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IEEE International conference on Data Mining Workshops (ICDM Workshops)

作者： Diletta Chiaro Edoardo Prezioso Stefano Izzo Fabio Giampaolo Salvatore Cuomo Francesco Piccialli Department of Mathematics and Applications “R. Caccioppoli” University of Naples Federico II Napoli Italy

The progress achieved in the field of information and communication technologies, particularly in computer science, and the growing capacity of new types of computational systems (cloud/edge computing) significantly contributed to the cyber-physical systems, networks where cooperating computational entities are intensively linked to the surrounding physical en-vironment and its on-going operations. All that has increased the possibility of undertaking tasks hitherto considered to be an exclusively human concern automatically: hence the gradual yet progressive tendency of many companies to adopt artificial intelligence (AI) and machine learning (ML) technologies to automate human activities. This papers falls within the context of deep learning (DL) for utility pattern mining applied to Industry 4.0. Starting from images supplied by a multinational company operating in the food processing industry, we provide a DL framework for real-time pattern recognition applied in the automation of peach pitters. To this aim, we perform transfer learning (TL) for image segmentation by embedding seven pre-trained encoders into multiple segmentation architectures and evaluate and compare segmentation performance in terms of met-rics and inference speed on our data. Furthermore, we propose an attention mechanism to improve multiscale feature learning in the FPN through attention-guided feature aggregation.

关键词： Representation learning image segmentation Transfer learning Companies Logic gates Real-time systems Information and communication technology

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Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase ii clusters

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PNAS NEXUS 2022年第3期1卷 pgac065页

作者： Hajiabadi, Hamideh Mamontova, Irina Prizak, Roshan Pancholi, Agnieszka Koziolek, Anne Hilbert, Lennart Helmholtz Informat & Data Sci Sch Hlth HIDSS4Hlth D-76131 Karlsruhe Germany Karlsruhe Inst Technol Inst Biol & Chem Syst Dept Biol Informat Proc D-76344 Eggenstein Leopoldshafen Germany Karlsruhe Inst Technol KASTEL Inst Informat Secur & Dependabil D-76131 Karlsruhe Germany Karlsruhe Inst Technol Zool Inst Dept Syst Biol & Bioinformat D-76131 Karlsruhe Germany

Fluorescence microscopy, a central tool of biological research, is subject to inherent trade-offs in experiment design. For instance, image acquisition speed can only be increased in exchange for a lowered signal quality, or for an increased rate of photo-damage to the specimen. Computational denoising can recover some loss of signal, extending the trade-off margin for high-speed imaging. Recently proposed denoising on the basis of neural networks shows exceptional performance but raises concerns of errors typical of neural networks. Here, we present a work-flow that supports an empirically optimized reduction of exposure times, as well as per-image quality control to exclude images with reconstruction errors. We implement this work-flow on the basis of the denoising tool Noise2Void and assess the molecular state and 3D shape of RNA polymerase ii (Pol ii) clusters in live zebrafish embryos. image acquisition speed could be tripled, achieving 2-s time resolution and 350-nm lateral image resolution. The obtained data reveal stereotyped events of approximately 10 s duration: initially, the molecular mark for recruited Pol ii increases, then the mark for active Pol ii increases, and finally Pol ii clusters take on a stretched and unfolded shape. An independent analysis based on fixed sample images reproduces this sequence of events, and suggests that they are related to the transient association of genes with Pol ii clusters. Our work-flow consists of procedures that can be implemented on commercial fluorescence microscopes without any hardware or software modification, and should, therefore, be transferable to many other applications.

关键词： image processing reliable deep learning fluorescence microscopy gene regulation transcription

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Optimized Deep neural Network for Accurate Detection of Malignant and Benign Brain Tumors

Optimized Deep Neural Network for Accurate Detection of Mali...

