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2023 International Conference on Intelligent Management and Software Engineering, IMSE 2023

作者： Yan, Yuhang Gao, Yujie Tian, Liqin School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China School of Computer North China Institute of Science and Technology Yanjiao China

ISBN: (纸本)9798350394146

Automated self-supervised learning frameworks have shown significant results in the recommendation domain. However, existing methods such as AutoCF use graph convolutional networks and graph attention mechanisms that are limited to a single expression granularity and cannot fully capture the multi-granularity of user interests and product attributes. To address this problem, we propose MS-AutoCF, a multi-scale automated self-supervised learning recommender system that obtains user and product representations at different granularities through multi-layer graph convolution to match the global and local details of their interest preferences. Meanwhile, we design a query key value-based multi-head graph attention module that can adaptively aggregate and enhance the representations with different granularities. Experimental results show that the MS-AutoCF system achieves significant improvements over existing single-scale methods on several public datasets. ©2023 IEEE.

关键词： Supervised learning

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Mortality Risk Prediction in Patients After Cardiac Surgery Using Machine Learning

Mortality Risk Prediction in Patients After Cardiac Surgery ...

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2024 International Conference on computer, Electronics, Electrical Engineering and their applications, IC2E3 2024

作者： Khan, Shahnawaz Thakur, Gopal Kumar Kulkarni, Shridhar Ayyalasomayajula, Madan Mohan Tito Reddy, Pundru Chandra Shaker Master of Computer Application Bundelkhand University Utter Pradesh Jhansi India Harrisburg University of Science and Technology Department of Data Sciences Pennsylvania United States School of Business and Technology Aspen University Phoenix Arizona United States School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (纸本)9798350388534

Norwood surgery restores systemic flow in neonates with solitary ventricle inherited heart abnormalities, but it is complicated and deadly. We created machinelearning(ML) strategies to anticipate cardiac surgery mortality using preoperative and intraoperative risk indicators. Mortality after surgery was the main consequence. Sensitivity, specificity, accuracy, and AUC were used to estimate model performance. The visualization algorithm used Shapley's additive explanations. Random-forest, neural-network, SVM, and gradient-boosting had AUC-values of 0.87, 0.79, 0.81, and 0.82 for anticipating cardiac surgery operative mortality, while EuroSCORE I and II, STS, and LR had 0.70, 0.73, 0.71, and 0.74. Shapley's additive explanations study of RF identified the top-20 predictors and individual-level explanations. ML strategies based on clinical data may anticpate heart surgery postoperative mortality better than clinical grading methods. Explanatory models can account for affecting factors to provide tailored risk profiles. These machine learning-driven models need more prospective multicenter studies to prove their therapeutic efficacy. © 2024 IEEE.

关键词： Cardiovascular surgery

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Avoid attacks: A Federated Data Sanitization Defense in IoMT Systems

Avoid attacks: A Federated Data Sanitization Defense in IoMT...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Chen, Chong Gao, Ying Huang, Siquan Yan, Xingfu School of Computer Science and Technology South China University of Technology Guangzhou China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China School of Computer Science South China Normal University Guangzhou China

ISBN: (纸本)9781665494274

Malicious falsification of medical data destroys the training process of the medical-aided diagnosis models and causes serious damage to Healthcare IoMT Systems. To solve this unsupervised problem, this paper finds a robust data filtering method for various data poisoning attacks. First, we adapt the federated learning framework to project all of the clients' data features into the public subspace domain, allowing unified feature mapping to be established while their data remains stored locally. Then we adopt the federated clustering to re-group their features to clarify the poisoned data. The federated clustering is based on the consistent association of data and its semantics. Finally, we do the data sanitization with a simple yet efficient strategy. Extensive experiments are conducted to evaluate the accuracy and efficacy of the proposed defense method against data poisoning attacks. © 2023 IEEE.

关键词： Semantics

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A Bone Metastatic Cancer Origin Prediction Using Deep Learning on Digital Pathological

A Bone Metastatic Cancer Origin Prediction Using Deep Learni...

