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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing"

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Optimization of Machine Learning and Deep Learning Algorithms for Diagnosis of Cancer 1

Optimization of Machine Learning and Deep Learning Algorithm...

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1st International conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Krishna, Hari Anand, M. Saravanan, D. Pushpalatha, K. Department of ECE B V Raju Institute of Technology Telangana India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of CSE IFET College of Engineering Villupuram India Department of Mathematics KoneruLakshmaiah Educational Foundation Vadedeswaram India

ISBN: (纸本)9781607685395

Machine learning and artificial intelligence has recently become a prominent technology. Given its popularity and strength in pattern recognition and categorization, many corporations and institutions have begun investing in healthcare research to improve illness prediction accuracy. Using these strategies, however, has several drawbacks. One of the primary issues is the lack of huge data sets for medical pictures. An introduction to deep learning in medical image processing, from theoretical foundations to real-world applications. The article examines the general appeal of deep learning (DL), a collection of computer science advances. The next step was to learn the basics of neural networks. That explains the use of deep learning and CNNs. So we can see why deep learning is rapidly advancing in various application fields, including medical image processing. The goal of this research was to use innovative methodologies on cancer datasets to explore the feasibility of combining machine learning and deep learning algorithms for cancer detection. This study used text and picture databases to classify cancer. The datasets are the Liver BUPA disorder database and brain MRI pictures. This article provides optimization methods that outperform the suggested approaches' accuracy. Using two alternative training methods, Levenberg Marquardt (lm) and Resilient back propagation (rp), two classification algorithms were evaluated with different groups of neurons to identify benign and malignant patients. Cascade correlation utilizing the train (rp) outperformed feed forward back propagation using the train (lm). The second deep neural network model presented a technique (based on CNN) for automated brain tumour identification using MRI data. The Water Cycle Algorithm is used to optimise CNN. The established approach is very accurate. The suggested framework examines innovative texture classification algorithms using Discrete Wavelet Transform (DWT) and the Gray Level Co occurrence

关键词： artificial neural Network Batch Normalization Deep Learning Gray Level Co-occurrence Matrix Water Cycle Algorithm

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Physical Exergames Movements and Pattern Recognition using Convolutional neural Network

Physical Exergames Movements and Pattern Recognition using C...

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International conference on Advancements in Computational Sciences (ICACS)

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

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

This paper proposes a novel framework for accurately recognizing physical exergames interaction in video sequences. The developed approach uses image processing and machine learning tools to correctly extract and classify the features. The framework comprises five main steps: Silhouette extraction, preprocessing, feature extraction, feature optimization and classification. image contrast is improved by power law transformation, silhouette is extracted by the Multiple Object Tracking (MOT) algorithm and graph-based segmentation. We extract ORB and geometric skeleton based keypoints to capture discriminative features, and apply Fast Independent Component Analysis (FICA) to optimize feature representation. Thirdly, the Convolutional neural Network algorithm is used to classify the optimized features, with a macro average accuracy of 0.85 and a weighted average accuracy of 0.87%. The proposed framework effectively recognizes physical exergames interaction and can be applied in many domains, such as human-human interaction and surveillance.

关键词： image segmentation Machine learning algorithms Accuracy Surveillance Video sequences Independent component analysis Feature extraction Classification algorithms Convolutional neural networks Optimization

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Machine Learning-Based Smart Waste Management in Urban Area

Machine Learning-Based Smart Waste Management in Urban Area

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International conference on Advances and applications of artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Barik, Sutanika Naz, Shaheen Tiwari, Usha Jain, Monika Sharda University Greater Noida India ITS Engineering Collage Muzaffarpur India

