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Supervised Machine Learning-Based Prediction of COVID-19

computers, Materials & Continua 2021年第10期69卷 21-34页

作者： Atta-ur-Rahman Kiran Sultan Iftikhar Naseer Rizwan Majeed Dhiaa Musleh Mohammed Abdul Salam Gollapalli Sghaier Chabani Nehad Ibrahim Shahan Yamin Siddiqui Muhammad Adnan Khan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of CIT Faculty of Applied StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan

COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the *** yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are *** around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely *** this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new *** situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this *** state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection *** study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential *** proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation *** experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation *** comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

关键词： COVID-19 CSDC-SVM artificial intelligence machine learning cloud computing support vector machine

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Meta-transfer Learning for Person Re-identification in Aerial Imagery 17th

Meta-transfer Learning for Person Re-identification in Aer...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Xu, Lili Peng, Houfu Wang, Linna Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang550025 China

ISBN: (纸本)9789819923847

Person re-identification (Re-ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In the last few years, the construction of aerial person Re-ID datasets has been appealing because visual surveillance through unmanned aerial vehicle (UAV) platforms has become very valuable in real-world scenarios. However, great differences are exhibited between the pedestrian images captured by ground cameras and those captured by UAVs. Person Re-ID methods based on ground person images have difficulty when performing Re-ID based on aerial person images. In this paper, we first propose a novel meta-transfer learning method for person Re-ID in aerial imagery;this approach trains a generalisable Re-ID model to learn discriminative feature representations for aerial person images. Specifically, a meta-learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilise and further improve the acquired meta-knowledge. To overcome the convergence speed and recognition accuracy reductions caused by the presence of difficult categories in the given dataset, we propose a learning strategy based on curriculum sampling that is harmonised with our meta-transfer learning framework. In addition, a new metric formulation of sample similarity is introduced based on Mahalanobis distance to improve the optimisation of the model. Extensive comparative evaluation experiments are conducted on the large-scale aerial Re-ID dataset, and the results obtained show that our method achieves a Rank-1 accuracy of 63.63 % and a mean average precision (mAP) of 38.02 %, demonstrating its potential for completing person Re-ID in aerial images. Ablation studies also validate that each component contributes to improving the performance of the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

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computer Systems science & Engineering 2024年第3期48卷 571-608页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Abdulatif Alabdulatif Ruba Abu Khurma Maribel Garcia-Arenas Pedro A.Castillo University Institute of Computing Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Uttaranchal UniversityUttarakhand248007India Department of Computer Science College of Computer Qassim UniversityBuraydah52571Saudi Arabia MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

As cloud computing usage grows,cloud data centers play an increasingly important *** maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance *** purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data *** aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management *** review revealed three task allocation research topics and seven performance management *** allocation research areas are resource allocation,load-Balancing,and *** management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network *** study proposes new techniques to enhance cloud computing work allocation and performance ***-comings in each approach can guide future *** research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and *** methodologies can steer future research to fill gaps in the literature.

关键词： Cloud computing data centre task allocation performance management resource utilization

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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Towards Automated Semantics-Driven Web Service Composition: Case Study on Question Answering Systems

Towards Automated Semantics-Driven Web Service Composition: ...

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International Conference on Semantic Computing

作者： Aleksandr Perevalov Andreas Both Mike Scherfner Department of Computer Science and Languages Anhalt University of Applied Sciences Köthen Germany Faculty of Computer Science and Media Leipzig University of Applied Sciences Leipzig Germany Technology Innovation Unit DATEV eG Nuremberg Germany

When following the Component-based Architecture (CBA), Web Services (WSs), as individual components, are composed to meet the functional and non-functional requirements of users. As the number of available WSs increases, this process becomes more complex. Therefore, designers must be able to easily (1) define, (2) select, (3) verify, and (4) execute composite WSs. While many papers focus on the execution phase, our work specifically aims at the definition and verification phases of combining WSs. To achieve this, we introduce a WS composition method that uses semantic descriptions to define functional information and select relevant WSs. A formal mathematical model is then used to verify the composite WSs. In our approach, we map OWL-based semantic service descriptions to Coloured Petri Nets model and use its algorithms to automatically compute reachable combinations. We apply our method to Question Answering (QA) systems, where WSs serve as functional services that are combined to answer natural-language questions. The results demonstrate that the proposed method helps to significantly reduce the search space of composite WSs during the selection phase.

