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Google pi using raspberry pi for visually impaired 3

Google pi using raspberry pi for visually impaired

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

作者： Katkar, Gowthami Vinod Posonia, A. Mary Ushanandini, D. Abirami, K. Ankayarkanni, B. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

This article introduces Google Pi, a new voice assistant system aimed at improving the lives of visually challenged people. Leveraging the affordability and versatility of the Raspberry Pi single-board computer, Google Pi integrates Google's voice assistant API with deep learning algorithms for object and currency *** voice assistants lack important features for visually impaired users. Although they provide voice-based control, they do not meet important requirements such as object identification and financial *** Pi bridges this gap with real-time object and currency recognition, increasing user independence and security. This paper details the technical implementation of Google Pi and highlights its user-friendly design and accessibility considerations. A hybrid approach that leverages Google's APIs and deep learning algorithms to address the challenges of providing efficient user interactions and immediate responses. Additionally, this paper highlights the benefits and importance of Google Pi for visually impaired people by promoting accessibility and promoting a safer and more independent lifestyle. © 2024 IEEE.

关键词： Object recognition

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Context-Based Adaptation of Neural Network Compression for Unmanned Aerial Vehicle (UAV) Weed Detection 20

Context-Based Adaptation of Neural Network Compression for U...

引用

20th IEEE international conference on Intelligent computer Communication and Processing conference, ICCP 2024

作者： Igret, Ioana C. MacHidon, Alina L. MacHidon, Octavian M. Babes-Bolyai University Faculty of Mathematics and Computer Science Cluj-Napoca Romania University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia

ISBN: (纸本)9798331539979

UAV computer vision in precision agriculture can enhance efficiency and reduce environmental impact compared to traditional techniques. However, the computational limitations and power constraints of UAVs hinder their performance, especially for real-time deep learning tasks. Compressed neural network models offer a solution, yet fixed compression levels may result in unacceptable accuracy loss. This paper proposes a novel context-aware approach for energy-efficient, real-time on- UAV weed detection. By incorporating contextual factors such as brightness, saturation, contrast, and vegetation indices, our method estimates input image difficulty and dynamically selects the optimal neural network compression level. Leveraging slimmable neural networks, which enable training a single model with various widths during inference, our approach ensures the best accuracy-resource consumption trade-off. Experimental results with two different slimmable networks architectures showed that our approach can lead to up to 35 % less computations while also increasing the quality of the inference with up to 2 %. This significantly improves the practicality and performance of UAV-based weed detection systems. © 2024 IEEE.

关键词： Image segmentation

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Human-computer interactive intelligent trash can 3

Human-computer interactive intelligent trash can

引用

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

作者： Chen, Ziyuan Guangdong University of Technology School of Information Engineering Guangdong China

ISBN: (纸本)9781665490788

With the demand for epidemic prevention and control and the encouragement of garbage recycling policy, how to enable users to empty garbage at the moment of garbage can idleness, timely disposal of some garbage that may become a medium of virus transmission and effective management and disposal of garbage cans become a problem worthy of attention. Therefore, this paper designs a smart trash can based on the STC89C52 microcontroller. This smart garbage can realize the functions of automatic opening of the lid by a garbage can personnel identification, real-time display of garbage capacity inside the garbage can, and human-computer interaction display by Bluetooth APP, which can effectively reduce the phenomenon that person needs to wait when emptying garbage, and also improve the efficiency of garbage can management for the administrator, and can provide the administrator with a suitable decision plan for when and where to put how many garbage cans. © 2023 IEEE.

关键词： Microcontrollers

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A Review of Available Standard Dataset for Automated Medicine Pill Recognition Technology 3

A Review of Available Standard Dataset for Automated Medicin...

引用

3rd international conference on Intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Islam, Sayeed Hossan, Md. Zakir Manarat International University Department of Electrical & Electronics Engineering Ashulia Dhaka Savar Bangladesh Computer Vision and Cybernetics Group Department of Computer Science & Engineering Independent University Bashundhara R/A Dhaka Bangladesh

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

In this paper, we'll review available standard datasets for automated medicine pill recognition technology. A process whereupon a dataset people discover or provide highly creative technique and tools for sorting and ranking consumer photos of prescription pills. To the need for healthcare providers and the general public to quickly identify unknown prescription medications all of these databases or datasets were created in response. Most of the prescription pills come in blister, alu-alu containers, or Ziploc packs. After unpacking the medical drugs, it is a very difficult tasks for a patient as well as professional medical staff to differentiate between them due to the lack of variability in size, shape, and color though it is a daily task for all of them. Often it is the case that label of the pack is somehow damaged, and for those how to have to take more than one medication at a time it is difficult to differentiate and find the right one between one and another from where the label is me deface or several pills are mistakenly come down from required containers. This error prone nature of the task as many of the pills have almost similar features, increases the medication error which sometimes appears as fatal for patients. The complications are even worse for elderly people with low vision and visually challenged people are at risk because they are not able to identify the color of the text printed on the pill. © 2022 IEEE.