ISBN: (纸本)9789819959730

The population of our country continues to grow, and the need for a good lifestyle is most visible in the urban region. Traditional waste management systems have failed, necessitating the development of a new strategy to address this problem. Traditional waste management systems have failed, necessitating the development of a new strategy to address this problem. Because we are unable to limit the amount of waste generated by citizens, a proper technique for managing this massive waste is essential. The proposed model is mainly focused on the emptying dustbin from time to time and recycling of the bio-wastages. Here we will install MACHINE LEARNING and IoT to provide a smart management system for the waste. Ultrasonic sensor, load measuring sensor, and microcontroller is used to make a smart dustbin so that when it is filled with enough waste, the municipality gets a message to clean it. Using IoT this smart dustbin is wirelessly connected with the base station. In machine learning CNN (Convolutional neural Network) model algorithms are used to identify the digestive and indigestive wasted so that we can easily sort them and recycle the waste. image processing methods of ML are used to detect the bio and non-bio wastes. CNN has some hidden convolutional layers with a particular no. of filters in each layer. These filters detect the pattern of an image and then compare it to the pre-trained image of the ML algorithm. It’s a smart way to monitor the waste and clear remediation of the wastage problem in big cities, organizations, etc. also this project makes us a step forward to the green environment. The efficiency and accuracy of the predicted model are evaluated by machine learning algorithms so changes of model failure will be less in percentage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： neural network models

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Automated Plant Disease Detection: CNN for Corn Maize, Tomato, and Potato 7th

Automated Plant Disease Detection: CNN for Corn Maize, Tomat...

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7th International conference on Soft Computing: Theories and applications, SoCTA 2023

作者： Angeline, R. Aruneshwaran, S. Department of Computer Science and Engineering with Specialization in Artificial Intelligence and Machine Learning SRM Institute of Science and Technology Ramapuram India

ISBN: (纸本)9789819720880

Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing crop yield reductions. Lately, advanced deep learning techniques, specifically Convolutional neural networks (CNNs), have exhibited encouraging outcomes across various tasks involving image recognition. This undertaking strives to create and execute a model founded on CNNs to prognosticate plant diseases through leaf images. The proposed strategy encompasses three main phases: compiling and preparing the data, developing the model architecture, and assessing performance. Initially, an extensive dataset of plant leaf images, encompassing leaves afflicted by diverse diseases, is assembled. The images undergo preprocessing to heighten quality and eliminate disturbances, ensuring a dependable model training process. Subsequently, a CNN structure is devised and trained to employ the dataset. The chosen CNN model adheres to a sequential design, where each layer possesses precisely one input and output. These layers are arranged sequentially to construct the entire network and incorporate multiple convolutional layers such as Conv2D, MaxPooling2D, Flatten, and Dense, enabling the learning of features from the input images. The findings underscore that the CNN-centered model for forecasting plant diseases attains remarkable training precision of 99.65%, accompanied by a testing precision of 99.44% and a validation precision of 98.61%, proficiently identifying prevalent ailments like common rust disease in corn plants, bacterial spot infection in tomato crops, and the early blight ailment in potato plants. In conclusion, the proposed CNN-driven prognostic model for plant diseases manifests encouraging outcomes in precisely recognizing these diseases from leaf images. The efficacious application of this model can assist farmers and agricultural specialists in inform

关键词： Fruits

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Distilled neural Network for Clothes Type Identification 20

Distilled Neural Network for Clothes Type Identification

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20th International conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Chongpiphattanasiri, Teerapon Wickramatilake, Pramod Moonrinta, Jednipat Ekpanyapong, Mongkol Dailey, Matthew N. Asian Institute of Technology Microelectronics and Embedded Systems Pathumthani Thailand Asian Institute of Technology Artificial Intelligence Center Pathumthani Thailand Asian Institute of Technology Computer Science and Information Management Pathumthani Thailand

ISBN: (纸本)9798350310467

Diverse applications such as criminal forensics and retail customer market segmentation would benefit from the ability to track individuals across multiple cameras. Clothing information is one of the keys to this capability when other identifying information such as the face cannot be reliably extracted. To quickly search for a criminal suspect or re-identify individuals across time and space, filtering by clothing over a given set of cameras for a given time range can dramatically narrow the search. To achieve this capability, we propose a training pipeline that adapts a knowledge distillation neural network to develop a lightweight but effective deep learning model for classifying clothing. The proposed training pipeline produces a small model that achieves 78.70% clothes type classification accuracy on the DeepFashion2 benchmark dataset. An experimental inference time of 0.019 per image on a Nvidia RTX 2070 Super GPU demonstrates that the system has potential for deployment over large-scale camera sensor networks. We expect the new approach to accelerate criminal investigations and enable other analytics requiring reidentification of individuals across multiple cameras at scale. © 2023 IEEE.

关键词： Pipelines

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A Multi-level Approach to Evaluate the Impact of GPU Permanent Faults on CNN's Reliability

A Multi-level Approach to Evaluate the Impact of GPU Permane...

