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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science Guangdong China Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. © 2024 IEEE.

关键词： Zero-shot learning

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A robust deformed convolutional neural network(CNN)for image denoising

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CAAI Transactions on Intelligence technology 2023年第2期8卷 331-342页

作者： Qi Zhang Jingyu Xiao Chunwei Tian Jerry Chun‐Wei Lin Shichao Zhang School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina School of Computer Science Central South UniversityChangshaChina School of Software Northwestern Polytechnical UniversityXi'anShaanxiChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina Yangtze River Delta Research Institute Northwestern Polytechnical UniversityTaicangChina Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway

Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image ***,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image *** relations of surrounding pixels can effectively resolve this *** by that,we propose a robust deformed denoising CNN(RDDCNN)in this *** proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding *** EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed *** address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean ***,we implement a blind denoising *** results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative *** can be obtained at https://***/hellloxiaotian/RDDCNN.

关键词： blind denoising CNN deformed block enhanced block

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The Early Detection of Dementia Disease Using Machine Learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kabir, Md. Shariar Khanom, Jakia Bhuiyan, Md. Atik Tumpa, Zerin Nasrin Rabby, S.K. Fazlee Bilgaiyan, Saurabh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350348217

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia. © 2023 IEEE.

关键词： Machine learning

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Energy Efficiency Optimization for UAV-Assisted Cellular Networks: A Periodic Clustering-Based MATD3 Approach

Energy Efficiency Optimization for UAV-Assisted Cellular Net...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Fuhao Chen, Haoqiang Miao, Jiansong Zhang, Tao Zhang, Chuan Kang, Jiawen Niyato, Dusit Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China Beijing Smartchip Microelectronics Technology Company Limited Beijing China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore

ISBN: (纸本)9798350351255

With the advancement of unmanned aerial vehicles (UAVs) technology, UAV-assisted cellular networks (UACNs) have emerged as a new communication paradigm aimed at enhancing the coverage and capacity of ground networks. Unfortunately, the limited energy capacity of UAVs significantly restricts their operational duration, so optimizing energy efficiency is of importance. However, existing optimization schemes often overlook the impact of ground user mobility on user association, lacking ability to achieve optimal energy efficiency. In this paper, the K-Means method is applied to optimize user association by periodically clustering users. Additionally, given the dynamic nature of the wireless channels, we utilize the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach to jointly optimize 3D trajectory and power allocation. The objective is to maximize the sum energy efficiency while meeting the constraints included maximum power, minimum achievable data rate and spatial limitation. Simulation results demonstrate the effectiveness of the proposed algorithm compared with other benchmark algorithms. © 2024 IEEE.

关键词： Mobile telecommunication systems

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Glaucoma Detection Using Convolutional Neural Network (CNN) 20

Glaucoma Detection Using Convolutional Neural Network (CNN)

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Tohye, Tewodros Gizaw Qin, Zhiguang Leka, Habte Lejebo Hundera, Negalign Wake School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua China

ISBN: (纸本)9798350318982

Today, glaucoma detection is a severe problem. Because it was predicted that there would be 111.8 million cases of glaucoma worldwide by 2040, up from the expected 76.0 million cases in 2020. Blindness results if early treatment is not given. The CNN model has been used in a variety of studies to identify glaucoma. The results obtained thus far are insufficient to reliably identify glaucoma. In order to detect glaucoma, which is a binary classification, we are utilizing the CNN model. We divided the REFUGUE data set into tanning and testing, with 80% of the photos being utilized for tanning and 20% for testing. Our model's accuracy score was 99.8% for the training set and 95.4% for the testing set. We get result of precision=98% and recall= 97%. We compare the model with other state of arts (SOTA) our experimental results reveal that, the proposed model for glaucoma detection using convolutional neural network is more proficient in glaucoma classification. © 2023 IEEE.

关键词： Convolution

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Enhancing Feature Selection in IoT Intrusion Detection Using the Ensemble Stacking 22

Enhancing Feature Selection in IoT Intrusion Detection Using...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zheng, Zhijian Gai, Weilin Zhang, Peng Zhou, Ming Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing210094 China Tca Institute of Software Cas Beijing102200 China CNCERT/CC Beijing102200 China School of Computer Science and Technology Ucas Beijing102200 China

ISBN: (纸本)9798331509712

The Internet of Things (IoT) is increasingly vulnerable to security risks due to new network attacks. Deep learning-based intrusion detection systems (DL-IDS) have emerged as a key solution, but they face challenges like imbalanced datasets and lengthy training times in complex environments. While feature selection algorithms are commonly employed to mitigate these issues, mainstream methods can yield inconsistent results, failing to reflect data characteristics accurately and potentially introducing noise. To address this problem, we propose an ensemble stacking approach to combine multiple feature selection algorithms, thereby minimizing errors from individual approaches. Each feature selection method acts as a base learner to assess feature importance, while logistic regression is a meta-learner to integrate the outputs into a final result. Additionally, we developed a CNN-based intrusion detection model enhanced with BiLSTM and attention mechanisms to improve detection performance. Our approach was tested on the UNSW-NB15 and CIC-IDS2017 datasets, with results indicating a significant improvement in detection performance compared to using a single feature selection method. © 2024 IEEE.

关键词： Network intrusion

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental Sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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Leveraging Blockchain-as-a-Certificate Authority for Authentication in 6G-Enabled Spatial Crowdsourcing Drone Services 100

Leveraging Blockchain-as-a-Certificate Authority for Authent...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Akram, Junaid Anaissi, Ali Sidana, Sagar Kumar, Deepak Jhaveri, Rutvij H. The University of Sydney School of Computer Science CamperdownNSW2008 Australia University of Technology Sydney Td School UltimoNSW2007 Australia McKinsey & Company Principal Software Engineer Dallas United States National Institute of Technology Meghalaya Department of Computer Science and Engineering India Pandit Deendayal Energy University School of Technology India

ISBN: (纸本)9798331517786

The integration of the Internet of Drone Things (IoDT) with spatial crowdsourcing, enhanced by 6G technology, has revolutionized environmental monitoring, particularly in managing Australian bushfires. This approach leverages drones' mobility, multidimensional motion, and ease of deployment to gather real-time data from hazardous or inaccessible areas. However, the unsecured wireless communication channels and limited computational resources of drones in typical IoDT scenarios make them susceptible to cyber-attacks, including spoofing, GPS manipulation, impersonation, man-in-the-middle, and hijacking. To counter these threats, we propose a robust security protocol that utilizes blockchain technology augmented by Hyperelliptic Curve Cryptography (HECC). By employing blockchain as a Certificate Authority (CA) and treating transactions as certifications, our framework, DronCert, eliminates the need for traditional CAs or Trusted Third Parties (TTP). This decentralized approach, combined with the high-speed, low-latency capabilities of 6G, significantly enhances data transmission security within the IoDT network. A comprehensive security analysis demonstrates DronCert's resilience against various attacks, such as Denial-of-Service (DoS), man-in-the-middle, replay, and unauthorized device representation. © 2024 IEEE.

关键词： Drones

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