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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

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Real-Time Insights and Dynamic Performance Visualizations for Unprecedented Impact in Ball Badminton 3rd

Real-Time Insights and Dynamic Performance Visualizations fo...

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3rd international conference on Sports engineering, ICSE 2023

作者： Rohith Gowda, M. Pai, Aditi N. Vishwanatha, C.N. Akash, M.P. Ranganath, G.H. Bhaskar, H.N. Department of Computer Science and Engineering R. V. College of Engineering Bengaluru India Department of Physical Education and Sports R. V. College of Engineering Bengaluru India Department of Physical Education and Sports SJB Institute of Technology Bengaluru India Department of Physical Education and Sports SBRRMFGC Mysuru India

ISBN: (纸本)9789819782963

The absence of advanced analytical tools has been a major obstacle in the world of ball badminton, a traditional sport. The lack of real-time data and valuable insights has long impeded players, coaches, and spectators. This paper introduces an innovative system aimed at transforming the ball badminton experience, enhancing player performance, engaging spectators, and providing valuable tools for coaches and fans through real-time insights and dynamic performance visualizations. This approach involves developing a system, preferably an application, to track the movement of the player and measure position, acceleration, and speed using computer vision and predicting tactics to assist during training, tracking the short trajectory of the ball to assist during practices for improvement, displaying live statistics such as player stats (points won, errors, aces), match progress (scoreboard), and historical data (head-to-head records) to provide context and analysis during the match, besides assisting the umpire in making decisions. Benefits to the viewers are the display of real-time statistics, fan engagement through quizzes on the sport, and result prediction. Coaches can use it to make strategic decisions, players to review their performance, and fans to gain deeper insights into the match. Additionally, it employs a markerless motion capture system, which relies on deep learning for human position estimation and computer vision techniques. To transform pixel data into court coordinates and enable the measurement of parameters like distance covered, court positioning, and average player speeds for ball badminton players, the methodology relies on the inverse perspective mapping method. One of the algorithms applied in this context is the Lucas-Kanade optical flow algorithm, which aids in player tracking and position estimation, contributing to the overall effectiveness of the technology. The technology will track player movements and tactical insights with great preci

关键词： Deep learning

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A Review on Emotion Detection from Text: Opportunities and Challenges 3rd

A Review on Emotion Detection from Text: Opportunities and C...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Mahmud, Anisur Rahman Mubtasim Fuad, Md. Jahid Hasan, Md. Minhazur Rafid, Md. Eusuf Khan, Md. Fazle Rabbi, M.M. Department of Computer Science and Engineering Bangladesh University of Business and Technology Road 9 Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

In this digital era, almost everyone can share their thoughts over the internet through texts in messages, posts or blogs, and people often convey emotions of different sorts as well. Sometimes it is about mental health, review about a bad product or simply opinions on different aspects of the modern world. Because of its massive popularity, emotion detection from text has become a burning topic for researchers. This review paper tries to provide understanding of different levels in various emotion models, different works that have been done in emotion identification from text and how it works along with introduction to various available datasets. Also how different models like LSTM, CNN, BERT and so on are utilized by researchers to provide better results in text-based emotion detection is also discussed with their advantages and disadvantages. Finally this paper also points out different obstacles and challenges that lie in the study of emotion extraction from text. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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IoT-Based Health Monitoring System for Post-Covid with Diabetes 3rd

IoT-Based Health Monitoring System for Post-Covid with Diabe...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Pankaja Lakshmi, P. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu Chennai600127 India

ISBN: (纸本)9789819726707

Internet of Things (IoT) devices are proliferating in the modern world, and this has a significant impact on healthcare. It can lower the price of medical care and offer early diagnosis of health issues. A patient that needs to be watched over constantly needs a healthcare monitoring system. In order to save their lives in dire circumstances, elderly patients, in particular, should periodically have their health state checked and informed to the doctor. Quiet people suffer the negative effects of true medical issues because of a lack of adequate health monitoring. Many IoT devices are available today to autonomously monitor patient health in order to address this problem. As of now, there is no other significant and adequate framework, to predict the impact of Covid-19 infection in diabetes patients. The literature has shown a separate ML/DL model for Covid-19 prediction and diabetes severity analysis. This is mainly because of the large gap in the database model. This paper has a detailed study of the machine learning (ML) model combined with IoT devices and the proposed framework would have been used for diabetic patients who were affected with Covid-19 and other symptoms for early prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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AI-Based Precision Farming for Sustainable Agriculture in Bangladesh 3rd

AI-Based Precision Farming for Sustainable Agriculture in ...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Chowdhury, Rup Islam, Md. Nazmul Das, Prapti Narin Nur, Fernaz Saiful Islam, A.H.M. Computer Science and Engineering Notre Dame University Bangladesh GPO Box 7 2/A Arambagh Motijheel Dhaka1000 Bangladesh

