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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

3rd International Conference on Intelligent Systems and Patt...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

ISBN: (纸本)9783031463372

The proceedings contain 44 papers. The special focus in this conference is on Intelligent Systems and pattern recognition. The topics include: Staged Reinforcement learning for Complex Tasks Through Decomposed Environments;policy Generation from Latent Embeddings for Reinforcement learning;deep learning Models for Aspect-Based Sentiment Analysis Task: A Survey Paper;A Real-Time Deep UAV Detection Framework Based on a YOLOv8 Perception Module;a Deep Neural Architecture Search Net-Based Wearable Object Classification System for the Visually Impaired;Multicarrier Waveforms Classification with LDA and CNN for 5G;Weeds Detection Using Mask R-CNN and Yolov5;Boruta-AttLSTM: A Novel Deep learning Architecture for Soil Moisture Prediction;graph Autoencoder with Community Neighborhood Network;Study of Support Set Generation Techniques in LAD for Intrusion Detection;question-Aware Deep learning Model for Arabic machine Reading Comprehension;a Hybrid Deep learning Scheme for Intrusion Detection in the Internet of Things;identifying Discourse Markers in French Spoken Corpora: Using machine learning and Rule-Based Approaches;A Comparative Study of the Impact of Different First Order Optimizers on the learning Process of UNet for Change Detection Task;Minimal Window Duration for Identifying Cognitive Decline Using Movement-Related Versus Rest-State EEG;modeling Graphene Extraction Process Using Generative Diffusion Models;bird Species recognition in Soundscapes with Self-supervised Pre-training;on the Different Concepts and Taxonomies of eXplainable Artificial Intelligence;Classifying Alzheimer Disease Using Resting State Coefficient of Variance BOLD Signals;proteus Based Automatic Irrigation System;How AI can Advance Model Driven Engineering Method ?.

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An Ensemble of CapsNet and KSVM Using Feature Fusion: Application to COVID-19 Detection 3

An Ensemble of CapsNet and KSVM Using Feature Fusion: Applic...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Aidoo, Evans Fiasam, Linda Delali Mensah, Acheampong Edward University of Electronic Science & Technology of China School of Information & Software Engineering Chengdu China University of Electronic Science & Technology of China School of Information & Software Engineering Network and Data Security Key Lab of Sichuan Province Chengdu China Zhejiang Gongshang University School of Computer & Information Engineering Hangzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural Networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

关键词： COVID-19

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Prediction of Students' Employment Prospect Based on GBDT Algorithm Model 3

Prediction of Students' Employment Prospect Based on GBDT Al...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Liu, Chang Wei, Zhengang Ocean Univ China Dept Comp Applicat Technol Qingdao Shandong Peoples R China Ocean Univ China Dept Software Engn Qingdao Shandong Peoples R China

ISBN: (纸本)9781665417907

The important indicator of students' employment prospect can help colleges and universities better stabilize the output of college students who meet the actual needs of society [1]. The definition of students' employment prospect and its influencing factors is not obvious. Based on this, this paper selects indicators, constructs three types of indicators such as career choice, salary and self realization to form students' employment prospect indicators, and uses random forest, SVM and gbdt models for analysis. The comparative analysis of data shows that gbdt model has good prediction ability, The first mock exam shows that this model is more suitable for the need of educational data mining.

关键词： Big data data mining Students' employment prospects Prediction model

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Productivity App for ADHD Affected People

Productivity App for ADHD Affected People

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data, Decision and Systems (ICDDS), IEEE international conference on

作者： Sandeep N Bhandarkar Poornima Manjunath Ramdas Dinesh Shanbhag Shravanth G N Information Science and Engineering CMR Institute of Technology VTU Bangalore India

ISBN: (数字)9798350363890

ISBN: (纸本)9798350363906

This project introduces the creation of a productivity application specifically designed to cater to the requirements of individuals impacted by Attention Deficit Hyperactivity Disorder (ADHD). Leveraging machine learning techniques, the application aims to augment emotional intelligence and promote self-reflection by analyzing written text. By identifying and acknowledging their emotions, users can cultivate deeper awareness of their mental conditions, fostering personal development and enhancing productivity.

