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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

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Scientific Reports 2025年第1期15卷 1-34页

作者： Lee, Sangkeum Almomani, Mohammad H. Alomari, Saleh Ali Saleem, Kashif Smerat, Aseel Snasel, Vaclav Gandomi, Amir H. Abualigah, Laith Department of Computer Engineering Hanbat National University 125 Dongseo-daero Yuseong-gu Daejeon South Korea Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa 13133 Jordan Faculty of Science and Information Technology Jadara University Irbid 21110 Jordan Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh 11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Faculty of Engineering and Information Technology University of Technology Sydney Ultimo 2007 NSW Australia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq University Research and Innovation Center (EKIK) Óbuda University Budapest 1034 Hungary Department of Computer Science Khazar University Baku Azerbaijan

Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets—NREL WIND, EMD WIND, WWSIS, and ERCOT GRID—and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil’s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications. © The Author(s) 2025.

关键词： Adaptive environmental response mechanism Artificial Protozoa optimizer Hybrid deep learning Renewable energy integration Time series prediction Wind power forecasting

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Automating app review response generation

arXiv

引用

arXiv 2020年

作者： Gao, Cuiyun Zeng, Jichuan Xia, Xin Lo, David Lyu, Michael R. King, Irwin Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong Faculty of Information Technology Monash University Australia School of Information Systems Singapore Management University Singapore

Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews. Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses. Copyright © 2020, The Authors. All rights reserved.

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Correction: Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone

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Neural Computing and Applications 2024年第21期36卷 13373-13373页

作者： Massaoudi, Ayman Echouchene, Fraj Ben Ayed, Mossaad Berguiga, Abdelwahed Harchay, Ahlem Al-Ghamdi, Sara Belmabrouk, Hafedh Department of Computer Science College of Science and Arts in Gurayat Jouf University Sakakah Saudi Arabia Olive Research Center Jouf University Sakakah Saudi Arabia Laboratory of Electronics and Microelectronics Faculty of Science of Monastir University of Monastir Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Department of Industrial Electronics ENISo University of Sousse Sousse Tunisia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Physics College of Science Majmaah University Al Majma’ah Saudi Arabia

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Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

arXiv

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arXiv 2021年

作者： Nápoles, Gonzalo Salgueiro, Yamisleydi Grau, Isel Espinosa, Maikel Leon The Department of Cognitive Science & Artificial Intelligence Tilburg University Netherlands The Department of Computer Science Faculty of Engineering Universidad de Talca Campus Curicó Chile The Information Systems Group Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands The Department of Business Technology Miami Herbert Business School University of Miami United States

Machine learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this paper, we propose a Long-Term Cognitive Network for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that our interpretable model obtains competitive results when compared to state-of-the-art white and black-box models. © 2021, CC BY-NC-ND.

关键词： Recurrent neural networks

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Proposing a Service Quality Framework for Mobile Commerce 2nd

Proposing a Service Quality Framework for Mobile Commerce

引用

2nd International Conference on Emerging Technologies in Computing, iCETiC 2019

作者： Desmal, Abdulla Jaafar Othman, Mohd Khalit Bin Hamid, Suraya Binti Zolait, Ali Hussein Kassim, Norliya Binti Ahmad Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Information Systems University of Malaya Kuala Lumpur Malaysia College of Information Technology University of Bahrain Sakheer Bahrain Shah Alam Malaysia

ISBN: (纸本)9783030239428

Customer satisfaction influences the profitability of organizations and can keep competitive advantages. One of the critical factors in customer satisfaction is the availability of a quality scale that measures the service. The service quality aims to ensure that the service delivered meets customer expectations. However, with the popularity of using mobile devices, there are many electronic businesses shifted to mobile platforms. Mobile platforms have unique features that differ from Personal computers, such as mobility, portable, wireless. Mobile business is a category of business development refers to new business platforms that enabled by using the technology of wireless and mobile devices. In this case, measuring of service quality of the mobile business is necessary nowadays to ensure the delivered services with the best quality. Due to the lack of a comprehensive framework to evaluate service quality at the mobile business, the business sector uses electronic service quality measurement to evaluate mobile business services, which results in difficulties in identifying accurate results. Using the theoretical base model of offline service quality "SERVQUAL" and the online service quality model "E-S-QUAL," the researchers were able to propose a framework of service quality to evaluate the services provided through mobile commerce. The proposed service quality framework is consisting of six dimensions that are application design, reliability, responsiveness, trust, efficiency, and system availability. The proposed service quality framework helps business providers for better development of business strategies and leads for best customers’ expectations due to the compatibility of the proposed model with the unique features of mobile devices with considerations of the environment of business sectors. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Customer satisfaction

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Geometric Brownian motion under stochastic resetting: A stationary yet nonergodic process

