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Image-Based Cognitive States Identification through Deep learning 2

Image-Based Cognitive States Identification through Deep Lea...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Kundan Rao, S. Ambannavar, Siddarth Sinha, G.R. Bms College of Engineering Dept. of Machine Learning Bangalore560019 India Gfsc University Gujarat Vadodara India

ISBN: (纸本)9798350306415

Yoga and mindfulness practices offer a variety of benefits, promoting both physical and mental well-being. This research intersects with AI, psychology, neuroscience, and more within cognitive science, contributing to the study of the brain, mind, memory, and disorders. The study presents a novel approach utilizing deep learning and image classification techniques to distinguish practitioners from non-practitioners of yoga based on pre-processed data. Data pre-processing involves down sampling, filtering, and Independent Component Analysis (ICA) to enhance the extraction of relevant features. The pre-processed data is transformed into images using Gramian Angular Field, facilitating the utilization of Convolutional Neural Network (CNN) models. The proposed methodology leverages custom-built CNN models to classify individuals into their respective groups. The models are trained on the image representations of the pre-processed data, enabling the extraction of intricate patterns and features for accurate classification. © 2024 IEEE.

关键词： Deep learning EEG Gramian Angular Field (GAF) Image Classification Mindfulness

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Lung Cancer Detection: A Machine learning Approach for Early Diagnosis 8

Lung Cancer Detection: A Machine Learning Approach for Early...

引用

8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Joshi, Praveen Kumar Prakash, Rahul Singh, Devendra Dhanik, Saurabh Tamta, Abhishek Sharma, Vaibhav Kumar Graphic Era Hill University School of Computing Department Bhimtal India National Institutes of Technology Department of Electronics Engineering Srinagar India Graphic Era Hill University Department of Computer Science and Engineering Bhimtal India

ISBN: (纸本)9798350367904

Lung cancer poses a significant health challenge, with early detection being crucial for better outcomes. Traditional diagnostic methods have limitations, leading to missed diagnoses. Machine learning offers promise in revolutionizing disease detection by leveraging data and advanced algorithms. This paper explores using machine learning for early lung cancer detection. Sophisticated algorithms enable accurate diagnostic systems, improving efficiency and scalability. Challenges like data quality and interpretability also need to be addressed. This study evaluates machine learning algorithms on real datasets to assess their effectiveness in clinical practice, contributing to the field of healthcare applications. We have found highest accuracy (96.77%), precision (98.33%), recall (98.33%) and F1-score (98.33%) in the Random Forest classifier. © 2024 IEEE.

关键词： Lung cancer

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Machine learning Insights into Predicting Crude Oil Prices 2

Machine Learning Insights into Predicting Crude Oil Prices

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Toppo, Akriti Mahajan, Jayant Singh, Vikas Pratap Paswan, Anand Shankar Saxena, Anshul Christ University Bengaluru India Ewec United Arab Emirates

ISBN: (纸本)9798350384277

This research paper delves into the complexities of crude oil, highlighting its extraction, composition, and transformation into valuable derivatives. Examining the pricing dynamics, it explores the intricate interplay of social and economic factors that shape crude oil's value, emphasizing its critical role in global energy and industrial sectors. A forecasting model is introduced, focusing on key factors - heating oil, SPX, GPNY, and EU DOL EX - utilizing five machine learning models. Historical data reveals the efficacy of conventional models, particularly Random Forest, in predicting crude oil prices, enhanced by feature engineering techniques. The paper concludes by suggesting avenues for further exploration, offering valuable insights for readers in this dynamic research domain. © 2024 IEEE.

关键词： Crude oil price

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A Comprehensive Approach to Brain Tumor Detection through MRI-CNN Fusion for Accurate and Efficient Imaging Analysis 2

A Comprehensive Approach to Brain Tumor Detection through MR...

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Deepa, N. Dhona, Nikita Singh, Atul Faculty of Engineering & Technology Srm Institute of Science & Technology Department of Networking and Communication Chennai India Srm Institute of Science & Technology Department of Networking and Communication Chennai India

ISBN: (纸本)9798350384277

In the field of medical imaging, prompt and precise brain tumor identification is critical for efficient treatment planning and better patient outcomes. The goal of this project is to improve diagnostic skills by creating a novel hybrid method for analyzing Magnetic Resonance Imaging (MRI) data in real-time. Our proposed methodology integrates a cutting-edge machine learning algorithm, Convolution Neural Network with traditional image processing techniques to achieve a synergistic fusion of speed and accuracy in brain tumor identification. By harnessing the power of deep learning, our model leverages a diverse dataset to train and optimize its predictive capabilities, ensuring adaptability to a spectrum of tumor types and anatomical variations. © 2024 IEEE.

