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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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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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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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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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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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Improved Kapur Entropy-Based Lung Nodule Segmentation in X-ray Images 7th

Improved Kapur Entropy-Based Lung Nodule Segmentation in X-r...

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7th International Symposium on Intelligent Informatics, ISI 2022

作者： Jaya, V. J. Mary Krishnakumar, S. Department of Computer Science Assumption Autonomous College Kerala Changanacherry India Department of Electronics School of Technology and Applied Sciences Mahatma Gandhi University Research Centre Edappally Kochi682024 India

ISBN: (纸本)9789811980930

Lung nodule segmentation is an essential aspect of two separate systems which deal with the diagnosis and prevention of lesions. A better computer-aided diagnostic (CAD) system attempts to increase the capacity to detect nodules and assist in determining whether they are malignant or benign. Lung segmentation precisely distinguish the areas and borders of the lung field from neighboring thoracic tissue and is indeed an important initial step in several clinical decision support methods. In pulmonary image analysis, one of the essential tasks is to segment the lung nodules that aid as an essential step for clinicians in distinguishing malignant and benign tumors. Yet, it is being noted that this might be a challenging task, particularly while nodules are attached to anatomic structures. This research presents a novel lung nodule segmentation method, which is executed in two steps: filtering and segmentation. The input lung image is pre-processed by employing an improved Wiener filtering and this filtered image is subjected to segmentation using an improved Kapur entropy-based segmentation method. The segmented output is then acquired in a practical manner. The outcome of the developed technique is compared to existing techniques in terms of various metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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PLOD: Point cloud level of detail for polygon mesh 32

PLOD: Point cloud level of detail for polygon mesh

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32nd International Conference in Central Europe on computer Graphics, Visualization and computer Vision, WSCG 2024

作者： Konks, Eric Shcherbakov, Alexandr Phystech School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny Oblast Moscow141701 Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow119991 Russia

Rendering high-polygonal models from distant perspectives has certain performance issues related to high density of subpixel triangles, which can be solved by levels of detail, a classical optimization method. Since a mesh occupies a small area on the screen, an alternative representation of the geometry in the form of a point cloud can replace the original geometry with little or no change to the image, and allowing for significant performance improvements due to the smaller number of primitives rendered. This paper presents a method for automatically generating a point cloud from a polygonal mesh with nested levels of detail. It also considers a method for rendering cloud with dynamically varying cloud density in real time based on view frustum and distance. © 2024 University of West Bohemia. All rights reserved.

关键词： Mesh generation

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New Approaches to the Prognosis and Diagnosis of Breast Cancer Using Fuzzy Expert Systems

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Journal of computer and Communications 2024年第5期12卷 151-169页

作者： Elias Ayinbila Apasiya Abdul-Mumin Salifu Peter Awon-Natemi Agbedemnab Department of Computer Science School of Computing and Information Sciences C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.

关键词： EMFES Breast Cancer Type-2 Fl Soft Computing Membership Functions Fuzzy Set Fuzzy Rules Risk Factors.

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Cardiac Arrest Prediction Model Using Ensemble Technique

Cardiac Arrest Prediction Model Using Ensemble Technique

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Dibakar Sinha Sarla More Jeyakrishnan V Durgesh Mishra Department of Computer Science Engineering Manipal University Jaipur Jaipur Rajasthan India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Indore MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The physician employs a diverse range of laboratory diagnostics, physical examinations, and on occasion, even invasive procedures to ascertain the presence of diseases. It is necessary to have a physician that possesses exceptional training, expertise and skill in the specific field, for detection of the disease in its initial phases. Despite the widespread use of ensemble techniques in disease prediction, here is a notable absence of comprehensive evaluation of regularly utilized ensemble algorithms for highly studied diseases. Consumers typically resort to seeking medical assistance from a doctor or other medical expert only after exhausting all other home remedies without finding relief for their ailments. This is probably a result of insufficient comprehension, as well as other factors related to behavior. This paper introduces a conventional ensemble LSTM model that incorporates data preparation and feature engineering. The model proposed in this research represents major gains in numerous statistical parameters like precision, f score, accuracy and ROC curve in comparison to the other models.

关键词： Heart Training Government Medical services Probability Predictive models Prediction algorithms Medical diagnostic imaging Long short term memory Diseases

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