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A Survey on Human Activity recognition Using Deep learning Techniques and Wearable Sensor data 4th

A Survey on Human Activity Recognition Using Deep Learning T...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Dua, Nidhi Singh, Shiva Nand Challa, Sravan Kumar Semwal, Vijay Bhaskar Kumar, M. L. S. Sai Natl Inst Technol Jamshedpur Jharkhand India MANIT Bhopal Bhopal MP India Vignans Inst Informat Technol Visakhapatnam India

ISBN: (纸本)9783031243516;9783031243523

HAR has attained major attention because of its significant use in reallife scenarios like activity and fitness monitoring, rehabilitation, gaming, prosthetic limbs, healthcare, smart surveillance systems, etc. HAR systems provide ways for monitoring human behaviors and detecting body movements and various activities by using sensor data. the collection of sensors available in the mobile and other wearable devices hasmade most of these HAR applications easily possible. Moreover, Deep learning (DL) has further accelerated the research on HAR using the data obtained via wearable devices. In this paper, we have discussed the overview of HAR, its applications, and popular benchmark datasets available publicly. Further, we discussed various DL techniques applied for HAR applications. We have also presented the challenges associated with the field and the future directions for performing more vital research in HAR.

关键词： Wearable sensors pattern recognition HAR Deep learning

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Implementation of SARSA-HMM Technique for Face recognition 4th

Implementation of SARSA-HMM Technique for Face Recognition

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Yadav, Anil Kumar Shukla, Shiv Shankar Prasad Jain, Vikas Kumar Pateriya, Rajesh Kumar VIT Univ Sch Comp Sci & Engn Bhopal India Maulana Azad Natl Inst Technol Comp Sci Dept Bhopal India

ISBN: (纸本)9783031243660;9783031243677

SARSA (State-Action-Reward-State-Action) is a Markov Decision Process Strategy learning method (MDP). there can be discrete, permanent, stationary, time variable or noisy observations in real-time processes. the main difficulty is to characterize observations by estimating their parameters using a welldefined mathematical model as a parametric random process. the hiddenMarkov model is one such statistical model to interpret the (unobserved) process by examining the pattern of a sequence. HMM-based methods frequently used in the study and prediction of the most likely sequence of states, such as usage and activity patterns, as well as transitions between different process stages. In this paper, we proposed SARSA facial recognition technique used to select overlapping subsets from each image, which estimates the hidden attributes for each image. In the result section, represent that our proposed algorithm having better recognition rate than PCA. It can be used for solving complex feature selection and other solution for the development of further image.

关键词： Hidden Markov Models (HMM) Self-learning SARSA learning

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A Classified Mental Health Disorder (ADHD) dataset Based on Ensemble machine learning from Social Media Platforms 4th

A Classified Mental Health Disorder (ADHD) Dataset Based on...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

作者： Mumu, Sabrina Mostafij Hoque, Hasibul Sakib, Nazmus Ahsanullah University of Science and Technology Dhaka Tejgaon1208 Bangladesh

ISBN: (纸本)9789811994821

machines in the modern world are becoming more intelligent. Recognizing patterns and categorizing enormous volumes of data into discrete values is one of the typical tasks performed by machine learning systems. Additionally, mental health illnesses are increasingly the most prevalent medical condition nowadays. Additionally, there are other ways that machines are utilized to identify mental health issues. In this study, machine learning is used as the baseline to diagnose the mental health condition known as Attention Deficit Hyperactivity Disorder (ADHD). An unsupervised dataset for ADHD was converted into a classified dataset. To get better results, an ensemble machine learning model made up of five classifiers has been deployed. After that, linguistic models were employed to improve the machine’s ability to predict. this recently created dataset offers the potential for future developments such as chatbots that can be developed to provide people with psychological support and the application of natural language processing to identify the behavior pattern of ADHD patients. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Deep Insights into Mango Scab: An AI-Powered Approach for Detection and Classification 4

