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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Shrestha, Avantika Tlachac, M.L. Flores, Ricardo Hickey, Kevin Rundensteiner, Elke A. Worcester Polytechnic Institute Data Science Department WorcesterMA United States Bryant University Center for Health and Behavioral Sciences Department of Information Systems and Analytics SmithfieldRI United States University of Concepcion Department of Computer Science Biobio Concepcion Chile Worcester Polytechnic Institute Data Science Department Computer Science Department WorcesterMA United States

ISBN: (纸本)9798350362480

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illness screening potential of language models on modalities such as transcripts. Such datasets suffer from a limited number of participants. To overcome these challenges, we take a two-pronged approach: (1) we leverage multi-task learning to model multiple illnesses concurrently namely MDD and PTSD screening, and (2) we plug in pre-trained language models as a backbone, namely, Bidirectional Encoder Representations from Transformers (BERT) variants - which are capable of learning the linguistic content of clinical interview transcripts. In particular, we experiment with three multi-task weighting strategies and five BERT variants, applying them to 15 transcript sets extracted from the Distress Analysis Interview Corpus. Our results indicate that leveraging multi-task learning, especially with meta-weighting, increases the screening performance compared to single-task learning. Our multi-task learning model strategies improved the F1 scores for all 15 datasets for both MDD and PTSD screening. Notably, multitask learning improved MDD screening ability by 20 percent with transcripts regarding 'regret' and achieved F1 of 0.89 and 0.82 respectively for MDD and PTSD screening with transcripts regarding 'medical history'. Our findings may help researchers develop more effective mental illness screening models. © 2024 IEEE.

关键词： Multi-task learning

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IoDseC++: authenticated key exchange protocol for cloud-enable internet of drone communication

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9529-9542页

作者： Amin, Ruhul Jayaswal, Sakshita Sureshkumar, Venkatasamy Rathore, Balram Jha, Ananya Abdussami, Mohammad Department of Computer Science and Engineering IIIT Naya raipur Sector 24 Chhattisgarh Naya Raipur493661 India Department of Applied Mathematics and Computational Sciences PSG College of Technology Tamilnadu Coimbatore641004 India

In recent times, sensor node or smart camera can be embedded in the drones for the collection of important data with the help of Internet Technology. Several security threats/vulnerabilities may hamper the system while collecting data. Hence, strong protection against security attacks is essential in drone based data collection system. In this work, we have considered a drone based cloud enable architecture for collecting and sharing information. The aim of this paper is to design a lightweight security protocol which will provide secure communication and mutual authentication among the entities. The proposed protocol establishes temporary session key for each session in order to make the complete system security attacks free. We have simulated the proposed protocol for measuring security strength using Scyther simulation whose results show all the claimed parameters are private during protocol run. It also ensures the correctness of mutual authentication. The informal analysis confirms strong protection on all security threats related to proposed architecture. The performance analysis has also made which shows that the protocol is lightweight and achieves better efficiency in comparison with related published works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Drones

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A Novel Pipeline for Virus Integration Sites Detection in Tumor Genomes Using Deep Learning

A Novel Pipeline for Virus Integration Sites Detection in Tu...

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IEEE International Conference on Big data

作者： Lorrayya Williams Vibhuti Gupta Department of Computer Science & Data Science School of Applied Computational Sciences Meharry Medical College Nashville TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Cancer is one of the leading causes of death worldwide. Pathogenic viruses are estimated to be responsible for 15% of all human cancers globally and pose significant threats to public health. Viruses integrate their genetic material into the host genome, increasing the risk of cancer promoting changes in it. To understand the molecular mechanisms of virus-mediated cancers, it is crucial to identify viral insertion sites in cancer genomes. However, this effort is hindered by the rapidly increasing volume of tumor sequencing data, along with the challenges of accurate data analysis caused by high viral mutation rates and the difficulty of aligning short reads to the reference genome. Thus it is crucial to develop an efficient method for virus integration site detection in tumor genomes. This paper proposes a novel pipeline to identify viral integration sites leveraging deep Convolutional Neural Networks (CNN). Our contributions are twofold: (i) We propose and integrate three novel matrix generation methods into the pipeline, developed after aligning the host and viral genomes with their respective reference genomes.; (ii) We employ one-hot encoded images with reduced computational complexity to represent viral integration sites and harness the capabilities of Deep CNN networks for detection. The paper illustrates our proposed approach and presents experiments conducted using both synthetic and real sequencing data. Our preliminary experimental results are promising, showcasing the effectiveness of the proposed methods in detecting viral integration sites.