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artificial Intelligence For Internet of Things (AiioT), International conference on

作者： J. Visumathi N. Kalaivani S V Hemanth K. Balasubramanian S. Karthikeyan G. Amirthayogam Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai TamilNadu India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana Department of Computer Applications Kalasalingam Academy of Research and Education (Deemed to be University) Krishnankoil Tamil Nadu India Department of Computer Science and Engineering Arunai Engineering College Tiruvanamalai TamilNadu India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Chennai Tamilnadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The rapid development of medical technology has ushered in a new age of crucial medical records. A brain tumor is an aberrant mass of rapidly expanding brain cells, some of which have the potential to metastasize. Brain tumors are often diagnosed in clinical practice using magnetic resonance imaging (MRI) scans. These tumors appear as clusters of irregular cells that could endanger patients by invading nearby tissues. However, manually spotting and segmenting brain tumors via MRI scans present complex challenges and are prone to mistakes. Thus, analysis of such data has influenced the prognosis, monitoring, diagnosis, and treatment of tumor-related conditions. To overcome these issues, we propose an Optimized Deep neural Network (ODNN) method to accurately predict benign or malignant brain tumor diseases. Moreover, pre-processing uses an Adaptive Histogram Contrast Normalization (AHCN) technique to enhance the distribution and contrast of MRI image density. Afterward, features can be extracted using a Texture-Based Gray Level Co-Occurrence Matrix (TGLCM) method to separate the dark and bright parts of the image. Finally, a Machine Learning (ML) based ODNN method is employed to classify benign or malignant diseases. Utilizing sensitivity, specificity, recall, and accuracy metrics, the simulation results of the recommended ODNN approach are contrasted with those of preexisting approaches.

关键词： Accuracy Sensitivity Magnetic resonance imaging Simulation artificial neural networks Feature extraction Prognostics and health management

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International conference on Applied Sciences ICAS2022

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Journal of Physics: conference Series 2023年第1期2540卷 011001页

作者： Dan, Ludovic LEMLE United States

Banja Luka, Bosnia and Herzegovina, May 25-28, 2022International conference on Applied Sciences ICAS2022 took place in Banja Luka, Bosnia and Herzegovina, in the period May 25–28, 2022, at the University of Banja Luka, with the aims to serve as a platform for exchange of information between various areas of applied sciences, and to promote the communication between the scientists of different nations, countries and *** conference has been focused on several fields of application, operation and influence of the applied sciences and technologies on *** of the conference covers a comprehensive spectrum of issues from:1. Fundamental Sciences: Numerical approximation and analysis, Numerical simulation, Numerical optimization, General statistical methods, Stochastic analysis methods, Analytical and numerical techniques, Finite element methods, Dynamical systems methods, Chemical composition analysis, Energy analysis, Heat transfer, Interdisciplinary applications of physics, Environmental aspects, Effects of pollution, Fuzzy logic, and others…2. Computers Engineering: Computer modeling and simulation, Algorithms, Software engineering, artificial intelligence, neural networks, image processing, Data acquisition: hardware and software, Data presentation and visualization, Data analysis: algorithms and implementation, Data management, Big Data, Internet and network applications, and others…3. Electrical Engineering: Electrical and electronic instruments and components, Circuits and circuit components, Signal processing electronics, Power electronics, Electric motors, Electric vehicles, Biomass energy, Wind energy, Solar energy, Solar cells (photovoltaics), Conventional hydropower, Hydroturbines, and others…4. Mechanical Engineering: General theory of classical mechanics, Computational methods in classical mechanics, Control of mechanical systems, Dynamics and kinematics of rigid bodies, Mechanical properties of solids, Applied mechanics and design, Defo

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Patronus: Countering Model Poisoning Attacks in Edge Distributed DNN Training

Patronus: Countering Model Poisoning Attacks in Edge Distrib...

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IEEE conference on Wireless Communications and Networking

作者： Zhonghui Wu Changqiao Xu Mu Wang Yunxiao Ma Zhongrui Wu Zhenyu Xiahou Luigi Alfredo Grieco State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Information Technology Zhejiang Financial College Zhejiang China Jiangxi Academy of Cyber Security Jiangxi China Department of Electrical and Information Engineering and CNIT Bari Italy

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

As Deep neural networks (DNNs) are evolving in complexity to meet the demands of novel applications, a single device becomes insufficient for training, leading to the emergence of distributed DNN training. However, this evolution exposes a gap in research surrounding security vulnerabilities on model poisoning attacks, especially in model parallel setups, an area that has been scarcely studied. To bridge this gap, we introduce Patronus, an approach that counters model poisoning attacks in distributed DNN training, accommodating both data and model parallelism. With the employment of Loss-aware Credit Evaluation, Patronus scores each participating client. Based on the continuously updated credit, malicious clients are isolated and detected after multiple epochs by Shuffling-based Isolation Mechanism. Additionally, the training system is reinforced by Byzantine Fault-tolerant Aggregation to minimize malicious client impacts. Comprehensive experiments confirm Patronus's superior reliable and efficient performance over the existing methods under attack scenarios.

