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IEEE General Meeting Power& Energy Society

作者： Jinhao Li Changlong Wang Hao Wang Department of Data Science and AI Faculty of IT Monash University Australia Department of Civil Engineering Monash University Australia Monash Energy Institute Monash University Australia

Wind energy has been rapidly gaining popularity as a means for combating climate change. However, the variable nature of wind generation can undermine system reliability and lead to wind curtailment, causing substantial economic losses to wind power producers. Battery energy storage systems (BESS) that serve as onsite backup sources are among the solutions to mitigate wind curtailment. However, such an auxiliary role of the BESS might severely weaken its economic viability. This paper addresses the issue by proposing joint wind curtailment reduction and energy arbitrage for the BESS. We decouple the market participation of the co-located wind-battery system and develop a joint-bidding framework for the wind farm and BESS. It is challenging to optimize the joint-bidding because of the stochasticity of energy prices and wind generation. Therefore, we leverage deep reinforcement learning to maximize the overall revenue from the spot market while unlocking the BESS’s potential in concurrently reducing wind curtailment and conducting energy arbitrage. We validate the proposed strategy using realistic wind farm data and demonstrate that our joint-bidding strategy responds better to wind curtailment and generates higher revenues than the optimization-based benchmark. Our simulations also reveal that the extra wind generation used to be curtailed can be an effective power source to charge the BESS, resulting in additional financial returns.

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Efficient Detection of DDoS Attacks in E-Government Clouds Using Sparse Neural Networks

Efficient Detection of DDoS Attacks in E-Government Clouds U...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A. Bixapathi H. Mohammed Ali R Maranan Sudhakar AVV Beschi I S M. Ramya Department of Computer Science Engineering -Data Science Geetanjali College of Engineering and Technology Department Mechanical Engineering SRM Institute of Science and Technology-Ramapuram Campus Chennai Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Electrical and Electronics Engineering SR University Warangal Telangana Department of Computer Applications St. Joseph's College (Arts & Science) Chennai Division of Research and Development Lovely Professional University Jalandhar Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

E-government (Electronic Government) refers to the use of information and communication technology (ICT) by governmental bodies to provide public services, manage government operations, and facilitate communication with citizens, businesses, and other government entities. E-government platforms have expanded significantly in recent years, offering a range of services such as online tax filing, license renewals, healthcare services, and more. These services, often hosted on cloud-based infrastructures, benefit from improved scalability, efficiency, and accessibility through the use of cloud computing technologies. However, the growing volume of legitimate traffic from a diverse range of users on e-government platforms makes distinguishing between normal and malicious traffic, such as DDoS (Distributed Denial of Service) attacks, increasingly difficult. To address this challenge, this manuscript proposes the use of Sparse Neural Networks (SNN) for DDoS attack detection in cloud-based e-government systems. Initially, data from the CSE-CIC-IDS2018 dataset is collected and processed through a feature selection phase. The selected features are selected using the Energy Valley Optimizer. These optimized features are fed into the Sparse Neural Network, which categorizes traffic as benign, DDoS, or DoS. To further enhance the accuracy of DDoS detection, the Ladybug Beetle Optimization (LBOA) is employed to optimize the Sparse Neural Network's parameters, ensuring optimal performance in classifying attacks. This approach aims to safeguard cloud-based e-government systems from cyber threats while maintaining reliable public service delivery. In comparison to current methods such as data clustering for the detection of DDoS attacks in the cloud for e-government (DBSCAN), e-government impact factor analysis cleverly using enhanced machine learning (ML-EGOV), and Problems with Machine Learning in Cloud Computing E-Government Models in Developing Nations, the proposed DCNN-QSA-PDO

关键词： Cloud computing Neural networks Machine learning Denial-of-service attack Feature extraction Information and communication technology Reliability Computer crime Optimization Electronic government

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6D Movable Antenna Based on User Distribution: Modeling and Optimization

arXiv

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arXiv 2024年

作者： Shao, Xiaodan Jiang, Qijun Zhang, Rui The Institute for Digital Communications Friedrich-Alexander-University Erlangen-Nuremberg Erlangen91054 Germany The School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

In this paper, we propose a new six-dimensional (6D) movable antenna (6DMA) system for future wireless networks to improve the communication performance. Unlike the traditional fixed-position antenna (FPA) and existing fluid antenna/two-dimensional (2D) movable antenna (FA/2DMA) systems that adjust the positions of antennas only, the proposed 6DMA system consists of distributed antenna surfaces with independently adjustable three-dimensional (3D) positions as well as 3D rotations within a given space. In particular, this paper applies the 6DMA to the base station (BS) in wireless networks to provide full degrees of freedom (DoFs) for the BS to adapt to the dynamic user spatial distribution in the network. However, a challenging new problem arises on how to optimally control the 6D positions and rotations of all 6DMA surfaces at the BS to maximize the network capacity based on the user spatial distribution, subject to the practical constraints on 6D antennas' movement. To tackle this problem, we first model the 6DMA-enabled BS and the user channels with the BS in terms of 6D positions and rotations of all 6DMA surfaces. Next, we propose an efficient alternating optimization algorithm to search for the best 6D positions and rotations of all 6DMA surfaces by leveraging the Monte Carlo simulation technique. Specifically, we sequentially optimize the 3D position/3D rotation of each 6DMA surface with those of the other surfaces fixed in an iterative manner. Numerical results show that our proposed 6DMA-BS can significantly improve the network capacity as compared to the benchmark BS architectures with FPAs or MAs with limited/partial movability, especially when the user distribution is more spatially non-uniform. Copyright © 2024, The Authors. All rights reserved.

