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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West University of Timisoara). All rights reserved.

关键词： COVID-19

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Load Balancing in Cloud Environment to Minimize Average Response Time 4

Load Balancing in Cloud Environment to Minimize Average Resp...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Mohanty, Suneeta Patra, Shaswati Sarkar, Sohini Dube, Prashant Pattnaik, Prasant Kumar KIIT DU School of Computer Engineering Bhubaneswar India KIIT DU School of Computer Science Engineering Information Technology Bhubaneswar India

ISBN: (纸本)9798350349856

Cloud computing is a recent and fastest-growing technology. Load balancing is a major challenge in cloud where the resources have to be directed to their respective servers so that the whole system works efficiently by distributing the workload efficiently. In this study, we compared the average response times of the six Load Balancing Algorithms, including Throttled, RR, ESET, Dynamic, Ip Hash, and Least Connection, to determine which one is the best. This research paper uses cloud analyst platform to perform a thorough comparative study of all six Load balancing algorithms along with three Service Broker Policies to find out with which best results are achieved. © 2024 IEEE.

关键词： Cloud computing

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Performance Analysis of Existing SDN Load Balancing Controller Using Mininet 1

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International Conference on Advanced engineering, technology and Applications, ICAETA 2024

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Agrawal, Atul Department of Computer Science and Engineering School of Engineeringand Technology IEC College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology ITS Engineering College Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized hardware. Networks have to adjust due to the load and also as a result of dealing with faults and complexity which are provided in the implementation and configuration process. Network engineers or administrators must be aware of selecting controllers according to the network requirement, which can lead to performance issues, scalability challenges, reliability and fault tolerance and many more problems in the network. To tackle this problem, this research analyses four different controllers in an experiment: In-band, open flow, OVS, and remote controller, which are the key aspects. The paper shows how these controllers, which are detected through a comprehensive evaluation of network performance attributes (e.g., Throughput and Jitter), affect the overall system’s performance. This information provides beneficial information for the researchers and practitioners who wish to make informed choices whenever they select suitable controls for specific network needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Remote control

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Bearing Fault Numerical Model for the Closed-slot Rotor Submersible Motor

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Chinese Journal of Electrical engineering 2023年第2期9卷 84-93页

作者： Zhe Ke Bokai Guan Chong Di Jingwen Yan Xiaohua Bao School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China School of Electrical and Computer Engineering The Ohio State UniversityColumbus OH 43210USA

A numerical model of the bearing fault of a motor with a closed-slot rotor using the finite element method(FEM)is *** rotor’s radial motion can be regarded as static eccentric at the defect time points and healthy at other time *** frequency of the harmonic component is analyzed corresponding to bearing fault in stator current according to the radial movement of the motor ***,the relative permeability variation region is established to achieve the radial motion of the rotor with bearing ***,the relative permeability variation region is established in the health and static eccentric ***,the defect time points are estimated and the static eccentricity model by transient field is ***,the relative permeability of the variable region in the static eccentric model is imported into the variable region of the health model at the defect time *** simulation results show that the air gap flux density of the bearing fault model is different from that of the health model and static eccentric *** addition,the stator current contains harmonic components of the bearing *** analysis results prove the applicability of the proposed model.

关键词： Bearing fault finite element method relative permeability variation region closed-slot rotor stator current

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Active disturbance rejection control for COVID-19 outbreak based on SEIR model

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International Journal of Systems, Control and Communications 2024年第3期15卷 292-311页

作者： Zhang, Haonan Tan, Wen Yu, Mei School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical and Control Engineering North China University of Technology Beijing100144 China

Public health and social measures (PHSMs) standardise the non-pharmacological intervention (NPIs) policies that countries around the world have implemented to curb the spread of COVID-19, and may also serve as a guide for the implementation of future complex health policies in response to new pandemics of infectious diseases. In this paper we aim to use feedback control to help decision-makers establish PHSMs, which can effectively constrain the spread of COVID-19 pandemic with minimal financial loss. The time-dependent susceptible-exposed-infectious-removed (SEIR) model is used for the dynamics of the COVID-19 pandemic. The feedback control is based on a modified nonlinear active disturbance rejection controller (ADRC) that includes modified nonlinear extended state and nonlinear state error feedback with parameters tuned by particle swarm optimisation. The final simulation results show that the modified nonlinear ADRC has better performances and is more robust against uncertainties in the parameters of the epidemiological model. © 2024 Inderscience Enterprises Ltd.

关键词： Disturbance rejection

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MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

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computers, Materials & Continua 2024年第4期79卷 983-1003页

作者： Xiangyan Tang Chengchun Ruan Xiulai Li Binbin Li Cebin Fu School of Computer Science and Technology Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China School of Cyberspace Security(School of Cryptology) Hainan UniversityHaikou570228China

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion *** enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial *** build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small *** replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small ***,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple *** module enhances the perception of spatial contextual features and the utilizationof multiscale feature *** the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

关键词： Small object detection YOLOv7 multi-scale attention spatial context

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Privacy-Enhanced Data Sharing in Multi-Institutional Healthcare 9

Privacy-Enhanced Data Sharing in Multi-Institutional Healthc...

