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Challenges and future directions of secure federated learning:a survey

Frontiers of computer science 2022年第5期16卷 173-180页

作者： Kaiyue ZHANG Xuan SONG Chenhan ZHANG Shui YU Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Faculty of Engineering and Information Technology University of Technology SydneySydney 2007Australia SUSTech-UTokyo Joint Research Center on Super Smart City Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big *** is an algorithm that does not collect users’raw data,but aggregates model parameters from each client and therefore protects user’s ***,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning *** addition,some recent studies have shown that attackers can recover information merely from ***,there is still lots of room to improve the current federated learning *** this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated *** open issues and existing solutions in federated learning are *** also point out the future research directions of federated learning.

关键词： federated learning privacy protection security

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Revolutionizing Healthcare through the Internet of Things: Current State and Future Implications 15

Revolutionizing Healthcare through the Internet of Things: C...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shantinath, Barwade Rohit Sabnis, Vikrant Jain, Jay Kumar Department of Computer Science Mansarovar Global University Bhopal India Department of Computer Science Faculty of Engineering and Technology Mansarovar Global University Bhopal India Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798331300579

The Internet of Things (IoT) has the potential to completely transform healthcare by allowing medical professionals to remotely monitor patients, gather data on their activity and health in real-time, and create more efficient treatment regimens. IoT technologies have the potential to save healthcare expenditures while also enhancing patient involvement and improving patient outcomes. However, the adoption of IoMT has been slow, and To ensure that the advantages of IoT are realised while reducing the hazards connected with its use, a number of hurdles must be overcome. The purpose of this research paper is to examine the current state of IoT implementation in healthcare, identify the benefits and challenges of IoMT, and explore the future implications of IoMT. The study starts off with a survey of the literature that has already been written on the use of IoT technology in healthcare, highlighting both the potential advantages and difficulties of doing so. The paper then presents the results of a survey of healthcare providers on the use of IoT technologies in healthcare, providing insights into the current state of IoT implementation in healthcare and the challenges that must be addressed to facilitate its adoption. © Grenze Scientific Society, 2024.

关键词： Patient treatment

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Prostate Cancer Prediction Using Convolutional Neural Networks 3

Prostate Cancer Prediction Using Convolutional Neural Networ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sethi, Bijaya Kumar Singh, Debabrata Rout, Saroja Kumar Department of Computer Science and Engineering Bhubaneswar India Department of Computer Applications Bhubaneswar India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350364569

Cancer-related mortality in men is highest among men who suffer from prostate cancer. The lack of clarity and consistency of early symptoms often makes diagnosis a challenge in the later stages (stages III and IV). Existing diagnostic techniques face challenges such as subjectivity, variability between observers, and lengthy testing processes involving biomarkers, biopsies, and imaging tests. This paper introduces a novel convolutional neural network (CNN) algorithm for prostate cancer diagnosis and prediction to overcome these drawbacks. An additional dataset of histopathology images was used to train and validate the system before it was put through its learning phase. In the study, 95.12% of cancerously derived cells were identified correctly and 93.02% were identified correctly, a remarkable accuracy of 98.07%. As a result of this study, Various challenges associated with expert evaluations by humans were successfully addressed, including higher misclassification rates, interdependencies between observers, and lengthy analysis periods. Prostate cancer diagnosis and prognosis have been made much simpler and faster by this research. Moving forward, to optimize the effectiveness of our proposed method, future investigations should explore the latest developments and innovations in this field. © 2024 IEEE.

关键词： Convolutional neural networks

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Comprehensive Analysis of Drug Reaction Opinions Through Deep Learning 13

Comprehensive Analysis of Drug Reaction Opinions Through Dee...

