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arXiv 2022年

作者： Amir, Ora Amir, Amihood Fraenkel, Aviezri Sarne, David NVIDIA United States Department of Computer Science Bar-Ilan University Ramat-Gan52900 Israel College of Computing Georgia Tech AtlantaGA30332 United States Dept of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel

The classical pattern matching paradigm is that of seeking occurrences of one string - the pattern, in another - the text, where both strings are drawn from an alphabet set Σ. Assuming the text length is n and the pattern length is m, this problem can naively be solved in time O(nm). In Knuth, Morris and Pratt’s seminal paper of 1977, an automaton, was developed that allows solving this problem in time O(n) for any alphabet. This automaton, which we will refer to as the KMP-automaton, has proven useful in solving many other problems. A notable example is the parameterized pattern matching model. In this model, a consistent renaming of symbols from Σ is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been suspected that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper we examine the practical efficiency of the KMP algorithm vs. the naive algorithm on a randomly generated text. We analyse the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts, and observe a similar behaviour as in the random text. We also show a very structured case where the automaton is much more efficient. © 2022, CC BY-NC-ND.

关键词： Pattern matching

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Kilometer-Scale E3SM Land Model Simulation over North America

arXiv

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arXiv 2025年

作者： Wang, Dali Wang, Chen Cao, Qinglei Schwartz, Peter Yuan, Fengming Krishna, Jayesh Wu, Danqing Ricciuto, Danial Thornton, Peter Kao, Shih-Chieh Thornton, Michele Mohror, Kathryn Environmental Sciences Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Department of Computer Science Saint Louis University Saint LouisMO United States Mathematics and Computer Sciences Argonne National Laboratory ArgonneIL United States

The development of a kilometer-scale E3SM Land Model (km-scale ELM) is an integral part of the E3SM project, which seeks to advance energy-related Earth system science research with state-of-the-art modeling and simulation capabilities on exascale computing systems. Through the utilization of high-fidelity data products, such as atmospheric forcing and soil properties, the km-scale ELM plays a critical role in accurately modeling geographical characteristics and extreme weather occurrences. The model is vital for enhancing our comprehension and prediction of climate patterns, as well as their effects on ecosystems and human activities. This study showcases the first set of full-capability, km-scale ELM simulations over various computational domains, including simulations encompassing 21.6 million land gridcells, reflecting approximately 21.5 million square kilometers of North America at a 1 km × 1 km resolution. We present the largest km-scale ELM simulation using up to 100,800 CPU cores across 2,400 nodes. This continental-scale simulation is 300 times larger than any previous studies, and the computational resources used are about 400 times larger than those used in prior efforts. Both strong and weak scaling tests have been conducted, revealing exceptional performance efficiency and resource utilization. The km-scale ELM uses the common E3SM modeling infrastructure and a general data toolkit known as KiloCraft. Consequently, it can be readily adapted for both fully-coupled E3SM simulations and data-driven simulations over specific areas, ranging from a single gridcell to the entire North America. © 2025, CC BY.

关键词： Digital elevation model

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Selection of Electrical Distribution System using SVAS based Einstein Averaging Operator

Selection of Electrical Distribution System using SVAS based...

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： B.P. Joshi Shweta Sachdeva Sangeeta Pant Shweta Goyal Anuj Kumar Pushpendra Singh Kharayat Department of Mathematics Department of Computer Science & Enggineering Graphic Era Hill University Bhimtal India School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Pune Maharashtra Department of Electrical Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engg. & Applications D. Y. Patil International University (DYPIU) Akrudi Pune Maharashtra

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The contribution of this study is to present the Einstein Weighted Averaging Operator (EWAO) based on the Einstein Sum (ES) under the Single-Valued Ambiguous Set (SVAS) information, which is termed as “SVASEA”. The SVAS theory (SVAST) was offered to address ambiguity as the generalization of fuzzy set (FS). It is powerful than the FS and Intuitionistic FS due to four different degrees of memberships. Then, to apply the SVAS $_{\text {ea }}$ operator to real life problems, a framework is presented to include it in the information fusion of decision making (DM) process. Moreover, the selection of Electrical Distribution System (EDS) is carried out using the proposed operator and proposed framework to show its applicability. The obtained results and comparative analysis show the feasibility and validity of the SVAS $_{\text {EA }}$ operator.

关键词： Fuzzy sets Decision making

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Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction

Development of Interpretable Machine Learning Models for COV...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqi Shi Mio Murakoso Xiaoyan Guo May D. Wang Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Life Science Kyoto University Kyoto Japan College of Computing Georgia Institute of Technology Atlanta USA Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta USA

With the extensive time and financial requirements incumbent on drug discovery, computational approaches, such as protein-ligand docking predictions, are increasingly crucial for accelerating the process of drug repurposing. However, the proliferation of identified protein targets has exposed a critical knowledge gap in developing robust models that offer both generalizability and interpretability for docking score prediction. Addressing this, our study presents a machine learning-based surrogate model, employing interpretable artificial intelligence techniques for accurate docking score prediction for SARS-CoV-2 protein targets. We demonstrate the model generalization on its expansion to accommodate unseen protein targets by integrating protein target information through feature concatenation. Moreover, we leverage the SHapley Additive exPlanations (SHAP) method to identify the data-driven feature importance of molecular substructures for knowledge-based validation. Our experiments reveal that the combination of data-driven prediction and knowledge-driven validation could provide biomedical insights into the interactions between drugs and SARS-CoV-2 proteins, elucidating their consequent effects on docking scores.