关键词： Training Fault tolerance image edge detection Fault tolerant systems Distributed databases artificial neural networks Parallel processing

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An Advanced Deep Learning Approach Combining image Analysis for Precise Retinal Disease Detection

An Advanced Deep Learning Approach Combining Image Analysis ...

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artificial Intelligence for Innovations in Healthcare Industries (ICAiiHI), International conference on

作者： Ankit Kumar Kamred Udham Singh Teekam Singh Gaurav Kumar Saroj Kumar Pandey Pushpendra Dhar Dwivedi Department of Computer Engineering & Apllications GLA University Mathura UP India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Department of CSE Government Engineering College Raipur (C.G.)

The purpose of this research is to showcase a unique hybrid image-centric deep learning system that was developed for the purpose of accurately identifying and diagnosing retinal diseases. Our method overcomes the limits that are connected with conventional imaging and computational analytics, specifically with regard to the accuracy of diagnostics and the level of processing speed. Our technique incorporates optimised models for retinal pictures in a seamless manner by combining the capabilities of convolutional neural networks (CNNs) with the flexibility of transfer learning. These models are then supplemented by advanced feature extraction algorithms. This new development makes it easier to accurately recognise small visual differences in retinal pictures, which are often missed by traditional approaches. Our study, which is based on a large dataset that includes a wide range of retinal anomalies, guarantees not only the correctness of our solution but also its flexibility to a variety of different situations. By demonstrating considerable gains in diagnostic accuracy, sensitivity, and specificity, as well as a notable decrease in the amount of manual feature engineering efforts, rigorous testing speeds up the diagnostic process without sacrificing its dependability. In addition to its use in retinal diagnosis, this model shows the potential of combining deep learning with conventional image analysis. It suggests that this combination might have a transformational influence on digital healthcare, with applications that could be extended to greater issues in medical imaging. In the rapidly developing field of digital healthcare, our results shed light on a potentially fruitful avenue. This avenue involves the use of advanced machine learning methods for the purpose of improving medical diagnosis.

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ADNet: An Advanced artificial neural Network for Early Diagnosis of Alzheimer's Disease

ADNet: An Advanced Artificial Neural Network for Early Diagn...

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Sustainable Technologies for Industry 4.0 (STI), International conference on

作者： Md. Arif Hasan Rifat Nusrat Jahan Tanha Taranum Muhammad Nazrul Islam Faiz Al Faisal Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Alzheimer's disease is a progressive neurological disorder, often referred to as dementia, that requires early and accurate diagnosis for effective treatment and management. It's a complex disease type that originates in the brain and only gets worse with timestamps, which is why it should be diagnosed early and accurately. Traditional diagnostic methods often fall short, posing challenges to healthcare professionals. This paper introduces a novel deep learning technique, ADNet, a specialized convolutional neural network (CNN) developed to classify Alzheimer's disease into four stages. For MRI image processing, we applied preprocessing techniques, including Green Fire Blue conversion and contrast enhancement using CLAHE, to improve image clarity. To address data imbalance, various data augmentation methods were implemented. During experimentation, pre-trained models like VGG16, MobileNet-V2, InceptionV3, and ResNet-50 achieved accuracies of 85%, 85%, 79 %, and 67 %, respectively, but fell short of our goals. Our ADNet model, however, attained an impressive 97.26% accuracy. This work offers significant contributions to Alzheimer's research and advances deep learning applications in medical imaging and neurological disease diagnosis.

关键词： Neurological diseases Deep learning Training Technological innovation Accuracy Magnetic resonance imaging Computational modeling Brain modeling Convolutional neural networks Alzheimer's disease

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Enhancing FPGA-Based YOLO Object Detection: Multi-Bank Storage Optimization and Model Refinement for Real-Time applications

Enhancing FPGA-Based YOLO Object Detection: Multi-Bank Stora...

