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AutoML Framework for Physical Activities Recognition 3

AutoML Framework for Physical Activities Recognition

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Kovalevsky, Vladislav Delhibabu, Radhakrishnan Zhukova, Nataly St. Petersburg Federal Research Center The Russian Academy of Sciences St. Petersburg Russia Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

The rise of measuring, computing, and storage capabilities in modern information systems has led to vast amounts of data in various fields of human activities. Various algorithms for machine learning have been developed to get valuable insights from this raw data. Tuning the hyperparameters of each algorithm is necessary. Finding the right algorithm and manually tuning its hyperparameters for a given task can be very time-consuming. The methods for automating this search are within the scope of AutoML (automated machine learning). The wide use of wearable devices such as smartwatches and fitness trackers enables the gathering of various information about the physical activities of the wearer. This information, in turn, can be used to produce useful insights, such as recognizing activities being performed. AutoML can automate the process of building models for this task, but the performance of the resulting models can be very different depending on the AutoML library that performed the task. In this work, we develop an AutoML framework that unites several commonly used AutoML libraries and enables the use of common constraints and metrics. The developed framework is then used to build models for the task of human activity recognition by processing a dataset containing information collected from smart devices worn by users. © 2024 IEEE.

关键词： Machine learning

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Hybrid CNN-GRU Model for Predicting Dysarthric Speech using Deep learning Approaches 3

Hybrid CNN-GRU Model for Predicting Dysarthric Speech using ...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chelliah, Suresh Kumar Arivazhagan, N. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331527549

This research study presents the novel method for dysarthria speech detection and classification. In order to create systems that are capable of accurately categorizing speech patterns that are impacted by dysarthria, it is vital to have speech detection and classification capabilities for dysarthria. The condition known as dysarthria, which is characterized by difficulties in articulation, phonation, resonance, and prosody, is a disorder that leads to a reduction in the intelligibility of speech. This method of classification necessitates the development of machine learning or deep learning models, more specifically a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), in order to accomplish the objective of automatically classifying dysarthric speech into categories such as severity or presence/absence of dysarthria. The primary purpose of dysarthria speech categorization is to bring about the development of a reliable instrument that can be used for identifying, categorizing, and managing dysarthric speech. As an added benefit, the classification of dysarthric speech allows for the adaptation of treatment methods and the acquisition of information about the underlying causes of the condition. The individuals who are affected by this speech difficulty are the target audience for this strategy, which ultimately aims to enhance the quality of life of those individual. The study addresses the challenge of classifying dysarthric speech caused by neurological impairments using a Hybrid CNN-GRU model, achieving accuracies of 70.3% (high-severity), 38% (severe-moderate), 34.5% (moderate), and 10.3% (mild dysarthria). © 2025 IEEE.

关键词： Convolutional neural networks

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Classification of Handwritten Sundanese Script via Transfer learning on CNN-Based Architectures 3

Classification of Handwritten Sundanese Script via Transfer ...

引用

3rd international conference on Electronic and Electrical engineering and intelligent System, ICE3IS 2023

作者： Prameswari, Masayu Anandita Dwi Sulistiyo, Mahmud Ihsan, Aditya Firman School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350327762

The Sundanese script is a cultural heritage of the Sundanese people that deserves to be preserved. With the advances in computer vision and deep learning, the study of Indonesian local character recognition still has room for improvement and therefore needs to be encouraged. In this study, the Sundanese script used was Swara and Ngalagena script. The classification of the Sundanese script poses a significant challenge due to the complexity and variability of the script's visual patterns. Therefore, this study classifies Sundanese script using the deep learning method, namely the Transfer learning method based on the Convolutional Neural Network. The architectures used in this research are ResNet-50, VGG-19, and MobileNet. Then to evaluate the performance of the three architectures, the evaluation metrics used in this study are Accuracy, Precision, Recall, and F1-scores will be used. The three CNN architectures show very satisfactory results in classifying Sundanese scripts. VGG-19 architecture Accuracy is 99% for training data and 95% for validation data, the ResNet-50 architecture is 97%, and for validation data is 91%. Meanwhile, the MobileNet architecture data train produces an Accuracy of 97% and data validation of 92%. Overall. The VGG-19 architecture is the best in this Sundanese script classification study. © 2023 IEEE.

关键词： Convolutional neural networks

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Bayesian Neural Networks for Direct Short-Term Net Load Forecasting in Solar-Integrated Residential Buildings 3

Bayesian Neural Networks for Direct Short-Term Net Load Fore...