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IEEE International Test conference (ITC)

作者： Condia, Josie E. Rodriguez Guerrero-Balaguera, Juan-David Dos Santos, Fernando F. Reorda, Matteo Sonza Rech, Paolo Politecn Torino Dept Control & Comp Engn DAUIN Turin Italy Univ Rennes INRIA Rennes France Univ Trento Dept Ind Engn Trento Italy

ISBN: (数字)9781665462709

ISBN: (纸本)9781665462709

Graphics processing units (GPUs) are widely used to accelerate artificial Intelligence applications, such as those based on Convolutional neural networks (CNNs). Since in some domains in which CNNs are heavily employed (e.g., automotive and robotics) the expected lifetime of GPUs is over ten years, it is of paramount importance to study the impact of permanent faults (e.g. due to aging). Crucially, while the impact of transient faults on GPUs running CNNs has been widely studied, an accurate evaluation of the impact of permanent faults is still lacking. Performing this evaluation is challenging due to the complexity of GPU devices and the software implementing a CNN. In this work, we propose a methodology that combines the accuracy of gate-level fault simulation with the speed and flexibility of software fault injection to evaluate the effects of permanent hardware faults affecting a GPU. First, we profile the executed low-level GPU instructions during the CNN inference. Then, using extensive gate-level fault injection campaigns, we provide an accurate analysis of the effects of permanent faults on the internal modules executing the targeted instructions. Finally, we propagate these effects using fast software-based fault injection. The method allows, for the first time, to estimate the percentage of permanent faults leading the CNN to produce wrong results (i.e., changing the result of its work). The method's feasibility, which allows for flexibly trade-off accuracy with the required computational effort, is shown using LeNet running on an Ampere Nvidia GPU as a case study. The method reduces the computational effort for the evaluation by several orders of magnitude with respect to plain gate- and RTL-level faults simulation.

关键词： Performance evaluation Computational modeling Graphics processing units Logic gates Software Hardware Convolutional neural networks

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Automatic Detection of Leukemia through Convolutional neural Network

Automatic Detection of Leukemia through Convolutional Neural...

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2022 International conference on Frontiers of Information Technology, FIT 2022

作者： Arif, Rida Akbar, Shahzad Farooq, Ahmad Bilal Ale Hassan, Syed Gull, Sahar Riphah International University Department of Computing Pakistan

ISBN: (纸本)9798350345933

Leukemia is a fatal cancer disease that develops in blood-forming tissue by the excessive development of white blood cells (WBCs) in the human body. However, a bone marrow test is recommended by the pathologist to diagnose leukemia and further types of leukemia. Leukemia has two classes i.e., acute and chronic leukemia. Therefore, early leukemias detection enables preventative actions to be taken to avoid any harm to human life. In addition, several manual and automatic methods have been proposed, however, they possess some drawbacks and are inefficient for the precise detection of leukemia. This research proposes a deep learning-based framework for precise and automatic leukemia identification using microscopic images. The proposed framework comprises four stages which are pre-processing, data augmentation, segmentation, and the classification of leukemia. Moreover, pre-processing is utilized to clean the dataset images and eliminate the noise. Following that, data augmentation approaches have been employed to increase the number of images, and remove the class imbalance, and overfitting problems. The modified Convolutional neural Network (CNN) based model is employed to segment the leukemia images. A well-known pre-trained AlexNet architecture has been used for classification. Besides that, a publicly available dataset Acute Lymphoblastic Leukemia image DataBase (ALL-IDB) has been utilized to train and test the proposed model. The proposed model yielded 98.05% accuracy, a specificity of 97.59%, 100% of recall, and a 99.06% of F1-score. The experimentation results demonstrate that this model is effective and reliable for leukemia identification using the ALL-IDB dataset and suitable for deployment in clinical applications. © 2022 IEEE.

关键词： Convolutional neural networks

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Proceedings of the 12th International conference on Information Technology and its applications, CITA 2023

Proceedings of the 12th International Conference on Informat...