Deep Insights into Mango Scab: An AI-Powered Approach for De...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Arshleen Kukreja, Vinay Rana, Deepak Singh Garg, Ashish Sharma, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350343670

the mango, which comes from the plant genus Mangifera indica L., is a fruit crop that is well-regarded all over the world due to its scrumptious flavor and noteworthy nutritional profile. the illness known as mango scab, which is brought on by the fungus Elsinoe mangiferae, is a serious obstacle for the mango business. this disease causes the mango fruit to develop scaly lesions, which in turn results in decreased market value and yield losses. To effectively manage a disease, timely discovery and correct assessment of the severity of the condition are both essential. this study provides a hybrid deep learning (DL) model that combines a Convolutional Neural Network (CNN) for the detection of disease with a Support Vector machine (SVM) for the categorization of the severity of the disease. the model was trained and tested using a broad dataset of mango photos that included a range of disease severity levels as well as different varieties of mango. the model's performance in illness detection resulted in an overall accuracy of 95.93%, with scores of best precisions of 97.17% and best recall of 96.28%, respectively. When it came to the classification of severity, the SVM classifier was able to achieve an accuracy of 95.93%. these findings provide evidence that the model is capable of precisely identifying the degree of mango scab illness and demonstrating its usefulness in detecting the condition. To provide mango farmers with a flexible instrument, the hybrid method makes use of the capability of CNNs to extract features as well as the SVM's skill in classifying data. this research provides growers with up-to-date information that enables targeted disease management. As a result, mango crop yields are increased, and economic losses are decreased. © 2024 IEEE.

关键词： Agriculture Technology Deep learning Image-based recognition Mango Crop Mango Scab Disease Severity Detection

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Multimodal Physiological Analysis of Cognitive Affective Effect of Immersive learning in Virtual Reality 4

Multimodal Physiological Analysis of Cognitive Affective Eff...

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4th international conference on Computer Science and Management Technology, ICCSMT 2023

作者： Li, Yu Shen, Yang Faculty of Psychology Beijing Normal University Beijing China Collaborative Innovation Center of Assessment Toward Basic Education Quality Beijing Normal University Beijing China

ISBN: (纸本)9798400709517

this paper used videos to assess the impact of emotion on learners"attention, and employed a Continuous Performance Test (CPT) task to evaluate the effectiveness of physiological signal pattern recognition, all under immersive virtual reality environment. A total of eighteen subjects were recruited for this study, and they were shown four videos with different emotional tendencies. After watching each video, the subjects were required to complete a mood scale and a CPT task. Simultaneously, EEG and PPG data were collected from the participants. this study found that the valence and arousal of emotions have an impact on attention, and that there are distinct physiological patterns that can be used to measure attention. In the future, it is hopefully captured through learners' multimodal data for computation to quantitatively assess learners' attention levels, providing effective means for status evaluation in learning analytics. © 2023 ACM.

关键词： Virtual reality

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Empirical Analysis of Hybrid Classical Variational Quantum Neural Networks for Target Classification from SAR data 4th

Empirical Analysis of Hybrid Classical Variational Quantum ...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Gajjar, Pranshav Saxena, Aayush Ranpariya, Divyesh Shah, Pooja Das, Anup Institute of Technology Nirma University Ahmedabad India SRM Institute Of Science and Technology Chennai India Space Applications Centre ISRO Ahmedabad India

ISBN: (纸本)9789819900848

the advancements in the field of quantum machine learning and the ability of quantum-enhanced networks to acquire a higher effective dimension are promising for remote sensing applications and synthetic aperture radar (SAR) data. this paper aims to incorporate variational quantum circuits with existing classification-based deep learning state-of-the-art architectures. Tests are proposed by using ResNets, DenseNets, and a SqueezeNet-based convolutional neural network. Various dressed quantum circuits associated with these encoders are created and tested for novel applicability analysis. A famous publicly available X-Band SAR image-based dataset called moving and stationary target acquisition and recognition (MSTAR) is utilized to demonstrate image-centric target classification. the utility of quantum-enhanced and hybrid classical-quantum transfer learning models is provided. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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Detection of Plant Diseases and Recommendation of Plant Cycle using machine learning 4

