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检索条件"机构=Dept. of Computer engineering and artificial intelligence"

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Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

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Advancing Low Resource Natural Language Processing: Techniques, Applications, and Future Directions 2

Advancing Low Resource Natural Language Processing: Techniqu...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Middha, Parth Agarwal, Harshit Rajput, Vaibhav Thakur, Ayush Singh, Sofia Saraswat, Shipra Amity University Dept. of Computer Science & Engineering Noida India Amity Institute of Information Technology Amity University Noida India Amity University Dept. of Artificial Intelligence Noida India

ISBN: (纸本)9798331519056

This paper explores the development and application of low resource Natural Language Processing (NLP) modules, addressing the challenges of processing underrepresented languages and domains with limited linguistic resources. It discusses key methodologies such as transfer learning, unsupervised and semi-supervised learning, and data augmentation techniques that enable effective NLP in resource-constrained environments. The paper presents case studies in machine translation, named entity recognition, and sentiment analysis, demonstrating the practical impact of these approaches. Additionally, it outlines persistent challenges in the field and proposes future research directions, emphasizing the importance of enhancing data acces-sibility, model robustness, computational efficiency, and ethical considerations in advancing low resource NLP. © 2024 IEEE.

关键词： Self-supervised learning

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FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data

FUN-AD: Fully Unsupervised Learning for Anomaly Detection wi...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Im, Jiin Son, Yongho Hong, Je Hyeong Hanyang University Dept. Electronic Engineering Korea Republic of Hanyang University Dept. Artificial Intelligence Korea Republic of

ISBN: (纸本)9798331510831

While the mainstream research in anomaly detection has mainly followed the one-class classification, practical industrial environments often incur noisy training data due to annotation errors or lack of labels for new or refurbished products. To address these issues, we propose a novel learning-based approach for fully unsupervised anomaly detection with unlabeled and potentially contaminated training data. Our method is motivated by two observations, that i) the pairwise feature distances between the normal samples are on average likely to be smaller than those between the anomaly samples or heterogeneous samples and ii) pairs of features mutually closest to each other are likely to be homogeneous pairs, which hold if the normal data has smaller variance than the anomaly data. Building on the first observation that nearest-neighbor distances can distinguish between confident normal samples and anomalies, we propose a pseudo-labeling strategy using an iteratively reconstructed memory bank (IRMB). The second observation is utilized as a new loss function to promote class-homogeneity between mutually closest pairs thereby reducing the ill-posedness of the task. Experimental results on two public industrial anomaly benchmarks and semantic anomaly examples validate the effectiveness of FUNAD across different scenarios and anomaly-to-normal ratios. Our code is available at https://***/HY-Vision-Lab/FUNAD. © 2025 IEEE.

关键词： Unsupervised learning

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Real-Time Traffic Intensity Estimation and Management using Deep Learning Techniques 2

Real-Time Traffic Intensity Estimation and Management using ...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Murhekar, Suryakant Kamble, Yash Dongre, Snehlata Dasarwar, Priya Khan, Tabassum H Patil, Vaishali G H Raisoni College Of Engineering Nagpur India Dept. of Computer Science and Engineering G H Raisoni College Of Engineering Pune India Dept. of Artificial Intelligence G H Raisoni Institute Of Engineering and Technology Nagpur India

ISBN: (纸本)9798350372892

In India, traffic flow estimation is crucial for effective transportation management and urban planning. This research proposes a comprehensive approach leveraging deep learning techniques, particularly the YOLOv8 object detection model, to estimate road traffic flow in urban environments. The model is trained on the widely used COCO dataset, ensuring robust performance in detecting various objects, including vehicles, pedestrians, and cyclists. Subsequently, the model is fine-tuned on a custom dataset comprising annotated traffic images, enabling it to adapt specifically to the complexities of urban traffic scenarios. The research methodology encompasses several key steps. Firstly, the pretrained YOLOv8 model is utilized to perform inference on sample images, demonstrating its capability to accurately detect vehicles within the urban environment. Visualization and analysis of the detection results showcase the model's effectiveness in identifying vehicles with high confidence levels, laying a foundation for further experimentation. Following the initial assessment, the model undergoes fine-tuning on the custom dataset, optimizing its performance for the task of traffic flow estimation. Training parameters such as epochs, image size, and learning rate are carefully tuned to achieve optimal results. Additionally, metrics such as box loss, classification loss, and distribution focal loss are monitored during training to assess model convergence and performance. Upon completing the training phase, the model's performance is evaluated using a validation set, providing insights into its generalization ability and effectiveness in real-world scenarios. The evaluation metrics, including precision, recall, and F1 score, offer quantitative measures of the model's performance, further validating its utility for traffic flow estimation tasks. Furthermore, the research extends beyond static image analysis to real-time traffic intensity estimation using video data. By integratin

关键词： Urban transportation

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Web-Based Smart Healthcare System 2

Web-Based Smart Healthcare System

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Daigavhane, Mansvi Kishorrao Gundewar, Swapnil Gadkari, Ayush Pravin Anpan, Sakshi Prakash Faculty of Engineering and Technology Wardha India Faculty of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Wardha India

ISBN: (纸本)9798331528713

In today's healthcare sector, utilizing information technology is essential for improving hospital operations. This study presents the creation and deployment of a web-based Smart Healthcare system (SHS) aimed at optimizing various administrative and clinical tasks within a healthcare setting. Some of the components are patient portal for signing in, practice calendar, assessment tools, invoicing, and reporting using webbased platform. Web based technologies and keeping a centralized database leads to secure data management, real time information and effective communication between different departments. This paper focuses on the description of the system's layout, structure and basic operations identifying it as a tool for raising organizational productivity, improving data accuracy and promoting patient care benefits. © 2024 IEEE.