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2024 International Conference on computer, Electronics, Electrical Engineering and their applications, IC2E3 2024

作者： Thakur, Gopal Kumar Kulkarni, Shridhar Khan, Shahnawaz Ayyalasomayajula, Madan Mohan Tito Reddy, Pundru Chandra Shaker Harrisburg University of Science and Technology Department of Data Sciences Pennsylvania United States Master of Computer Application Bundelkhand University Utter Pradesh Jhansi India School of Business and Technology Aspen University PhoenixAZ United States School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (纸本)9798350388534

The key to effective treatment for bone cancers is a precise imaging diagnosis. The diagnosis process might be improved with the introduction of AI and machine-learning(ML) techniques for characterizing and evaluating bone tumors on different imaging modalities. When it comes to clinical treatments, knowing where bone metastasis cancer (OBMC) originated is crucial. With little clinical data and bone biopsy, OBMC determination is difficult for pathologists. In order to forecast the OBMC from hematoxylineosin (H&E) stained slides alone, we developed a regional multiple-instance learning algorithms. We trained the model with data from 1041 instances across 8 institutions and 26,431 areas of interest that were tagged. External validation and ten-fold cross validation were used to evaluate the model's performance. Using top3 predictions as a guide, we ran an IHC test on 175cases with unclear origins to see how well the model's predictions matched up with the results shown by the markers. We also used the model to determine if a tumor was present in a specific area and to differentiate between neuroendocrine tumors, squamous cell carcinoma, and adenocarcinoma. With a 98.98% success rate in tumor detection and a 93.85% success rate in differentiating between three cancer kinds, the suggested model was a clear winner. Our model showed promise in assisting pathologists in accurately diagnosing the OBMC and performed well when used to predict the OBMC from standard histology. © 2024 IEEE.

关键词： Diseases

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A Novel Two-Stream Algorithm for Spatio-Temporal Fusion in Remote Sensing

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Wang, Shiying Zou, Xuechao Wang, Wentao Liu, Yao Zhang, Shun Cao, Tengfei Tao, Pin Qinghai University School of Computer Technology and Application Xining China Qinghai University Intelligent Computing and Application Laboratory of Qinghai Province China School of Computer Science & Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China Ministry of Education Key Laboratory of Pervasive Computing China

The spatio-temporal fusion technology can effectively solve the problem of missing time series data. However, existing algorithms often struggle to accurately capture surface feature changes and fine details. To overcome these limitations, this paper introduces FemSTF, a flexible two-stream algorithm for remote sensing spatio-temporal fusion, which consists of three main stages: image sharpening, image fusion, and edge optimization. In the first stage, techniques such as interpolation, classification, and blending are employed to address missing pixel values caused by minimal input data. In the second stage, a two-stream strategy is introduced to enhance fusion accuracy, enabling the algorithm to capture regional detail changes with high sensitivity to surface variations. The third stage focuses on edge optimization, generating high-resolution predictive images under minimal input conditions (three images) while retaining rich texture and edge details. Notably, FemSTF achieves a performance comparable to that of methods requiring five input images. Experimental results demonstrate that FemSTF outperforms mainstream algorithms across multiple datasets. Ablation experiments confirm the effectiveness of the two-stream strategy, highlighting its role in enhancing remote sensing data processing accuracy. This study offers an efficient solution for spatio-temporal fusion, demonstrating great potential in the field. © 2004-2012 IEEE.

关键词： Spatio-temporal data

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Blockchain application in Healthcare Domain: Industry 5.0 Perspective 6

Blockchain Application in Healthcare Domain: Industry 5.0 Pe...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Verma, Sandeep Kumar Nadeem, Mohd. Verma, Vishal Sayeed, Mohd Asim Agrawal, Alka Khan, Raees Ahmad Babasaheb Bhimrao Ambedkar Department of Information Technology UP Lucknow India School of Computer Application Babu Banarasi das University UP Lucknow India

