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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Vidhi Wankhade Soyal Nimbalkar Kajal Dakhare Rohan Jangamwar Meher Langote Prateek Verma Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

As the abdominal and chest injuries are high morbidity and mortality around the globe, these pose an acute challenge in emergency care. Current data from recently released sources indicate that more than half of the patients visiting ER and also being admitted into hospital account for trauma; and the burden of both abdominal and chest injuries is quite high. Imaging plays an important role in diagnosis and treatment planning in the management of abdominal and chest injuries. The most widely practiced modality is radiograph imaging simply because it produces quality and easy results. Improving Automated pathology object detection of things in radiographs with deep learning algorithms promises much for the precision of diagnosis and optimization in health care. This paper discusses and provides a very detailed review of optimized CNNs, primarily ResNet18, ResNet34, along with a custom-built neural network for the identification of disease from X-ray pictures. Such models are trained and validated to reach excellent sensitivity and specificity on a wide variety of diseases by using the annotated RSNA 2023 dataset by professionals in radiology. It extended the scope of earlier research in this field by looking at other imaging conditions and diseases as well. This puts its contribution into the larger, ongoing discussion of AI in radiology. The results of this study help to contribute to the thesis that the promise of deep learning in radiological diagnosis is substantial, and it could be used to better enhance patient outcomes in the practice of emergency radiology.

关键词： Deep learning Pathology Ethics Neural networks Imaging Radiology artificial intelligence Injuries X-ray imaging Diseases

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Robust Real-Time PCB Defect Detection Using Improved DETR

Robust Real-Time PCB Defect Detection Using Improved DETR

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wu, Qingtian Yang, Jinfeng Yang, Ou Zhang, Liming Guan, Yisheng Institute of Applied Artificial Intelligence Guangdong-Hong Kong-Macao Greater Bay Area School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China University of Macau Faculty of Sciences and Technology Department of Computer and Information Science SAR Macau China School of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou Guangdong China

ISBN: (纸本)9781665481090

This paper presents a PCB defect detection system utilizing the Real-Time Detection Transformer (RT-DETR) model. Originally designed for general object detection, we adapt RT-DETR for the specific task of PCB defect detection, achieving both high accuracy and rapid inference. Building on this baseline, we introduce re-parameterization techniques to further enhance the model's performance with less parameters computational cost. Additionally, we employ data augmentation strategies to enrich the diversity of the dataset, thereby improving the model's robustness and generalization capability. Through these efforts, the developed PCB defect detection system based on the RT-DETR model meets the expected requirements in terms of high accuracy and real-time inference speed. The code and models will be made publicly available. © 2024 IEEE.

关键词： Object detection

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Unlocking the Potential of Model Merging for Low-Resource Languages

Unlocking the Potential of Model Merging for Low-Resource La...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tao, Mingxu Zhang, Chen Huang, Quzhe Ma, Tianyao Huang, Songfang Zhao, Dongyan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Center for Data Science Peking University China College of Engineering Peking University China

ISBN: (纸本)9798891761681

Adapting large language models (LLMs) to new languages typically involves continual pre-training (CT) followed by supervised finetuning (SFT).However, this CT-then-SFT approach struggles with limited data in the context of low-resource languages, failing to balance language modeling and task-solving *** thus propose a new model merging solution as an alternative for low-resource languages, combining models with distinct capabilities into a single model without additional *** use model merging to develop task-solving LLMs for low-resource languages without SFT data in the target *** experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce *** performance saturation in model merging with increasingly more training tokens, we further analyze the merging process and introduce a slack variable to the model merging algorithm to mitigate the loss of important parameters, thereby enhancing model *** hope that model merging can benefit more human languages suffering from data scarcity with its higher data efficiency. © 2024 Association for Computational Linguistics.