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Pricing Policy with the Effect of Fairness Concern, Imprecise Greenness, and Prices in Imprecise Market for a Dual Channel

Pricing Policy with the Effect of Fairness Concern, Imprecis...

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International Conference on Nonlinear applied Analysis and Optimization, ICNAAO-2021

作者： Ganguly, Sanchari Das, Pritha Maiti, Manoranjan Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur WB Howrah711103 India Department of Applied Mathematics Oceanology and Computer Programming Vidyasagar University WB Midnapore721102 India

ISBN: (纸本)9789819905966

Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manufacturing to maintain sustainable development. The adoption of online shopping by customers around the globe has altered the dynamics of competitiveness in the retail supply chain (SC). Considering the above facts, the effect of fairness concern on optimal prices and channel members’ profits in a two-level dual channel SC is investigated. Here, we consider an SC comprising a green manufacturer and a fair-minded retailer where the manufacturer produces and sells the green product through both offline and online channels to consumers. Due to uncertainty in the real world, the fuzziness is associated with market demand, price elasticity, and the coefficient of greenness. The fuzzy objectives and constraints are reduced to crisp ones using expectation and possibility measures, respectively. Both centralized and decentralized models (with and without cognitive bias) are formulated and solved by the Stackelberg game for the optimal prices and product greenness level. The models’ optimal solutions are analyzed and compared with the deterministic models numerically. The effects of the fairness concern coefficient on the optimal prices, product greenness level, and channel members’ profits are investigated. The sensitivity analyses are presented to study the effects of the fuzzy degree of customers’ sensitivity toward greenness on channel members’ profits. Retailer’s fairness is harmful to the manufacturer but beneficial for her own profit. Finally, some conclusions and managerial insights are presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Game theory

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Data driven Approaches for Demand Forecasting in Supply Chain for Business Decisions

Data driven Approaches for Demand Forecasting in Supply Chai...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Salman Javed Rizwan Akhtar Dept. IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Pakistan

Demand forecasting is viewed as a complex challenge for industries as it relies heavily on various factors. The Supply Chain Management (SCM) integrates processes vital for corporate environments across industries. This paper presents a comprehensive data-driven framework for sales demand forecasting in SCM aiming to enhance accuracy of forecasts. Using Rossmann Stores data case study the sales are predicted up to 6 weeks ahead with a comparative analysis conducted for long-term predictions. Multi-dimensional data complexity is addressed by identifying relevant features and selecting appropriate algorithms. The proposed framework utilizes four data-driven machine learning algorithms: Extreme Learning Machine (ELM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) aiming to construct a high-precision sales demand forecasting model. The experimental validation using Rossmann Stores data demonstrates the effectiveness of the proposed framework, highlighting its significance in improving the accuracy of forecasts in SCM and enhancing decision-making processes.

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Social Media Analytics through Big Data Using Hadoop Framework 2

Social Media Analytics through Big Data Using Hadoop Framewo...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Almajed, Rasha Khan, Bilal Shoaib Bassam Nassoura, Ayman Irshad, Muhammad Sohail Amjad, Muhammad Hassan, Muhammad Maqbool, Shahid Pradhan, Manas American University College of Computer and Information Technology Dubai United Arab Emirates Akhuwat College University Department of Informatin Technology Lahore Pakistan City University Ajman Management Information Systems Department United Arab Emirates National College of Business Administration and Economics Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan National University of Science & Technology Pakistan Skyline University College School of Information Technology Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