Detection of Plant Diseases and Recommendation of Plant Cycl...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Alivn Ancy, A. Vivekanandan, G. Swapna, A. Kaniha, P. Indhu Priyadharshini, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Plant diseases create significant challenges to productivity in agriculture, food security, and ecological stability. Pathogens and environmental variables interact to cause plant diseases. Pathogens include various microorganisms like bacteria, fungi, and viruses, each with unique modes of spread. Environmental conditions such as temperature, humidity, and soil quality impact disease development and severity. the impacts of plant diseases are far-reaching. It affects crop yields and quality. Outbreaks of plant diseases can lead to economic losses, and food shortages which ultimately lead to hunger and poverty. Effective management of plant diseases requires an integrated approach that combines detection, preventive measures, cultural practices, and chemical interventions. the solution has two modules. the first module focuses on detection of plant diseases and its management. the second module focuses on recommendations of the plant cycle. Our proposed system utilizes CNN algorithms trained on a large amount of datasets. Using image analysis the features are extracted and patterns are recognized. therefore, the system can quickly identify the disorders.. the system also includes suggestions for preventive measures. the system integrates modules for recommending optimal plant cycles to the farmers and gardeners. It generates personalized recommendations for planting schedules and crop varieties. this system increases crop yields and decreases the risk of disease outbreaks. Our solution offers a holistic approach to plant health monitoring and management. © 2024 IEEE.

关键词： Productivity Plant diseases Pathogens Schedules Soil Feature extraction Stability analysis pattern recognition Crop yield thermal stability

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Video matting based on local-global features fusion 4

Video matting based on local-global features fusion

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Dong, Niuniu Liang, Yihui Zou, Kun Li, Wensheng Feng, Fujian School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Video matting aims at accurately separating foreground from videos. Recent video matting researches pursue to eliminate auxiliary inputs. However, due to the limited ability of extracting global correlation features, these methods suffer from performance degradation when dealing with complex scenes or natural background videos. To address this challenge, we propose a video matting method called Video Matting Based on Local-Global Features Fusion (VMBLGFF) which can extract both comprehensive global correlation features and local subtle features. VMBLGFF contains two closely connected networks: a transformer network that utilizes window and global attention mechanisms to obtain global correlation features within and cross windows, and a fusion network that integrates local subtle features into the global correlation features to supplement the local detail information which may be overlooked by the attention mechanisms. VMBLGFF alleviates the issue of limiting global correlation features and has been benchmarked on both synthetic and real datasets, and the results demonstrate that VMBLGFF improves the quality of video matting and exhibits good generalization performance. © 2024 SPIE.

关键词： Computer vision

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249846

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical Image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image Processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised machine learning Model Using Facial recognition;crime data Analysis Using machine learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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Gaussian Process for the machine learning-based Smart Fire Detection System 4

Gaussian Process for the Machine Learning-based Smart Fire D...

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4th international conference on Computers, Information Processing and Advanced Education, CIPAE 2023

作者： Li, Guangyu Yang, Boyu Chang, Shaocong Yi, Hongbin Zhang, Yingbo Wang, Yao Baishan Power Supply Company State Gird Jilin Electric Power Co. Ltd Baishan City134300 China

ISBN: (纸本)9798350342710

Artificial intelligence, machine learning, imaging devices, and computer vision are a few examples of contemporary technology that potentially replace traditional methods fire-detection systems with vision-based ones, thereby ensuring society's fire safety. Using the computer-aided system for quick and accurate fire detection could prevent a large-scale fire. Fire detection models perform poorly because they do not take into account the analysis of the fire particles (also known as pattern recognition) or the numerous moving objects in the background. To resolve this issue and concentrate a fire improvement in light of worldly edges from a film, this study gives a powerful even reenactment model of dynamic fire and a half-and-half fire division. In the model's preprocessing step, the candidate fire and non-fire Areas are segmented with the Hue-Saturation-Value (HSV) colour space and the Gaussian's Mixture Model (GMM) to isolate them from the multi-moving item on the complicated background. a unique approach that divides sensory data employing a Gabor filter library with desired orientations and estimates the process of fire. the energy of the wavelengths is also added as an additional aspect to the attribute vector when a temporally-differed image is employed. When all of these traits are added up, a support vector machine (SVM) is produced. to ensure accurate fire detection and better distinguish our data. the suggested fire detection model is validated using multi-conditional video clips. the results of the experiments indicate that the suggested model beats cutting-edge algorithms. © 2023 IEEE.

关键词： Support vector machines

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