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International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Ilayaraja V S.P. Rajamohana Jagadeesan S V. Aishwarya M. Rakesh School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science School of Engineering & Technology Pondicherry University Karaikal Puducherry India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability.

关键词： Deep learning Surveys Accuracy Tropical cyclones Disasters Sea measurements Estimation Transformers Satellite images Reliability

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Deep Learning Algorithm for Brain Tumor Detection and Classification using MRI Images 1

Deep Learning Algorithm for Brain Tumor Detection and Classi...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Harshavardhan, A. Maheswari, N. Uma Prakash, M. Sammeta, Naresh VNR Vignana Jyothi Institute of Engineering and Technology Department of CSE Hyderabad India Panimalar Engineering College Department of Computer Science and Business Systems India Vellore Institute of Technology School of Computing Science and Engineering Vellore India R.M.K College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350323795

Brain tumor classification is beneficial for identifying and diagnosing the tumor's specific location. According to the medical imaging system, early diagnosis and categorization of a tumor extend a person's life. Clinical specialists rely heavily on magnetic resonance imaging (MRI) among numerous imaging modalities since it provides contrast information on brain malignancies. The primary purpose of this project is to use a competent automated approach that improves tumour identification accuracy. Several segmentation strategies have been developed throughout the years to achieve and improve the categorization precision of brain tumours. Brain picture segmentation has long been recognised as a difficult and time-consuming aspect of medical image processing. This method for detecting brain tumors Brain pictures are classified using the Full Resolution Convolutional Network (FRCN) classification architecture after pre-processing and segmentation. This study presents a Full Resolution Convolutional Network (FRCN) with Support Vector Machine (SVM) approach for detecting tumors on MRI scans. The procedure is broken down into four steps. In the first phase, the anisotropic filter is utilized to pre-process raw MRI images, followed by segmentation using the Support vector machine (SVM) and skull classification. The singular value decomposition and primary component analysis operations are performed in the third step. Tumors are then detected and classified using the Full Resolution Convolutional Network (FRCN) approach. Simultaneously, the Support Vector Machine (SVM) technique is employed to improve the classification precision of the study model. The experimental results showed an amazing accuracy rate of nearly 100% in detecting both normal and diseased tissues from brain MR images, confirming the efficacy of the suggested technique. © 2023 IEEE.

关键词： Support vector machines

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Demonstration of Effectiveness of Nativeness in Stepwise Learning by Performing Sentiment Analysis

Demonstration of Effectiveness of Nativeness in Stepwise Lea...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Al-Mahmud Shimada, Kazutaka Kyushu Institute of Technology Dept. of Computer Science and Systems Engineering 680-4 Kawazu Iizuka 850-8502 Japan

ISBN: (纸本)9798350316001

Nowadays, more people and entities express opinions on online platforms. With this increasing trend of online opinion mass information, it is impossible to analyze them manually. For this reason, an automatic approach to opinion and sentiment analysis is essential and useful in the real world. Several approaches based on machine learning techniques in sentiment analysis have been proposed. However, machine learning essentially needs massive data to generate a robust model. On the other hand, collecting the training data is costly, especially in low-resource language. To solve this problem, we introduce stepwise learning. Stepwise learning is a technique that improves the model capacity for a target task (i.e., main task) by exploiting auxiliary information from source tasks. It is effective when the data in the target tasks are inadequate. The target task of this paper is sentiment analysis for reviews in the Bangla language. Our proposed method consists of two stages: source and target tasks. For the source task, we train a transformers-based model, such as BanglaBERT, by a large dataset translated into the Bangla language;the original dataset is written in English. For the target task, we retrain and evaluate the model with a relatively small target dataset. We further investigate the effect of opinion expression style of writers between source and target data in stepwise learning. We call it 'nativeness.' The experimental result shows that nativeness has an important role in improving performance. © 2023 IEEE.

关键词： Sentiment analysis

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Emotion recognition research of eye-movement feature extraction classification network in online video learning environment 3

Emotion recognition research of eye-movement feature extract...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Liu, Shengxi Li, Ze Tao, Xiaomei College of Computer Engineering Henan Institute of Economy and Trade Henan Zhengzhou China School of information science and engineering Guilin University of Technology Guangxi Guilin China School of Computer Science and Engineering School of Software Guangxi Normal University Guangxi Guilin China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

With the rapid development of artificial intelligence technology, emotion recognition has been applied in all aspects of life, using eye movement tracking technology for emotion recognition has become an important branch of emotion computing. In order to explore the relationship between eye movement signals and learners' emotional states in the online video learning environment, we used machine learning and convolutional neural network methods to recognize eye movement signals, and classify learners' emotional states into two categories, positive and negative. The study of eye movement data under different time windows mainly includes four stages: data collection, data preprocessing, classifier modeling, training and testing. In this paper, a Eye-movement Feature Extraction Classification Network(EFECN) based on convolutional neural network is proposed for small sample data and the classification of emotion state based on eye movement. The eye movement data were transformed into images through cross-modal conversion as input of multiple different deep convolutional neural networks, and the emotional states were classified in positive and negative directions. The accuracy was used as the evaluation index to evaluate and compare the different models. The accuracy of the eye movement emotion recognition model reached 72% in the SVM model and 91.62% in the EFECN model. Experimental results show that the convolutional neural network based on deep learning has a significant improvement in recognition accuracy compared with traditional machine learning methods. © 2023 SPIE.