关键词： computer vision

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Fuzzy Decision-Making on Strategies for Maximizing the Utility of Electric Vehicles 3

Fuzzy Decision-Making on Strategies for Maximizing the Utili...

引用

3rd IEEE international conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Imran, Shaik Moinuddin Rameshan, N. Anand, M. Clement Joe Martin, Nivetha Tiwari, Mohit Mohan, Varun Sri Venkateswara Institute of Technology Electronics & Communication Engineering Andhra Pradesh Anantapur515001 India Srm Institute of Science and Technology Faculty of Engineering & Technology Department of Mathematics Vadapalani Campus Chennai600026 India Department of Mathematics Karnataka Bengaluru560052 India Department of Mathematics Tamil Nadu Karumathur625514 India Bharat Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi110063 India Sharda University Department of Mathematics Greater Noida201310 India

ISBN: (纸本)9798350387193

Environmental sustainability is one of the sustainable development goals and the invention of electric vehicles marks the beginning of alternatives to fuel-based vehicles. Electric vehicles have penetrated the global market to reduce carbon emissions and create a carbon-neutral community. Though these vehicles are environmentally friendly a large population of people in developing nations are still hesitant to purchase these vehicles as they are expensive. This research work proposes strategies for maximizing the utility of E-vehicles based on the obstructing factors using the multi-criteria decision-making method of the Fuzzy Base criterion method (FBCM). The strategies proposed in this paper are ranked based on the score values obtained using representations by trapezoidal fuzzy numbers. The consistency of the results is compared with other decision-making methods and the results obtained using FBCM were found to be more optimal. In this work, the special focus is made concerning developing nations. This paper also presents the extensive role of multi-criteria decision-making methods in designing optimal solutions to the problems of different dimensions associated with electric vehicles. © 2024 IEEE.

关键词： Sustainable development goals

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Development of an Explainable AI (XAI) system for the interpretation of ViT in the diagnosis of medical images 3

Development of an Explainable AI (XAI) system for the interp...

引用

3rd international conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Hachi, Soufiane Sabri, Abdelouahed Faculty of Sciences Dhar El Mahraz Dept. of Computer Science Fez Morocco

ISBN: (纸本)9798331522056

ViT for vision Transformer have emerged as powerful deep learning architectures for medical image analysis, achieving performance in various diagnostic tasks. However, their complex internal mechanisms often hinder interpretability, limiting trust and hindering wider adoption in clinical applications. This study explores the development of an Explainable AI (XAI) system to interpret the decision-making process of a ViT model trained for skin lesion severity classification using the HAM10000 dataset. We leverage five explainability techniques-Attention Rollout, Beyond Attention, Generic Attention, Beyond Intuition, and Transition Attention Maps-to generate saliency maps, providing visual insights into the regions and features deemed most influential by the model for its predictions. Through saliency map visualizations, we demonstrate that the ViT model often learns to focus on clinically relevant features consistent with dermatological criteria, even without explicit feature annotations during training. However, the analyses also reveal potential biases, such as an over-reliance on color variations, and challenges in handling cases presenting with multiple diagnostic features. Our findings provide valuable insights for refining ViT models for skin lesion diagnosis and highlight the essential role of XAI in facilitating transparent and trustworthy AI-driven clinical applications. © 2024 IEEE.

关键词： Medical imaging

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Research on multi-label classification of Chinese judicial texts based on BERT-TextCNN 3

Research on multi-label classification of Chinese judicial t...

引用

3rd international conference on computer vision and Data Mining, ICCVDM 2022

作者： Zhang, Yawen Li, Xiaohui Gu, Pingli Zhang, Ke North China Institute of Computing Technology Smart Plat Rd Department Beijing100083 China Artificial Intelligence Institute of China Electronics Technology Group Corporation Beijing100041 China

ISBN: (纸本)9781510661363

Multi label classification of judicial text data is a hot issue in judicial artificial intelligence. However, judicial data has the characteristics of strong professionalism and long text. The performance of multi label classification task of judicial text using BERT model is poor. Aiming at the above problems, this paper proposes a BERT-TextCNN model, which uses BERT to extract text vectors, and introduces TextCNN to construct multi label classifiers for training, so as to extract semantic information features at different levels of abstraction and improve classification precision. In addition, the input character limit of BERT model will affect the classification results, so this paper rebuild the data set to make the data set meet the maximum input limit of BERT. This paper is tested on the multi label classification data set in the "China legal research Cup "in 2021. The experimental results show that compared with the BERT model, the proposed method has significantly improved the performance and can effectively improve the effect of multi label classification of Chinese judicial texts. © COPYRIGHT SPIE.