关键词： Electronic health record

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An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity

An Al Powered Exam Proctoring: Comprehensive Monitoring for ...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Mewada, Dhruv Gaikwad, Shlok Gharat, Bhavika Kamble, Priyanshu Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence&Data Science Mumbai India

ISBN: (纸本)9798350359688

In recent times, remote learning has experienced significant growth, yet the challenge of conducting academic examinations in a secure manner persists. Various approaches have been adopted by universities, such as collecting assignments susceptible to plagiarism or implementing remote proctoring with manual supervision. As our current lifestyle appears to be transitioning towards a new normal, it becomes imperative to explore effective solutions. This paper introduces a proposed remedy: the development of an AI-based integrated system designed to prevent cheating during examinations. The system not only detects fraudulent activities but also records evidence, ensuring both security and cost-effectiveness. © 2024 IEEE.

关键词： online tests proctoring remote learning

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Boothless Toll Collection System 4

Boothless Toll Collection System

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sarkar, Swagata Selvabharathi, M. Sarveswaran, D. Arun Raja, M. Raju, Aswin Sri Sairam Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

As a developing country, it is critical that we prioritize the construction and improvement of our road infrastructure in order to stimulate economic growth and promote easier logistics operations. However, the current toll collection system is hampered by difficulties such as overcharging and traffic congestion, reducing its effectiveness. To solve these issues, we propose implementing an advanced toll collection system that includes GPS technology, an ANPR camera, and a variety of machine learning algorithms. This new method will transform toll collecting by properly calculating toll charges based on the actual distance traveled by vehicles on the highway, removing the need for drivers to stop at toll booths. The system uses GPS technology to precisely compute the distance traveled by each car, while ANPR cameras guarantee the journey's validity. As a result, vehicles will only be paid for what distance they have traveled, reducing overcharging concerns and congestion at toll gates. Our major goal is to create a smooth and efficient tolling mechanism that overcomes the limitations of traditional tollgate systems. We hope to improve the toll collection process, shorten wait times, and reduce the consumption of fuel associated with long lines at toll booths by utilizing cutting-edge technologies. By applying this innovative strategy, we hope to improve not only toll collecting efficiency but also commuters' overall driving experience. © 2024 IEEE.

关键词： Traffic congestion

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TEA-VITS: Emotion Voice Synthesis Based on Temporal Emotion Analysis

TEA-VITS: Emotion Voice Synthesis Based on Temporal Emotion ...

引用

2023 International Conference on Innovation, Communication and engineering, ICICE 2023

作者： Park, Jaesung Son, Seungjae Jung, Yuchul Dept. of Computer Engineering Kumoh National Institute of Technology Gumi Korea Republic of Dept. of Artificial Intelligence Engineering Kumoh National Institute of Technology Gumi Korea Republic of

ISBN: (纸本)9781839539954

Previous emotional TTS models based on two-stage pipelines are expensive to label to construct natural speech and complicated in the learning process. In addition, there is a problem in which there is a difference between the pitch of the voice and the labelling of the emotion. To address this problem, this study presents TEA-VITS, an End-to-End TTS using Speech-Emotion-Diarization(SED) to generate words with a clear emotional accent. TEA-VITS uses the SED method to recognize temporal emotion in a single voice and label temporal emotion classification changes in voice data for model learning. The results show that they become clearer in terms of emotion than previous emotional TTS models and present new directions for efficient and natural emotional expression synthesis. © The Institution of engineering & Technology 2023.

关键词： Contrastive Learning

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Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

引用

4th IEEE International Conference on Emerging Techniques in Computational intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Analysis of Prediction of Pneumonia from Chest X-ray images using CNN and Transfer Learning 5

Analysis of Prediction of Pneumonia from Chest X-ray images ...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Vinay, K. Shashanka Kodipalli, Ashwini Swetha, P. Kumaraswamy, S. Dept. of Computer Science and Engineering Global Academy of Technology Karnataka Bengaluru India Dept of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350361155

A good design for image classification problems is the CNN architecture you presented, which consists of 5 convolution blocks followed by 4 fully connected layers. From the input X-ray images, the convolutional blocks extract pertinent features, and the fully connected layers assist in determining the final classification based on those learned features. You have integrated various approaches to improve the performance of your model. The inputs to each layer are normalized through batch normalization, which can speed up training and enhance generalization. By removing certain neurons at random during training, dynamic dropout helps avoid overfitting. L2 regularization weight decay and learning rate decay are two efficient strategies for preventing overfitting and enhancing the model's capacity to expand to new data. Popular optimization algorithm Adam optimizer effectively neural network training. For binary classification problems like the diagnosis of pneumonia, the loss function for binary Cross-Entropy is the best option. To determine your model's efficacy, you must validate it using benchmark datasets that are available to the general public. You can evaluate your model's effectiveness by comparing its performance to that of current methods by conducting experimental investigations on these datasets. Your model performs well as evidenced by accuracy scores of 90.93%, 89.17% for multi-class classification and binary classification. tasks. Automated methods, such as the one you suggested, might help medical practitioners recognize pneumonia and spot diseased spots in chest X-ray pictures. However, it's crucial to remember that automated systems shouldn't take the place of professional radiologists' and doctors' skills and judgment;rather, they should be used as supportive tools. Medical To ensure accurate diagnosis and suitable patient care, specialists should always review and interpret the system's data. It's also crucial to take into account potential drawback

关键词： Convolutional neural networks

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