关键词： Modeling languages

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Diverse Geographical Regions Based Biodiversity Conservation by LiDAR Image with Deep Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 738-749页

作者： Babu C. N, Gireesh Reddy, Chilukala Mahender Kumar, Mungara Kiran Nagaraju, Komatigunta Maheswari, G. Uma Kiran, Ajmeera Department of Computer Science and Engineering BMS Institute of Technology & amp Management Karnataka Bengaluru India Department of Artificial Intelligence and Data Science GITAM (Deemed to be University) Hyderabad India Department of CSE School of Technology GITAM (Deemed to Be University) Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P India Department of Computer Science and Engineering RMK College of Engineering and Technology Pudhuvoyal Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Reliable data on the composition and structure of forests at various spatial scales is necessary for the conservation and monitoring of forest biodiversity. However, because field sampling techniques can be challenging, comprehensive data regarding the features of forest habitat over wide areas are frequently lacking. In order to get over this restriction, we created variables that describe the structure of the forest landscape over a wide environmental gradient using a nationally accessible light detection and ranging (LiDAR) remote sensing dataset. This research proposes a novel technique in forest management based on biodiversity analysis using LiDAR image processing by deep learning techniques. Here, the input LiDAR image has been collected as temperature pattern dataset in major deforestation regions and processed for noise removal and normalization. This image feature has been extracted using recurrent graph learning-based SegNet component analysis. Then, this extracted feature has been classified using Gaussian deep belief region-based adversarial neural networks. The experimental analysis has been carried out for various temperature pattern datasets in terms of random accuracy, average precision, recall, NSE, and AUC. The results of this study will help determine policy objectives for the restoration of mountain areas and enhance the effectiveness of monitoring damaged mountain forests. With the help of the suggested method, 96% random accuracy, 94% average precision, 49% NSE, 93% recall, and 95% AUC were achieved. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial neural networks Deep learning techniques Deforestation rate LiDAR image processing Temperature rise analysis

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Decentralized Intrusion Detection in Peer-to-Peer Networks using Stacking Ensemble Model 1

Decentralized Intrusion Detection in Peer-to-Peer Networks u...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Devi, R. Renuga Riadhusin, Rame Anu, D. Swarnateja, R. Revathi, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, due to the rise in Peer to Peer (P2P) networks has brought a significant way in which the data is shared and services are offered over the internet. These P2P networks make use of decentralized architecture in which nodes directly interact and exchange resources. However, due to the use of decentralized architecture, the P2P networks are exposed to various security threats such as data breaches, and other malicious attacks. In this research, Stacking Ensemble Model (SEM) is proposed for network intrusion detection in P2P networks. Initially, the data is considered from network intrusion detection dataset and then preprocessed with data imputation and min-max normalization effectively handles the missing values and rescales the data into significant range. After that, Recursive Feature Elimination (RFE) is incorporated to select optimal features from preprocessed data based on feature importance. Finally, the proposed SEM effectively detects the intrusion in P2P networks which enhances the security. From the results, the proposed SEM model obtained a better outcome in terms of accuracy, precision, recall, and F1-score compared to the existing K-Nearest Neighbor (KNN) by accomplishing 97.20%, 94.71%, 96.62%, and 95.65%, respectively. © 2025 IEEE.

关键词： Network intrusion

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An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling 2nd

An Inconsistency-Based Hybrid Feature Selection Approach for...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zhao, Rong ALDharhani, Ghassan Saleh Ratnavelu, Kuru Thambiayya, Sathiyaraj Jasser, Muhammed Basheer Majeed, Anwar P. P. Abdul Luo, Yang Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Institute of Information Science and Technology Dalian University of Science and Technology Dalian China Faculty of Information Science and Technology UKM University Selangor Bangi43000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur56000 Malaysia Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

artificial intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Image Caption Generation using CNN and Audio Conversion 2

Image Caption Generation using CNN and Audio Conversion

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Priya, K. Babu, R.Vijay Reddy, M.Muralidhar Mohan Reddy, T.H. Maanesh, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Artificial Intelligence & Data Science Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Arizona state university Department of Data Science Tempe United States

ISBN: (纸本)9798350368413

This research focuses on developing a system that can generate descriptive captions for images and convert them into audio output. The system employs a Convolutional Neural Network (CNN) to extract visual features from images, which are then fed into a Gated Recurrent Unit (GRU) to generate textual descriptions. The generated captions are subsequently converted into audio using text-to-speech techniques. By training the model on a large dataset of image-caption pairs, the system learns to associate visual information with textual descriptions, enabling accurate and coherent caption generation. This research contributes to the advancement of image understanding and generation, with potential applications in various fields such as image search, accessibility, and content creation. © 2024 IEEE.

