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5th International Conference on Big Data and artificial Intelligence and Software Engineering, ICBASE 2024

作者： Xu, Xinhe Wang, Zhuoer Zhang, Yihan Liu, Yizhou Wang, Zhaoyue Xu, Zhihao Zhao, Muhan Luo, Huaiying University of Illinois Urbana-Champaign Urbana United States Washington University in St. Louis St. Louis United States Georgia Institute of Technology Atlanta United States Carnegie Mellon University Pittsburgh United States Cornell University Ithaca United States

ISBN: (纸本)9798331506612

This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing pattern images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background. The traditional method excels in creating artistic abstractions but can struggle with seamlessness, whereas the machine learning method preserves the integrity of foreground elements while enhancing the background, offering improved aesthetic quality and computational efficiency. Our study indicates that machine learning-based methods are more suited for real-world applications where detail preservation in foreground elements is essential. © 2024 IEEE.

关键词： Adversarial machine learning

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ATTENTION-BASED neural NETWORK FOR ILL-EXPOSED image CORRECTION 29

ATTENTION-BASED NEURAL NETWORK FOR ILL-EXPOSED IMAGE CORRECT...

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IEEE International Conference on image processing (ICIP)

作者： Messias, Lucas R. V. Drews-, Paulo L. J., Jr. Botelho, Silvia S. C. Fed Univ Rio Grande FURG Ctr Comp Sci Rio Grande Brazil

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The present work presents an artificial neural network architecture for the restoration of images damaged by underexposure and overexposure. The problem is relevant in computer vision applications that are applied in conditions where the limitation of the sensor prevent the scene details from being adequately represented in the captured image. This research presents an attention-based architecture composed of two convolutional neural networks, where one performs a preprocessing of the input image, while the other performs the restoration and enhancement of the degraded image. Regarding the evaluation of research results, a broad range of image quality metrics is used to assess the quality of the results produced by the model. The obtained results indicate that the proposed architecture is able to enhance images damaged by exposure heterogeneity, offering gains over state-of-art models in real data.

关键词： Deep Learning image processing Enhancement Attention

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Deep Spiking neural networks Based on STDP Rules for image Recognition 20

Deep Spiking Neural Networks Based on STDP Rules for Image R...

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20th International Computer Conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Yan, Can Wang, Guang-Shuo Li, Jian-Ping School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Spiking neural networks (SNN) can transmit and process information through a sequence of spikes. Compared with the first two generations of artificial neural networks, spiking neural networks are closer to biological neurons and have high biological interpretability. In addition, SNN have low power consumption and good applications in neurochips. SNN learning algorithms include unsupervised algorithms and supervised algorithms. The STDP rule, as an archetype of unsupervised algorithms, is widely used and has been derived from several versions. This review concentrates on the application of STDP algorithm in the areas of image recognition. © 2023 IEEE.

关键词： image recognition

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Convolutional neural networks based classifications of soil images

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MULTIMEDIA TOOLS AND applications 2022年第7期81卷 10313-10336页

作者： Lanjewar, M. G. Gurav, O. L. Goa Univ Sch Phys & Appl Sci Taleigao Plateau 403206 Goa India Sci Coll Pimpri Pune 411018 Maharashtra India

The utilization of artificial Intelligence (AI) and Machine Learning(ML) in the image processing domain is useful to detect and recognize the types of soil. The main aim of the present work is to process the soil images and classify them accurately by using Tensorflow and Keras Deep Learning (DL) frameworks with pre-trained weights. There are several ML models already implemented for the classification of soil images. A dataset has 903 soil images of four different types of soil (alluvial, black, clay, and red). These images were divided into a training dataset and a validation dataset. The image augmentation process was applied to the dataset, and then the models are trained with these augmented images. In the present work, the Convolutional neural Network (CNN) model was implemented to classify the soil images and achieved an accuracy of 99.86% for training and 97.68% for validation. Furthermore, six Deep Convolution neural Network (DCNN) models were implemented, such as Rsnet152V2, VGG-16, VGG-19, Inception-ResNetV2, Xception, and DenseNet201, to classify the soil images. The accuracy of a Rsnet152V2, VGG-16, VGG-19, Inception-ResNetV2, Xception, and Densnet201 DCNN models were 99.15%, 97.58%, 98.44%, 98.15%, 98.86%, and 98.58%, respectively. The performance of CNN and DCNN models was evaluated using a confusion matrix and K-fold technique. The proposed CNN model has outperformed the DCNN models, and also literature reported works.

