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A Machine Learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering 3

A Machine Learning-Enhanced IoT Wearable Device for Monitori...

引用

3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Suttarwala, Hussain Kumar, Ritesh Deorankar, Madhavi L. Daulat Sarpate, Dikshendra Shingade, Pallavi Kumar Sharma, Surender More, Vrushali Patkar, Uday Chandrakant Sharma, Deepak K. American Express Senior Software Engineer PhoenixAZ85054 United States Dr. Akhilesh Das Gupta Institute of Professional Studies Dept. of AI and Data Science Delhi New Delhi110053 India Marathwada Mitramandal College of Engineering Dept. of Electronics and Telecommunications Maharashtra Pune411052 India Zeal College of Engineering and Research Dept. of AI and Data Science MaharashtraNarhe Pune411041 India D Y Patil College of Engineering Dept. of AI and Data Science Akurdi Pune411041 India Poornima University Dept. of Electrical Engineering Jaipur India Genba Sopanrao Moze College of Engineering Dept. of AI and Data Science Balewadi Maharashtra Pune411045 India Bharati Vidyapeeth's College of Engineering Department of Computer Engineering Lavale Pune412115 India Savitribai Phule Pune University Dept. of AI and Data Science Maharashtra Pune India

ISBN: (纸本)9798350361391

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device's superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device's contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders. © 2024 IEEE.

关键词： Convolutional neural networks

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Phishing URLs Using Machine Learning Hybrid Stacking Classifier Approach with XGBoost, Random Forest and Extra Trees

Phishing URLs Using Machine Learning Hybrid Stacking Classif...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Chitteti, Chengamma Divya Sree, Tanguturi Sai Madhavi, K. Reddy Pooja, Pikkili Jayanth, Sannapaneni Reddy, Middi Harshavardhan Dept. of Data Science Tirupathi India Sree Vidyanikethan Engineering College Dept. of Information Technology Tirupathi India Mohan Babu University Dept. of Ai & Ml Tirupati India

ISBN: (纸本)9798350382693

Within the dynamic field of cybersecurity threats, phishing remains a prevalent and sophisticated tactic employed by malicious actors to deceive consumers. This paper presents a novel approach that leverages the strengths of Random Forest, ExtraTrees, and XGBoost in a machine learning framework to enhance the detection of phishing URLs through a hybrid stacking classifier. The suggested method combines the best features of all three ensemble learning techniques to provide a phishing detection model that is reliable and precise. An ensemble of classifiers is formed by combining XGBoost, which is well-known for its boosting abilities, with the highly randomized ExtraTrees method and the adaptable Random Forest algorithm. By using the various decision limits of each base learner, the hybrid stacking model combines their particular capabilities in the best possible way to provide higher prediction performance. Preprocessing is done on the training dataset to extract pertinent characteristics such as content, URL structure, and contextual data. The efficacy of the suggested method in detecting phishing URLs is assessed by extensive testing on an extensive dataset. The results indicate how well the hybrid stacking classifier performs in comparison to separate classifiers and highlight the possibility for improved phishing detection in practical situations. The study has importance as it presents a novel and efficient machine learning-based approach to counteract the rising danger of phishing assaults, hence propelling the area of cybersecurity forward. © 2024 IEEE.

关键词： Phishing

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An Evolutionary Algorithm for the Travelling Sales Man problem 3

An Evolutionary Algorithm for the Travelling Sales Man probl...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Waseem, Shaik Mohammad Jeshwanth, N.L. Lingesh, A. Jayanth, Matta Bhargavi, C.H. Dhanwanth, Batini Dept. of Computer Science and Engineering Chennai India Dept. of Artificial Intelligence and Data Science Chennai India Sai Spurthi Institute of Technology Dept. of Computer Science and Engineering Gangaram Sathuaplly India Jönköping University Dept. of Ai Engineering Jönköping Sweden

ISBN: (纸本)9798331509675

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques. © 2025 IEEE.

关键词： Traveling salesman problem

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A Vision-Based Autonomous ai Tic-Tac-Toe Manipulation System 12

A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System

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12th International Electrical engineering Congress, iEECON 2024

作者： Rakprayoon, Panjawee Ounsiri, Akechai Chongyoa, Ratchanon Bangkok University Faculty Of Engineering Dept. Ai Engineering And Data Science Pathum Thani Thailand Dna Robotics And Automation System Company Limited Pathum Thani Thailand Bangkok University Faculty Of Engineering Dept. Computer And Robotics Engineering Pathum Thani Thailand

ISBN: (纸本)9798350383591

For an ai tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and dept. images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects. © 2024 IEEE.

关键词： Object detection

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Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention 13

Enhancing Lighter Neural Network Performance with Layer-wise...

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13th International Conference on Computer and Knowledge engineering, ICCKE 2023

作者： Zaravashan, Siavash Torabi, Sajjad Zaravashan, Hesam Iran University of Science and Technology Dept. Computer Engineering Tehran Iran Data Scientist Part Research Center of Ai Tehran Iran

ISBN: (纸本)9798350330151

Deep neural networks have made a revolution in different areas of Artificial Intelligence such as image classifi-cation and object detection. However, most of these models are heavy and time-consuming at the inference stage. If we desire to use real-time applications, it's necessary to lighten them up. However, this could negatively affect the performance of models. One suggested approach for enhancing the capabilities of smaller models involves a technique called knowledge distillation. In this method, a less advanced model, referred to as the student model, learns from and emulates the more advanced teacher model to enhance its performance. This method has been proven as an effective technique to compress the model and increase its accuracy. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. In this paper, we investigate the effect of connections between different layers of teacher and student networks and reveal their great importance. We have employed three different loss functions - attention-driven distillation, hierarchical context loss, and non-local loss - to enhance the classifier's overall performance. Extensive experiments on the CIFAR-100 dataset show that the proposed method has a great performance improvement. For the benefit of the research community, we will make the code for this study available on GitHub. © 2023 IEEE.

