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PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing

IEEE Transactions on Cloud Computing 2025年第2期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China Sichuan University West China Biomedical Big Data Center West China Hospital Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

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Design and Implementation of a Web-Based Application for Managing Agricultural Health Products 4

Design and Implementation of a Web-Based Application for Man...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Oluwagbemi, Olugbenga O. Onyeka, Emebo Ishieka, Ikashi S. Wieclaw, Anna Azeta, Ambrose A. Middlesex University Department of Computer Science London United Kingdom Virginia Tech University Department of Computer Science Blacksburg United States Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia

ISBN: (纸本)9798350353266

Caring for the health of food crops and plants can help boost food production and eradicate hunger according to the second United Nations (UN) Sustainable Development Goal (SDG). The procurement of agricultural health products such as fertilizers, fungicides, pesticides, and herbicides are a major problem for an average Nigerian farmer. This results in low crop yield and negatively impacts on food production which eventually causes hunger in the land. Existing methods of procuring these health products have majorly been through offline platforms such as agricultural stores and other relevant stores. This has its disadvantages such as inaccessible roads to stores where such products can be obtained, varying prices, health products out of stock, ignorance in the application of such products. These barriers deter farmers from procuring these health products. In this paper, a survey was conducted on the needs and problems of farmers. The results of the survey informed the design and implementation of an agricultural health Informatics web application for health product management for food crops. The application was designed using unified modelling language class diagram and implemented using Python. SQLite3 serves as the backend database. The agricultural informatics solution will help farmers in Nigeria to easily procure health products for food crops and get the products delivered without stress and worry when fully deployed. This will boost food production, thus eradicating hunger;it will also help in exporting food crops to other countries. © 2024 IEEE.

关键词： Agriculture AI Application Chatbot Farmers Health Informatics

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Deep Learning in ChatGPT - A Survey 7

Deep Learning in ChatGPT - A Survey

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Hashana, A.M. Jasmine Brundha, P. Ahamed Ayoobkhan, Mohamed Uvaze Fazila, S. Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Software Engineering New Uzbekistan University Tashkent Uzbekistan

ISBN: (纸本)9798350397284

Abstract-As a subset of machine learning, deep learning makes use of multiple-layer neural networks to learn with available data and make decisions or predictions. A large language model called ChatGPT is based on deep learning, specifically a type of neural network called a transformer. ChatGPT's transformer architecture uses attention mechanisms to focus on the most important parts of the input, allowing it to process and comprehend a large amount of text data. In order for the model to comprehend the context and meaning of natural language text, it is trained on a huge database of text, including articles and books. One of the main importance of using deep learning in ChatGPT is its intelligence to understand relationships and patterns from the input text and generate or predict new text that is homogeneous to the input/training data. Because of this, ChatGPT is able to respond to questions and prompts in a manner that is comparable to that of a human, making it useful for a wide scope of natural language processing missions like translating languages, summarizing texts, and responding to questions. It's worth noting that, while deep learning has been highly effective in ChatGPT, it is not without its limitations. To train, deep learning models can be very complex and require a lot of data and computing power. © 2023 IEEE.

关键词： Network layers

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Forecasting Mental Stress Using Machine Learning Algorithms

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computers, Materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi Sultan Almakdi Mohammed Alshehri M.Gazi Golam Faruque Wahidur Rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the *** general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient ***,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical ***,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and *** methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the *** addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss ***,the Features Importance Interpretation is also delineated in this *** forecasts could help individuals connect with expert mental health specialists more quickly and *** to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately *** study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree *** after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)***,several factors are identified in terms of predicting depression through statistical data *** the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

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Group Recommendation Method Based on Graph Neural Network

Group Recommendation Method Based on Graph Neural Network

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Lu, Mengye Zhu, Peng Miao, Chun Wang, Youjian Wang, Jiacun Nanjing University of Science and Technology School of Economics and Management Nanjing China Monmouth University Department of Computer Science and Software Engineering NJ United States

ISBN: (纸本)9798350376739

Group activities are becoming more and more common on the Internet in the big data environment. Which makes many scholars focus on how to recommend items or activities to a group. However, conventional recommendation systems grapple with issues like data sparsity and the cold start problem, which limits the relevance and accuracy of recommendation. Therefore, this paper conducts an in-depth exploration of group recommendation methods utilizing graph neural networks and introduces a novel recommendation framework aimed at enhancing the efficacy of group recommendations. Firstly, this paper discusses how to construct an effective graph structure to encode the social interaction and individual characteristics of users. Then, an improved graph neural network architecture is proposed. Finally, extensive experimental evaluation is carried out on actual datasets, and the findings demonstrate that our framework markedly enhances the accuracy of recommendations. © 2024 IEEE.