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Physical Review E 2021年第1期104卷 014121-014121页

作者： Viktor Stojkoski Trifce Sandev Ljupco Kocarev Arnab Pal Faculty of Economics Ss. Cyril and Methodius University 1000 Skopje Macedonia Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 1000 Skopje Macedonia Institute of Physics and Astronomy University of Potsdam D-14776 Potsdam-Golm Germany Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 1000 Skopje Macedonia Faculty of Computer Science and Engineering Ss. Cyril and Methodius University P.O. Box 393 1000 Skopje Macedonia School of Chemistry The Center for Physics and Chemistry of Living Systems Tel Aviv University Tel Aviv 6997801 Israel

We study the effects of stochastic resetting on geometric Brownian motion with drift (GBM), a canonical stochastic multiplicative process for nonstationary and nonergodic dynamics. Resetting is a sudden interruption of a process, which consecutively renews its dynamics. We show that, although resetting renders GBM stationary, the resulting process remains nonergodic. Quite surprisingly, the effect of resetting is pivotal in manifesting the nonergodic behavior. In particular, we observe three different long-time regimes: a quenched state, an unstable state, and a stable annealed state depending on the resetting strength. Notably, in the last regime, the system is self-averaging and thus the sample average will always mimic ergodic behavior establishing a stand-alone feature for GBM under resetting. Crucially, the above-mentioned regimes are well separated by a self-averaging time period which can be minimized by an optimal resetting rate. Our results can be useful to interpret data emanating from stock market collapse or reconstitution of investment portfolios.

关键词： Brownian motion Stochastic processes Ergodic theory

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Optimal Measurement Structures for Contextuality Applications

arXiv

引用

arXiv 2022年

作者： Liu, Yuan Ramanathan, Ravishankar Horodecki, Karol Rosicka, Monika Horodecki, Pawel Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Institute of Informatics National Quantum Information Centre Faculty of Mathematics Physics and Informatics University of Gdańsk Wita Stwosza 57 Gdańsk80-308 Poland International Centre for Theory of Quantum Technologies University of Gdańsk Wita Stwosza 63 Gdańsk80-308 Poland Faculty of Applied Physics and Mathematics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland

The Kochen-Specker (KS) theorem is a corner-stone result in the foundations of quantum mechanics describing the fundamental difference between quantum theory and classical non-contextual theories. Recently specific substructures termed 01-gadgets were shown to exist within KS proofs that capture the essential contradiction of the theorem. Here, we show these gadgets and their generalizations provide an optimal toolbox for contextuality applications including (i) constructing classical channels exhibiting entanglement-assisted advantage in zero-error communication, (ii) identifying large separations between quantum theory and binary generalised probabilistic theories, and (iii) finding optimal tests for contextuality-based semi-device-independent randomness generation. Furthermore, we introduce and study a generalisation to definite prediction sets for more general logical propositions, that we term higher-order gadgets. We pinpoint the role these higher-order gadgets play in KS proofs by identifying these as induced subgraphs within KS graphs and showing how to construct proofs of state-independent contextuality using higher-order gadgets as building blocks. The constructions developed here may help in solving some of the remaining open problems regarding minimal proofs of the Kochen-Specker theorem. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum entanglement

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Semantic-Preserving Adversarial Text Attacks

TechRxiv

引用

TechRxiv 2021年

作者： Yang, Xinghao Gong, Yongshun Liu, Weifeng Bailey, James Zhu, Tianqing Tao, Dacheng Liu, Wei The School of Computer Science University of Technology Sydney 15 Broadway UltimoNSW2007 Australia The School of Software Shandong University Jinan China Qingdao266580 China The Department of Computing and Information Systems University of Melbourne Australia The JD Explore Academy China The Digital Science Institute Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Deep learning models are known immensely brittle to adversarial image examples, yet their vulnerability in text classification is insufficiently explored. Existing text adversarial attack strategies can be roughly divided into three categories, i.e., character-level attack, word-level attack, and sentence-level attack. Despite the success brought by recent text attack methods, how to induce misclassification with the minimal text modifications while keeping the lexical correctness, syntactic soundness, and semantic consistency simultaneously is still a challenge. To examine the vulnerability of deep models, we devise a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences. We also present a hybrid attack strategy that collects substitution words from both synonyms and sememe candidates, to enrich the potential candidate set. Besides, a Semantic Preservation Optimization (SPO) method is devised to determine the word substitution priority and reduce the perturbation cost. Furthermore, we constraint the SPO with a semantic Filter (dubbed SPOF) to improve the semantic similarity between the input text and the adversarial example. To estimate the effectiveness of our proposed methods, BU-SPO and BU-SPOF, we attack four victim deep learning models trained on three real-world text datasets. Experimental results demonstrate that our approaches accomplish the highest semantics consistency and attack success rates by making the minimal word modifications compared with competitive methods. © 2021, CC BY.

