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

共 2495 条记录，以下是81-90 订阅

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Detecting Suspicious Military Tents from Optical Images using Deep Learning Model 3

Detecting Suspicious Military Tents from Optical Images usin...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vasavi, S. Praneeth Polu, Venkata Sai Manikya Venkata Saketh Nandula, Bala Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350364569

Locating suspicious military tents or encampments is crucial for national security. Recently, deep learning methods have become increasingly popular for detecting military objects at country borders. Traditional methods, which rely on manual checks and morphological algorithms, are often inaccurate and slow at detecting such unauthorized activities. To overcome this issue, a solution that automatically detects suspicious tents from satellite images using deep learning techniques is crucial. An enhanced YOLOv8x-obb model is proposed and trained with a satellite image dataset that is well-prepared with various preprocessing and augmentation techniques. This model was validated and produced an accuracy of 91.9% and false alarm rate of 6.08%, specifying that the model works well in different terrains and situations, and it has the advantage of better border surveillance. © 2024 IEEE.

关键词： Military satellites

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Detection of Airports and Runways From Aerial Imagery Using Deep Learning 8

Detection of Airports and Runways From Aerial Imagery Using ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vasavi, S. Paul, Valeti Abhishek Abhilash, Vandanala Gnaneswar, Kotha Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331505462

Disaster management calls for as quick a detection of critical infrastructure-like airports and runways. Most of the traditional object detection models, which include CNNs, R-CNN s, SSDs, and earlier versions of YOLO, have high computational costs and low accuracy, particularly for smaller objects within aerial and satellite imagery. It presents the YOLOv9 object detection model with very significant improvements in feature extraction, bounding box prediction, and several modern techniques involving attention mechanisms and multi-scale feature fusion. For the purpose of improving model robustness, extensive data augmentation was adopted in training. The proposed model was tested and validated on the diverse dataset of aerial and satellite images, attaining an accuracy as high as 91.3% with a low false alarm rate of just 7.08%. These further improvements will make YOLOv9 highly scalable to support disaster management with faster infrastructure assessments, improvement in border surveillance, among others. © 2024 IEEE.

关键词： Disaster prevention

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Decoding the News: An Integrated Approach for News Text Classification and Expert System Recommendations 18

Decoding the News: An Integrated Approach for News Text Clas...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prajapati, Vivek Mishra, Arpit Shinde, Prathmesh Raut, Neha Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

In the era of information overload on online platforms, efficient access to news content has become crucial for individuals leading busy lives. This paper addresses the need for categorizing vast amounts of online news data into specific sections like sports, entertainment, business, finance, etc., catering to users' personalized interests. Leveraging Machine Learning and Deep Learning algorithms, the study utilized a BBC News dataset from Kaggle and supplemented it with an additional dataset generated through web scraping using BeautifulSoup. With up to 20,000 rows covering sports, business, entertainment, politics, and technology categories, the research applied Random Forest, Support Vector Machine (SVM), Gaussian Naive Bayes (NB), and Multi-Head Attention Transformer algorithms. The findings and recommendations extend to technologies suitable for both news text classification and user-specific content recommendation in this evolving digital landscape. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： information overload machine learning media analysis news text classification trend identification

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Revolutionizing Education for Visually Impaired Students Using Augmented Reality 5

Revolutionizing Education for Visually Impaired Students Usi...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Janapati, Malathi Naveen, Budamcharla Venkata Sanuth Reddy, Mettu Sai Dinesh, Balabomma Sai Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331530013

This research explores the use of Augmented Reality (AR) to enhance learning experiences for visually impaired students. By utilizing an Android application, the system captures images of classroom boards and processes them using the EasyOCR library for text extraction. The extracted text is then rendered in 3D using Unity, allowing visually impaired students to interact with the content more effectively. The AR experience enables students to zoom in on the text, adjust font sizes, and explore the information at their own pace. This innovative approach integrates the power of AR to overcome the challenges faced by visually impaired students in traditional classroom settings, promoting inclusivity and enhancing their learning experience. © 2024 IEEE.

