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13th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2024

作者： Schulz, Steffen Haueisen, Jens Bar, Karl Jurgen Voss, Andreas Seifert, Georg Berlin Germany Institute of Biomedical Engineering and Informatics University of Technology Ilmenau Ilmenau Germany Department for Psychosomatic Medicine and Psychotherapy Germany

ISBN: (纸本)9798350392050

The detection and quantification of the coupling strength and direction are important aspects for a more detailed understanding of physiological regulatory processes in the field of Network Physiology. Here, we introduced a new directionality index (D HRJsd ) derived from the pattern family density matrix from the High Resolution Joint Symbolic Dynamics approach (HRJSD) to determine the coupling direction and driver-response relationships. For the validation of this index different linear and non-linear multivariate coupled systems (Gaussian autoregressive models) with different mutual influences were generated. The simulation results revealed that D HRJSd was able to correctly detect the dominating coupling direction in linear bivariate coupled systems, but is only partly able to detect the dominating coupling direction in non-linear bivariate coupled systems. © 2024 IEEE.

关键词： Physiological models

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Enhanced Berth Allocation Using the Cuckoo Search Algorithm

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SN Computer Science 2022年第4期3卷 325页

作者： Aslam, Sheraz Michaelides, Michalis P. Herodotou, Herodotos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

Berth allocation is one of the most important optimization problems in container terminals at ports worldwide. From both the port operator’s and the shipping lines’ point of view, minimizing the time a vessel spends at berth and minimizing the total cost of berth operations are considered fundamental objectives with respect to terminal operations. In this study, we focus on the berth allocation problem (BAP), where berth positions are assigned to arriving ships with the objective of reducing the total service cost, which includes waiting cost, handling cost, and several penalties, such as a penalty for late departure and a penalty for non-optimal berth allocation. First, the BAP is formulated as a mixed-integer linear programming (MILP) model. Since BAP is an NP-hard problem and cannot be solved by mathematical approaches in a reasonable time, a metaheuristic approach, namely, a cuckoo search algorithm (CSA), is proposed in this study to solve the BAP. To validate the performance of the proposed CSA-based method, we use two benchmark approaches, namely, the genetic algorithm (GA) and the optimal MILP solution. Next, we conduct several experiments using a benchmark data set as well as a randomly-generated larger data set. Simulation results show that the proposed CSA algorithm has higher efficiency in allocating berths within a reasonable computation time than its counterparts. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Berth allocation problem Cuckoo search algorithm Intelligent sea transportation Maritime container terminal Metaheuristic optimization

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Energy leakage in OFDM sparse channel estimation:The drawback of OMP and the application of image deblurring

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Digital Communications and Networks 2024年第5期10卷 1280-1288页

作者： Gang Qiao Xizhu Qiang Lei Wan Hanbo Jia Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Underwater Acoustic Communication and Marine Information Technology(Xiamen University) Ministry of EducationXiamen 361005China

In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting ***,we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method,Orthogonal Matching Pursuit(OMP).The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval,which prevents OMP-based methods from achieving better *** overcome this drawback,the image deblurring-based channel estimation method,in which the channel estimation problem is analogized to one-dimensional image deblurring,was proposed to improve the large compensation distance of traditional *** advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

关键词： Sparse channel estimation Orthogonal matching pursuit(OMP) Orthogonal frequency division multiplexing (OFDM) Linear fitting Image deblurring

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Development of an e-governance system to support cooperative environmental policy actions

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Environment Systems and Decisions 2024年第4期44卷 763-778页

作者： Princz-Jakovics, Tibor Szemenyei, Márton Department of Environmental Economics and Sustainability Faculty of Economic and Social Sciences Budapest University of Technology and Economics Műegyetem rkp. 3. Budapest 1111 Hungary Department of Control Engineering and Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. Budapest 1111 Hungary

The quality of the digital infrastructure supporting decisions is an important factor in the preparation phase of any environmental policy action, and this quality can greatly reduce the level of transaction costs. However, the development of a solution for Coaseian bargaining raises several unsolved technical problems to be able to efficiently decrease external costs. Here, we show the recommended structure of an information and communication technology (ICT)-based software infrastructure that aims to guide relevant stakeholders to achieve environmental deals and enhance green innovations. After reviewing the existing methods, practical solutions, and economics of transaction costs, we selected the design process of a new platform. We identified the functionality modules of the ICT infrastructure with descriptions of the features. Our results demonstrated that the establishment of an ICT-based e-governance system featuring participatory modeling could efficiently reduce transaction costs based on the widely used typology of them. We found that our platform could improve the involvement of any group of stakeholders through collaboration. Furthermore, we elaborate on the assessment of risks and evaluate the potential to avoid serious risks during the implementation or operation of the system. We anticipate our paper to be an initial step in the implementation of an innovative platform for the interested parties of environmental policy actions. © The Author(s) 2024.