关键词： Convolutional neural networks

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An Experimental Evaluation of Flagging Sensitive Contents Over Social Networking Environment Using Elevated Classification Logic

An Experimental Evaluation of Flagging Sensitive Contents Ov...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Devadas, Mercy Sharon Sabitha, R. Shankar, K. Edlabadkar, A.P. Aljawawdeh, H.J. Pramitha, K. Jeppiaar Institute of Technology Department of Ece Chennai India Rajalakshmi Engineering College Department of Cse Thandalam Chennai India Easwari Engineering College Department of Cse Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of Software Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798331543617

The amount of sensitive content blankets on social networking platforms is rising quickly, necessitating the necessity for swift identification and removal measures. In this paper, our focus is on the capacity of social network flagging systems to recognize and block offensive content. In the rapidly evolving landscape of social networking, the ability to effectively flag sensitive content is crucial for maintaining platform integrity and user safety. This paper introduces an advanced methodology for identifying and managing sensitive content through the Elevated learning based Classification Logic (ELCL) framework, in which the work is cross-validated with the conventional learning algorithm called Recurrent Neural Network (RNN) to evaluate the efficiency of the proposed scheme. The proposed approach ELCL integrates sophisticated classification algorithms with contextual analysis to enhance the accuracy and efficiency of content moderation in social networking environments. By employing a multi-tiered approach that includes deep learning models and semantic analysis, ELCL can detect nuanced and context-dependent sensitive content that traditional systems often overlook. The framework utilizes a combination of supervised learning for training on labeled datasets and unsupervised learning to adapt to emerging content trends. Empirical evaluations demonstrate that ELCL significantly improves the precision of content flagging, reducing false positives and missed detections. This research highlights ELCL's potential to advance content moderation practices, offering a robust solution for safeguarding user experiences and ensuring compliance with community standards. The impact of user behavior, site affordances, and content type on flagging are also taken into account. This knowledge will help to design better content moderation systems which are both efficient and ethical, in order to make real benefits for the safety and well-being of users on online platforms. © 2024

关键词： Self-supervised learning

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Practical Exploration of PAD Class teaching Model in Software Technology Foundation 23

Practical Exploration of PAD Class Teaching Model in Softwar...

引用

8th international conference on Distance Education and learning, ICDEL 2023

作者： Chen, Lin Sun, Qiao Zhang, Xianfeng Zheng, Di School of Electronic Engineering Naval University of Engineering Wuhan430033 China

ISBN: (纸本)9798400700422

Software Technology Foundation is an important professional foundation course in engineering colleges. To resolve the problems of the traditional teaching mode, which relies on lectures by teachers and hard to improve students' learning initiative and cultivate their algorithmic thinking, a novel teaching model called Presentation-Assimilation-Discussion (PAD) Class is applied in this paper. For three successive years, online PAD Class, offline PAD Class, and online and offline blended PAD Class were adopted respectively for the practical exploration of the PAD Class teaching model. After practice, the results of the three PAD Classes were evaluated by questionnaires and examination results, and the data and reasons were analyzed. The experiment results show that the online and offline blended PAD Class model is more effective for this course, and it also has a certain reference for other professional foundation courses to practice PAD Class. © 2023 ACM.

关键词： Foundations

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Big Data and Machine learning: Barriers and Methods 4

Big Data and Machine Learning: Barriers and Methods

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Agrawal, Monika Goel, Swati William, P. Agwan, Ritesh Bhangale, Sonal C. Ghige, Onkar Kopargaon Sppu Sanjivani College of Engineering Department of Computer Engineering MH Pune India Inderprastha Engineering College Department of Computer Science and Engineering UP Ghaziabad India Kopargaon Sppu Sanjivani College of Engineering Department of Information Technology MH Pune India Kopargaon Sppu Sanjivani College of Engineering Department of Mechatronics MH Pune India Kopargaon Sppu Sanjivani College of Engineering Department of Electrical Engineering MH Pune India

ISBN: (纸本)9798350307757

In this study, the researchers investigate the mutually beneficial relationship that exists between machine learning (ML) and big data, highlighting both the benefits and the challenges that are brought about by the combination of these two domains. Large datasets provide a difficult environment for machine learning (ML) algorithms to extract meaningful insights from, but ML also provides sophisticated tools to assist in navigating through the complexity of these datasets. Even while there is the potential for synergy, their full-fledged adoption is delayed by significant constraints such as the quality of the data, the need for computing, concerns around privacy, and the skills gap. In addition to highlighting recent advancements in algorithmic efficiency, data preparation, and ethical frameworks, this paper investigates the strategic strategies and techniques that are used to address these issues. An in-depth investigation of machine learning models on a variety of datasets is presented, which offers insights into the efficacy of these models and their applicability for instances that occur in the real world, particularly those that involve the pharmaceutical industry. The research sheds light on the ways in which the environment is undergoing transformations and the ways in which practitioners are actively searching for and putting these challenges into practice in order to fully grasp the revolutionary potential of big data and machine learning. © 2024 IEEE.