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Proceedings of the12th International conference on Information Technology and its applications, CITA 2023

ISBN: (纸本)9783031368851

The proceedings contain 33 papers. The special focus in this conference is on Information Technology and its applications. The topics include: An Improved Transfer Learning-Based Approach for Detecting Abnormal Cervical Cells from Pap Smear Microscopic images;emotional Intelligence and Social Media Site Experiences’ Effects on College Choice Behavior: The Mediating Role of Brand Attitude;a Multi Context Decoder-based Network with applications for Polyp Segmentation in Colonoscopy images;AMG-Mixer: A Multi-Axis Attention MLP-Mixer Architecture for Biomedical image Segmentation;Investigating YOLO Models for Rice Seed Classification;Extending OCR Model for Font and Style Classification;STG-SimVP: Spatiotemporal GAN-Based SimVP for Video Prediction;tritention U-Net: A Modified U-Net Architecture for Lung Tumor Segmentation;multi-modal with Multiple image Filters for Facial Emotion Recognition;differentially-Private Distributed Machine Learning with Partial Worker Attendance: A Flexible and Efficient Approach;few-Shots Novel Space-Time View Synthesis from Consecutive Photos;Information Technology Skills Extractor for Job Descriptions in vku-ITSkills Dataset Using Natural Language processing;comprehensive Study on Semantic Segmentation of Ovarian Tumors from Ultrasound images;VR VanMieu: The Interactively Virtual Temple of Literature;neural Sequence Labeling Based Sentence Segmentation for Myanmar Language;a Compact Version of EfficientNet;using Deep Learning for Obscene Language Detection in Vietnamese Social Media;A New Approach for Enhancing MVDR Beamformer’s Performance;a Spectral Mask - Based on Method for Applying into Generalized Sidelobe Canceller Beamformer;An Integrated Model of Placement Optimization and Redundancy Elimination in RFID Network Planning;Building Legal Knowledge Map Repository with NLP Toolkits;conjunctive Query Based Constraint Solving for Feature Model Configuration;ESpin: Analyzing Event-Driven Systems in Model Checking;blockchain Solution for

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DERIVING EXPLAINABLE DISCRIMINATIVE ATTRIBUTES USING CONFUSION ABOUT COUNTERFACTUAL CLASS 47

DERIVING EXPLAINABLE DISCRIMINATIVE ATTRIBUTES USING CONFUSI...

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47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Yang, Nakyeong Kang, Taegwan Jung, Kyomin Seoul Natl Univ Dept Artificial Intelligence Seoul South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781665405409

Recently, Integrated Gradients-based (IG) methods have been commonly used to explain the decision process of deep neural networks (DNNs). However, they have only considered the information of the predicted class while neglecting the information of the rest classes. In this paper, we propose a novel counterfactual explanation method, Discriminative Gradients (DiscGrad) that derives explainable discriminative attributes by considering not only the predicted class but also the counterfactual classes. Specifically, we calculate the discriminative attributes by removing the attribute of the counterfactual classes, and this process makes it possible to derive only key discriminative attributes that contrast with other decisions. Also, we determine the weights for discriminative attributes using the degree of confusion about counterfactual classes. We evaluated our method by measuring how much logit decreases by perturbing important attributes. Experimental results on the widely used image and text datasets show that our proposed method outperforms the strong baseline, IG. In addition, we examine the relationship between class correlation and the performance of discriminative attribute to demonstrate the effectiveness of our method.

关键词： explainable AI counterfactual explanation deep learning

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Multimodal Adversarial Defense Trained on Features Extracted from images and Brain Activity

Multimodal Adversarial Defense Trained on Features Extracted...

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IEEE Global conference on Consumer Electronics (GCCE)

作者： Tasuku Nakajima Keisuke Maeda Ren Togo Takahiro Ogawa Miki Haseyama Hokkaido University Sapporo Hokkaido Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Deep neural networks (DNNs) have achieved significant advancements in artificial intelligence (AI) but remain vulnerable to adversarial attacks. In contrast, the human cognitive system, integrating multimodal processing and brain activity, shows high robustness against such attacks. This paper introduces a novel multimodal adversarial defense method that incorporates brain activity data to enhance the DNN robustness. We use functional magnetic resonance imaging (fMRI) data recorded while subjects view images and combine it with visual data to create a more robust classifier. Experimental results using the NSD dataset demonstrate that our method improves robustness against adversarial attacks compared to traditional methods. This research underscores the potential of leveraging brain activity data to develop more robust AI systems.

关键词： Brain Visualization Accuracy artificial neural networks Functional magnetic resonance imaging Feature extraction Robustness Data models Data mining image classification

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