关键词： Emotion Recognition

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Artificial Intelligence-Based Centralized Resource Management Application for Distributed systems 13

Artificial Intelligence-Based Centralized Resource Managemen...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Hettiarachchi, Lasal Sandeepa Jayadeva, Senura Vihan Bandara, Rusiru Abhisheak Vikum Palliyaguruge, Dilmi Arachchillage, Udara Srimath S. Samaratunge Kasthurirathna, Dharshana Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Due to the decentralized nature and emergence of new practices, tools, and platforms, microservices have become one of the most widely spread software architectures in the modern software industry. Furthermore, the advancement of software packaging tools like Docker and orchestration platforms such as Kubernetes enable developers and operation engineers to deploy and manage microservice applications more effectively and efficiently. However, establishing and managing microservice applications are still cumbersome due to the infrastructure configuration and array of disjoint tools that fail to understand the application's dynamic behavior. As a result, developers need to configure multiple tools and platforms to automate the deployment and monitoring process to provide the optimal deployment strategy for microservices. Even though many tools are available in the industry, the fully automated product which comprises deployment, monitoring, resiliency evaluation and optimization were not developed yet. In response to this issue, we propose an artificial intelligence (AI)-based centralized resource management tool, that provides an automated low latency container management, cluster metrics gathering, resiliency evaluation and optimal deployment strategy behave in dynamic nature. © 2022 IEEE.

关键词： Machine learning

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Smart Diagnostic System for Early Detection and Prediction of Polycystic Ovary Syndrome

Smart Diagnostic System for Early Detection and Prediction o...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Harish, K.P. Dhivyanchali, M.N. Devi, K.Nirmala Krishnamoorthy, N. Dhana Sree, R. Dharanidharan, R. Kongu Engineering College Department of Computer Science & Engineering Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tamilnadu Tiruchengode637 205 India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Department of Software Systems and Engineering Tamilnadu 632014 India

ISBN: (纸本)9798350348217

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset. © 2023 IEEE.

关键词： Adaptive boosting

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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The internal circulations on internal mass transfer rate of a single drop in nonlinear uniaxial extensional flow

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Chinese Journal of Chemical engineering 2023年第7期59卷 51-60页

作者： Anjun Liu Jie Chen Meng Guo Chengmin Chen Meihong Yang Chao Yang Qilu University of Technology(Shandong Academy of Sciences) Shandong Computer Science Center(National Supercomputer Center in Jinan)Jinan 250101China CAS Key Laboratory of Green Process and Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Jinan Key Laboratory of High Performance Industrial Software Jinan Institute of Supercomputer TechnologyJinan 251013China

The internal flow of a droplet in the nonlinear extensional flow field will exhibit more than two internal circulations with the variation of nonlinear intensity(E).In this paper,the effect of positions and sizes of internal circulations on internal mass transfer rate of a single spherical droplet in a nonlinear extensional flow field is studied and compared with that in a linear extensional flow *** simulation results show that when E≥0,there are two symmetrical internal circulations in the droplet,which is the same with that in a linear extensional *** limit value of mass transfer rate Sh is 15,which is equal to that in a linear extensional flow,no matter how large E *** E≤-3/7,the number of internal flow circulation of a droplet increase to four and the transfer rate Sh *** E=-1,the maximum internal transfer rate Sh equals 30 which is twice of that in a linear extensional *** generation of new flow circulations in droplets and the circulation positions will enhance mass transfer when E≤-3/7,which provides a new idea for enhancing the internal mass transfer rate of droplets.

关键词： Internal mass transfer Nonlinear extensional flow field Spherical droplet Internal circulations

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A Retrospective Study on Obesity to Evaluate Omnipotence of Physical Condition Feature Set

A Retrospective Study on Obesity to Evaluate Omnipotence of ...

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2022 IEEE International Conference on Data science and Information System, ICDSIS 2022

作者： Sengupta, Diganta Mondal, Subhash Banerjee, Susanta Navin, Harsh Meghnad Saha Institute of Technology Department of Computer Science & Engineering Department of Computer Science & Business Systems Kolkata India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

One of the growing medical concerns globally is obesity. The age old popular notion for the disease lies in physical conditions (PC), and eating habits (EH), leading to much observed debate for the root cause of obesity. This study establishes the omnipotence of PC over EH as a leading cause of obesity. The dataset used for the study comprised of 16 features which were divided into two feature subsets (FSS);one FSS containing 9 PC features, and the other FSS containing features related to EH. Initially obesity was classified using the complete feature dataset, followed by classification using the PC and EH FSSs respectively. Eight Machine Learning (ML) algorithms were used for the study. Regular performance metrics were used to evaluate the results. It was observed that the PC features unanimously contributed to obesity in contrast to EH features. Moreover, boosting was done using six algorithms, and results reflected that all the boosting algorithms enhanced the results. Of all the boosting algorithms, Hist-Gradient Boost generated the best results. The prime focus of the study is to analyze the major features for obesity using ML algorithms including boosting. This study computationally concludes that physical conditions have a greater impact on obesity with respect to eating habit conditions. © 2022 IEEE.

关键词： Classification (of information)

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