引用

3rd international conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Tziolis, Georgios Livera, Andreas Lopez-Lorente, Javier Herodotou, Panagiotis Makrides, George Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus DNV Arnhem Netherlands

ISBN: (纸本)9798350375923

Short-term net load forecasting (STNLF) is of utmost importance for the efficient operation and control of residential buildings, with integrated photovoltaic (PV) systems. The intermittent PV generation increases the uncertainty of STNLF and therefore the error. This study presents a machine learning model using Bayesian neural networks (BNNs) for direct STNLF in solar-integrated residential buildings. Historical data from PV-integrated households in Cyprus, with self-sufficiency between 88.59% and 294.12%, were used for assessing the performance of the BNN model. The proposed model was applied to data from 68 individual and aggregated households in Cyprus. The results indicated that the machine learning model exhibited normalized root mean square error of 7.20% for the aggregated data, rendering it suitable for STNLF in residential buildings. Finally, the model was compared to a naïve persistence model, achieving a skill score of 46.37% at the aggregated household level. © 2024 IEEE.

关键词： Adversarial machine learning

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Applications of Machine learning and Deep learning in Intrusion Detection System: A Comprehensive Survey 3

Applications of Machine Learning and Deep Learning in Intrus...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Riba, Moge Askari, Sikdar Md S Prasad, Rabinder Kr. Rajiv Gandhi University Dept. of CSE 791112 India Dibrugarh University DUIET Dept. of CSE Dibrugarh786004 India

ISBN: (纸本)9798331523893

The information stored within an organization plays a vital role in determining its success. When someone intentionally targets a computer's stored data, both the computer itself and the connected network become vulnerable to ***, to address these issues, one of the most important security solutions is the use of IDS. Many scholars have focused on AI-based intrusion detection systems, & this paper presents a brief summary of several AI-based intrusion detection systems and provides deeper insights so that next researchers may grasp the different ways AI is being used for intrusion detection systems. © 2025 IEEE.

关键词： Deep learning

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3rd international conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

3rd International Conference on Intelligent Computing Techni...

引用

3rd international conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

ISBN: (纸本)9789819784639

The proceedings contain 50 papers. The special focus in this conference is on intelligent Computing Techniques For Smart Energy Systems. The topics include: Analysis of Fault and Current-Limiting Methods in the Power System;UDS for data Communication in Temperature Monitoring;approximation in the Weighted Lipschitz Class by (C,α,θ)(E,q) Mean of Its Fourier Series;demand Response and Load Profile Analysis in Modern Electricity Systems;First-Principles Study of the Structural, Optical and Electronic Properties of Zinc Blende GaN Through PBEsol and TB-mBJ Functionals;optimization-Based Cost Analysis for Energy Management System in Microgrids;classification and Prediction of Knee Osteoarthritis by Deep learning Approach;Predicting Stock Price and Market Direction Using Statistical and LSTM Models;Comprehensive Evaluation of Time Management with Balancing Resistors in Passive Cell Balancing Using the 1RC Model;PAWnnect: Pioneering IoT-ML Driven Pet Monitoring;transactive Energy Management for Microgrids: Peer-To-Peer Trading Using Stackelberg Game Approach;Unravelling the Mind's Emotions: A Review of EEG-Based Emotion Detection Using Machine learning and Deep learning;unlocking IoT Security: Enabling the Future with Lightweight Cryptographic Ciphers;Optimal Design of Nano Scale Voltage Amplifier Using Evolutionary Techniques for DG-MOSFET;flower Patterned with Elliptical Petals Multiband Microstrip Fractal Patch Antenna for Wireless Applications;Optimal Scheduling of Solar PV Generators and Network Assets in Distribution Network;revolutionizing Biomedical engineering with Microcontroller Applications: A Comprehensive Review;investigation of Battery Temperature Impact on High Voltage Cable of Electric Vehicles;deep learning Technique for Computer Vision-Based Pose Estimation for Augmented Reality;Rectangular Shape Patch Antenna with Defected Ground Plane for 5G Sub 6 GHz Wireless Communication Systems.

