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Annual IEEE Symposium on computer-Based Medical Systems

作者： Gisele Helena Barboni Miranda Edson Garcia Soares Junior Barrera Joaquim Cezar Felipe Department of Computing and Mathematics Faculty of Philosophy Sciences and Languages of Ribeirão Preto University of São Paulo Ribeirao Preto Brazil Department of Pathology Faculty of Medicine of Ribeirão Preto University of São Paulo Ribeirao Preto Brazil Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Brazil Department of Computing and Mathematics Faculty of Philosophy Sciences and Languages of Ribeirão Preto University of São Paulo Ribeirao Preto Brazil

Histopathology is considered one of the most important tools for diagnosis in medical routine. It is characterized by the study of structural and morphological changes of the cells, caused by diseases in biological tissues. The use of computational techniques in the processing of histopathological images allows the identification of structural elements as well as the determination of inherent characteristics, supporting the study of the structural organization of tissues and their pathological changes. Within this perspective, the overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the analysis of cervical intraepithelial neoplasia (CIN) from histopathological samples through techniques based on Neighbourhood Graphs theory and Complex Networks. The proposed method was evaluated concerning the detection of the presence of lesions in the tissue. The maximum accuracy obtained in the evaluation of the detection of abnormalities was 88%. Since this method is generic, it can be applied to other types of lesions and tissues.

关键词： Lesions Measurement Image segmentation Accuracy Cancer Training Organizations

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Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model Using CNN

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Computational Intelligence and Neuroscience 2022年第0期2022卷 3098604页

作者： Kandhro, Irfan Ali Uddin, Mueen Hussain, Saddam Chaudhery, Touseef Javed Shorfuzzaman, Mohammad Meshref, Hossam Albalhaq, Maha Alsaqour, Raed Khalaf, Osamah Ibrahim Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan College of Computing and IT University of Doha for Science and Technology Doha Qatar Department of Information Technology Hazara University KP Mansehra21120 Pakistan National University of Modern Languages Islamabad Pakistan Department of Computer Science College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Technology College of Computing and Informatics Saudi Electronic University Riyadh93499 Saudi Arabia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research Center Baghdad Baghdad Iraq

When it comes to conveying sentiments and thoughts, facial expressions are quite effective. For human-computer collaboration, data-driven animation, and communication between humans and robots to be successful, the capacity to recognize emotional states in facial expressions must be developed and implemented. Recently published studies have found that deep learning is becoming increasingly popular in the field of image categorization. As a result, to resolve the problem of facial expression recognition (FER) using convolutional neural networks (CNN), increasingly substantial efforts have been made in recent years. Facial expressions may be acquired from databases like CK+ and JAFFE using this novel FER technique based on activations, optimizations, and regularization parameters. The model recognized emotions such as happiness, sadness, surprise, fear, anger, disgust, and neutrality. The performance of the model was evaluated using a variety of methodologies, including activation, optimization, and regularization, as well as other hyperparameters, as detailed in this study. In experiments, the FER technique may be used to recognize emotions with an Adam, Softmax, and Dropout Ratio of 0.1 to 0.2 when combined with other techniques. It also outperforms current FER techniques that rely on handcrafted features and only one channel, as well as has superior network performance compared to the present state-of-the-art techniques. © 2022 Irfan Ali Kandhro et al.

关键词： Chemical activation

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Enforcing morphological information in fully convolutional networks to improve cell instance segmentation in fluorescence microscopy images

arXiv

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arXiv 2021年

作者： Zamora-Cárdenas, Willard Mendez, Mauro Calderon-Ramirez, Saul Vargas, Martin Monge, Gerardo Quiros, Steve Elizondo, David Torrents-Barrena, Jordina Molina-Cabello, Miguel A. Computing School Costa Rica Institute of Technology Costa Rica Department of Computer Technology De Montfort University United Kingdom Tropical Diseases Research Center Microbiology Faculty. University of Costa Rica Costa Rica Department of Computer Engineering and Mathematics Rovira i Virgili University Spain Department of Computer Languages and Computer Science University of Malaga Spain Instituto de Investigación Biomédica de Málaga – IBIMA Spain

Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic processes such as proliferation. This enables customized and more precise cancer treatments. However, accurate cell instance segmentation, necessary for further cell tracking and behavior analysis, is still challenging in scenarios with high cell concentration and overlapping edges. Within this framework, we propose a novel cell instance segmentation approach based on the well-known U-Net architecture. To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model. The DDT output is subsequently used to train a top-model. The following top-models are considered: a three-class (e.g., foreground, background and cell border) U-net, and a watershed transform. The obtained results suggest a performance boost over traditional U-Net architectures. This opens an interesting research line around the idea of injecting morphological information into a fully convolutional model. © 2021, CC BY.

关键词： Convolutional neural networks

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms

Integration of Artificial Intelligence in Software Defined N...

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Lamhot Naibaho Pulkit Raj Saxena Kuldeep Sharma Badria Sulaiman Alfurhood L. Pallavi Bhasker Pant English Language Education Study Program Faculty of Letters and Languages Christian University of Indonesia College of Computing Science and Information Technology Teer Thanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India Department of Computer Sciences College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia CSE B V Raju Institute of Technology Narsapur Telangana India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

A future networking design called "software-defined networking" combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN.

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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency departments

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： N V Shibu Sujatha M M. Xavier Suresh K. Sindhuja T Saju Raj C. Srinivasan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. Results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology.

关键词： Support vector machines Cloud computing Technological innovation Accuracy Scalability Medical services Real-time systems Safety Resource management Research and development

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Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis

Cloud-Enabled Predictive Vehicle Health Monitoring and Maint...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： Viyyapu Lokeshwari Vinya K. K. Manivannan S. K. Manivannan C. Sathiyamoorthy M. Jagadeeswaran B. Meenakshi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Maximizing operational efficiency and customer satisfaction in the automobile sector depends on maintaining vehicle health and avoiding maintenance downtime. Vehicle Internet of Things (IoT) devices capture massive volumes of sensor data, enabling predictive maintenance. This research offers cloud-based predictive vehicle health monitoring and maintenance systems using Long Short-Term Memory (LSTM) networks for time-series analysis. System architecture uses IoT sensors in automobiles to gather real-time data on engine temperature, oil pressure, and vibration. This data is stored, analyzed, and predicted on clouds. To assess time-series sensor data and forecast failures or maintenance requirements, LSTM networks, a form of recurrent neural network, are used. The proposed system uses data preprocessing to manage missing values and outliers, feature extraction to find significant sensor data patterns, and LSTM-based predictive modeling to predict vehicle health conditions. Technology may predict future faults by continually monitoring and evaluating the vehicle's state, allowing proactive maintenance to avoid expensive breakdowns and downtime. The cloud-based architecture is scalable and flexible, providing smooth connection with fleet management systems and remote vehicle health monitoring across scattered sites. The suggested method optimizes maintenance schedules and resource allocation using predictive insights from real-time sensor data to improve operational efficiency and save costs. It may improve predictive vehicle health monitoring and maintenance in the automobile sector, improving dependability, safety, and cost.

关键词： Cloud computing Costs Time series analysis Real-time systems Maintenance Safety Automobiles Internet of Things Monitoring Long short term memory

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Studying the history of the arabic language: Language technology and a large-scale historical corpus

arXiv

引用

arXiv 2018年

作者： Belinkov, Yonatan Magidow, Alexander Barrón-Cedeño, Alberto Shmidman, Avi Romanov, Maxim MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States Department of Modern and Classical Languages and Literatures University of Rhode Island United States Qatar Computing Research Institute HBKU Doha Qatar Department of Hebrew Literature Bar-Ilan University Israel Dicta: The Israel Center for Text Analysis Department of History University of Vienna Vienna Austria