ISBN: (纸本)9798350304480

Blockchain is an advanced digital technology that has been increasingly used during the past decade to store sensitive information in all small and large businesses that can benefit from incorporating blockchain to increase confidentiality, integrity, security, privacy, and data openness. Modern manufacturing techniques that enable manufacturers to hit their target more quickly are combined to form Industry 5.0. Various manufacturing verticals like smart healthcare, manufacturing plant, smart education, cloud/fog/Edge computing, and supply chain Management (SCM) ecosystems are anticipated to benefit from Industry 5.0. However, trustworthy safe data transfer is necessary to synergistically balance and secure the industrial perimeter since data is shared between extended networks and heterogeneous networks in many authorized sectors. Due to its intrinsic ability to provide immutability, transparency, chronology, and traceability of data, and trust in industrial systems, blockchain (BC) is a preferred choice as security and privacy are key concerns for Industry 5.0 ecosystems. In this article, keeping in mind the need of existing industry, a literature review of some limited different types of security parameters has been done and the journey of the industry has been shown with the healthcare, and blockchain revolution and also discusses the various verticals of Industry 5.0. A comparative study of the blockchain revolution has been done in this article and explains the blockchain's role as a better security facilitator in Industry 5.0 is presented in this article. © 2023 IEEE.

关键词： Blockchain Health care Industry 4.0 Industry 5.0 Privacy security

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Comparative study of IoT- and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

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Hybrid Transformer-CNN Model for Automated Diagnosis of Pomegranate Fruit Diseases 10

Hybrid Transformer-CNN Model for Automated Diagnosis of Pome...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Tiwari, Raj Gaurang Misra, Alok Maheshwari, Himani Agarwal, Ambuj Kumar Sharma, Manish Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University School of Computer Application Punjab Phagwara India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350353778

Improving crop quality and output relies on early and precise disease diagnosis in pomegranate fruits. This study presents a new Feature Fusion Network for disease classification in pomegranate fruits, which combines Transformer and CNN modules. The proposed model classifies pomegranate fruit into five categories that are Healthy, Bacterial Blight, Anthracnose, Cercospora Fruit Spot, and Alternaria Fruit Spot. While the CNN module is great at collecting local visual aspects, the Transformer module is used to gather global contextual information. The model uses both global and local information for robust disease categorization by combining two complimentary feature representations. Adam, Adadelta, Gradient Descent, and RMSProp optimization algorithms are used to fine-tune the suggested Feature Fusion Network. After extensive training with Adam Optimizer, the suggested method attained a maximum accuracy of 96.45% in disease classification of pomegranate fruits. In addition, when the performance of the proposed model is compared with other popular deep learning architectures like ResNet, Inception, VGG, and MobileNet, it outperforms. © 2024 IEEE.

关键词： Fruits

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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Multi-label Feature Selection Based on Fuzzy Neighborhood Mutual Discrimination Index 12

Multi-label Feature Selection Based on Fuzzy Neighborhood Mu...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Wang, Chenxi Chen, E. Ren, Mengli Yu, Xiehua Lin, Yaojin Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Minnan Science and Technology University School of Computer Science Lab of Data Science and Intelligence Application Quanzhou China Xiamen University School of Computer Science Lab of Data Science and Intelligence Application Xiamen China

ISBN: (纸本)9798350310153

Multi-label feature selection advances the performance of multi-label classification models by eliminating irrelevant and redundant features. Most multi-label feature selection algorithms assume that the training set containing logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances of labels. To solve the problem, we proposes a multi-label feature selection method based on fuzzy neighborhood mutual discriminant index. Firstly, logical labels are transformed into label distribution by label enhancement. Secondly, the information entropy is referenced to the label distribution learning, the label neighborhood similarity matrix is constructed to describe the ambiguity between labels. Finally, linking the fuzzy neighborhood discrimination index under the selected feature subset with label neighborhood similarity matrix is proposed, which is used for judging the distinguishing ability of the feature subset to label. Comprehensive experiments on 6 multi-label datasets show that the algorithm outperforms other algorithms in classification performance. © 2022 IEEE.

关键词： Feature Selection

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