关键词： Graph neural network Group recommendation Recommendation system

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Multi-Optimiser Training for GANs Based on Evolutionary Computation 13

Multi-Optimiser Training for GANs Based on Evolutionary Comp...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhang, Yixia Xue, Yu Neri, Ferrante School of Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350308365

Generative adversarial networks (GANs) are widely recognized for their impressive ability to generate realistic data. Despite the popularity of GANs, training them poses challenges such as mode collapse and instability. To address these issues, many variants enhance GAN performance through improvements in network architecture, modifications to loss functions, and the inclusion of regularization techniques. However, a limited number of methods focuses on optimising GAN performance from the optimiser's perspective, despite the distinct roles different optimisers play in training. Existing GANs typically employ a single optimiser throughout, with Adam being the default choice for GAN training. Approaches using multiple optimisers have shown improved performance but are often task-specific. This paper introduces a novel, portable approach that lever-ages the strengths of multiple training methods, with an evolutionary supervisor coordinating the training of different sections of the network. Initially, real-number-encoded vectors representing the optimiser for each sub-parameter layer undergo pro-gressive enhancement using a standard evolutionary algorithm (SGA). Through the evolutionary process, optimal optimiser combinations are retained based on the performance of the trained GAN. The proposed method, SGA-GANs, is validated on the CIFAR10 dataset by integrating the steps into five benchmark GAN models: GAN, DCGAN, BEGAN, WGAN, and WGAN-GP. Experimental results demonstrate that SGA-GANs outperforms single optimiser training methods, achieving superior evaluation results and generating higher-quality images. Source code can be found at https://***/lizzhang-spec/SGA-GANs. © 2024 IEEE.

关键词： Generative adversarial networks

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QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization 38

QUEST: Quadruple Multimodal Contrastive Learning with Constr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Qi Gong, Tianxiang Gao, Shiqi Zhou, Haoyi Li, Jianxin School of Software Beihang University China School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China

Multimodal contrastive learning (MCL) has recently demonstrated significant success across various tasks. However, the existing MCL treats all negative samples equally and ignores the potential semantic association with positive samples, which limits the model's ability to achieve fine-grained alignment. In multi-view scenarios, MCL tends to prioritize shared information while neglecting modality-specific unique information across different views, leading to feature suppression and suboptimal performance in downstream tasks. To address these limitations, we propose a novel contrastive framework named QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization. In the QUEST framework, we propose quaternion contrastive objectives and orthogonal constraints to extract sufficient unique information. Meanwhile, a shared information-guided penalization is introduced to ensure that shared information does not excessively influence the optimization of unique information. Our method leverages quaternion vector spaces to simultaneously optimize shared and unique information. Experiments on multiple datasets show that our method achieves superior performance in multimodal contrastive learning benchmarks. On public benchmark, our approach achieves state-of-the-art performance, and on synthetic shortcut datasets, we outperform existing baseline methods by an average of 97.95% on the CLIP model. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

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EventLens: Enhancing Visual Commonsense Reasoning by Leveraging Event-Aware Pretraining and Cross-modal Linking

EventLens: Enhancing Visual Commonsense Reasoning by Leverag...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Mingjie Yu, Zhihuan Ma, Yichao Li, Guohui Yang, Zhong School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions, and to explain the rationale behind their answers. While Large Language Models (LLMs) offer potential for this task, VCR's complex scenes require specialized approaches to activate their commonsense reasoning abilities, as existing Multimodal LLMs struggle with VCR's visual events and unique reference tags. To address these challenges, we propose EventLens, which enhances VCR through Event-Aware Pretraining and Cross-modal Linking in Supervised Fine-tuning. First, we introduce a new pretraining stage that emulates human cognitive processes to improve LLM comprehension of complex scenarios. Second, during supervised fine-tuning, we leverage reference tags to explicitly bridge RoI features with text, maintaining semantic integrity across modalities. Additionally, instruct prompts and task-specific adapters help integrate LLMs' knowledge with new commonsense reasoning. Experimental results demonstrate competitive performance with state-of-the-art methods and ablation studies verify the effectiveness of proposed EventLens. © 2025 IEEE.

关键词： Multimedia Understanding Pretrained Language Models Visual Commonsense Reasoning

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Enhancing Cloud Resource Management through Predictive Neural Networks and Reinforcement Learning 15

Enhancing Cloud Resource Management through Predictive Neura...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Lakshya Khan, Mohammad Ilham Hasan Bajarange, Anish P. Delhi Technological University Software Engineering Delhi India Jamia Millia Islamia Electrical Engineering Delhi India Parul University Computer Science and Engineering Gujarat India

ISBN: (纸本)9798350370249

Prior strategies for scaling microservices encompassed various techniques, including diverse processing approaches and mathematical models. However, these methodologies often exhibited limitations in predictive accuracy and management complexity. This study employs Reinforcement Learning as an innovative alternative, particularly the Double Deep Q-Networks Algorithm. This approach autonomously monitors and evaluates essential scaling parameters, promptly triggering on-site actions when warranted. It adeptly balances the dual aspects of environment exploitation and exploration, optimizing computational resource usage while enhancing cost-effectiveness. Empirical evaluations substantiate the superiority of this approach in dynamically scaling cloud services with precision and resource optimization. By integrating LSTM and RNN predictive capabilities with Reinforcement Learning, this research contributes to efficient cloud resource management, a vital facet of contemporary cloud computing paradigms. © 2024 IEEE.

关键词： autoscaling Average Memory Utilization SLA double DQN reinforcement learning

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