关键词： Sequential analysis Accuracy computer viruses Pipelines Genomics Convolutional neural networks Bioinformatics Viruses (medical) Cancer Tumors

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Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

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computers, Materials & Continua 2025年第4期83卷 381-405页

作者： Fahad Ahmad Saad Awadh Alanazi Kashaf Junaid Maryam Shabbir Asim Ali School of Computing Faculty of TechnologyUniversity of PortsmouthWinston Churchill Ave SouthseaPortsmouthPO12UPUK Portsmouth Artificial Intelligence and Data Science Center University of PortsmouthWinston Churchill AveSouthseaPortsmouthPO12UPUK Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia School of Biological and Behavioural Sciences Queen Mary University of LondonLondonE14NSUK Department of Computer Sciences Bahria UniversityLahorePunjab54700Pakistan

Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in ***,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through *** diagnosis through image processing can significantly improve survival *** encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years *** damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of *** such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung ***,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory *** proposed system utilizes convolutional neural networks(CNN)to diagnose *** this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural *** network undergoes training to recognize patterns and make accurate predictions based on the learned *** leveraging DL techniques,the system aims to enhance the precision and reliability of CB *** research specifically focuses on a subset of 189 lung disease images,carefully selected for model *** further refine the training process,various data augmentation and noise removal techniques are *** techniques significantly enhance the quality of the training data,improving the model’s robustness and *** a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing *** h

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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FTHP-MPI: Towards Providing Replication-based Fault Tolerance in a Fault-Intolerant Native MPI Library

arXiv

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arXiv 2025年

作者： Joshi, Sarthak Vadhiyar, Sathish Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

Faults in high-performance systems are expected to be very large in the current exascale computing era. To compensate for a higher failure rate, the standard checkpoint/restart technique would need to create checkpoints at a much higher frequency resulting in an excessive amount of overhead which would not be sustainable for many scientific applications. To improve application efficiency in such high failure environments, the mechanism of replication of MPI processes was proposed. Replication allows for fast recovery from failures by simply dropping the failed processes and using their replicas to continue the regular operation of the application. In this paper, we have implemented FTHP-MPI (Fault Tolerance and High Performance MPI), a novel fault-tolerant MPI library that augments checkpoint/restart with replication to provide resilience from failures. The novelty of our work is that it is designed to provide fault tolerance in a native MPI library that does not provide support for fault tolerance. This lets application developers achieve fault tolerance at high failure rates while also using efficient communication protocols in the native MPI libraries that are generally fine-tuned for specific HPC platforms. We have also implemented efficient parallel communication techniques that involve replicas. Our framework deals with the unique challenges of integrating support for checkpointing and partial replication. We conducted experiments emulating the failure rates of exascale computing systems with three applications, HPCG, PIC and CloverLeaf. We show that for large scale systems where the failure intervals are expected to be within a hour, our replication-based library provides higher efficiency and performance than checkpointing-based approaches. We show that under failure-free conditions, the additional overheads due to replication are negligible in our library. © 2025, CC BY.

关键词： Large scale systems

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A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

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computer Systems science & Engineering 2023年第5期45卷 1001-1016页

作者： Muhammad Junaid Umer Muhammad Sharif Majed Alhaisoni Usman Tariq Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusWah CanttPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)P.O.BOX 84428Riyadh11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every *** BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total *** diagnosis and detection of breast cancer disease with proper treatment may reduce the number of *** gold standard for BC detection is biopsy analysis which needs an expert for correct *** diagnosis of BC is a complex and challenging *** work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology *** evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is *** proposed automatic diagnosis of BC detection and classification mainly involves three ***,a DL model is proposed for feature ***,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best ***,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are *** outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

关键词： Breast cancer deep learning FS IDC ML SVM

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A causal multiomics study discriminates the early immune features of Ad5-vectored Ebola vaccine recipients

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The Innovation 2024年第3期5卷 77-87页

作者： Zhe Zhang Wenjing Yang Zhengshan Chen Haoang Chi Shipo Wu Wanru Zheng Ruochun Jin Busen Wang Yudong Wang Nan Huo Jinlong Zhang Xiaohong Song Liyang Xu Jun Zhang Lihua Hou Wei Chen Institute of Biotechnology Academy of Military Medical SciencesBeijing 100071China Department of Intelligent Data Science College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Intelligent Game and Decision Lab Academy of Military ScienceBeijing 100091China

The vaccine-induced innate immune response is essential for the generation of an antibody *** date,how Ad5-vectored vaccines are influenced by preexisting anti-Ad5 antibodies during activation of the early immune response remains ***,we investigated the specific alterations in GP1,2-specific IgG-related elements of the early immune response at the genetic,molecular,and cellular levels on days 0,1,3,and 7 after Ad5-EBOV *** a causal multiomics analysis,distinct early immune responses associated with GP1,2-specific IgG were observed in Ad5-EBOV recipients with a low level of preexisting anti-Ad5 *** study revealed the correlates of the Ad5-EBOV-induced IgG response and provided mechanistic evidence for overcoming preexisting Ad5 immunity during the administration of Ad5-vectored vaccines.