关键词： Rotation

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PropNet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans

arXiv

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arXiv 2023年

作者： Chen, Zifan Li, Jiazheng Zhao, Jie Liu, Yiting Li, Hongfeng Dong, Bin Tang, Lei Zhang, Li Center for Data Science Peking University Beijing China Radiology Department Peking University Cancer Hospital & Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Center for Health and Medical Data Science Peking University Beijing China Peking University Beijing China

Background: Accurate segmentation of gastric tumors on 3D CT scans is essential for diagnosis and treatment planning. However, irregular shapes and blurred boundaries of tumors make automatic segmentation challenging. Existing 2D-based methods do not model 3D morphology effectively, and 3D-based methods are computationally expensive and require annotated samples. Some studies have used few-shot learning (FSL) to train models with prior knowledge, but these methods cannot handle irregular shapes and blurred boundaries. Purpose: In this pilot study, we anticipate that the human-guided prior knowledge and specially designed modules will address the challenges of 3D segmentation of gastric tumors described above. Drawing inspiration from FSL, we aim to enable the model to learn from information propagation between slices rather than from specific segmentation annotation. Methods: We propose the PropNet framework to achieve 3D segmentation of gastric tumors by propagating the radiologist's prior knowledge from the 2D annotated slice into the entire 3D CT space. The proposing stage uses an inter-slice context module to extract contextual information between adjacent slices and generate a coarse segmentation. The refining stage then corrects difficult-to-segment pixels for improved segmentation. We use two-way branches for boundary and region in each stage to enhance the segmentation performance at tumor boundaries. Furthermore, we adopt an up-down parallel propagation strategy to speed up our model's predictions. Results: Our study utilizes 98 patients (173 CT scans) as a training set and 30 patients (59 CT scans) as a validation set. The proposed method achieves a comparable agreement with manual annotation (Dice of 0.803 with a p-value of 0.563) and radiologists (Dice of 0.797) on the validation set while significantly improving efficiency with only 40 seconds for annotation and segmentation of a tumor. Furthermore, we test our proposed method on an independent external

关键词： Refining

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Deep Convolutional Neural Network Framework for Brain Tumor Classification using MRI Images 2

Deep Convolutional Neural Network Framework for Brain Tumor ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sakthi, U. Thangaraj, K. Tamizhselvi, A. Kirubakaran, M.K. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu TN Chennai603203 India Department of Information Technology Sona College of Technology Junction Main Road TN Salem636005 India Department of Information Technology St. Joseph's College of Engineering TN Chennai600119 India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology TN Chennai600119 India

ISBN: (纸本)9798350340235

Brain cancer is one of the high-risk diseases and increases the death rate in all countries, affecting both men and women. The early diagnosis and severity of brain cancer leads to better medical treatment and save people's lives. The machine learning procedure has been applied for early detection and treatment of brain cancer in the biomedical field by classifying them into low-risk and high-risk groups. In cancer research, the predictive and classification model has been developed using Deep Convolutional Neural Network (DCNN) algorithms for accurate decision making. The Magnetic Resonance Image (MRI) classification technique DCNN is advanced to detect and match feature points of training and test images. The DCNN classifier based on the outcome of feature points then classifies images. The key notion of this proposed research effort is to implement and execute the proposed DCNN algorithm on cancer patient datasets for risk level classification. The brain cancer affected patient details are collected from UCI machine learning data repository for experimental analysis. In this research study, the DCNN algorithm is proposed and it gives better accuracy and faster than the KNN, CNN and SVM. © 2023 IEEE.

关键词： Brain Cancer Convolutional Neural Convolutional Network Deep Learning Medical Resonance Image Classification

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Applying Machine Learning to Strengthening Cyber Security

Applying Machine Learning to Strengthening Cyber Security

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International Conference on Computing and Networking Technology (ICCNT)

作者： M. Vigenesh M Shalini P Mandar Inzimam Ul Hassan Sukhvinder Singh Dari S. Annamalai Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune INDIA Department of Computer Science & Engineering Vivekananda Global University Jaipur India Symbiosis Law School Nagpur Symbiosis International (Deemed University) Pune India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cyber safety threats have become a central problem in the contemporary world, with hackers constantly finding new approaches to make the most systems and users to gain unauthorized admission. Gadget learning has been determined to be a promising approach to combat these threats, considering the improvement of state-of-the-art algorithms capable of detecting malicious activities in near real-time. Through supervised studying, neural networks, or other sample popularity techniques, these algorithms can learn how to locate malicious conduct and lift an alert to take appropriate countermeasures. Additionally, device getting to know can be used to identify new threats and recognize formerly unknown flaws in present defenses, allowing them to be patched or upgraded promptly. Given the ever-converting nature of the cyber security landscape, gadget studying programs are worthwhile for staying ahead of the curve and retaining information secure.

