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Class Distribution Pyramid Experts for Long-tailed Visual recognition 3

Class Distribution Pyramid Experts for Long-tailed Visual Re...

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3rd IEEE international conference on Information Technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Li, Yijie Huang, Xiao Zhang, Yifan Li, Lin Ma, Jie School of Artificial Intelligence and Automation Wuhan China China Ship Development and Design Center Wuhan China

ISBN: (纸本)9781665490788

Deep long-tailed visual recognition is a challenging task due to its extreme imbalance in classes. Existing methods improve the overall performance by making a trade-off between head and tail classes. We conduct experiment find that in this way, not only the performance of the head classes is compromised, but also the performance of the tail classes is not maximized. Based on these observations, we propose Class Distribution Pyramid Experts(CDPE), a clean and simple framework for end-to-end training.T each expert are calculated by insta hrough the idea of divide and conquer, we maximize the representation of classes at different quantitative levels by different experts and further propose a novel instance-aware aggregation network that dynamically integrates the predictions of different experts. We conducted extensive experiments on CIFAR10/100-LT, ImageNet-LT and iNaturalist2018 datasets to demonstrate our method's effectiveness, practicability, and state-of-the-art performance. We hope this simple method will motivate people to rethink the divide-and-conquer strategy in long-tailed visual recognition. © 2023 IEEE.

关键词： Economic and social effects

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Harnessing Artificial intelligence and Robotics to Combat Water Hyacinth Invasion for Sustainable Environment : A Review 3

Harnessing Artificial Intelligence and Robotics to Combat Wa...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Pawar, Mrunal Ranjan, Nihar Patil, Vaibhav Naik, Sanika Shetty, Rutika Divekar, Rajiv JSPM's Rajarshi Shahu College of Engineering Department of Information Technology Pune India Symbiosis Institute of Management Studies Pune India

ISBN: (纸本)9798350354348

Water hyacinth (Eichhornia crassipes), an aquatic free-floating plant, is highly invasive and can now be found in most freshwater bodies in the world's subtropical and tropical climates. Because of the potential harm these plants do to the environment and civilization, it is critical to track their seasonality and spatial distribution in order to develop effective prevention techniques at an early stage. With an emphasis on the application of machine learning algorithms and image analysis techniques for weed detection, this study investigates and examines the usage of artificial intelligence (AI) to overcome these difficulties. Sophisticated algorithms such as the Single Shot Detector (SSD) approach are used for real-time object detection, as well as the integration of machine learning techniques such as CART, RF, and SVM, CNN, and KNN helps for object detection. Modern advances in robotics and artificial intelligence (AI) have shown promise in addressing issues related to the environmental challenges. This review examines prior studies, innovative methodologies, and case studies to identify gaps, challenges, and opportunities for future study and application for controlling the growth of water hyacinth. © 2024 IEEE.

关键词： Convolutional neural networks

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Explainable Artificial intelligence (XAI) Approach to Heart Disease Prediction 3

Explainable Artificial Intelligence (XAI) Approach to Heart ...

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

作者： Sethi, Aryan Dharmavaram, Sahiti Somasundaram, S.K. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Cardiovascular continue to stand as the leading cause of mortality globally. The World Health Organization (WHO) noted that in 2019, 32% of all fatalities were attributed to heart-related issues. In India, the Ministry of Health and Family Welfare reported that cardiovascular diseases accounted for 28.1% of the overall deaths. [1] Approximately 85% of individuals diagnosed with heart failure survive the first year after diagnosis, with rates decreasing to around 55% at two years, approximately 33% at five years, and about 35% at ten years. [2] Cardiovascular diseases, in 2019, claimed the lives of approximately 17.9 million people worldwide, making them the primary cause of death, constituting around 32% of all global deaths. [3] Underscoring the critical need for predictive models in early detection, our proposal integrates machine learning with Explainable AI (XAI) and a user interface (UI) tailored for medical professionals to address cardiovascular diseases (CVDs). We have developed a predictive model for heart disease with 96.07% accuracy, integrating factors like peak exercise ST segment slope, maximum heart rate, and exercise-induced angina. Our model prioritizes transparency, offering clear explanations for decisions to build trust in AI-driven healthcare. Emphasizing feature importance, it enables interpretable predictions, facilitating early detection and informed management. Our objective is two-fold: accurate risk identification and providing medical professionals with a user-friendly interface for transparent, reliable decision-making. This research advances healthcare by bridging AI with human understanding, enhancing outcomes in heart disease management. © 2024 IEEE.

关键词： Decision making

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A Concentration Prediction and Gas Classification Model Based on LSTM-Attention Multi-task Learning Framework Network 3

A Concentration Prediction and Gas Classification Model Base...