关键词： CNN Deep Convolutional neural Network K-fold Confusion matrix Soil types

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Recent Advances of Chimp Optimization Algorithm:Variants and applications

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Journal of Bionic Engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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An integrated machine learning and genetic algorithm approach for properties prediction of fly ash-based steel fiber-reinforced concrete

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Asian Journal of Civil Engineering 2024年第3期26卷 1175-1191页

作者： Keote, Rashmi Keote, Minal Balpande, Rupali S. Masram, Bharati Dubey, Pragati Pinjarkar, Latika Muley, Manjushree Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Computer Science and Applications Ramdeobaba University Maharashtra Nagpur 440013 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India Department of Mathematics S. B. Jain Institute of Technology Management and Research Maharashtra Nagpur 441501 India Department of Electronics and Telecommunication Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India

Enhancement of concrete strength is critically important for increasing construction materials’ lifespan and sustainability. Traditionally, concrete mixture optimization methods—especially those used for fly ash and steel fiber concretes—normally fail to accurately predict the strength due to the high degree of complexity and non-linearity involved in the interaction of their components. These limitations are overcome in this study, which uses advanced artificial intelligence techniques—The Multilayer Perceptron (MLP) neural networks, Gradient Boosting Machines (GBM), and Convolutional neural networks (CNN) to optimize concrete mixtures for improved strength. Among these, the MLP neural network was selected for this work because of its ability to model highly complex, nonlinear relationships and hence will be able to capture the intricate interactions among fly ash, steel fibers, and other additives. For this reason, Gradient Boosting Machine was chosen for its robustness against overfitting and high accuracy in handling linearity or nonlinearity in an optimization problem. Traditionally, CNN has been applied to image processing, but in this work, it had been uniquely adapted to include the spatial distribution of concrete mix components, hence giving a new dimension in strength prediction. In this study, every method was used with a comprehensive data set and the input variables were taken as the percentages of fly ash and steel fibers, the water-cement ratio, aggregate size distribution, and curing delays. The accuracies of prediction for the proposed models were improved significantly, with the Mean Absolute Error (MAE) for compressive strength by the MLP model and an R² value of 0.90–0.95 by the GBM model. It is interpreted from CNN that there could be a potential reduction in prediction error by 10–15% compared to traditional methods. The work provides a robust framework for concrete strength optimization with substantial improvements in the reliability and per

关键词： Concrete strength Fly ash Gradient boosting MLP neural networks Steel fibers Sustainable concrete

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Protons Evaluation of 7nm Versal™ AI Engine (AIE) based Radiation Tolerant Platform for Deep Learning applications

Protons Evaluation of 7nm Versal™ AI Engine (AIE) based Rad...

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2024 Radiation Effects Data Workshop

作者： Maillard, Pierre Dhavlle, Abhijitt Chen, Yanran Paula Fraser, Nicholas Chen, Yushan Vacirca, Nicholas Barton, Jeff Voogel, Martin L. Sawant, Minal AMD XILINX 2100 Log Dr San Jose CA 95124 USA

ISBN: (纸本)9798350382082;9798350382075

This paper presents the SEE evaluation of a platform and design approach for enabling radiation-tolerant deep learning acceleration, based on the 7nm Versal (TM) VC1902 AI Engine (AIE), for aerospace and space 2.0 proton environments. The presented platform is suitable for deep neural network (DNN) implementations emphasizing image classification and uses 396 AIE cores. It includes solutions to mitigate both radiation-induced Single Event Functional Interrupts (SEFIs) and network datapath corruptions. The radiation-tolerant deep learning platform combines AMD's AI engine (AIE), Deep Learning processing Unit (DPU) IP, and AMD Soft Error Mitigation (XilSEM) to mitigate SEFIs. Furthermore, a technique known as Fault Aware Training (FAT) was applied to mitigate single-event effects (SEE) in the datapath effectively. Test results from a 64MeV proton beam experiment using the ResNet-50 Convolutional neural Network (CNN) for image classification are presented. The Single Event Upset (SEU) rate, system-level SEFI rate, and neural network classification/datapath performance are compared between the standard non-mitigated and radiation-tolerant platforms. Compared to standard non-mitigated training techniques, the radiation-tolerant platform using fault-aware training methods dramatically improves overall system response. SEFI error rates are similar to 1.4 x 10(-10) p/cm(2) at > 60 MeV and twice a year at 500km in LEO orbit. Datapath events are reduced by similar to 4X and events classification accuracy degradation > 5% are completely mitigated.