关键词： Students

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Wearable Ear EEG Device for Emotion Recognition in Human-Robot Interaction 23

Wearable Ear EEG Device for Emotion Recognition in Human-Rob...

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23rd International Conference on Cyberworlds, CW 2024

作者： Mai, Ngoc-Dau Go, Kentaro Mao, Xiaoyang Chung, Wan-Young Pukyong National University Dept. Artificial Intelligence Convergence Busan48513 Korea Republic of University of Yamanashi Kofu Dept. Computer Science and Engineering Yamanashi400-8511 Japan

ISBN: (纸本)9798331527174

Wearable electroencephalography (EEG) devices are emerging as crucial tools in human-robot interaction (HRI), enabling intuitive and effective communication between humans and robots. These devices non-invasively measure brain activity, providing real-time insights into a user's mental state, intentions, and cognitive load. This paper explores the advancements and applications of wearable ear EEG technology in HRI, with a focus on detecting human intent, monitoring emotional and cognitive states, and delivering real-time feedback for adaptive robot behavior. The benefits of wearable EEG over traditional scalp EEG, such as enhanced user comfort, reduced setup time, and improved long-term wearability, are thoroughly examined. Additionally, the paper covers advanced applications, including emotion-aware adaptive interactions, neurofeedback training, and direct robot control via brain-computer interfaces (BCIs). A review of the latest advancements in device miniaturization, integration with other wearable sensors, and applications in virtual reality, gaming, and healthcare is provided. Future directions focus on expanding applications, enhancing human-AI collaboration, and improving accuracy and reliability in HRI. The studies discussed in this paper represent our state-of-the-art research across various fields and applications of wearable EEG systems. This study highlights the potential of wearable Ear-EEG devices to revolutionize human-robot interactions by enhancing customization, responsiveness, and overall efficacy. © 2024 IEEE.

关键词： Human robot interaction

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Enhanced Deep Learning-Based Malware Classification Using MobileNetV2 4

Enhanced Deep Learning-Based Malware Classification Using Mo...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Manasa, V.Gnana Sri Gopinath, M. Ram, M.Aditya Khan, Md.Sajid Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Kanuru India

ISBN: (纸本)9798331529635

In the rapidly evolving landscape of cybersecurity, the classification of malware has become increasingly critical due to the exponential growth in the volume of malware, which poses significant security threats to financial institutions, businesses, and individuals. Traditional signature-based detection methods have struggled to keep up with the growing variety and complexity of new threats, making it necessary to use more advanced techniques. Among these, deep learning has emerged as a promising approach for malware detection, particularly through the classification of malware images by transforming binary files into grayscale representations. While previous research has predominantly utilized deep learning architectures for this task. However, in this research work, we developed a model with Mobilenet to enhance classification accuracy. The results of this research demonstrate the revolutionary potential of deep learning-based methods for malware family classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards Robust and Efficient Yoga Pose Recognition using Deep Learning: A Comprehensive Review 15

Towards Robust and Efficient Yoga Pose Recognition using Dee...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Senthil Kumari, P. Mythili, S. Dept. of Artificial Intelligence PSNA College of Engineering and Technology Dindigul India Dept. of Bio Medical Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331300579

Yoga is a popular practice that promotes physical and mental well-being. The lack of regulation and standardization in yoga teaching and learning can lead to unsafe practices and health issues for practitioners, such as injury or exacerbation of pre-existing medical conditions. Accurate classification of yoga postures is essential for effective teaching and learning of yoga. Deep Learning (DL) techniques have shown significant potential in automating the process of posture classification, leading to improve accuracy and efficiency. This paper provides a comprehensive review of the use of DL techniques for yoga posture classification. Different DL algorithms, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Generative Adversarial Network are explored. Additionally, various datasets used for training and testing deep learning models for yoga posture classification are discussed. Challenges involved in yoga posture classification, such as variability in posture performance and the lack of standardized datasets, are also highlighted. A comparison of different DL techniques for yoga posture classification is presented, mentioning their respective strengths and weaknesses. This review emphasizes the potential benefits of DL for accurate yoga posture classification and provides insights into future research directions in this field. © Grenze Scientific Society, 2024.