关键词： Productivity Emotion recognition Refining machine learning Feature extraction Complexity theory

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Integration of Complex System Simulation and data mining 3

Integration of Complex System Simulation and Data Mining

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Zhang, Fa Zhuhai Sch Beijing Inst Technol Dept Business Adm Zhuhai Peoples R China

ISBN: (纸本)9781665417907

Simulation is a common method for studying the behavior of complex systems and revealing the mechanism of the system. However, complex systems have many parameters, non-linear interactions, and complex evolutionary dynamics. It is difficult to reveal the mechanism of complex systems. Especially complex system simulation experiments may produce a large amount of data. How to summarize the macroscopic mode of the system, identify key factors, and discover the relationship between input and output variables, still lacks an effective method. This paper proposes an integrated framework for simulation modeling and data mining, which combines data mining and simulation modeling to conduct iterative experimental exploration and analysis of complex systems. data mining techniques were used in multiple stages ofmodeling and simulation, including: ETL on raw data, text mining and process mining to build conceptual models, uniform experimental design to generate simulation data, and clustering ofsimulation data to identify system macro patterns, use stepwise regression, neural network, etc. to build a meta-model of a complex system. The introduction of data mining can improve the ability and efficiency of complex system modeling and simulation.

关键词： simulation complex system data mining

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OpenCV and Python for Emotion Analysis of Face Expressions

OpenCV and Python for Emotion Analysis of Face Expressions

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： MD Khadimul Islam Zim Department of Computer Science and Engineering Lovely Professional University Phagwara India

If someone showed you a picture of themselves and asked you to describe how they feel, you'd probably have a good idea. Think about how useful it would be if your computer could do that! But what if you could enhance the things you have? It seems like a completely absurd idea. In the past, it was easy to infer a person's emotional state simply by observing their face. However, it is much more challenging for a computer to perform this task. Emotion recognition in photographs is now feasible with the help of machine learning and computer vision. Facial expression recognition is a growing subset of the field of facial recognition. Despite the fact that there are methods that use machine learning and artificial intelligence to accomplish the same goals, this work attempts to use the OpenCV approach to recognise expressions and classify the expressions based on the photos.

关键词： Training Emotion recognition Technological innovation Computer vision Face recognition Training data machine learning

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Sentiment Analysis using deep learning for use in recommendation systems of various public media applications 3

Sentiment Analysis using deep learning for use in recommenda...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Arava, Karuna Chaitanya, Rudraraju Sri Krishna Sikindar, Shaik Praveen, S Phani Swapna, D. Jntu Kakinada Department of Cse Kakinada India Sir C R Reddy College of Engineering Department of C.S.E Eluru India Vignan's Foundation for Science Technology & Research Department of C.S.E Guntur India Pvp Siddhartha Institute of Technology Department of C.S.E Vijayawada India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Sentiment Analysis is a method of analyzing text and extracting opinions from it. It's also known as emotion or opinion extraction, and it's part of the machine learning as well as data mining categories. There are numerous ways to convey one's sentiments. It can be articulated in a variety of ways, such as through feelings, making judgments, or expressing one's vision or insight. Sentiment investigation is the act of detecting, recognizing, and categorizing a user's emotion or view for any service, such as movies, product issues, events, or any other attribute that can be good, negative, or neutral. This analysis is based on social communication channels such as websites that included ratings, forum conversations, blogs, micro blogs, Twitter, and other social media platforms. The important goal of suggested system is to improvise accuracy and to generate recommendation system using deep learning algorithms. © 2022 IEEE.