关键词： 7nm Versal artificial Intelligence AIE DPU neural networks SEU SEFI Resnet-50 Fault Aware Training Adaptive Compute Platform

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Fast and Efficient Real-Time Facial Recognition System Using Raspberry Pi and IoT 8

Fast and Efficient Real-Time Facial Recognition System Using...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Shanker, Ravi Chouhan, Santosh Prakash Bhardwaj, Shivam Kumar, Ankur Bhattacharya, Mahua Indian Institute of Information Technology Ranchi Jharkhand Ranchi India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Madhya Pradesh Gwalior India

ISBN: (纸本)9798331531539

Recent advancements in technologies such as artificial intelligence, computer vision, and the Internet of Things have expanded into numerous fields, particularly enhancing surveillance systems that demand real-time facial recognition for security. This study introduces a novel integration of edge computing and computer vision to create an efficient, cost-effective facial recognition system with real-time capabilities. Unlike traditional systems that rely on high-powered servers or cloud infrastructure, which often result in latency, privacy concerns, and increased costs, this approach utilizes Convolutional neural networks (CNNs) and the You Only Look Once (YOLO) algorithm on edge devices like the Raspberry Pi. By combining CNNs for precise image classification with YOLO for fast object detection, the system performs high-speed processing directly on the edge device, enabling secure and efficient facial recognition without the need for continuous cloud connectivity. This research thoroughly examines both hardware and software optimizations, showing how this edge-based solution not only reduces latency but also provides scalability and flexibility, making it well-suited for practical security applications. © 2024 IEEE.

关键词： Convolutional neural networks

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artificial neural Network using image processing for Digital Forensics Crime Scene Object Detection 2

Artificial Neural Network using Image Processing for Digital...

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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Devasenapathy, Deepa Raja, M. Dwibedi, Rajat Kumar Vinoth, N. Jayasudha, T. Ganesh, V. Dilli Computing & Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University United States Cmr Institute of Technology Department of Computer Science and Engineeting Bengaluru India Aarupadai Veedu Institute of Technology Vmrf Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350347579

Digital forensics science places a significant emphasis on the detection of objects as one of the most vital areas of study. Several industries and institutions may benefit from the object detection method, including those concerned with medical diagnostic scanning, traffic monitoring, airport security, law enforcement, and data rescue on a local and global scale. This study aims to detect weapons in video surveillance images by using various enhancement, segmentation, feature extraction, and classification methods by artificial neural Network to improve the detection accuracy. Yet, several mathematical and algorithmic models are computed to provide the appropriate approaches. © 2023 IEEE.

关键词： neural networks

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A comprehensive assessment of artificial intelligence applications for cancer diagnosis

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artificial INTELLIGENCE REVIEW 2024年第7期57卷 179-179页

作者： Singh, Gaurav Kamalja, Anushka Patil, Rohit Karwa, Ashutosh Tripathi, Akansha Chavan, Pallavi DY Patil Deemed Be Univ Ramrao Adik Inst Technol Informat Technol Sect 7 Nerul Navi Mumbai 400706 Maharashtra India

artificial intelligence (AI) is being used increasingly to detect fatal diseases such as cancer. The potential reduction in human error, rapid diagnosis, and consistency of judgment are the primary motives for using these applications. artificial neural networks and Convolution neural networks are popular AI techniques being increasingly used in diagnosis. Numerous academics have explored and evaluated AI methods used in the detection of various cancer types for comparison and analysis. This study presents a thorough evaluation of the AI techniques used in cancer detection based on extensively researched studies and research trials published on the subject. The manuscript offers a thorough evaluation and comparison of the AI methods applied to the detection of five primary cancer types: breast cancer, lung cancer, colorectal cancer, prostate cancer, skin cancer, and digestive cancer. To determine how well these models compare with medical professionals' judgments, the opinions of developed models and of experts are compared and provided in this paper.

关键词： artificial intelligence Cancer detection Cancer diagnosis Prediction Deep learning Machine learning image processing

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