关键词： Decision-making E-governance Environmental policy Green innovations Information and communication technology (ICT) Transaction costs

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Text Clustering of Tafseer Translations by Using k-means Algorithm: An Al-Baqarah Chapter View

Annals of Emerging Technologies in Computing

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Annals of Emerging Technologies in Computing 2023年第4期7卷 27-34页

作者： Ahmed, Mohammed A. Baharin, Hanif Nohuddin, Puteri N. E. Institute of Visual Informatics Universiti Kebangsaan Malaysia Malaysia Network Engineering Department College of Engineering Al-Iraqia University Baghdad10053 Iraq Higher Colleges of Technology United Arab Emirates

Al-Quran is Muslims’ main book of belief and behaviour. The Al-Quran is used as a reference book by millions of Muslims worldwide, and as such, it is useful for Muslims in general and Muslim academics to gain knowledge from it. Many translators have worked on the Quran’s translation into many different languages around the world, including English. Thus, every translator has his/her own perspectives, statements, and opinions when translating verses acquired from the (Tafseer) of the Quran. However, this work aims to cluster these variations among translations of the Tafseer by utilising text clustering. As a part of the text mining approach, text clustering includes clustering documents according to how similar they are. This study adapted the (k-means) clustering technique algorithm (unsupervised learning) to illustrate and discover the relationships between keywords called features or concepts for five different translators on the 286 verses of the Al-Baqarah chapter. The datasets have been preprocessed, and features extracted by applying TF-IDF (Term Frequency-Inverse Document Frequency). The findings show two/three-dimensional clustering plotting for the first two/three most frequent features assigned to seven cluster categories (k = 7) for each of five translated Tafseer. The features ‘allah/god’, ‘believ’, and ‘said’ are the three most features shared by the five Tafseer. © 2023 by the author(s).

关键词： Cluster analysis

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Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques 3

Comparative analysis of Start-up Success Rate Prediction Usi...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Belgaum, Mohammad Riyaz Viswanath, Ediga Kasi Chakradhar, Pavukolla Goud, Ediga Chenna Kesava Chandu, Kuruva G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350391565

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success. © 2024 IEEE.

关键词： Ada (programming language)

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Matrix Based Approach to Structural and Semantic Analysis Supporting Software Product Line Evolution 10

Matrix Based Approach to Structural and Semantic Analysis Su...

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10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Perdek, Jakub Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia

The evolution of product families is complex due to the exponential growth of new products with a modified codebase. It is caused by the introduction of new such important or restrictive features that allow transforming an existing solution into a new product as the basis for another one. Specifically, both the structure of resulting components and semantic information need to be taken into account to provide in-depth supporting materials by capturing feature interactions and their capabilities. Additionally, data from heterogeneous applications usually differ and their handling needs account for different scoring. We tackled these problems by creating various supporting views based on structural and semantic information. Related applications are modeled as graphs, also instances of their components are optionally included, and various matrix based algorithms based on similarity metrics are integrated. The final integrated and automated approach is efficient because it runs in polynomial time, is extensively focused on dependencies/connections between nodes, can be adapted to big data, and is extendable and enhanced to support different metrics. Its capability to organize analyzed parts into hierarchies by applying hierarchic clustering helps to specify the context to support comprehension or find a related position of features based on their interaction, especially their coupling. With regards to possible design issues, each updated product should be checked by an expert or more autonomously against performed predictions, especially its complexity and coupling between components. Additionally, extendability can be measured from the chosen sequence of such updates. An approach including an automated matrix based feature recognition process is presented in the analysis of modular Angular applications. Our future work will be more focused on semantic enhancements, and its applications on big data by generating and analyzing various types of fractals including extracte

关键词： feature modeling feature trees graph comparison hierarchical clustering matrix clustering

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Putra, Qori Alfatah Sari, Christy Atika Rachmawanto, Eko Hari Cahyo, Nur Ryan Dwi Mulyanto, Edy Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers. © 2023 IEEE.

关键词： Image segmentation

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