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13th International Conference on system Modeling and Advancement in Research Trends, SMART 2024

作者： Rastogi, Ajay Singh, Ravendra Khan, Mohammad Zubair Naim, Iram Mjp Rohilkhand University Department of Computer Science and Information Technology Bareilly243006 India Taibah University Department of Computer Science and Information Madinah Saudi Arabia

ISBN: (纸本)9798350380583

Now days social media is growing exponentially and people post their opinions upon different platforms. We are concerned about patient's opinions related to the medicines or drugs. Drug reaction opinion is one of the most important dimension to evaluate the performance of the healthcare system. Sentiment analysis is a field of NLP that provide the meaningful information about sentiment, emotion or attitudes from reviews. In this article we have used Deep learning based opinion mining to extract the most relevant features. We embedded our inputs by two types of embedding technique - Glove and Word2Vec. These embedding passed in deep learning methods like CNN, GRU, LSTM, RNN, and CNN+LSTM. These models have good performance. We first proposed SWIN transformer model that is used in image processing. This model provides state-of-the-art performance, and then we compared it with other transformers like BERT, DistillBERT, RoBERTa. We also compare SWIN performance from the previous research which is still lacking in the performance in terms of accuracy. © 2024 IEEE.

关键词： Deep learning

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Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

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Tsinghua science and technology 2024年第1期29卷 86-98页

作者： Rui Yuan Shunmei Meng Ruihan Dou Xinna Wang Department of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210056China Faculty of Mathematics University of WaterlooWaterloo N2L 3G1Canada

Edge computing platforms enable application developers and content providers to provide context-aware services(such as service recommendations)using real-time wireless access network *** to recommend the most suitable candidate from these numerous available services is an urgent ***-through rate(CTR)prediction is a core task of traditional service ***,many existing service recommender systems do not exploit user mobility for prediction,particularly in an edge computing *** this paper,we propose a model named long and short-term user preferences modeling with a multi-interest network based on user *** uses a logarithmic network to capture multiple interests in different fields,enriching the representations of user short-term *** terms of long-term preferences,users'comprehensive preferences are extracted in different periods and are fused using a nonlocal *** experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.

关键词： recommender system logarithmic network nonlocal network

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T5-LDSum: Leveraging T5 Transformer in Hybrid Abstractive-Extractive Long Document Summarization 6

T5-LDSum: Leveraging T5 Transformer in Hybrid Abstractive-Ex...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Magdy, Mostafa Abdelbaky, Abdelrahman M. Mohamed, Ensaf Hussein Helwan University Master of Computer Science Faculty of Computers and Artificial Intelligence Egypt Helwan University Bachelor of Computer Science Faculty of Computers and Artificial Intelligence Egypt Nile University Computer Science School of Information Technology and Computer Science Giza Egypt Helwan University Faculty of Computers and Artificial Intelligence Computer Science Department Egypt

ISBN: (纸本)9798350378511

In the era of digital information overload, the ability to summarize books efficiently emerges as an invaluable skill. Book summarization condenses extensive texts into digestible, concise summaries, enabling readers to grasp the essence of a book without committing to reading it in its entirety. In scholarly research, various techniques for text summarization are employed, including extractive summarization, abstractive summarization, and hybrid approaches. This paper presents a novel hybrid method that leverages the strengths of both extractive and abstractive techniques, with a particular focus on the T5 text-to-text transformer model. Our methodology begins with a BERT-based extractive model paired with the LexRank algorithm to generate preliminary summaries. These summaries are subsequently refined using the T5 transformer model, known for its powerful text generation capabilities. We conducted two empirical studies using Oscar Wilde's 'The Picture of Dorian Gray,' specifically focusing on Chapter 13. Our experiments analyzed the efficacy of summaries derived from the top 20% and top 60% of sentences ranked by the extractive model. The findings indicate a consistent pattern: summaries based on the 60% sentence extraction significantly outperform those from the 20% extraction across all assessed metrics-Rouge, precision, recall, F1 scores, and human evaluations. The integration of the T5 transformer model in the refinement process is highlighted as a key component in achieving high-quality, coherent summaries. © 2024 IEEE.

关键词： abstractive summarization Book summarization evaluation metrics extractive summarization hybrid summarization

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Design an optimization based ensemble machine learning framework for detecting rice leaf diseases

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Multimedia Tools and Applications 2024年第36期83卷 84401-84424页

作者： Rajasekhar, Veeramreddy Arulselvi, Gnanasekaran Babu, K. Suresh Department of Computer Science Engineering Annamalai University Tamilnadu Chidambaram India Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Chidambaram India International School of Technology and Sciences for Women East Gonagudem Rajanagaram A.P Rajamahendravaram India