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2023 3rd international conference on Electronics, Circuits and Information Engineering, ECIE 2023

作者： Zhao, Linju Li, Wang Wang, Shun College of Artificial Intelligence Southwest University Chongqing China

The electronic nose (E-nose), a bionic olfactory system, has been widely used in gas identification and concentration prediction. However, these tasks are usually based on separate systems, leading to high detection costs. To address this issue, a multi-task learning network model based on LSTM-Attention (MTL-LSTMA) as a skeleton has been proposed to simultaneously train both species recognition and concentration prediction tasks. The introduction of an attention mechanism greatly reduces interference in gas feature information, improving the electronic nose pattern recognition algorithm's efficiency. The MTL-LSTMA model was tested through five sets of comparison experiments, showing the best performance in both concentration prediction and species identification. The proposed model has broad application prospects and may revolutionize gas detection technology. © Published under licence by IOP Publishing Ltd.

关键词： Electronic nose

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D3still: Decoupled Differential Distillation for Asymmetric Image Retrieval

D3still: Decoupled Differential Distillation for Asymmetric ...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Xie, Yi Lin, Yihong Cai, Wenjie Xu, Xuemiao Zhang, Huaidong Du, Yong He, Shengfeng South China Univ Technol Guangzhou Peoples R China State Key Lab Subtrop Bldg Sci Guangzhou Peoples R China Ocean Univ China Qingdao Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Shenzhen Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (纸本)9798350353006

Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these one-to-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain, which is then decomposed into three components: feature representation knowledge, inconsistent pairwise similarity differential knowledge, and consistent pairwise similarity differential knowledge. This strategic decomposition aligns the retrieval ranking of the query network with the gallery network effectively. Extensive experiments on various benchmark datasets reveal that D3still surpasses state-of-the-art methods in asymmetric image retrieval. Code is available at https://***/SCY-X/D3still.

关键词： Asymmetric Image Retrieval Knowledge Distillation

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Sequential Style Consistency Learning for Domain-Generalizable Text recognition 3rd

Sequential Style Consistency Learning for Domain-Generalizab...

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Zhang, Pengcheng Liu, Wenrui Wang, Ning Shen, Ran Sun, Gang Jiang, Xinghua Chen, Zheqian wu, Fei Zhao, Zhou Zhejiang Univ Hangzhou Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Yiwise AI Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9789819988495;9789819988501

As a task aiming to recognize text from images, text recognition is of great significance in both industry and academia. The vast majority of existing text recognition methods use text images with the same styles as training and testing samples. However, when these models encounter images with new styles, their recognition accuracy will be significantly reduced. In this paper, we mainly explore Domain-Generalizable Text recognition (DGTR), a challenging but meaningful setting focusing on enhancing the generalization ability of text recognition models. For this reason, we propose a practical framework called Sequential Style Consistency Learning (SSC), disentangling the style-specific and task-specific representation. Specifically, our SSC first constructs samples of augmented visual feature sequences, then disentangles the original and augmented feature sequences into style-specific features and task-specific features. To better separate the task-specific representation from the style-specific representation, the Style-Consistency Learning (SCL) is designed for learning the style consistency between original and augmented sequences. The disentangled module and style-consistency learning could provide complementary information for each other. Besides, our SSC is encouraged to meta-learn the style-specific and task-specific features during training based on text images with seen styles, generalizing better to text images with other styles. Numerous experiments and analyses conducted on the benchmark dataset MSDA have shown that SSC can achieve very competitive experimental results compared to state-of-the-art methods.

关键词： Text recognition Domain Generalization Style Consistency Learning

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Detection of Faulty Solar Panels Using Artificial intelligence and machine Learning Methods 3

Detection of Faulty Solar Panels Using Artificial Intelligen...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Faniar, Asmail A. Seker, Cihat Karabuk University Electrical Electronics Department Karabuk Turkey

ISBN: (纸本)9798350340358

One of the most promising approaches for detecting faults in solar panels is using computer vision techniques based on deep learning algorithms. Deep learning has proven to be highly effective in image classification tasks, and several studies have demonstrated its potential for detecting faults in solar panels. This paper, proposes a method for the classification of faulty solar panels using Convolutional Neural Networks (CNNs). CNN based on the encoder method is used for extracting features from the faulty and non-faulty images which received from the PV solar systems. Four classes that have 0%, 33%, 66%, and 100% defective rates have been considered to train the machine learning method. The Decision Tree, Support Vector machine (SVM), KNN, Ensemble, and Discriminant methods were used for the machine learning and the accuracy for these methods has been obtained as 94.88%, 76.20%, 97.45%, 98.34%, and 80.23%, respectively. © 2023 IEEE.