关键词： Sentiment analysis

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Parallel Selective Meta-Heuristic Algorithms for Real-Time Non-Convex Optimization in Big data Analytics 3

Parallel Selective Meta-Heuristic Algorithms for Real-Time N...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ghosal, Shreya Shankar, Amar Agarwal, Trapty Suchithra, R. Raj, Nimesh Hansda, Prity Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

With big data in modern analytics becoming more common, a lot of it is real-time optimization, which is an essential problem. This is because big data requires efficient and accurate optimization methods, leading to developing parallel selective viable meta-heuristic algorithms for nonconvex optimization in large analytics. They can be used to find optimal solutions rapidly by merging the complementary advantages of meta-heuristics and parallel computing. Algorithms do this using an optimization process, but not always for the whole data set used, saving computational performance. As information is updated in real-time, its approach will constantly be improved, resulting in a better and more accurate solution. A parallel implementation of these algorithms makes them effective for real-time optimization using large datasets. However, the performance of big data analytics may be significantly improved by implementing these algorithms because they have achieved satisfactory results in a number of applications, including machine learning, data mining, and pattern recognition. © 2025 IEEE.

关键词： Large datasets

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Determination and Diagnosis of Autism Spectrum Disorder using Efficient machine learning Algorithm

Determination and Diagnosis of Autism Spectrum Disorder usin...

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Intelligent Technologies (CONIT), international conference on

作者： Shiva Kumar R Naik Deepa M Ruhi P B Sneha Prakash U Jhanavi Royal School of Computer Science and Engineering REVA University

Autism Spectrum Disorder (ASD) is gaining momentum faster than ever before. Screening tests for autistic features are very expensive and time-consuming. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to predict autism relatively early. Autism, commonly known as "Autism Spectrum Disorder" (ASD), is a difficult, chronic developmental impairment that includes issues like lack of focus, repetitive behavior, and nonverbal communication. ASD has been growing more rapidly in recent years, necessitating an early diagnosis. Autism detection necessitates some time-consuming and costly screening tools. Predictive analytics, which is another name for a variety of mathematical models, has become increasingly popular in recent years. machine learning and pattern recognition in medical research machine learning and pattern recognition are two multidisciplinary study fields in medicine that offer efficient methods to detect ASD. So, we proposed a system to predict autism spectrum disorder based on various factors given by the user on the front end, using machine learning techniques and algorithms such as KNN, Logistic regression, decision trees random forest, ,the Naive Bayes and XGB classifier.

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Intelligent Waste Bot Using Internet of Things and Deep learning for a Smart City 3rd

Intelligent Waste Bot Using Internet of Things and Deep Lear...

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3rd international conference on Information Technology, InCITe 2023

作者： Shirwaikar, Rudresh Lamgaonkar, Yogini D’souza, Lyzandra Khorjuvenkar, Diksha Prabhu Department of Computer Engineering Agnel Institute of Technology and Design Goa University Taleigão India

ISBN: (纸本)9789819959938

The buildup of solid garbage in metropolitan areas is causing environmental contamination and may be harmful to human health if not adequately controlled. A sophisticated/intelligent waste management system is required to handle various wastes. Separating garbage into its component parts is a crucial stage in waste management and is generally done manually by hand- picking. We propose a bot-based waste management system with nodes consisting of mechanical robotic arms to pick up waste material. Deep learning is a kind of machine learning that harvests information by layering data representation and feature extraction. The robotic arm has been designed to move in the same way as a human arm does. This is accomplished by using an Arduino Nano microcontroller with servo motor control which is programmed in the Java language. Convolution neural network, a deep, feed- forward artificial neural network, was used to accurately assess the image. This technique is offered as a part of a solid waste sorter. The combination of robotic waste management with a deep learning system allows trash to be classified and disposed off automatically. As a result, trash segregation with deep learning includes image acquisition from a camera, object recognition, prediction, and categorization into biodegradable and non-biodegradable categories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Robotic arms

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