ISBN: (纸本)9789819739363

Agriculture, as an indispensable facet of human civilization and a vital economic contributor, plays a pivotal role in sustaining societies. Bangladesh, a nation heavily reliant on agriculture, currently employs traditional methods, resulting in a significant impact on its economic stability. Improvements in agricultural technology and practices have the potential to increase productivity, reduce waste, and improve the quality of agricultural products. As the global population swells and food demand rises, sustainable agricultural methods become increasingly crucial. To address this challenge, we have developed an AI-based precision farming system employing machine learning algorithm, including CNN networks, to analyze and predict crop health and potential yield. Additionally, we propose an AI-based decision-making module that forecasts crop yields, pest detection, and disease detection. Our system, rigorously evaluated using real-world data, has demonstrated substantial improvements in crop yield, fostering sustainability and contributing to global food security while minimizing environmental impact. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Feature Techniques with a Custom Convolutional Model for Breast Tumor Surveillance in Mammograms 3rd

Feature Techniques with a Custom Convolutional Model for Bre...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Mahmud, Md. Tanim Islam, Md. Shamiul Yasar, Samin Rahman, Md. Saifur Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The most occurring cancer worldwide is breast cancer in which abnormal breast cells grow and form tumors. If left unchecked, it can be more fatal and can spread throughout the body. Globally, 2.3 million cases have been diagnosed and there were 685,000 fatalities in 2020, according to the World Health Organization (WHO). To overcome the fatalities, an early diagnosis is the best approach. Manual diagnosis is not an easy process for this cancer, using mammography images, and always requires an expert person. In this paper, we have used a VGG16-based model to eradicate the feature from the intermediate layers and concatenate the intermediate layers. Then, we used a Convolutional Neural Network (CNN) to train the feature and predict the accuracy and classification. We have evaluated our performance analysis with confusion matrix analysis, ROC curve analysis, and other methods. In our results, we have accomplished an accuracy of 94% among the datasets. Comparing our suggested framework to state-of-the-art (SOTA) technology demonstrates that it increased accuracy, and additional study may enable it to be enhanced to decrease false positives and false negatives in screening mammography findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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A Privacy Preserving Decision Tree Classification Algorithm for Prediction of Health Care Monitoring System 3rd

A Privacy Preserving Decision Tree Classification Algorithm ...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Raj, Thaluka Mohan Maheswari, K. Raju, K. Srujan Shweta, N. Dharmireddi, Srinivasarao Reddy, Avala Raji Department of Computer Science and Engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9789819783540

In recent years, health monitoring systems have become popular due to increasing medical costs and advances in wireless technology. Organisations use information systems to develop and distribute information as well as gather, filter, and process data. Patients are responsible for sharing information with particular types of healthcare providers in order to make an accurate diagnosis and decide on the most appropriate model of treatment. In health monitoring systems, hospitals outsource clinical decision-making models to cloud service providers, who then use the models to make clinical decisions based on the biomedical information they receive from remote clients. Biomedical data as well as clinical decision-making models must be protected since privacy issues are extremely serious. An effective categorization technique for health monitoring systems using a privacy-preserving decision tree (PPDT) is given in this analysis. Decision tree classification with privacy preservation is carried out by using the encrypted token to search the encrypted index. According to a performance analysis, PPDT is extremely effective in terms of accuracy, time, storage, and communication. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Deploying DenseNet for Cotton Leaf Disease Detection on Deep Learning 3rd

Deploying DenseNet for Cotton Leaf Disease Detection on De...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Bappi, Md. Basitur Rahman Swapno, S. M. Masfequier Rahman Rabbi, M. M. Fazle Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

Cotton trees are tropical and subtropical trees that flourish in warm climates. It is a popular, costly as well as a cash crop. Farmers face difficulties selling their products when their production is decreased due to diseases harming cotton plants. It is critical to manage any dangerous illnesses as soon as feasible in order to increase quality and production. This problem prompted the development of novel technologies for detecting and diagnosing cotton plant diseases, as well as expert systems for disease prevention. The use of computer vision in agriculture has grown in popularity as a result of its ability to give critical information in real time. To achieve our goal, we built a dataset that included 4 classes of diseased and leaf images. We have applied InceptionV3, DenseNet, and MobileNetV2 to our dataset. By using DenseNet, we achieve the best accuracy of 99.24%. Our system with the capability of identifying leaf diseases of cotton has been built. As a result, we believe that our initiative will help the user by allowing us to make more items, which will impact our economy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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An Ensemble Technique for Classification of Oral Cancer by Using Histopathological Imaging 3rd