关键词： Semantics

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3rd Workshop on Ethical Artificial Intelligence: Methods and Applications (EAI) 24

3rd Workshop on Ethical Artificial Intelligence: Methods and...

引用

30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Zhao, Chen Chen, Feng Wu, Xintao Li, Jundong Chen, Haifeng Baylor Univ Waco TX 76706 USA Univ Texas Dallas Richardson TX USA Univ Arkansas Fayetteville AR USA Univ Virginia Charlottesville VA USA NEC Labs Princeton NJ USA

ISBN: (纸本)9798400704901

Ethical AI has become increasingly important, and it has been attracting attention from academia and industry, due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science. Despite these successes, ethical AI still faces many challenges, such as a lack of interpretable and explainable methods for fairness-aware deep learning models, etc. Consequently, there is an urgent need to bring experts and researchers together at prestigious venues to discuss ethical AI, which has been rarely seen in previous KDD conferences. This workshop will provide a premium platform for both research and industry from different backgrounds to exchange ideas on opportunities, challenges, and cutting-edge techniques in ethical AI.

关键词： Ethical artificial intelligence Fairness-aware Machine Learning

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GACNNXAI: Employing Genetic Algorithm-Enhanced Convolutional Neural Networks and Explainable Artificial Intelligence and its Applications 3

GACNNXAI: Employing Genetic Algorithm-Enhanced Convolutional...

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3rd IEEE international conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Khanna, Sakshi Taaresh Khatri, Sunil Kumar Sharma, Neeraj Kumar Amity Institute of Information Technology Amity University Uttar Pradesh Noida201313 India Ram Lal Anand College Department of Computer Science New Delhi110021 India

ISBN: (纸本)9798350387193

We have proposed a Genetic Algorithm Enhanced Convolutional Neural Network coupled with Explainable Artificial Intelligence for oral cancer detection in medical imaging. Our approach associated CNN with an explanation method to make CNN-based diagnostic decisions more understandable for a clinician. A high resolution of oral imaging is a challenge due to downsampling in several CNN networks. By optimizing the hyperparameters of CNN using Genetic Algorithms, we compensated for downsampling. Our model's hyperparameters, such as the number of layers, filter size, and learning rate, were adjusted based on performance in the validation set. Advancing further in the interpretability, we have used Layer-wise Relevance Propagation, a Local Interpretable Model-agnostic Explanation technique to indicate CNN's decision-making rationale by selecting influential regions of input images. Our proposed GA-CNN model applied to a comprehensive oral image dataset resulted in a classification accuracy of 93.7%. Our sensitivity and specificity rates were 92.5% and 94.9% improvement over other CNN models available in the literature. We have demonstrated that our GA-based optimization mechanism performs better for parameter searching in the CNN. Our proposed AI tool with an explanation for oral cancer screening will assist clinicians in the early identification of disease, establishing a complementary relationship between human health service providers and machine learning technologies for improved patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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PLC-based Mechatronic-Hydraulic Integration Experimental Platform Measurement and control System Development 3

PLC-based Mechatronic-Hydraulic Integration Experimental Pla...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Yu, Junhua Applied Electronics Department Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

With the development of industrial automation technology and computer control technology, the traditional mechanical and electrical equipment can no longer meet the requirements of modern industrial production for high precision, high efficiency and intelligence. Therefore, the organic combination of advanced sensors, controllers and actuators has become one of the current research hotspots. In this paper, we design and develop a comprehensive measurement and control system integrating data acquisition and processing, process monitoring, fault diagnosis, and remote communication with "PLC-based electromechanical-hydraulic integration experiment platform" as the background. The system has the features of compact structure, complete functions, simple and convenient operation and high reliability, which can realize the real-time monitoring of the operation status information of the object under test, improve the quality of experimental teaching, and also can be used as an important means to transform the results of scientific research projects. In the current industrial control field, PLC plays an important role due to its multiple advantages, which is a common module in industrial control. PLC is a programmed control device, this is specially designed for functional control in industrial conditions, and it can realize the storage of programs. © 2023 SPIE.

关键词： Integration

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