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International Conference on Inventive Computation Technologies (ICICT)

作者： Fathima Rumaiza S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavarm Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Antimicrobial resistance (AMR) is a meaningful and growing public health concern that is largely driven by improper antibiotic use. An important factor contributing to AMR is the failure to complete prescribed antibiotic courses, which allows resistant bacteria to survive as well as spread. This study examines the effect of awareness of AMR on the likelihood that people will adhere to their prescribed antibiotic regimens. Using a chi-square test for independence, the analysis identifies a statistically meaningful association between AMR awareness and the completion of antibiotic courses. Results indicate that people who are aware of AMR and its risks are more likely to complete their prescribed courses than those with lower awareness levels. The study explores several factors influencing adherence, including personal health beliefs, access to adequate medical advice and common misconceptions about antibiotic use, along with this key finding. Many people discontinue antibiotics after symptoms subside, completely unaware of the meaningful risks linked with incomplete treatment. Effectively dealing with these issues is important for considerably reducing the spread of AMR. The study suggests multiple public health interventions to improve antibiotic compliance, which include educational campaigns, increased patient counseling and stricter prescription guidelines. These strategies can considerably help curb the rising threat of AMR by importantly increasing awareness and promoting responsible antibiotic use.

关键词： Employee welfare Antibiotics Bacteria Public healthcare Immune system Guidelines

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Enhanced Prediction Of Rheumatoid Arthritis Flares Through Ensemble Learning Methods 2

Enhanced Prediction Of Rheumatoid Arthritis Flares Through E...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Priyadharshini, K. Vinitha, S. Sivaprakash, S. Kayalvili, S. Thilakavathy, P. Vasavi, M. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Artificial Intelligence Kongu Engineering College Erode India Vels Institute of Science Technology and Advanced Studies Chennai India ET Department CVR College of Engineering Hyderabad India

ISBN: (纸本)9798350383867

RA is an inflammatory, autoimmune disease with an unpredictable course, during which patients experience exacerbations (increases in symptoms) and its associated complications, such as deterioration of the joint and decreased quality of life. Tipping the exact point when remission is reached or RA flares is most essential for devising optimal treatment and enhancing patient's well-being. In this study, we put forward the usage of various types of ensemble learning algorithms aimed at improved the predictive power of RA flares. We use diverse data sources, including clinical data, medical imagery, genetic profiles and patient-reported data, and ensemble methods like Random Forest, Gradient Boosting, AdaBoost and Stacking processors to help us identify the patterns automatically. Thus ensembles of models are shown to be leading in performance, reaching superior accuracy, accuracy precision, recall rate, F1-score and area under the receiver operating characteristic curve (AUC-ROC). Feature importance analysis highlights the significant impact of clinical features which include the levels of CRP, ESR, age of the patient, duration of the disease on the likelihood of RA flares among the individual variants. The sensitivity analysis shows how well the ensemble model behaves under the generation of different model parameters and input data. The presented study emphasizes the immense facilitative ability of the ensemble methods of learning as predicting RA flares and this can also provide great information about the disease process and personalized medical decision-making. The areas for future research could be improved ensembles models, identification of novel feature selection approaches and validation of model performance in a variety of clinical settings. This will give the researchers an opportunity to implement the models in practice and make clinics more effective. © 2024 IEEE.

关键词： Sensitivity analysis

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Medical image registration and its application in retinal images:a review

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Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Analysing various algorithms applied to Alzheimer's disease dataset to help the Future Generation 2

Analysing various algorithms applied to Alzheimer's disease ...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Athinarayanan, S. Navaz, K. Tamilselvi, P. Shajeena, J. Vishwa Priya, V. Joel, M. Robinson School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai Tamil Nadu Avadi600062 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai600117 India Vels Institute of Science Technology and Studies Department of Computer Science Chennai India Kings Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350306415

A sort of brain illness that affects the brain is Alzheimer's. Nowadays, older adults are more likely to have these ailments. People who have this illness have very poor memories and decreased memory. Additionally, their brain isn't working correctly. As a result, their regular practice is progressively beginning to decline. Alzheimer's is a kind of dementia for which there is no definitive treatment. This essay will help us learn more about Alzheimer's illness. We shall learn more about its causes, symptoms, and remedies. Additionally, we investigate several illness prediction methods employing Graph Neural Networks denoted as GNNs. Decision Tree, Random Forest algorithms, and more. However, the Random forest classifier gave good results with 86% accuracy and good recall values. © 2024 IEEE.

