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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Premkumar Ramu A. N. Sasikumar D. Kavitha K. Suresh G. Anushree Manjunathan Alagarsamy Department of Artificial Intelligence & Data Science Sree Sastha Institute of Engineering & Technology Chennai Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai Tamil Nadu India Department of Computer Science and Engineering PSNA College of Engineering & Technology Dindigul Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Deep learning has revolutionized natural language processing by enabling complex models to classify text. This paper investigates using DistilBERT, a pre-trained transformer model, to classify news stories into World, Sport, Business, and Sci/Tech. To ensure model robustness, class imbalance, missing values, and duplicate entries in the dataset were carefully rectified. Five-crop augmentation and random flips were utilized to boost data variability and generalization. Model performance and embeddings were also visualized using t-SNE embeddings. Learning rate modifications and batch size trials optimized the model and balanced computing cost and performance. The project examined manual linear layer configuration to demonstrate mathematical transformations and deep learning architecture adaptability. DistilBERT was developed to extract important textual elements. After processing, these traits were categorized. Model metrics such as accuracy, precision, recall, and the F1 score demonstrated performance improvements. Deep learning pipelines can overcome data challenges and produce cutting-edge NLP results. This study explains gradient behaviour and pre-trained weight sensitivity. This study also reveals the need for hyperparameter tuning to optimize pre-trained models across domains. The case study advances transfer learning research and advises NLP practitioners. By increasing the learning rate, researchers and developers can improve pre-trained language models like DistilBERT.

关键词： Deep learning Training Adaptation models Computational modeling Pipelines Transformers Natural language processing data models Mathematical models Sports

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Random Forest and Sobol Method Based data Center PUE Prediction 12

Random Forest and Sobol Method Based Data Center PUE Predict...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Yan, Longchuan Luan, Zhaofang He, Ling Jiang, Congfeng Liu, Junming Xu, Chuanqi Zhang, Liangbin State Grid Corporation of China Information & Telecommunication Branch Beijing China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Information Technology Center Hangzhou Dianzi University Hangzhou China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9798350355802

data centers are critical infrastructures that support a multitude of business loads such as big data, artificial intelligence, and cloud computing. While providing the necessary computing power, the problem of high energy consumption in data centers has become a major obstacle limiting their scale-up, reliability and quality of service. Improving the power usage effectiveness (PUE) of data centers is an important way to improve the energy efficiency of data centers. With the development of theories and methods of machine learning, modeling, analysis and prediction of data center energy consumption using machine learning techniques has become a feasible option. In this study, several accurate PUE prediction models are constructed using neural networks and machine learning techniques for the PUE of a small air-cooled data center in actual operation, among which the mean absolute error of the optimal model reaches 0.29%. In addition, this paper also uses Random Forest and Sobol methods to model and conduct sensitivity analyses of key factors affecting data center PUE, revealing the relationship between data center operating parameters and PUE, and finding that the cooling system exhibits different energy consumption patterns under different IT equipment power conditions, which has a decisive impact on PUE. Based on these sensitivity analyses, we argue that for small air-cooled data centers, improving internal insulation and humidity is an important strategy for optimizing PUE. © 2024 IEEE.

关键词： Sensitivity analysis

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Green Energy AI Cloud: Intelligent Community-Based Electricity Management System 8

Green Energy AI Cloud: Intelligent Community-Based Electrici...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Mehta, Yukta Lo, Vincent Mehta, Vijen Agarwal, Kunal Madabathula, Charan Teja Gao, Jerry San Jose State University Department of Applied Data Science San JoseCA United States Evergreen Valley High School Department of Analysis San JoseCA United States San Jose State University Department of Computer Engineering San JoseCA United States

ISBN: (纸本)9798331523558

According to the U.S. Energy Information Administration, 60 percent of the world's electricity is generated from fossil fuels, 18 percent from nuclear power, and only 21 percent from green energy resources. These statistics highlight the pressing need to transition to renewable energy for a sustainable future. However, a major challenge that arises from this increased demand for green energy is the risk of electricity shortage. Overcoming this challenge requires a shift from conventional energy management to an AI-based electricity management system that can more efficiently balance supply and demand. In this paper, we have developed a comprehensive cloud-based Green AI service Management System to accommodate three important aspects of green energy services: electricity consumption, electricity generation, and shortage analysis. In this system, we propose using a community-based model that manages and analyzes multiple buildings' energy usage, allowing the model to be trained further via distributed and aggregated training. The models developed for these components use three approaches: deep learning, machine learning, and reinforcement algorithms, and achieved a shortage analysis accuracy of 98.2 percent. The model is deployed on the cloud and results are accessible from the user interface. Overall, we provide a unique Green AI solution to the market using a system that can handle various aspects of green energy and gives better accuracy than existing systems. © 2024 IEEE.

关键词： Consumption prediction Electricity Management System Electricity shortage forecasting Green AI Usage prediction

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The effect of visual advanced driver assistance systems on a following human driver in a mixed-traffic condition

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Procedia computer science 2023年 216卷 221-229页

作者： Dedy Ariansyah Bens Pardamean Giandomenico Caruso Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers’ ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-traffic condition.