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9th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2024

作者： Xiao, Jixiang Zeng, Shengke Zhou, Tiantian Feng, Yawen School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350373554

In multi-institutional patient data sharing scenarios, maintaining fine-grained access control while safeguarding privacy and adapting to real-world environments is crucial. Traditional attribute-based encryption (ABE) schemes may inadvertently expose sensitive information through access policies and lack support for dynamic policy updates. To tackle these challenges, this paper proposes an ABE scheme with partially hidden access policies to bolster privacy protection for encrypted data stored on servers across healthcare institutions. Our scheme also facilitates access policy updates, ensuring accurate and efficient adaptation to evolving real-world needs. Through rigorous security analysis, we validate the efficacy of our approach in safeguarding patient data integrity, confidentiality, and adaptability to dynamic environments. Our scheme offers a robust solution for secure and efficient multi-institutional patient data sharing, supporting fine-grained access control, privacy preservation, and dynamic policy updates. © 2024 IEEE.

关键词： Access control

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Boost diagnostic performance in retinal disease classification utilizing deep ensemble classifiers based on OCT

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Multimedia Tools and Applications 2024年 1-21页

作者： Khan, Umer Sadiq Khan, Saif Ur Rehman School of Computer and Information Science Hubei Engineering University Xiaogan432000 China School of Computer Science and Information Engineering Hubei University Wuhan430062 China Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China School of Computer Science and Engineering Central South University Yuelu District 932 Lushan S Rd Changsha Hunan China

Damage to the retinal blood vessels is critical in diabetic retinopathy, a progressively emerging health concern that often advances quietly without explicit symptoms. Optical coherence tomography-OCT has emerged as a favored noninvasive imaging technique for diagnosing diabetic retinopathy promptly and accurately. However, timely and precise diagnoses from OCT images are essential in prevention of blindness. Moreover, accurate interpretation of OCT images is challenging. Single model learning debilitates in managing diverse data types and structures, constraining its adaptability to varied environments. Its limitations become apparent in tasks requiring expertise from multiple domains, delaying overall performance. Moreover, learning may exhibit susceptibility to overfitting with large and heterogeneous datasets, resulting in compromised generalization capabilities. In this study, we propose a hybrid learning model for the classification of four distinct classes of retinal diseases in OCT images with improved generalization capabilities. Our hybrid model is constructed upon the well-established architectural foundations of ResNet50 and EfficientNetB0. By pre-training the hybrid model on extensive datasets like ImageNet and then fine-tuning it on publicly available OCT image datasets, we capitalize on the strengths of both architectures. This empowers the hybrid model to excel in discerning intricate image patterns while efficiently extracting hierarchical prediction from various regions within the images. To enhance classification accuracy and mitigate overfitting, we eliminate the fully connected layer from the base model and introduce a concatenate layer to combine two objective learning prediction. A dataset comprising 84,452 OCT images, each expertly graded for illnesses. we conducted training and evaluation of our proposed model, which demonstrated superior performance compared to existing methods, achieving an impressive overall classification accuracy of 97.

关键词： Optical tomography

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EELMCDA: Combining evolutionary ensemble learning with matrix feature decomposition for predicting circRNA-disease associations

EELMCDA: Combining evolutionary ensemble learning with matri...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Zheng Wang, Lei You, Zhu-Hong Wang, Lei Li, Yang Wang, Zhenyu Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Northwestern Polytechnical University School of Computer Science Xi'an710048 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Lanzhou University of Technology School of Electrical Engineering and Information Engineering Lanzhou730050 China

ISBN: (纸本)9798350386226

Recent studies have indicated that circular RNAs (circRNAs) play a significant role in the diagnosis and treatment of disease. However, the prediction of associations between circRNAs and diseases using conventional biological methods is constrained by numerous factors. In this study, we proposed a novel computational model called EELMCDA that combines evolutionary ensemble learning (EEL) approach and matrix feature decomposition method to predict potential circRNA-disease associations. The model firstly integrates circRNA function information, disease semantic information, and circRNA and disease gaussian interaction profile kernel (GIPK) information into an integrated matrix and constructed the corresponding feature matrix, then uses the matrix feature decomposition algorithm to obtain its important feature, and finally adopted evolutionary ensemble learning module to predict circRNA-disease associations. The average accuracy of the EELMCDA model by 5-fold cross-validation on CircR2Disease, CircAtlasv2.0, Circ2Disease, and CircRNADisease datasets were 92.40%, 92.90%, 88.91%, and 90.74%, respectively. Moreover, in case studies, the 21 of the top 30 circRNA-disease pairs with the highest EELMCDA scores were validated in recent literatures. These results further demonstrate the effectiveness of EELMCDA in predicting circRNA-disease associations. © 2024 IEEE.

关键词： Contrastive Learning

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Probabilistic Multi-Product Trading of Hydro Power Plants in Sequential Intraday and Frequency-Regulation Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 449-464页

作者： Mohammadi, Saeed Khodadadi, Abolfazl Shinde, Priyanka Blom, Evelin Hesamzadeh, Mohammad Reza Soder, Lennart KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived through a series of transformation techniques. Additionally, we present two reformulations that re-frame the problem as a mixed-integer linear programming problem with uncertain parameters. Various aspects of the model are thoroughly examined to observe the optimal multi-product trading behavior of hydro power plant assets, along with numerous case studies. Leveraging historical data from Nordic electricity markets, we construct realistic scenarios for the uncertain parameters. Furthermore, we then proposed an algorithm based on the No-U-Turn sampler to provide probability distribution functions of cleared prices in frequency-regulation and day-ahead markets. These distribution functions offer valuable statistical insights into temporal price risks for informed multi-product optimal-trading decisions. © 2024 IEEE.

关键词： Power markets

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