关键词： Large datasets

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A Systematic Literature Review on the Feasibility of Dialogue learning Based on Generative AI 24

A Systematic Literature Review on the Feasibility of Dialogu...

引用

2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Tian, Yusen Tu, Binghao College of Educational Science and Technology Zhejiang University of Technology Zhejiang Hangzhou China

ISBN: (纸本)9798400711237

This study systematically reviews the feasibility of dialogue learning based on generative AI. Research on mixed reality and generative AI in enhancing teaching effectiveness is increasingly in-depth both domestically and internationally. The development and application of mixed reality and generative AI are accelerating, evolving classroom structures and methods. In the context of rapid social, economic, and technological development, students require more personalized and innovative education, which dialogue learning interventions based on generative AI can effectively address. Through a systematic literature review method, 128 relevant studies from the past decade were systematically analyzed to explore the feasibility of dialogue learning in the context of generative AI. The study finds that the application of generative AI in dialogue learning not only enhances learning outcomes but also excels in personalized learning and timely feedback. However, the diversity and operability of technological implementation and application scenarios require further exploration. This research provides references and insights for future studies by related scholars. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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FedRFD: Heterogeneous Federated learning via Refining Feature-level Distillation 24

FedRFD: Heterogeneous Federated Learning via Refining Featur...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Yu, Peng Yuan, Yuwen Wang, Youquan Shi, Wei Shi, Zhihui Hua, Yue College of Information Engineering Nanjing University of Finance and Economics Jiangsu Nanjing China School of Computer Science and Engineering Taizhou Institute of Sci.&Tech. NJUST Jiangsu Taizhou China College of Liberal Arts and Sciences University of Illinois at Chicago ChicagoIL United States Department of Financial Technology Jiangsu Sushang Bank Co. Ltd. Jiangsu Nanjing China School of Marxism Jiangsu University Jinagsu Zhenjiang China

ISBN: (纸本)9798400717482

Federated learning faces a significant challenge in effectively aggregating knowledge from diverse local models with varying learning capabilities while preserving privacy through collaborative, local updates on private data. Previous methods for addressing such challenges involve combining distilled logits-based knowledge from various local models using weights derived from their respective learning capabilities as weighting coefficients. However, logits-based knowledge distillation suffers from information loss, expression diversity limitations, and model dependency, leading to a less affluent knowledge transfer. Therefore, we propose heterogeneous federated learning via refining feature-level distillation (FedRFD), which leverages richer knowledge of intermediate layer features for distillation. Specifically, (1) To facilitate communication between heterogeneous models, we align their feature-level with public data, eliminating the need for an additional global model. (2) To address feature alignment challenges in models with varying feature-level numbers, we propose the knowledge refining mechanism (KRM). (3) To increase the efficiency of collaborative learning, we propose a novel method to dynamically assign weights to each client. Extensive experiments confirm that our approach significantly outperforms state-of-the-art algorithms in heterogeneous model scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Survey of Quantum Machine learning in IoT Application Field 5

Survey of Quantum Machine Learning in IoT Application Field

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Pradhan, Moumita IT Dr. B.C. Roy Engineering College Durgapur India

ISBN: (纸本)9798331518097

Due to massive interest in emerging technology for different types of computing and storing data, Quantum computing plays a significant role. Physics based strategy like superposition, entanglement, inference, decoherence make this technology more efficient. Well structured quantum hardware provides time efficiency for different types of operation. Machine learning model, a subset of artificial intelligence, furnish predictive model for unknown data exploring training and testing mechanism. Quantum Machine learning may upgrade classical machine learning algorithms. Running computationally expensive algorithms efficiently on a quantum computer using concepts from physics, open a new aspect. IoT provides effective data (air quality, temperature etc.), gathering from physical environment through sensor. Digital India, smart agriculture, smart city, health care, smart home, remote monitoring etc., are benefited by emerging technology, called IoT. This research is preliminary lay the cornerstone of making novice researcher interested to learn about QML in different types of IoT field. © 2024 IEEE.

关键词： Quantum computers

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