关键词： sustainable transport management platooning car-following visual advanced driver assistance system virtual realtiy

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Symmetrical Supervision with Transformer for Few-shot Medical Image Segmentation

Symmetrical Supervision with Transformer for Few-shot Medica...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Niu, Yao Luo, Zhiming Lian, Sheng Li, Lei Li, Shaozi Song, Haixin Xiamen University Department of Artificial Intelligence Fujian China Fuzhou University College of Computer and Data Science Fujian China Xiamen University Department of Software Engineering Fujian China

ISBN: (纸本)9781665468190

Few-shot learning can potentially learn the target knowledge in extremely few data regimes. Existing few-shot medical image segmentation methods fail to consider the global anatomy correlation between the support and query sets. They generally adopt a weak one-way information transmission that can not fully explore the knowledge to segment query data. To address this problem, we propose a novel Symmetrical Supervision network based on traditional two-branch methods. We raise two main contributions: (1) The Symmetrical Supervision Mechanism is leveraged to strengthen the supervision of network training;(2) A transformer-based Global Feature Alignment module is introduced to increase the global consistency between the two branches. Experimental results on two challenging datasets (abdominal segmentation dataset CHAOS and cardiac segmentation dataset MS-CMRSeg) show a remarkable performance compared to other comparing methods. © 2022 IEEE.

关键词： Image segmentation

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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Invisible Watermark and Hypothesis Testing for Multi-Ownership Verification Framework of Deep Neural Network

Invisible Watermark and Hypothesis Testing for Multi-Ownersh...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ruoxi Wang Yujia Zhu Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid development of information technology, artificial intelligence models have become the core asset of tech giants, highlighting the importance of copyright protection. Black-box watermarking has gained attention due to its ability to verify the legality of models remotely without relying on internal model information. The challenges lie in embedding and verifying watermarks without accessing internal model parameters, while ensuring the robustness and stealth of the watermark. This paper focuses on discussing effective ways to protect the copyright of black-box watermarking models and detect and prevent unauthorized user behavior that infringes on model ownership. Therefore, this paper proposes a multi-ownership verification framework based on invisible watermark and hypothesis testing, which mainly consists of two parts: watermark generation and copyright verification. The watermark generation uses a dynamic watermarking method to generate invisible backdoor watermark and verifies copyright through hypothesis testing. This framework has high assurance and effectiveness under various models of CIFAR-10 and MNIST datasets and shows strong robustness in VGG and Resnet model pruning attacks. This research provides new ideas and methods for the protection of model copyrights in deep neural networks.

关键词： Deep learning Education Closed box Watermarking Artificial neural networks Copyright protection Benchmark testing Robustness Protection Information technology

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A Design of an Effective Methodology to Detect Glaucoma Disease using Intelligent Learning Strategy 9

A Design of an Effective Methodology to Detect Glaucoma Dise...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Shobana, D. Akilarasu, G. Sangeetha, J. Priya, V. Banu Karkuzhali, S. Malathi, P. Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Lovely Professional University Department of Computer Science and Engineering Punjab India S. A. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Mathematics Tamil Nadu Chennai India R.M.K. Engineering College Department of General Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

Globally, glaucoma is now among the leading causes of blindness. It is a chronic, irreversible illness. Screening for glaucoma and detecting it early are crucial in the fight against this illness. Here, deep learning shows great promise. Two deep learning architectures, the Convolutional Neural Network (CNN) model and the Improved Learning Strategy to Predict Glaucoma (ILSPG) model, have been evaluated in this article. In order to diagnose glaucoma, the DCNN method is utilized. Ultimately, this area of study aims to help doctors diagnose glaucoma by developing methods to spot the disease in retinal fundus pictures. Because glaucoma can have both beneficial and bad effects on the model, the result of the model might be good or bad. Several measures are utilized in the process of evaluating models, including accuracy, precision, specificity, and reliability. In order to determine the accuracy of the proposed model, an additional comparison study is conducted. The results are contrasted with a deep learning-based classification system that uses convolutional neural networks. The results demonstrate, without a doubt, that the proposed system is effective. © 2023 IEEE.

关键词： Convolutional neural networks

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An Optimized Hybrid Deep Learning and IoT-Enabled Health System in Retina Image Analysis for Blindness Detection

An Optimized Hybrid Deep Learning and IoT-Enabled Health Sys...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： J Yamini T Kanimozhi A Jenefa K Bagyalakshmi K Aarthi P Dhivya Department of Artificial Intelligence and Machine learning Kalaignar Karunanidhi Institute of Technology Coimbatore India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering PPG Institute of Technology Coimbatore India Department of Mathematics SNS College of Technology Coimbatore India Department of Artificial Intelligence and Data Science Kalaignar Karunanidhi Institute of Technology Coimbatore India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of $95.65 \%$ with a loss of 0.18 without overfitting and underfitting.

关键词： Optical filters Accuracy Filtering Noise Predictive models Retina Feature extraction Convolutional neural networks Diseases Overfitting

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Knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： He, Kai Mao, Bing Zhou, Xiangyu Li, Yufei Gong, Tieliang Li, Chen Wu, Jialun Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge. © 2022 IEEE.

关键词： Model structures

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