关键词： vaccine immunity alterations

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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

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Neural Computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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An Automated Python Script for data Cleaning and Labeling using Machine Learning Technique

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Informatica (Slovenia) 2023年第6期47卷 219-232页

作者： Oladipupo, Matthew Abiola Obuzor, Princewill Chima Bamgbade, Babatunde Joseph Olagunju, Kazeem M. Adeniyi, Abidemi Emmanuel Ajagbe, Sunday Adeola Department of Data Science School of Science Engineering and Environment University of Salford United Kingdom Jericho GRA Ibadan Nigeria Department of Computer Engineering Ladoke Akintola University of Technology LAUTECH Ogbomoso Nigeria Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria Department of Computer & Industrial Production Engineering First Technical University Ibadan Nigeria

Every employee in the company who deals with data needs to have clean, noise-free data. Since data warehouses store and update enormous amounts of data from several sources, there is a potential that some of those references may contain inaccurate data. Due to the noise, inefficacy, and poor characterization of the vast amount of accessible data, as well as the ensuing insensitivity and inefficiencies of human data cleaning and labeling, the presentation of the data has become ambiguous, and the assessment of the information has become difficult. A hole in the creation of a better data analysis method was identified. This helped to guide the creation of a Python script for automatically cleaning and labeling data. The first step in the strategy used in this study to accomplish its goals and objectives was to obtain a financial dataset from the top database, "Kaggle". Create a machine learning (ML) approach in Python that intends to automate the financial dataset cleaning. This covers ingesting data, addressing incomplete data, addressing anomalies, one-hot wrapping and label encoding, extracting date and time values, and data normalization. Implementing an unsupervised machine learning method that attempts to automate financial dataset labeling (k-means). Using the method includes the elbow principle, k-means clustering, data modeling of "age" versus "arrival," dimensionality reductions, computer vision, and dataset categorizing using the groupings. An empirical assessment of the cleaned and labeled automated trading dataset utilizing a comparison of the cleaned dataset before and after PCA adoption. The results show that the developed ML technique not only improved the performance of the audit data used in this study, but also classified the data after cleaning it and removing the unpleasant section and incomplete data, as shown by the k-means segmentation result and grouping by PCA. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Python

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Preparing Wearable data for AI-Powered Mood and Compliance Prediction in HCT Patients and Caregivers

Preparing Wearable Data for AI-Powered Mood and Compliance P...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Ziegenbein, Charles B. Ortiz, Bengie L. Gupta, Vibhuti Choi, Sung Won University of Michigan Department of Pediatrics Michigan Medicine Ann ArborMI United States Peraton Labs Autonomous Systems Research Department Basking RidgeNJ United States School of Applied Computational Sciences Meharry Medical College Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

Hematopoietic stem cell transplantation (HCT) is a potentially life-saving treatment that uses healthy blood-forming cells from donors to replace dysfunctional or damaged hematopoietic cells in patients with various blood disorders. This procedure is often employed to treat conditions such as hematological malignancies (e.g., leukemia, lymphoma, myeloma) and other severe blood or immune system diseases. Monitoring post-transplant complications is essential for tracking physiological effects and aiding in clinical decision-making. Biobehavioral aspects of care partners (i.e., unpaid caregivers) can also be influenced during the post-transplant stage of HCT. Wearable devices offer a non-invasive way to continuously track physiological parameters, making them a valuable resource for health monitoring. However, the physiological data collected from wearables is highly unstructured, often containing missing values, outliers, redundant features, and erroneous measurements leading to false conclusions/prediction. Therefore, enhancing data quality is essential for deriving meaningful insights. This paper introduces novel pre-processing methods to build a high quality, comprehensive, standardized, AI/ML ready, and clinically meaningful wearable dataset of HCT patients and caregivers. To test our data cleaning implementation, our cleaned, high-quality dataset is utilized to predict mood and compliance in HCT patients and their caregivers using machine learning algorithms. The paper illustrates our proposed approach and presents experimental results conducted on the data collected from Michigan Medicine for HCT patients and caregivers. Our preliminary experimental results are promising, demonstrating the effectiveness of the proposed methods and the high-quality dataset in predicting mood and compliance for the participants. © 2024 IEEE.

关键词： Diseases

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