关键词： Generative adversarial networks

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Military Aircraft Detection from Optical Satellite Images Using Convolutional Neural Networks 8

Military Aircraft Detection from Optical Satellite Images Us...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vasavi, S. Kalahasti, Tejaswini Kotturu, Manvitha Sri Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331505462

Aircraft detection using optical images plays a vital role in various military operations contributing to security, safety, efficiency, and environmental sustainability in the aviation sector. Recent works such as CGC-net, YOLOv5, DenseNet have showed remarkable outcomes in military aircraft detection. Although the models demonstrate significant results, they often exhibit low speed and unreliable accuracy in detecting aircrafts from foggy optical images. To address these issues, we plan on developing a deep learning model that detects military aircrafts with high speed and accuracy particularly in challenging weather conditions. This paper proposes an enhanced CNN model that has been well trained with MAR20 dataset, with various pre-processing techniques applied. The trained model has consistently shown reliable results when tested with unseen data. This model was validated and it yielded an accuracy of 92.35 % and over all false positive rate is 0.41 %, indicating that it performs well in a variety of landscapes and circumstances, with an additional advantage of improved border surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards Scalable Handwriting Communication via EEG Decoding and Latent Embedding Integration 13

Towards Scalable Handwriting Communication via EEG Decoding ...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Kim, Jun-Young Kim, Deok-Seon Lee, Seo-Hyun Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

In recent years, brain–computer interfaces have made advances in decoding various motor–related tasks, including gesture recognition and movement classification, utilizing electroencephalogram (EEG) data. These developments are fundamental in exploring how neural signals can be interpreted to recognize specific physical actions. This study centers on a written alphabet classification task, where we aim to decode EEG signals associated with handwriting. To achieve this, we incorporate hand kinematics to guide the extraction of the consistent embeddings from high–dimensional neural recordings using auxiliary variables (CEBRA). These CEBRA embeddings, along with the EEG, are processed by a parallel convolutional neural network model that extracts features from both data sources simultaneously. The model classifies nine different handwritten characters, including symbols such as alphabets, exclamation marks and commas. We evaluate the model using a quantitative five–fold cross–validation approach and explore the structure of the embedding space through visualizations. Our approach achieves a classification accuracy of 91 % for the nine–class task, demonstrating the feasibility of fine–grained handwriting decoding from EEG signals. © 2025 IEEE.

关键词： Gesture recognition

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EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia 13

EEG Spectral Analysis in Gray Zone Between Healthy and Insom...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Jo, Ha-Na Kweon, Young-Seok Lee, Seo-Hyun Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences sleep disturbances yet does not fully meet the clinical criteria for chronic insomnia. Thirteen healthy participants and thirteen individuals from the gray zone were assessed using polysomnography and electroencephalogram to analyze both sleep architecture and neural activity. Although no significant differences in objective sleep quality or structure were found between the groups, gray zone individuals reported higher insomnia severity index scores, indicating subjective sleep difficulties. Electroencephalogram analysis revealed increased delta and alpha activity during the wake stage, suggesting lingering sleep inertia, while non-rapid eye movement stages 1 and 2 exhibited elevated beta and gamma activity, often associated with chronic insomnia. However, these high-frequency patterns were not observed in non-rapid eye movement stage 3 or rapid eye movement sleep, suggesting less severe disruptions compared to chronic insomnia. This study emphasizes that despite normal polysomnography findings, EEG patterns in gray zone individuals suggest a potential risk for chronic insomnia, highlighting the need for early identification and tailored intervention strategies to prevent progression. © 2025 IEEE.

关键词： Electroencephalography

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