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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning

Classification and Discovery of Brain Tumor Using Ensemble D...

引用

Network, Multimedia and Information Technology (NMITCON) international conference on

作者： Kasanagottu Srilatha Chekka Sravani Mikkilineni Vasudevarao Ajmeera Kiran D Chitra Pundru Chandra Shaker Reddy Department of Data Science Geethanjali College of Engineering and Technology Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India Department Of Computer Applications Dayananda Sagar Business Academy Karnataka India Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Telangana India Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor.

关键词： Accuracy Computational modeling Medical treatment Predictive models Brain modeling Feature extraction Convolutional neural networks Lesions Data mining Diseases

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The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias*

The Unconstrained Ear Recognition Challenge 2023: Maximizing...

引用

IEEE international Joint conference on Biometrics (IJCB)

作者： Ž. Emeršić T. Ohki M. Akasaka T. Arakawa S. Maeda M. Okano Y. Sato A. George S. Marcel I. I. Ganapathi S. S. Ali S. Javed N. Werghi S. G. Işık E. Sarıtaş H. K. Ekenel V. Hudovernik J. N. Kolf F. Boutros N. Damer G. Sharma A. Kamboj A. Nigam D. K. Jain G. Cámara-Chávez P. Peer V. Štruc University of Ljubljana (UL SI) Shizuoka University (SU JP) Idiap Research Institute (IDIAP CH) Electrical Engineering and Computer Science Department Khalifa University (KU UAE) Department of Computer Engineering (ITU TR) Istanbul Technical University Microelectronic Guidance and Electro-Optical Group ASELSAN Fraunhofer Institute for Computer Graphics Research IGD (IGD DE) Technische Universität Darmstadt (TUD DE) Indian Institute of Technology Mandi (IIT IN) HCL Imaging and Robotics R&D Lab (HCL IN) School of Artificial Intelligence Dalian University of Technology (DLUT CN) Federal University of Ouro Preto (UFOP BR)

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://***/

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Supporting Music pattern Retrieval and analysis: An Ontology-Based Approach 2020

Supporting Music Pattern Retrieval and Analysis: An Ontology...

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10th international conference on Web intelligence, Mining and Semantics, WIMS 2020 and 5th international conference on Real-time Intelligent Systems, RTIS 2020

作者： El Achkar, Charbel Atéchian, Talar Antonine University - TICKET Lab Baabda Lebanon

ISBN: (纸本)9781450375429

Analyzing music notations is found useful for musicology purposes. This can be applied by retrieving semantic information from digitally annotated music scores. In this paper, we propose an ontology that structures the knowledge extraction process of a music pattern analysis algorithm. In addition to mandatory elements that describe music scores, the proposed ontology relies on contextual elements and attributes for pattern analysis. The ontology then supports the semantic information retrieval and analysis processes of music score contents. We illustrate the whole mechanism by explaining the workflow of the ontology integrated inside a music encoding platform for eastern music. © 2020 ACM.

关键词： Ontology

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analysis of Open-Loop Grasping From Piles

Analysis of Open-Loop Grasping From Piles

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IEEE international conference on robotics and Automation (ICRA)

作者： Előd Páll Oliver Brock Robotics and Biology Laboratory Technische Universität Berlin Germany

This paper offers an explanation of why humans can effortlessly grasp objects from a pile. We identified a regularity in objects’ motion when pushed, namely, an object separates and stabilizes in front of the pusher. We devise an open-loop grasping strategy leveraging this regularity in piles of nearly identical objects. Our real robot robustly grasps round objects beside a wall with success rates between 95% and 100% without visual or tactile feedback. We analyze our grasping strategy extensively both in real-world and simulated experiments. We observe that object roundness improves grasping and the motion pattern also manifests in small piles beside a wall. Our qualitative simulation can approximate the real robot’s grasping behavior, and we apply open-loop grasping in an warehouse pick-and-place application.

关键词： Visualization Automation conferences Tactile sensors Grasping Object detection Aerospace electronics

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Experience of Artificial intelligence Implementation in Japan 1

Experience of Artificial Intelligence Implementation in Japa...

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1st international conference on Business Technology for a Sustainable Environmental System, BTSES 2020

作者： Belova, Loudmila Lomonosov Moscow State University 46 Leninskie Gory Moscow119991 Russia

The main purpose of this article is to identify the essence of artificial intelligence (AI), to determine its impact on various aspects of life and to specify the knowledge gained by studying the directions of AI application in one of the most advanced countries - Japan, the world leader in robotics. Main conclusions from the study: important characteristics of the modern approach to the AI phenomenon are: first, the human pattern that once guided and motivated AI research has largely been pushed out of sight;second, this is why certain views on what we should know and do with intelligent machines are gradually emerging;third, there is a growing need to combine the efforts of research institutes in different countries in studying the theoretical aspects of AI. Japan has very good prospects for establishing and developing bilateral AI cooperation with Russia and Kazakhstan. © The Authors, published by EDP Sciences, 2020.

关键词： Artificial intelligence

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Computer data processing mode in the era of big data: From pattern recognition to intelligent sensing 3

Computer data processing mode in the era of big data: From p...

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

作者： Huaiyong, Deng Qing, Ma ChongQing Water Resources and Electric Engineering College Chong qing402160 China

ISBN: (纸本)9781728170893

Computer data processing mode in the era of big data from pattern recognition to intelligent sensing is discussed in this paper. intelligence refers to the interconnection between things and things and people through the Internet of Things and mobile Internet, combined with the general big data computing, communication, artificial intelligence and other technologies. Neighborhood dependence reflects the proportion of approximate samples to all samples under decision class in mixed information system, and it is also a measure to describe the degree of relationship between conditional attribute set and decision attribute, so it can be used as a heuristic function to reduce the attribute of mixed information system. This paper designed the model from the collection to processing, from pattern analysis to the intelligent sensing. The experimental results have proven that the proposed model can sense the data effectively. © 2020 IEEE.