关键词： Image classification

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Leveraging data Mining for Cybersecurity Threat Detection 1

Leveraging Data Mining for Cybersecurity Threat Detection

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Chouhan, Ganga Singh Mishra, Atul Kumar Bandil, Deepika Kumari, Meenakshi Chandra Sekar, P. Chandigarh University Dept. of CSE Chandigarh India Madhav University Dept. of Commerce & Management Pindwara India Institute of Busi. Mang. & Commerce Dept. of Management Aligarh India Usha Martin University Dept. of Busi. Mang. & Commerce Jharkhand India Dhanalakshmi Srinivasan College of Engineering Dept. of AI & Data Science Coimbatore India

ISBN: (纸本)9798350356816

In the face of increasingly sophisticated cyber threats, the application of data mining techniques has become essential for effective cybersecurity threat detection. This paper examines how data mining methodologies, including clustering, classification, and anomaly detection, can be leveraged to enhance cybersecurity defenses. By systematically analyzing large datasets from various sources such as network traffic, system logs, and user activities, data mining tools can identify patterns and anomalies that signify potential security threats. Will discuss the integration of these techniques with machine learning algorithms to improve the accuracy and efficiency of threat detection systems. The study explores several data mining approaches, evaluating their strengths and limitations in detecting both known and novel cyber threats. Additionally, it addresses the challenges associated with data privacy, the volume and complexity of data, and the need for real-time analysis. The results illustrate that data mining not only aids in early threat detection but also provides a foundation for developing adaptive and proactive cybersecurity strategies. The paper concludes by outlining future directions for research and advancements in data mining for cybersecurity, emphasizing the need for ongoing innovation to counter emerging threats. © 2024 IEEE.

关键词： Phishing

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aiML for Road Accident Analysis 4

AIML for Road Accident Analysis

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Rathore, Saurabh Pratap Singh Sanitha, P.C. Varshney, Manoj Verma, Anupama Vashisht, Manisha Saharan, Aaryan Icapsr Management & Research New Delhi India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Business Management Ranchi India Lingayas Vidyapeeth University Dept. of Cse Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

Road traffic accidents pose a significant threat to both developed and developing nations, prompting global efforts to enhance road safety. With cars and drivers on the roads, pedestrians also face risks, often becoming innocent victims of accidents. Various factors contribute to road accidents, including driving under the influence, weather conditions, speed, road type, light conditions, road surface, and design. These accidents can cause severe, sometimes unseen injuries with lasting health implications. This study aims to explore road accidents in India using the random forest classifier algorithm and machine learning techniques. © 2024 IEEE.

关键词： Highway accidents

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Improving Image Classification Using Attention Mechanisms in Deep Learning Models 4

Improving Image Classification Using Attention Mechanisms in...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sharma, Vinod Mishra, Atul Kumar Kumar, Anil Shakti, Nanda Sharma, Madhuri Srinju, M. Slp University Dept of Cse Gwalior India Madhav University Dept. of Commerce & Management Pindwara India Mangalayatan University Dept. of Eee Aligarh India Usha Martin University Dept. of Engineering and Applied Sciences Ranchi India Chri Campus University Dept. of Life Sciences Gwalior India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India

ISBN: (纸本)9798350364729

In this study, we explore the application of attention mechanisms to enhance deep learning models in the context of image classification. We assess several types of attention mechanisms across diverse image classification datasets. Our findings indicate substantial enhancements in model accuracy facilitated by these attention mechanisms. Additionally, we delve into implementation challenges such as computational overhead and the necessity for extensive datasets. Furthermore, we investigate the potential benefits of transfer learning and fine-tuning pretrained models integrated with attention mechanisms to optimize performance and streamline training efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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Gaze Analysis of Children with Autism During Robot-Assisted Therapy 25

Gaze Analysis of Children with Autism During Robot-Assisted ...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Nursena Boluk Hatice Kose Computer Engineering Dept. Istanbul Technical University Istanbul Turkiye AI and Data Engineering Dept. Istanbul Technical University Istanbul Turkiye

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Smart City Solutions Leveraging Internet of Things (IoT) Technology 1

Smart City Solutions Leveraging Internet of Things (IoT) Tec...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumawat, Bhawesh Shalini, E. Tomar, Vinay Upadhyay, Rajesh Kumar Gupta, Divyanshu Geetha, P.V. Madhav University Dept. of CSE Pindwara India Meenakshi A.H. Edu. & Research Dept. of Computer Science Chennai India Jiwaji University Dept. of Chemistry Gwalior India Usha Martin University Dept. of EEE Jharkhand India J.S. University Dept. Electronics & Comm. Engineering Shikohabad India Dhanalakshmi Srinivasan College Dept. of AI & Data Science Coimbatore India

ISBN: (纸本)9798350356816

The widespread adoption of the Internet of Things (IoT) is facilitating the development of Smart City initiatives globally. Everyday objects are increasingly equipped with electronic gadgets and connected through various protocol suites, becoming part of a vast network. According to a recent Gartner study, by 2020, smart cities are projected to deploy 50 billion connected devices. This advancement is expected to make urban environments more adaptive and resilient to future changes. This paper explores current and future trends in smart cities and IoT, highlighting the technologies and applications driving their growth. We examine how the expansion of IoT directly impacts the evolution of smart cities and discuss the interplay between the two, as well as the factors motivating their development. Smart cities have the potential to reduce manual labor, mitigate disaster risks, and streamline daily life. Lastly, we address some of the challenges associated with IoT in the context of smart cities and propose potential solutions to these issues. © 2024 IEEE.

关键词： Smart city

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