In the current decades, data from social networks has increased dramatically. Since outmoded tools and techniques are not appropriate for working with such a large amount of data, a good structure is needed to accumulate these vast volumes of data and provide the opportunity for finale users to analyze them. In this research work, we use data analysis techniques to support businessmen for the development of new strategies. We use Apache Hadoop framework and apache flume platform for obtaining social media analytics. Apache flume works as a local host for the Hadoop data file system (HDFS). Iron man 3 movie reviews on twitter have been executed and analyzed by using apache flume and hive. By observing the table created by apache hive from twitter tweets it is shown that only 2 movie viewers observe that it is negative, some of them observe the movie not of standard while giving a neutral opinion or negative option, but most of them like the movie by giving positive remarks. © 2023 IEEE.

关键词： Sentiment analysis

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Active Authorization Control of Deep Models Using Channel Pruning 17th

Active Authorization Control of Deep Models Using Channel P...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Wang, Linna Song, Yunfei Zhu, Yujia Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang550025 China

ISBN: (纸本)9789819923847

In recent work, researchers have proposed deep neural network(DNN) model active authorization control protection strategies. Because active authorization strategies can prevent attackers from stealing models in advance, they have become a focus of DNN model copyright protection research. At present, most active authorization methods significantly reduce the accuracy by encrypting the parameters of a DNN model or modifying the structure of a DNN model, to prevent malicious infringers from using the model. The active authorization method of modifying the structure of a DNN model impacts the original task accuracy of the DNN model. Moreover, in active authorization methods that encrypt DNN model parameters, authorized users need to perform many calculations to decrypt the DNN model parameters. Therefore, this paper uses a channel pruning algorithm to control the authorization of the DNN model. In this work, the pruning rate or threshold is used as the secret key of the DNN model, the secret key is used to prune and fine-tune the original DNN model before the DNN model is distributed to authorized users, and the fine-tuned DNN model can restore performance similar to that of the original DNN model. Due to the advantages of the pruning mechanism, the DNN model retains the performance of the original task during the active authorization process, and reducing the number of calculations. We perform our work with the CIFAR-10 and CIFAR-100 datasets, and the experimental results show that we only need to prune a small number of channels in the DNN model to determine whether a user is authorized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Intrusion Detection Systems Based on Machine Learning Approaches: A Systematic Review

Intrusion Detection Systems Based on Machine Learning Approa...

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2023 International Conference on science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Ogundokun, Roseline Oluwaseun Basil, Ugochukwu Babatunde, Akinbowale Nathaniel Abdulahi, Abdul Rahman Tosho Adenike, Ajiboye Raimot Adebiyi, Ayodele Ariyo Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Sdg 11. Sustainable Cities and Communities Omu Aran Nigeria Landmark University Department of Computer Science Omu Aran Nigeria Kwara State University Department of Computer Science Malete Nigeria Institute of Information and Communication Technology Department of Computer Science Kwara State Polytechnic Nigeria Institute of Applied Sciences Department of Statistics Kwara State Polytechnic Nigeria Landmark University Omu-Aran Department of Computer Science Omu Aran Nigeria Landmark University Omu Aran Sdg 3. Good Health and Wellbeing Nigeria

ISBN: (纸本)9798350324785

The proliferation of Internet use poses certain security problems for networks. Intrusion detection (ID) in cybersecurity technology is to recognize unexpected entry to or assaults on secured networked computers. In the research, many machine learning (ML) and deep learning (DL) algorithms have been used to tackle intrusion detection systems (IDS). Nevertheless, few publications examine and explain the present state of employing ML approaches to tackle ID issues. This systematic review (SR) analyzes 11 papers published between 2016 and 2021 that focused on developing single, hybrid, and ensemble classifiers. Similar research is evaluated based on their classifier designs, the datasets they used, and their conceptual frameworks. Recent accomplishments and limits in developing IDS systems using ML are presented and analyzed. In addition, many prospective study possibilities are offered. © 2023 IEEE.

关键词： Intrusion detection

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