关键词： Semantics

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A probability-based fuzzy algorithm for multi-attribute decision-analysis with application to aviation disaster decision-making

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Decision Analytics Journal 2023年 8卷

作者： Agrawal, Anurag Vijay Soni, Mukesh Keshta, Ismail Savithri, V. Abdinabievna, Pardaeva Shakhnoza Singh, Shweta Department of Electronics and Communication Engineering J.P. Institute of Engineering and Technology Uttar Pradesh Meerut 250001 India Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali 140413 India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of science and humanities/Mathematics Karpagam Academy of Higher education (Deemed to be University) Eachanari Tamil Nadu Coimbatore 641021 India The department of Bank Accounting and Audit Tashkent Institute of Finance Tashkent Uzbekistan Electronics and communication department IES college of technology Bhopal India

This research addresses the challenges of multi-attribute group decision-making in a hesitant fuzzy environment, where randomness and uncertainty introduce biases into practical making choices. A probability-based double uncertain fuzzy (PDHF) algorithm is proposed to overcome these challenges. The algorithm constructs probability-based double hesitant fuzzy information matrices for each decision expert. The maximum deviation and entropy methods determine objective weights for decision experts and attribute indicators. Combining an improved scoring function and deviation function generates a comprehensive decision evaluation information matrix for decision experts. Subsequently, a decision algorithm that integrates probability-based double hesitant fuzzy sets with the preference ranking organization method (PROM) is employed to obtain the final decision result. As part of a case study, the algorithm is used to evaluate emergency action plans for aviation disaster accidents. Comparing the results with those from the TOPSIS, VIKOR, and PDHFS decision-making algorithms shows that the suggested PDHF algorithm works well and can be trusted for the multi-attribute group decision-making problems in hesitant fuzzy environments. © 2023 The Author(s)

关键词： Aviation disaster accidents Entropy method Hesitant fuzzy environment Making choices Maximum deviation method Multi-attribute Probability-based

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Public Goods Games in Disease Evolution and Spread

arXiv

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arXiv 2024年

作者： Morison, Christo Fic, Mallgorzata Marcou, Thomas Mohamadichamgavi, Javad Antón, Javier Redondo Sayyar, Golsa Stein, Alexander Bastian, Frank Krakovská, Hana Krishnan, Nandakishor Pires, Diogo L. Satouri, Mohammadreza Thomsen, Frederik J. Tjikundi, Kausutua Ali, Wajid School of Mathematical Sciences Queen Mary University of London London United Kingdom Department of Theoretical Biology Max Planck Institute for Evolutionary Biology Plön Germany Center for Computational and Theoretical Biology University of Würzburg Würzburg Germany Centre for Mathematical Biology University of South Bohemia České Budějovice Czech Republic Institute of Applied Mathematics and Mechanics University of Warsaw Warsaw Poland Department of Computer Science University of Turin Turin Italy Bolyai Institute University of Szeged Szeged Hungary Evolutionary Dynamics Group Centre for Cancer Genomics and Computational Biology Barts Cancer Institute Queen Mary University of London London United Kingdom School of Mathematical Sciences University College Cork Cork Ireland Section for Science of Complex Systems Medical University of Vienna Vienna Austria Complexity Science Hub Vienna Vienna Austria HUN-REN Centre for Ecological Research Institute of Evolution Budapest Hungary Department of Mathematics City University of London London United Kingdom Institute for Health Systems Science Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands Institute of Applied Mathematics Delft University of Technology Delft Netherlands ISI Foundation Turin Italy Department of Mathematical Sciences University of Liverpool Liverpool United Kingdom

Cooperation arises in nature at every scale, from within cells to entire ecosystems. In the framework of evolutionary game theory, public goods games (PGGs) are used to analyse scenarios where individuals can cooperate or defect, and can predict when and how these behaviours emerge. However, too few examples motivate the transferal of knowledge from one application of PGGs to another. Here, we focus on PGGs arising in disease modelling of cancer evolution and the spread of infectious diseases. We use these two systems as case studies for the development of the theory and applications of PGGs, which we succinctly review and compare. We also posit that applications of evolutionary game theory to decision-making in cancer, such as interactions between a clinician and a tumour, can learn from the PGGs studied in epidemiology, where cooperative behaviours such as quarantine and vaccination compliance have been more thoroughly investigated. Furthermore, instances of cellular-level cooperation observed in cancers point to a corresponding area of potential interest for modellers of other diseases, be they viral, bacterial or otherwise. We aim to demonstrate the breadth of applicability of PGGs in disease modelling while providing a starting point for those interested in quantifying cooperation arising in healthcare. © 2024, CC BY.

关键词： Diseases

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