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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Sports Prediction Model through Cloud computing and Big Data Based on Artificial Intelligence Method

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Journal of Intelligent Learning Systems and Applications 2024年第2期16卷 53-79页

作者： Aws I. Abu Eid Achraf Ben Miled Ahlem Fatnassi Majid A. Nawaz Ashraf F. A. Mahmoud Faroug A. Abdalla Chams Jabnoun Aida Dhibi Firas M. Allan Mohammed Ahmed Elhossiny Salem Belhaj Imen Ben Mohamed Faculty of Computing Studies Arab Open University Amman Jordan Computer Science Department Science College Northern Border University Arar Kingdom of Saudi Arabia Artificial Intelligence and Data Engineering Laboratory LR21ES23 Faculty of Sciences of Bizerte University of Carthage Tunis Tunisia Applied College Northern Border University Arar Kingdom of Saudi Arabia Faculty of Specific Education Mansoura University Mansoura Egypt

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

关键词： Artificial Intelligence Machine Learning Spark Apache Big Data SAIM

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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Engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image

arXiv

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arXiv 2025年

作者： Huang, Yehong Zhao, Chen Dhakal, Rochak Zhao, Min Hung, Guang-Uei Jiang, Zhixin Zhou, Weihua Department of Computer Science Kennesaw State University MariettaGA United States Department of Applied Computing Michigan Technological University HoughtonMI United States Department of Nuclear Medicine Xiangya Hospital Central South University Changsha China Department of Nuclear Medicine Chang Bing Show Chwan Memorial Hospital Changhua Taiwan Nanjing China Center for Biocomputing and Digital Health Institute of Computing and Cyber-systems Health Research Institute Michigan Technological University HoughtonMI United States

Background and Purpose: Functional assessment of the left ventricle using gated myocardial perfusion (MPS) single-photon emission computed tomography relies on the precise extraction of the left ventricular contours while simultaneously ensuring the security of patient data. Methods: In this paper, we introduce the integration of Federated Domain Adaptation with TimeSformer, named "FedDA-TSformer" for left ventricle segmentation using MPS. FedDA-TSformer captures spatial and temporal features in gated MPS images, leveraging spatial attention, temporal attention, and federated learning for improved domain adaptation while ensuring patient data security. In detail, we employed Divide-Space-Time-Attention mechanism to extract spatio-temporal correlations from the multi-centered MPS datasets, ensuring that predictions are spatio-temporally consistent. To achieve domain adaptation, we align the model output on MPS from three different centers using local maximum mean discrepancy (LMMD) loss. This approach effectively addresses the dual requirements of federated learning and domain adaptation, enhancing the model's performance during training with multi-site datasets while ensuring the protection of data from different hospitals. Results: Our FedDA-TSformer was trained and evaluated using MPS datasets collected from three hospitals, comprising a total of 150 subjects. Each subject's cardiac cycle was divided into eight gates. The model achieved Dice Similarity Coefficients (DSC) of 0.842 and 0.907 for left ventricular (LV) endocardium and epicardium segmentation, respectively. Conclusion: Our proposed FedDA-TSformer model addresses the challenge of multi-center generalization, ensures patient data privacy protection, and demonstrates effectiveness in left ventricular (LV) segmentation. © 2025, CC BY.

关键词： Single photon emission computed tomography

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Movie recommendation system using clustering mining with Python 1

Movie recommendation system using clustering mining with Pyt...

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1st International Conference on Advances in Computational science and Engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Cera, Juan Manuel Deepa, R. Satheshkumar, K. Muthukumaran, V. Satheesh Kumar, S. Angeline Kavitha, M. Vellore Institute of Technology Vellore India Universudad de la Costa Barranquilla Colombia Telangana Hyderabad India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Department of Mathematics School of Applied Science REVA University Bengaluru64 India Department of Computer Science School of Applied Science REVA University Bengaluru64 India Voorhees College Tamilnadu Vellore India

The research we are pursuing here is about building a system which we will use and mine a movie review database using concepts of statistics like Euclidean Distance algorithm. This will help us determine similar review according to the ratings. What we plan on achieving through this approach is to analyze the reviews to suggest a suitable critic according to one's preferences and one's taste. We will do this according to the review we select and our algorithm which we use upon it. This algorithm will be the key to find those similar reviews. We are going to use Python programming to program this experiment. We have chosen to pursue this because of not the reviewers being incorrect or correct but merely different according to their preferences and their lean towards a certain genre and we know that a reviewer should also match the taste of the person who want to feel benefited of the review itself. So, we plan to make system that could help with that. © Published under licence by IOP Publishing Ltd.

关键词： High level languages

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Daily rainfall modeling using Neural Network 28

Daily rainfall modeling using Neural Network

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28th Simposium Kebangsaan Sains Matematik, SKSM 2021

作者： Permai, S.D. Ohyver, M. Aziz, M.K.B.M. Statistics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia

In the early 2020, Indonesia experienced flooding in several areas. This disaster caused a lot of damage and losses. One of the causes of flooding in Indonesia is due to high rainfall. This was not anticipated beforehand so there was a flood. Therefore, research on rainfall in Indonesia is very important to anticipate floods. If it is predicted that rainfall is very high and conditions do not allow it to accommodate, the government can prepare watersheds so that rainwater can flow and not be trapped. In this research, the rainfall data were obtained from Meteorological, Climatological, and Geophysical Agency (BMKG Indonesia), then the analysis of rainfall data in Indonesia was carried out. There are several statistical methods that can be used. There are ARIMA and Neural Network. In this research, the results of ARIMA model are used as input variables in the Neural Network model. Then there are several numbers of hidden layer in the Neural Network model that are compared. The results of ARIMA model and Neural Network model showed that Neural Network model is better than ARIMA model, because the mean square error (MSE) value of Neural Network model is smaller than ARIMA model. © Published under licence by IOP Publishing Ltd.

关键词： Neural network models

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