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International conference on Integrated Circuits and Microsystems (ICICM)

作者： Shiwei Ma Zhisong Bie Xianchen Liang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

Object detection algorithms are widely used in image classification and detection tasks recently. Among the other algorithms, You Only Look Once (YOLO) performs better in real-time detection. Convolutional neural networks (CNNs) in the algorithm require a large amount of computation and memory access. However, researchers tend to access parameters through the external memory in accelerator design, which leads to slow processing speed and high power consumption. Therefore, we propose a Multi-Bank storage structure that flexibly combines URAM and BRAM in storing feature maps, which significantly improves the throughput. We also improve the YOLOv3-Tiny model by distillation, quantization and pruning and make it more suitable for deployment in FPGAs. These methods can significantly reduce the number of parameters in the model while ensuring almost no decrease in detection accuracy. Finally, tested on VOC 2007, our model achieves a frame rate of 67.6 FPS with a reduction in mAP of 0.38%.

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Deep Learning based Object Detection Algorithm for the Detection of Dental Diseases and Differential Treatments

Deep Learning based Object Detection Algorithm for the Detec...

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IEEE India conference (INDICON)

作者： Athira Thulaseedharan Lal Priya P S Electrical Engineering Department College of Engineering Trivandrum APJ Abdul Kalam Technological University Kerala India

ISBN: (纸本)9781665452724

With a wide range of applications in cosmetic dentistry treatments, and treatment planning, the use of artificial Intelligence (AI) and Machine Learning is having a growing influence in the field of dentistry and is enhancing the growth of digital tools and technology. The surgical sector, automatic disease diagnosis, and recently established customized medicine are just a few of the disciplines that might benefit from AI support. Personalized medicine provides the inclination to diseases, diagnosis, and selection of the best treatment for a specific individual. In fields like image processing, deep learning has been able to match and improve human performance for exceedingly complicated tasks such as object detection models with classification, and prediction capabilities and for the identification of numerous dental diseases and their differential treatments. Many of these neural network applications face significant challenges due to the lack of labeled medical datasets, and oral disease datasets are no exception. This pilot study makes it possible to identify various dental diseases and the treatments for them by utilizing a dataset of 664 dental panoramic X-ray images which is considered a small number. These images have been used for training after being manually labeled for 9 separate classes. You Only Look Once (YOLO) version 5, a one-step search method primarily utilized for real-time object detection, is the deep neural network that was used in this study. Bounding boxes helped identify six differential treatments and three classes of dental diseases. Results show that this strategy was able to attain a Precision of 1.00, F1 score of 0.74, Recall of 0.83, and mAP of 72.4 percent by training with this smaller number of images, which shows tremendous potential for dentists and radiologists.

关键词： Deep learning Training neural networks Object detection Dentistry Planning Object recognition

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Bibliometric Research: Tracking How the artificial Intelligence Inntegration Changed the Medical Systems

Bibliometric Research: Tracking How the Artificial Intellige...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International conference on

作者： Jasneet Kaur S. Barath Kuamr Pradnya Mehta Shalini Ninoria Vishal Sharma C. Karthikeyan Symbiosis college of Nursing University and symbiosis international deemed University pune India Department of Biotechnology Karpagam Academy of Higher Education Coimbatore Computer Engineering Vishwakarma Institute of Information Technology Pune CCSIT TMU School of Media Film&Television Studies IIMT University Meerut Department of Computer Science Engineering Karpagam Institute of Technology Coimbatore

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

In latest generations, there has been a significant increase in study interest in the growing applications of artificial intelligence in health and medicine. The purpose of this study is to present a worldwide and chronological overview of AI study in the areas of medical care and health. The online science tool was used to obtain a total number of articles that were released between. The detailed study looked at the number of publications, as well as the cooperation between writers and nations. Generally, indicate that a factor, along with robotics, algorithms, neural networks, artificial cognitive computing, and natural language processing, were identified through a vast network of researchers' key words and phrases and message review of pertinent academic papers. These methods are regularly used in clinical forecasting and rehabilitation. The most articles were on cancer, followed by those on cardiovascular disease, stroke, blindness, Early onset dementia, and sadness. Additionally, the lack of study on applying AI to some illnesses with a high disease prevalence indicates potential paths for Ai development. The study proposes the creation of international and national guidelines and laws on the rationale and application of pertaining to medical products and provides a first and complete image of the global efforts made in this significant and lucrative study area.

关键词： Training Industries Machine learning algorithms Databases Reviews Medical services Natural language processing artificial intelligence Robots Biomedical imaging

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