关键词： pattern recognition

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Visual Saliency Oriented Vehicle Scale Estimation

Visual Saliency Oriented Vehicle Scale Estimation

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international conference on pattern Recognition

作者： Jiali Ding Tie Liu Qixin Chen Zejian Yuan Yuanyuan Shang School of Information Engineering Capital Normal University Beijing China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China

Vehicle scale estimation with a single camera is a typical application for intelligent transportation and it faces the challenges from visual computing while intensity-based method and descriptor-based method should be balanced. This paper proposed a vehicle scale estimation method based on salient object detection to resolve this problem. The regularized intensity matching method is proposed in Lie Algebra to achieve robust and accurate scale estimation, and descriptor matching and intensity matching are combined to minimize the proposed loss function. The visual attention mechanism is designed to select image patches with texture and remove the occluded image patches. Then the weights are assigned to pixels from the selected image patches which alleviates the influence of noise-corrupted pixels. The experiments show that the proposed method significantly outperforms state-of-the-art methods with regard to the robustness and accuracy of vehicle scale estimation.

关键词： Visualization Image resolution Algebra Estimation Transportation Object detection Visual systems

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Time Series analysis of COVID-19 Cases in Humboldt County

Time Series Analysis of COVID-19 Cases in Humboldt County

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international conference on Computational Science and Computational intelligence (CSCI)

作者： Soeon Park Mohammed Mahmoud Sherrene Bogle Mathematics Humboldt State University Arcata CA Computer Science and Engineering Oakland University Rochester MI Computer Science Humboldt State University Arcata CA

ISBN: (纸本)9781665458429

The time series of COVID-19 daily cases in the U.S is analyzed by utilizing the county-level temporal data, from January 22, 2020 to October 18, 2021. Autocorrelation and partial autocorrelation show that time series of daily cases in Humboldt county has a 7-day seasonal pattern. Visualization and augmented Dickey-Fuller test show that time series of daily cases in Humboldt county is non-stationary. The seven-order difference reveals that the time series is stationary. There is a moderate positive correlation between daily cases and fully vaccination rate. Clustering analysis describes 33 counties have similar daily case pattern with Humboldt County by standard deviation of 0.003. This analysis can be used for future time-series forecasting and planning.

关键词： COVID-19 Reactive power Scientific computing Time series analysis Predictive models Market research Vaccines

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An sEMG-based Hill-type Model for Estimation of Swallowing Motion

An sEMG-based Hill-type Model for Estimation of Swallowing M...

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IEEE international conference on robotics and Biomimetics

作者： Zhenhui Guo Yue Wang Song Zhang Jinqiao Wu Yang Yu Yu Dang Ningbo Yu Jialing Wu Jianda Han College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China Tianjin Huanhu Hospital Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China Laboratory of Cerebral Vascular and Neurodegenerative Diseases Tianjin Neurosurgical Institute Tianjin Huanhu Hospital Tianjin China

ISBN: (纸本)9781665405362

Dysphagia is a common clinical symptom which can cause dehydration, malnutrition, pneumonia, and even death. Surface electromyography (sEMG) signals, which can be obtained in a non-invasive way, have been used for screening dysphagia, and estimation of the swallowing motion based on the sEMG signals is essential. In this paper, we propose an sEMG-based Hill-type model for estimation of swallowing motion. Firstly, the sEMG signals of suprahyoid and infrahyoid muscles and acceleration signals of larynx which represent the swallowing motion were collected simultaneously and preprocessed. Secondly, the relationship between the preprocessed sEMG signals and swallowing motion was established based on Hill-type muscle model and dynamics of larynx. Then experiments with four kinds of swallowing tasks were conducted to verify the proposed method. analysis of the experimental results with normalized root-mean-square (NRMSE) and correlation coefficient (CC) verified the effectiveness of the proposed method.

关键词： Correlation coefficient Pulmonary diseases conferences Dynamics Estimation Muscles Larynx

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Factors affecting future of work: Insights from Social Media Analytics

Factors affecting future of work: Insights from Social Media...

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international conference on Computational intelligence and Data Science (ICCIDS)

作者： Sarin, Pooja Kar, Arpan Kumar Kewat, Kiran Ilavarasan, P. Vigneswara IIT Delhi DMS New Delhi 110016 India

Twitter is one of the most comprehensive sources of public conversations over the world. As a platform, it provides a medium to a large group of audience to express their views and opinions. Owing to the snowballing of the cheaper smartphone market coupled with cheaper data services, the platform has experienced an enormous increase in its use and has become a good medium of influence. Hence, Twitter data i.e. around 1.1 lakh tweets over a period of three months (January to March, 2019) are used to extract the tweets surrounding the discussion of future of work which is the result of the emerging technologies vis-a-vis automation, IoT and Industry 4.0. to understand the perceptions of people, organizations and businesses regarding the same. Furthermore, themes surrounding the discussions are identified and technology enablers that are perceived to bring about a change in the future of work. The social media discussions can strongly influence the opinions and perceptions of people, businesses or organizations regarding Future of Work. The results of sentiment analysis exhibit that there is not much negative sentiment regarding the change in nature of work. Artificial intelligence and robotics are identified as the main technology enablers. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： IoT automation future of work Industry 4.0 technology digitization employment Artificial intelligence

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