An Ensemble Technique for Classification of Oral Cancer by...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Saikia, Trishna Dhamaniya, Ashutosh Gupta, Puneet Singh, Koushlendra Kumar Department of Computer Science and Engineering IIT Indore Indore India Machine Vision and Intelligence Lab NIT Jamshedpur Jamshedpur India

ISBN: (纸本)9789819743582

Oral cancer primarily affects the oral chamber within the head and neck area, and underscores the critical need for effective classification to initiate timely treatment. Deep learning (DL)-based computer-aided diagnostic (CAD) systems have demonstrated notable success in various applications, offering accurate and prompt diagnosis of oral squamous cell carcinomas (OSCC). One of the challenges in biomedical image classification is the acquisition of a sufficiently large training dataset. Transfer learning presents an efficient solution by extracting general features from natural image datasets and adapting them to new image datasets. In this study, we focus on classifying OSCC histopathology images to develop a productive DL-based CAD solution. To this end, we employ an average weighted ensemble technique, harnessing the strengths of deep learning-based models. To address the limitation of a small dataset, we fine-tune pre-trained deep CNN models, specifically VGG-16, VGG-19, MobileNet-V2, and Inception-V3, within our proposed method. Additionally, we conduct a comprehensive comparative analysis of these models, considering classification accuracy, precision, recall, and F-score as metrics. Our experimental findings reveal that VGG-19 consistently delivers substantially superior performance compared to the other fine-tuned deep CNN models. However, our proposed weighted ensemble technique outperforms all these deep CNN models, particularly when employing the RMSProp optimizer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ensemble Histopathological imaging Inception-V3 MobileNet-V2 OSCC Transfer learning VGG-16 VGG-19

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Enhanced Salient Object Detection from Single Haze Images 3rd

Enhanced Salient Object Detection from Single Haze Images

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Dhara, Gayathri Kumar, Ravi Kant Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati522020 India

ISBN: (纸本)9789819743582

Salient Object Detection experiences significant difficulties when trying to identify objects from single haze images due to the deterioration of visibility and low contrast. To subdue this challenge, this study introduces a computational model of visual saliency as a solution. Object detection in hazy environments presents a major challenge due to reduced visibility and contrast. The proposed methodology begins by determining whether an image is hazy, and if so, leverages the Dark Channel Prior (DCP) to extract essential haze-related information. The DCP calculation serves as the basis for subsequent dehazing, achieved through the Multiscale Retinex algorithm. In the dehazing phase, the Multiscale Retinex algorithm is applied to improve image clarity and obtain a dehazed version. This haze-free image is given as input to a trained U-Net architecture, which gives a saliency map that identifies notable and prominent regions within the image. Simultaneously, it undergoes region-based segmentation. The geodesic saliency map is calculated using geodesic distance, considering both spatial proximity and feature similarity. In the final step, the saliency maps generated from the U-Net and geodesic saliency computation are fused to generate the ultimate saliency map. The effectiveness of the suggested method in detecting salient objects in hazy images is supported by the experimental findings, which showcase state-of-the-art performance in dehazing. The integration of DCP, multiscale Retinex, and dual saliency maps enhances both dehazing and object detection, making this method valuable in a variety of computer vision applications, including autonomous driving, video surveillance, and image restoration. The experimental results of AUC and MAE provide confirmation for the effectiveness and accuracy of the saliency computational model that has been proposed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Demulsification

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NAS for Automated ML Deployment on Extreme Edge Devices 3rd

NAS for Automated ML Deployment on Extreme Edge Devices

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3rd international conference on Signal and Data Processing, ICSDP 2023

作者： Mahendran, Gayathri Thirumaran, Gokhulnath Ali, Syed Imam Jadon, Vishwas Raj SSN College of Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Amity School of Engineering and Technology Noida India

ISBN: (纸本)9789819795772

Machine Learning (ML) and Artificial Intelligence are omnipresent topics nowadays. Especially since small personal devices, such as smartphones, are capable of running computation intensive tasks on their own, they can improve our lives in many different ways, e.g. computer vision or speech detection. Deploying neural network models on tiny and therefore performance, memory and power-constrained devices, such as microcontrollers, is currently an important research topic. For mobile and other embedded target devices, Neural Architecture Search, or NAS for short, is a novel technique to find appropriate network models without requiring manual error-prone adjustments by engineers, which allows to automate the workflow from the definition of a real work problem to a deployable solution. While there are a lot of comprehensive surveys on NAS methods in general, none of them considers the application of those ideas on edge devices like microcontrollers (MCUs). This work provides an overview of the recent developments and compares different approaches, which appeared over the last few years, against one another and with well-known reference applications intended for the usage on mobile phones. In the end some opportunities for future work on this topic are discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AutoML MCUNet Once-for-All SpArSe The MicroNet models

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