Arabic is a widely-spoken language with a long and rich history, but existing corpora and language technology focus mostly on modern Arabic and its varieties. Therefore, studying the history of the language has so far been mostly limited to manual analyses on a small scale. In this work, we present a large-scale historical corpus of the written Arabic language, spanning 1400 years. We describe our efforts to clean and process this corpus using Arabic NLP tools, including the identification of reused text. We study the history of the Arabic language using a novel automatic periodization algorithm, as well as other techniques. Our findings confirm the established division of written Arabic into Modern Standard and Classical Arabic, and confirm other established periodizations, while suggesting that written Arabic may be divisible into still further periods of development. Copyright © 2018, The Authors. All rights reserved.

关键词： Linguistics

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Book reviews

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Minds and Machines 1996年第2期6卷 239-285页

作者： Abbott, Barbara Herskovits, Annette Peterson, Philip L. Mele, Alfred R. Cole, David J. Crevier, Daniel Pelletier, Francis Jeffry Berkeley, Istvan S. N. Kitts, Brendan J. Brown, Mike Paliouras, George Department of Linguistics and Languages Michigan State University East Lansing USA Department of Philosophy Michigan State University East Lansing USA Program in Applied Linguistics Boston University Boston USA Department of Philosophy Syracuse University Syracuse USA Department of Philosophy Davidson College Davidson USA Department of Philosophy University of Minnesota Duluth USA Department of Electrical Engineering École de Technologie Supérieure Université du Québec Montréal Canada Department of Philosophy University of Alberta Edmonton Canada Department of Computing Science University of Alberta Edmonton Canada Biological Computation Project University of Alberta Edmonton Canada Center for Complex Systems Brandeis University Waltham USA Department of Computer Science University of Manchester Manchester U.K.

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Human-centered artificial intelligence in education: Seeing the invisible through the visible

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computers and Education: Artificial Intelligence 2021年 2卷

作者： Yang, Stephen J.H. Ogata, Hiroaki Matsui, Tatsunori Chen, Nian-Shing Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Academic Center for Computing and Media Studies Kyoto University Yoshidahonmachi Sakyo Ward Kyoto606-8501 Japan School of Human Sciences Waseda University 1-104 Totsukamachi Shinjuku-ku Tokyo169-8050 Japan Department of Applied Foreign Languages National Yunlin University of Science and Technology 123 University Road Section 3 Douliou CityYunlin64002 Taiwan

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives. © 2021 The Author(s)

关键词： Artificial intelligence

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Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation

arXiv

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arXiv 2023年

作者： Aksoy, Nurbanu Sharoff, Serge Baser, Selcuk Ravikumar, Nishant Frangi, Alejandro F. CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine United Kingdom School of Computing University of Leeds Leeds United Kingdom School of Languages Cultures and Societies University of Leeds Leeds United Kingdom School of Health Sciences Faculty of Biology Medicine and Health The University of Manchester United Kingdom School of Computer Science Faculty of Science and Engineering The University of Manchester United Kingdom Department of Radiology Kastamonu Training and Research Hospital Kastamonu Turkey

Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. In this paper, we present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical *** introduce a conditioned cross-multi-head attention module to fuse these heterogeneous data modalities, bridging the semantic gap between visual and textual data. Experiments demonstrate substantial improvements from using additional modalities compared to relying on images alone. Notably, our model achieves the highest reported performance on the ROUGE-L metric compared to relevant state-of-the-art models in the literature. Furthermore, we employed both human evaluation and clinical semantic similarity measurement (Bio-ClinicalBERT Score1) alongside word-overlap metrics to improve the depth of quantitative analysis. A human evaluation, conducted by a board-certified radiologist, confirms the model’s accuracy in identifying high-level findings, however, it also highlights that more improvement is needed to capture nuanced details and clinical context. © 2023, CC BY-NC-SA.

关键词： Deep neural networks

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