关键词： Solar panels

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A Hybrid Decision-Tree and ANN Learning Model for Air Contamination Forecasting 3

A Hybrid Decision-Tree and ANN Learning Model for Air Contam...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Gunasekar, S. Pius Agbulu, G. Joselin Retna Kumar, G. Raghavendra Reddy, N.V. Dileep Kumar, Y. Ramesh Prasad, R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Chinhoyi University of Technology Department of Mechatronic Engineering School of Engineering Sciences & Technology Private Bag 7724 Chinhoyi Zimbabwe SRM Institute of Science and Technology Department of Electronics and Instrumentation Engineering Chennai India RV Institute of Technology and Management Department of Mechanical Engineering Karnataka India Mohan Babu University Department of Electronics & Communication Engineering School of Engineering Tirupati India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350391565

Development of urban intelligent cities introduces a severe rise in environmental pollution, ranging from air and water pollution to noise pollution. Explicitly, the upsurge in deforestation, industrialization, production, and automobile activities presents increased air pollution. To recommend a suitable preventive method, a proper forecasting model is required. Presently, (ML) machine learning methods aid intelligent data evaluation and are employed in many domains. This study examines the forecasting of air contamination employing distinct ML methods. A hybrid decision-tree and (ANN) artificial neural network model is recommended for proper gauging. The analyses reports reveal that the suggested model outclasses other classical algorithms in terms of accuracy. Precisely, the presented model realized 99% and 99.88% accuracy and precision, with 99% and 99.88% sensitivity and specificity. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial intelligence's Function in Chatbots That Use NLP and SVM Algorithms 3

Artificial Intelligence's Function in Chatbots That Use NLP ...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Panduro-Ramirez, Jeidy Melanielourens Anitagehlot Gangodkar, Durga Prasad Tanushreesarkar Sivakumarsattanathan Universidad Tecnológicadel Peru Durban University of Technology Berea Department of Human Resources Management Durban South Africa Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Department of English India Hindustan Institute of Technology and Science Department of Mechatronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350399264

A healthy lifestyle depends heavily on proper healthcare. Nevertheless, it might be exceedingly challenging to schedule a doctor's appointment for any health issue. Before contacting a doctor, the goal is to develop a medical chatbot utilizing machine learning that can identify the illness and offer basic information about it. By using a medical robot, this will save healthcare expenses and increase access to medical information. The bots are computational models that communicate with people by using speech recognition. In order to recognize the language terms, make a query choice, and provide a response, the chatbot saves the data in a database. N-gram, TF-IDF, and mutual information are used to calculate ranking and text similarity. Each verb in the supplied input word will be assigned a score and additional comparable phrases will be found for the specified query. When a bot formulates a query that is unclear or not found in the system, a supplier, an expert software, responds. © 2023 IEEE.

关键词： Natural language processing systems

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Transfer Learning based Deep Learning Model for the Identification of Facial Expression 3

Transfer Learning based Deep Learning Model for the Identifi...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ekambaram, Gothai Rakkiannan, Thamilselvan Natesan, P. Jayakkavin, E. Dept of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Dept Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Dept of Artificial Intelligence Kongu Engineering College Tamilnadu Perundurai India Kongu Engineering College Tamilnadu Perundurai India

ISBN: (纸本)9798331527549

The changes that occur on a person's face to communicate their internal emotional states, intentions, or social cues are known as facial expressions. The human face is a crucial tool for communicating with other sand determining their emotional states from their facial expressions. Because it is essential in human interaction, real-time facial expression analysis has become a research topic. Face reading is used in various areas, such as social interaction, emotional intelligence, computer vision, biometric security, and social intelligence. This work focuses on an overview of the various facial expression recognition methods and their advantages and disadvantages. The body's natural response to internal and external stimuli is emotional. Whether it's a common emotion, stress, anger, etc., it can cause serious harm to health. Both psychological and physiological manifestations of these responses emotions and facial expressions appear. The model is used to analyse real-time snapshots of the user's face and detect the existence of stressful information as well as convolutional neural network capable of recognizing emotions, and facial emotions, a proof of concept for the emotion detector was developed. The classification model was developed via transfer learning and fine-tuning. For deep learning process, two classifier designs were taken into account. Following a numberoftests, it was shown that a classifier built using a Separable Conv2D convolutional layer and LeakyReLU as activation method CNN performed the best in categorising emotions. The results are encouraging, and the proposed emotion detection system is minimallyintrusive as it only needs a webcam to monitor the user's facial expressions. © 2025 IEEE.

关键词： Contrastive Learning

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