art checkpointing schemes. We have released the opensource codes for public use in https://***/LighT-chenml/ ***.

关键词： Computer systems Checkpointing systems Failure tolerance Deep neural networks

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International Conference on Innovative Methods and Practical applications for Cognizant Transportation Systems, IMPACTS 2023

International Conference on Innovative Methods and Practical...

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International Conference on Innovative Methods and Practical applications for Cognizant Transportation Systems, IMPACTS 2023

ISBN: (纸本)9789819772995

The proceedings contain 48 papers. The special focus in this conference is on Innovative Methods and Practical applications for Cognizant Transportation Systems. The topics include: Scientific Crash Investigation: A Case Study of the Bus Crash at Vadakkencherry, India;modelling of Lateral Positioning of Vehicles on Undivided Urban Roads in Mixed Traffic Conditions;application of Gap Acceptance Concept to Investigate Behaviour of Drivers at Roundabouts Under Mixed Traffic Conditions;intersection Traffic Data Extractor—A Tool for Extracting Traffic Volume and Speed;influence of Geometric and Traffic Characteristics on Priority Violations at Uncontrolled Intersections;road Safety Audit of Kollam Bypass and Traffic Management Measures for Safety Improvement;impact of Pedestrian Flow on Midblock Traffic Characteristics—A Comprehensive Review;road Safety Evaluation of Stretches Along Safe Corridor Demonstration Project in Kerala, India;modification of Crumb Rubber Modified Binders Using Fly Ash-Based Geopolymers;pavement Distress Detection Using image processing from Unmanned Aerial Vehicle Data;formulation of Design Catalogue for Low-Volume Interlock Cement Block Pavements;performance Analysis of Coir Reinforced Pavement For Rural Roads;pavement Deterioration Prediction Models for Non-urban Road networks in Kerala;rice Husk Ash Modified Asphalt: A High-Performing Bio-Binder for Asphalt Pavements;development of a 3-D Simulation Model of Flexible Pavement Responses Reinforced with Glass Fiber Geogrid Under Moving Load;effect of Soil Stabilization on Pavement Design and Its Cost-Effectiveness Using Cement in Soil Subgrade;prediction of California Bearing Ratio from Soil Index Properties Using artificial neural Network;influence of Different Warm Mix Chemical Additives on Characteristics of Warm Mix Asphalt.

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Cervical cancer classification based on a bilinear convolutional neural network approach and random projection

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ENGINEERING applications OF artificial INTELLIGENCE 2024年第PartA期127卷

作者： Abd-Alhalem, Samia M. Marie, Hanaa Salem El-Shafai, Walid Altameem, Torki Rathore, Rajkumar Singh Hassan, Tarek M. Delta Univ Sci & Technol Fac Artificial Intelligence Gamasa 35712 Egypt Delta Univ Sci & Technol Fac Engn Gamasa 35712 Egypt Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt King Saud Univ Community Coll Comp Sci Dept POB 11433 Riyadh Saudi Arabia Cardiff Metropolitan Univ Cardiff Sch Technol Dept Comp Sci Llandaff CampusWestern Ave Cardiff CF5 2YB Wales

Cervical cancer (CC) is one of the most prevalent malignancies affecting women globally, with a particularly notable impact and high mortality in regions with limited economic resources. This underscores the imperative for the expeditious development of techniques that facilitate timely and precise detection, thereby augmenting treatment efficacy, enhancing survival rates, and mitigating the burden of healthcare expenditure. The intricacies of cervical cancer detection are inherently aligned with the challenges of fine-grained visual classification. This study focuses on the integration of bilinear pooling within convolutional neural networks (CNNs) and addresses the problem of the computational complexity of bilinear features with the fortification of the bilinear CNN with a random projection paradigm (RP-BCNN). The aim is the simultaneous achievement of improved classification precision and streamlined processing temporalities. The proposed methodology entails the introduction of a dyadic feature extraction protocol in which the input cellular image is subjected to twin feature extraction modalities. The feature maps obtained from this process undergo element-wise multiplication via an outerproduct operation, thereby engendering composite feature representations. Subsequently, a judiciously designed random projection procedure is invoked to reduce dimensionality, yielding a more succinct yet informative image descriptor. Empirical evaluations of the introduced model predicted on the RP-BCNN framework yielded commendable outcomes. Notably, an accuracy of 0.9983 was achieved for the dual-label classification scenarios, and an accuracy of 0.9530 was realized in the context of multiclass classification encompassing seven distinct labels. The proposed model achieves an optimal equilibrium between classification accuracy and processing efficiency, thus constituting a potent instrument for the classification of cervical cancer. Further, it holds promise for the refinemen

关键词： Cervical cancer Bilinear pooling Random projection Convolutional neural networks RP-BCNN image classification

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