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An Advanced Wasserstein-Enabled Generative Adversarial Network Enabled Attack Detection for Blockchain-Assisted intelligent Transportation System 3

An Advanced Wasserstein-Enabled Generative Adversarial Netwo...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Ananthan, Bhuvanesh Christopher, Sj.Ben Surya, R. University of the Cumberlands Department of Information Technology Williamsburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineeing Tirunelveli India

ISBN: (纸本)9798350372120

In day-to-day life, intelligent transport system (ITS) is playing an indispensable role in enhancing mobility and minimizing the carbon track. The security and enduring maintenance of the ITS from cyberattacks are essential. To achieve highly enhanced protection over attacks, a new blockchain (BC) technology with a deep learning (DL) model is introduced to secure the ITS system. Initially, the signature is created and verified by BC blocks, and hash values are chained to resist various attacks that try to falsify sensitive data along with the associated transactions. Moreover, the Advanced Wasserstein Generative Adversarial Network (Advanced-WGAN) technique is introduced to detect various distributed attacks that pause the immediate and confidential data of the shareholders. The simulation progression is carried out on the Python platform and the openly accessible CICDDoS2019 database is considered for the training process. The outcomes prove that the developed method attains an accuracy of 99%, F-score (FS) of 98.5%, precision of 99%, and recall of 99.2%. The experimental results show that the developed approach is highly secure against vulnerable cyberattacks. © 2024 IEEE.

关键词： Smart contract

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Harnessing the Power of CNN to Detect Late and Early Blight in Potato with Enhanced Accuracy 3

Harnessing the Power of CNN to Detect Late and Early Blight ...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Tripathi, Kaustubh Taneja, Neeti Ahmed, Ibrar Soni, Dhruv Kumar Taneja, Ashu Noida Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354171

Potatoes are a ubiquitous vegetable in every household. Potato planting has been widespread in India for the past decades. However, potato output is being impeded by diseases such as late blight and early blight [1], which result in higher production costs. The objective of this project is to develop a rapid disease detection procedure using deep learning techniques. The goal is to diagnose diseases in potato crops at an early stage, hence increasing potato production. The diagnosis will be done using digital methods. Our primary objective is to identify and diagnose potato diseases by utilizing leaf images that have been enhanced and trained using the Convolutional Neural Network (CNN) technique. This study provides a comprehensive explanation of image processing and deep learning algorithms used in automated systems to detect and classify potato leaf diseases. Image processing is the optimal choice for the detection and diagnosis of agricultural illnesses. This study involves the division of around 2100 images of healthy and afflicted potato leaves, which were obtained via Kaggle and other pre-prepared models. The program forecasts an accuracy of 97.27% when tested with 20% test and validation data and 80% training data. Our results demonstrate that we successfully completed all essential duties in detecting potato diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Construction and Implementation Strategies of Personalized learning Paths Based on Artificial Intelligence 24

Construction and Implementation Strategies of Personalized L...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Chen, Xiaoyun Liang, Jinru School of Management Guangdong University of Science and Technology Guangdong Dongguan China School of Business Administration Guangzhou Institute of Science and Technology Guangdong Guangzhou China

ISBN: (纸本)9798400712692

With the increasingly popular application of intelligent technology in the field of education, innovation in educational practice continues to emerge, and personalized learning experience becomes inevitable. On the basis of in-depth analysis of the application status of educational intelligence technology, this paper constructs a personalized learning path theoretical model based on artificial intelligence, and summarizes the implementation strategy of the path. The purpose of this paper is to realize the precise match between the individual needs of learners and the individual teaching strategies with the help of intelligent education technology, so as to realize the tailor-made education. Establishing intelligent learning data analysis system, accurately pushing learning resources, orderly arranging learning process, establishing learning progress monitoring and feedback mechanism, creating interactive learning environment, paying attention to learner privacy protection and other strategies are the key to ensure the smooth implementation of personalized learning path. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Educational robots

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Design of intelligent decision support system based on artificial intelligence 3

Design of intelligent decision support system based on artif...

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3rd international conference on Internet of Things and Smart City, IoTSC 2023

作者： Zidong, Yang Communication University of China Beijing Beijing100020 China

ISBN: (纸本)9781510666375

With the rapid development of information technology, the internal decision-making mode of enterprises has been greatly affected. intelligent decision support system (IDSS) is a system supported by information science and modern management science, which can help executives make better decisions. Starting from data support and technical support, this article discusses the support framework of intelligent decision support system. After that, the article analyzes the composition and partial application of its constituent system. On this basis, the mechanism and implementation path of decision support system are introduced, and a digital and intelligent decision support platform is designed. This will provide corresponding reference for enterprises to build intelligent decision support system. © 2023 SPIE.

关键词： Machine learning

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