关键词： Convolutional neural networks

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Unlocking the Potential: A Review of Digital Twins and Integration with Emerging Technologies 1

Unlocking the Potential: A Review of Digital Twins and Integ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Dinesh Babu, V. Geetha, S.K. Aneeshkumar, M. Revathi, S. Arulprakash Arulprakash, P. Karpagam Institute of Technology Department of Information Technology Coimbatore India Department of Computer Science and Engineering Coimbatore India Arjun Collge of Technology Department of Artificial Intelligence and Data Science Thamaraikulam India Erode Sengunthar Engineering College Department of Computer Science and Engineering Erode India Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359756

The Digital Twin (DT) is primarily used to digitally represent a physical object, process, or service. Simply put, DT is a clone representation of a physical object in the real world, such as wind farms, objects like a building or even an entire city. The key uses of technology are to collaborate, train, and simulate processes or products. The results of DT vary greatly depending on the sector and application, but a few key advantages include reducing costs and risks before investing in real-world resources and helping to enhance products or processes over their entire life cycles. The integration of the digital Twin with so many various industries, including manufacturing, healthcare, and the growth of urban and rural areas, has enhanced the prominence of this emerging technology. This paper's major goal is to identify ongoing initiatives by numerous engineering fields to apply digital twin technology for sustainable development. © 2023 IEEE.

关键词： Blockchain

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Boosting Graph Convolution with Disparity-induced Structural Refinement 25

Boosting Graph Convolution with Disparity-induced Structural...

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34th ACM Web Conference, WWW 2025

作者： Huang, Sujia Pi, Yueyang Zhang, Tong Liu, Wenzhe Cui, Zhen Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Fuzhou University College of Computer and Data Science Fuzhou China Huzhou University School of Information Engineering Huzhou China Beijing Normal University School of Artificial Intelligence Beijing China

ISBN: (纸本)9798400712746

Graph Neural Networks (GNNs) have expressed remarkable capability in processing graph-structured data. Recent studies have found that most GNNs rely on the homophily assumption of graphs, leading to unsatisfactory performance on heterophilous graphs. While certain methods have been developed to address heterophilous links, they lack more precise estimation of high-order relationships between nodes. This could result in the aggregation of excessive interference information during message propagation, thus degrading the representation ability of learned features. In this work, we propose a Disparity-induced Structural Refinement (DSR) method that enables adaptive and selective message propagation in GNN, to enhance representation learning in heterophilous graphs. We theoretically analyze the necessity of structural refinement during message passing grounded in the derivation of error bound for node classification. To this end, we design a disparity score that combines both features and structural information at the node level, reflecting the connectivity degree of hopping neighbor nodes. Based on the disparity score, we can adjust the aggregation of neighbor nodes, thereby mitigating the impact of irrelevant information during message passing. Experimental results demonstrate that our method achieves competitive performance, mostly outperforming advanced methods on both homophilous and heterophilous datasets. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Synergy of artificial intelligence and Music 6

Synergy of Artificial Intelligence and Music

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6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Patil, Ruchira Pramod Bhor, Prabhu Anil K. J. Somaiya Institute of Technology Department of Artificial Intelligence and Data Science Sion Mumbai India Datta Meghe College of Engineering Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350359817

This comprehensive paper delves into the intricate relationship between Music and artificial intelligence (AI). It presents an extensive overview of the current developments in this emerging field and explores the potential future applications. The paper goes beyond the surface, emphasizing that music is not just a melodious tune but a complex data set that AI can efficiently manipulate. It investigates how AI can affect human brain functionality through music, discusses AI's role in automated music composition, and highlights its significance in assisting impaired individuals in playing music. Furthermore, the paper predicts the future of AI in the music industry, considering its possibility for transformation. © 2023 IEEE.

关键词： Sentiment analysis

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