The agriculture industry is currently dealing with serious issues with rice plants as a result of illnesses that decrease the quantity and output of the harvest. Numerous fungi and bacteria diseases harm plants that affect the growth of yield. So identification of the detection of crop diseases is essential for enhancing the productivity of crops. Many techniques have been developed to improve disease detection in crops but there are still some issues such as noise, time consumption, delay, poor segmentation, and less accuracy. To overcome these issues, this paper, designed a Spider Monkey-based Random Forest (SMbRF) model for accurate detection and classification of rice leaf diseases and the designed model was implemented in the Python system. The main aim is to develop an optimization-based random forest technique for accurate prediction of rice leaf disease by exact segmentation. Initially, preprocessing is performed to enhance the data quality and remove errors. Furthermore, separate the lesions using mean shift image segmentation and extract the shape and color features to improve the detection results of the developed model. As well, spider monkey fitness is updated in the RF classification layer for accurate classification of diseases and improves accuracy with less execution time. The experiment results show the reliability and efficiency of the designed model, it attains better performance in accuracy, sensitivity, specificity, and precision. The designed model attained 99.29% accuracy, 99.52% sensitivity, 98.76% precision, and 99% specificity proving the scalability of the designed model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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An Automated system for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

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computers, Materials & Continua 2023年第4期75卷 1291-1304页

作者： Sumayh S.Aljameel Malak Aljabri Nida Aslam Dorieh M.Alomari Arwa Alyahya Shaykhah Alfaris Maha Balharith Hiessa Abahussain Dana Boujlea Eman S.Alsulmi Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Obstetrics and Gynecology College of MedicineImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand avoid any danger. Therefore, an intelligent automated solution must bedeveloped to predict the risk factors for pregnancy loss at an early stage toassist with accurate and effective diagnosis. Machine learning (ML)-baseddecision support systems are increasingly used in the healthcare sector andhave achieved notable performance and objectiveness in disease predictionand prognosis. Thus, we developed a model to help obstetricians predictthe probability of miscarriage using ML. And support their decisions andexpectations about pregnancy status by providing an easy, automated way topredict miscarriage at early stages using ML tools and techniques. Althoughmany published papers proposed similar models, none of them used Saudiclinical data. Our proposed solution used ML classification algorithms tobuild a miscarriage prediction model. Four classifiers were used in this study:decision tree (DT), random forest (RF), k-nearest neighbor (KNN), andgradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiveroperating characteristic area under the curve (ROC-AUC) were used to evaluatethe proposed model. The results showed that GB overperformed the otherclassifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposedmodel can assist in the early identification of at-risk pregnant women to avoidmiscarriage in the first trimester and will improve the healthcare sector inSaudi Arabia.

关键词： Miscarriage pregnancy abortion machine learning gradient boosting

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Automatic Bowel Preparation Assessment Using Deep Learning 26th

Automatic Bowel Preparation Assessment Using Deep Learning

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Haithami, Mahmood Salah Ahmed, Amr Liao, Iman Yi Altulea, Hamid Jalab School of Computer Science University of Nottingham Semenyih Malaysia School of Computer Science University of Nottingham Nottingham United Kingdom Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Dhi Qar Iraq

ISBN: (纸本)9783031376597

Bowel preparation is considered a critical step in colonoscopy. Manual bowel preparation assessment is time consuming and prone to human errors and biases. Automatic Bowel evaluation using machine/deep learning is a better and efficient alternative. Most of the relevant literature have focused on achieving high validation accuracy, where private handy-picked dataset does not reflect real-environment situation. Furthermore, treating a video dataset as a collection of individual frames may produce overestimated results. This is due to the fact a video contains nearly identical consecutive frames, hence, dividing them into training and validation sets yields two similar distributed datasets. Given a public dataset, Nerthus, we show empirically a significant drop in performance when a video dataset is treated as a collection of videos (depicting the real environment/context), instead of a collection of individual frames. We propose a model that utilizes both sequence and none-sequence (spatial) information within videos. The proposed model achieved on average 83% validation accuracy across 4 validation sets, whereas, the state-of-the-art models achieved on average a range of 66%–72% validation accuracy. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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Specification of Complex Analytics Workflows: A Formal Language Model of Decision Options 20th

Specification of Complex Analytics Workflows: A Formal Lang...

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20th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2024

作者： Miri, Pouriya Kochovski, Petar Tuler de Oliveira, Marcela Stankovski, Vlado Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Engineering Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031812255

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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