关键词： Security management Neural networks Machine learning Reconnaissance Real-time systems Hazards Object recognition Computer crime Protection Testing

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Enhanced Cardiovascular Disease Diagnosis Using Multi-Label Machine Learning and Stacking Cross-Validation 4

Enhanced Cardiovascular Disease Diagnosis Using Multi-Label ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhabliya, Dharmesh Bhuvaneshwari, K.S. Kalra, Hitesh Senthurya, S. Vashisht, Nitish Rao, Batani Raghavendra Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Cse Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798331518981

The necessity for early access to reliable models is heightened by the fact that cardiovascular disease (CVD) remains a leading global killer. This study also looks at how multi-label algorithms for machine learning may be used to better diagnose cardiovascular diseases. In this study we utilized different types of ML procedures to successfully analyze various heart disease datasets, including LR Classifier, SVM, KNN, Choice Tree, Random Forestry, and Gradient Boosting Classifiers. Conventional methods provide the groundwork for prediction, according to comparative research, but they often fall short of what is needed for peak performance and accuracy. We provide a refined model that addresses these limitations by use of a Stacking Cross-Validation (CV) Classifier. To get the best results, this model takes the best parts of many approaches and merges them. The proposed model's prediction power for cardiac illness detection is substantially enhanced by using classifiers like random forest models and Gradient Boosting. By optimizing the reproduction's generalizability, recall, accurateness, correctness, and F1-score, the Stacking CV approach ensures consistent and reliable performance across many datasets. The experimental findings show that the created Stacking CV Classifier outperforms the other methods utilized in this study with an accuracy of 98%. More accurate diagnoses of cardiovascular disease may be possible with this method, and it also outperforms the most advanced ML methods when it comes to predicting CVD in its early stages. © 2024 IEEE.

关键词： Cardiology

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Text2NKG: fine-grained N-ary relation extraction for N-ary relational knowledge graph construction 24

Text2NKG: fine-grained N-ary relation extraction for N-ary r...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoran Luo Haihong E. Yuhao Yang Tianyu Yao Yikai Guo Zichen Tang Wentai Zhang Shiyao Peng Kaiyang Wan Meina Song Wei Lin Yifan Zhu Luu Anh Tuan School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331314385

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available https://***/LHRLAB/Text2NKG.

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Dual-Scale Enhanced and Cross-Generative Consistency Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Gu, Yunqi Zhou, Tao Zhang, Yizhe Zhou, Yi He, Kelei Gong, Chen Zhu, Huafu PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Information Science and Technology Nanjing210094 China School of Computer Science and Engineering Southeast University Nanjing211189 China Medical School Nanjing University National Institute of Healthcare Data Science Nanjing210023 China Institute of High Performance Computing A*STAR Singapore

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in lesion sizes and improves the robustness of the model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To produce more accurate segmentation maps, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales. Extensive experimental results on polyp segmentation from colonoscopy images, skin lesion segmentation from dermoscopic images, and Brain glioma segmentation from MRI images demonstrate the effectiveness of our DEC-Seg against other state-of-the-art semi-supervised segmentation approaches. © 2024, The Authors. All rights reserved.

关键词： Computer aided diagnosis

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Diagnosing Skin Lesion Using Multi-Modal Analysis

Diagnosing Skin Lesion Using Multi-Modal Analysis

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International Conference on Computational Intelligence and Networks (CINE)

作者： Rahul Nijhawan Devansh Bhatnagar Sudipta Roy Computer Science and Engineering University of Petroleum and Energy Studies School of Computer Science College of Engineering Roorkee Artificial Intelligence & Data Science Jio Institute Navi Mumbai India

ISBN: (纸本)9781665464666

The vast expansion of modalities in the medical field has helped identify and cure many diseases. Each modality has its specific characteristic feature. Multi-modal data analysis fuses different characteristic features and generates helpful information. With recent advances, deep learning has gain much importance in analyzing medical images. Generally, in the medical field, images are used for extracting features. Multi-modal image analysis uses deep learning models. The knowledge of the patient history and laboratory data helps create a simple clinical context. Combining information about the patient history and distinct modalities can be effective in obtaining an accurate diagnosis. The proposed model performs reasonably better than the other deep learning models, taking the image as input and additional helpful information about the patient. We have used the PAD-UFES-20 dataset for this study.

关键词： Deep learning Analytical models Image analysis Fuses Feature extraction Skin History

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