关键词： Alzheimer dementia GNN Graph Neural Network Prediction

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Integrating a Sequential Model Into GNN-Based Social Recommendation for Relieving Over-Smoothing Problem

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IEEE Access 2025年 13卷 100219-100233页

作者： Janekhwan Kitsupapaisan Saranya Maneeroj Atsuhiro Takasu Department of Mathematics and Computer Science Faculty of Science Advanced Virtual and Intelligent Computing Center Chulalongkorn University Bangkok Thailand National Institute of Informatics Tokyo Japan

In recent years, social recommendation systems have increasingly integrated Graph Neural Networks (GNNs) to capture social relationships among users from both direct and higher-order neighbors. However, these state-of-the-art models face limitations regarding the number of iterations they can propagate across the graph. Messages from higher-order neighbors often dilute the unique characteristics of the target node. This causes node representations to become similar and indistinguishable, leading to a phenomenon known as the over-smoothing problem. Several works have attempted to address this problem with the expectation of preserving user characteristics. They add auxiliary information to the GNN-based social recommendation models without considering the dynamic preferences of users, which frequently change over time. Therefore, those works still struggle with the same problem after propagating in a few iterations. To mitigate this phenomenon, we propose a novel approach to integrate user characteristics extracted from users’ historical interactions using a sequential model to slow down node convergence. Integrating user characteristics into the GNN-based social recommendation will allow the model to capture user preferences that dynamically change over time. Consequently, the proposed model will be able to leverage informative data from higher-order neighbors over social networks for user representation learning, leading to better performance and effectiveness of the recommendations. Our experimental results demonstrate that the proposed model outperforms state-of-the-art social recommendation models across three benchmark datasets.

关键词： Representation learning Graph neural networks Social networking (online) Convergence Redundancy Recommender systems Diffusion processes Smoothing methods Nickel Feature extraction

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Detection of Brain Tumor Using Transfer Learning Using Conventional Autoencoder with Long Short Term Memory Method

Detection of Brain Tumor Using Transfer Learning Using Conve...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： D Agalya S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in health care industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications.

关键词： Training Recurrent neural networks Computational modeling Autoencoders Transfer learning Predictive models Convolutional neural networks Long short term memory Image classification Testing

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Malware Classification using LSTM-CF Framework for Disk Forensic Analysis 2

Malware Classification using LSTM-CF Framework for Disk Fore...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Thirupathi, Chaithanyaka Yeddeli Sundaram, Jawahar Yeo, Kheng Cher Khan, Shujahat Ali Pritika Sellappan, Devaraju Energy and Resources Institute Charles Darwin University Faculty of Science and Technology Darwin Australia Christ Academy Institute for Advanced Studies Department of Computer Science and Applications Bengluru India Charles Darwin University Faculty of Science and Technology Darwin Australia School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (纸本)9798350383652

In the current era, digital forensic investigators need specialized tools to extract digital footprints from hard disks. The number of crimes is rising dramatically. This has resulted in an increased number of unresolved cybercrime cases including malicious software, hacking and cyberfraud. Disk forensics or disk investigation is a massive task. It takes weeks to collect the traces of the 1TB hard drive and analyze them. It is a challenging task to detect reliable evidence because of the worldwide use and advancement of digital communication technologies. By using automated tools, only predictable areas of the disk are often investigated, and as a result, latent evidence in the hidden area might be missed. In this paper, we present Chaithu's Framework (CF) that gives us a clear picture of the steps to follow and implement a machine learning algorithm which is able to predict whether the request is related to malware or not by analyzing the large world Microsoft Malware Prediction dataset collected from Kaggle. It consists of a total of 7.8 million data samples with 84 features collected in real time from Windows 7,8,9 and 10 systems. Using predictive machine learning algorithms, frauds can be detected automatically and autonomous actions can be taken to prevent them. © 2024 IEEE.

关键词： Hard disk storage

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Robust Domain Generalization for Multi-modal Object Recognition 5

Robust Domain Generalization for Multi-modal Object Recognit...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Qiao, Yuxin Li, Keqin Lin, Junhong Wei, Rong Jiang, Chufeng Luo, Yang Yang, Haoyu Northern Arizona University Department of Computer Information Technology FlagstaffAZ United States Ama University Department of Computer Science Quezon Philippines Massachusetts Institute of Technology Electrical Engineering & Computer Science Department CambridgeMA United States Peking University Academy for Advanced Interdisciplinary Studies Beijing China The University of Texas at Austin Department of Computer Science AustinTX United States University of Southern California Department of Computer Science Los AngelesCA United States Georgia Institute of Technology College of Computing AtlantaGA United States

ISBN: (纸本)9798350366174

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual *** address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion. © 2024 IEEE.

关键词： Visual languages

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Preface to the 13th Workshop on Bibliometric-enhanced Information Retrieval at ECIR 2023 13

Preface to the 13th Workshop on Bibliometric-enhanced Inform...

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13th International Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2023

作者： Frommholz, Ingo Mayr, Philipp Cabanac, Guillaume Verberne, Suzan Brennan, Jordan School of Engineering Computing and Mathematical Sciences University of Wolverhampton United Kingdom GESIS Leibniz-Institute for the Social Sciences Cologne Germany University of Toulouse Computer Science Department IRIT UMR 5505 France Leiden Institute of Advanced Computer Science Leiden University Netherlands

This preface summarizes the 13th Workshop on Bibliometric-enhanced Information Retrieval (BIR). BIR 2023 was held as hybrid event at April 2nd, 2023, co-located with the 45th European Conference on Information Retriev...

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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