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SSRN 2022年

作者： Sedik, Ahmed El-Hag, Noha A. El-Hoseny, Heba M. El-Banby, Ghada M. Khalaf, Ashraf A.M. Abd El-Samie, Fathi E. El-Shafai, Walid Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Egypt Menoufia University Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Deptartment of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified for anomaly *** MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, braintumors are represented as areas composed of abnormal cells that need to be detected. In addition, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

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Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin

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Arabian Journal of Geosciences 2022年第10期15卷 1-14页

作者： Elbeltagi, Ahmed Zerouali, Bilel Bailek, Nadjem Bouchouicha, Kada Pande, Chaitanya Santos, Celso Augusto Guimarães Towfiqul Islam, Abueza Reza Md. Al-Ansari, Nadhir El-kenawy, El-Sayed M. Department of Agricultural Engineering Faculty of Agriculture Mansoura University Mansoura Egypt Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Chlef Algeria Energies and Materials Research Laboratory Department of Matter Sciences Faculty of Sciences and Technology University of Tamanrasset Tamanrasset Algeria Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS) Centre de Développement des Energies Renouvelables (CDER) Adrar Algeria Department of Geology Sant Gadge Baba Amravati University Amravati India Department of Civil and Environmental Engineering Federal University of Paraíba João Pessoa Brazil Department of Disaster Management Begum Rokeya University Rangpur Bangladesh Department of Civil Environmental and Natural Resources Engineering Lulea University of Technology Lulea Sweden Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt

Predicting rainfall amount is essential in water resources planning and for managing structures, especially those against floods and long-term drought establishment. Machine learning techniques can produce good results using a minimum dataset requirement, making it a leader among the prediction algorithms. This work develops a hybrid learning model for monthly rainfall prediction at four geographical locations representing Mediterranean basins in Northern Algeria and desert areas in Egypt. The study proposes an adaptive dynamic-based hyperparameter optimization algorithm to improve the accuracy of hybrid deep learning models. The proposed model provided a good fit, based on the obtained Nash-Sutcliffe efficiency index (NSE ≈ 0.90) with a high correlation coefficient of R ≈ 0.96, providing improvements of up to 62% in the RMSE. The proposed method proved to be an encouraging and promising tool to simulate water cycle components for better water resources management and protection.

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5g Enhanced and Sustainable Power Distribution Network Design Incorporating Renewable Energy Generation and Dynamic Energy Pricing

SSRN

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SSRN 2022年

作者： Tsao, Yu-Chung Beyene, Tsehaye Dedimas Gebeyehu, Sisay G. Kuo, Tsai-Chi Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Artificial Intelligence for Operations Management Research Center National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration Asia University Taichung Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia

Power distribution network design optimization is the principal concern for power companies. To address both environmental issues and increased energy demand, the need to obtain energy from distributed renewable energy resources is increasing. This study integrates 5G with a power distribution network design. The design results include the interconnection of the power company with the distributed renewable energy resource sites and its customers. The 5G enhanced power distribution network design model addresses the uncertainties related to power generation and customers’ energy demand to maximize company profits. The model principally determines the energy generation capacity of the distributed renewable energy resource units, dynamic pricing, and the level of 5G implementation. The numerical analysis shows that the company earns an optimal profit of $1.6E+07 with the degree of 5G implementation being 63.1% for to enhance the sustainable power distribution network design. © 2022, The Authors. All rights reserved.

关键词： Profitability

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Measuring poverty in India with machine learning and remote sensing

arXiv

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arXiv 2021年

作者： Daoud, Adel Jordan, Felipe Sharma, Makkunda Johansson, Fredrik Dubhashi, Devdatt Paul, Sourabh Banerjee, Subhashis Institute for Analytical Sociology Department of Management and Engineering Linköping University Norrköping Sweden The Division of Data Science and Artificial Intelligence The Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden UC Santa Barbara Santa BarbaraCA United States Wadhwani AI Computer Science Indian Institute of Technology Delhi India Humanities & Social Sciences Indian Institute of Technology Delhi India Computer Science and Engineering Indian Institute of Technology Delhi India

In this paper, we use deep learning to estimate living conditions in India. We use both census and surveys to train the models. Our procedure achieves comparable results to those found in the literature, but for a wid... 详细信息

关键词： Remote sensing

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EXPLAINABLE artificial intelligence (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque artificial intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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Green biogenic fabrication of AgNPs derived from Pulicaria jaubertii leaf extract: Investigation of antimicrobial and cytotoxic properties

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OpenNano 2025年 24卷

作者： Ansam Wadia Alshameri Bilal Abdualgalil Mahdi Mutahar Medical Sciences College Medical Laboratories Department ALjanad University Of Science and Technology Taiz City Yemen Applied Science College Biology Sciences Department Thamar University Yemen Artificial Intelligence and Data Science Department Faculty of Engineering and Information Technology Al janad University of Science and Technology Taiz Yemen School of Dental Health and Care Professions William Beatty Building University of Portsmouth Hampshire Terrace Portsmouth PO1 2QG United Kingdom

"Plant-mediated synthesis of nanomaterials offers an eco-friendly and cost-effective alternative for biomedical applications. In this study, silver nanoparticles (AgNPs) were green-synthesized using a leaf extract of Pulicaria jaubertii , which served as both a reducing and stabilizing agent. The biophysical characterization of synthesized AgNPs confirmed their successful fabrication through Ultraviolet-Visible spectroscopy, Dynamic Light Scattering, Fourier Transform Infrared spectroscopy, Transmission Electron Microscopy, and Scanning Electron Microscope- Energy Dispersive X-ray Analysis. The synthesized AgNPs demonstrated potent antibacterial activity against gram-negative ( Escherichia coli ) and gram-positive ( Staphylococcus aureus ) pathogens, as evidenced by agar well diffusion, time-kill, and antibiofilm assays. Notably, the nanoparticles exhibited minimal cytotoxicity toward Peripheral Blood Mononuclear Cells and Human Red Blood Cells, underscoring their biocompatibility. These findings highlight the potential of Pulicaria jaubertii -derived AgNPs as promising candidates for antimicrobial therapies and biomedical applications."

关键词： Silver nanoparticles Green synthesis Cytotoxic Microbes Antimicrobial

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Comparison of K-means and K-medoids algorithm in grouping dengue fever patient data (Case study: Kaliasin health center)

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AIP Conference Proceedings 2023年第1期2536卷

作者： Media Cahyo Untoro Nuranisda Triawati Yutika Amelia Effendi Holina Natalia M. Syamsuddin Wisnubroto Teknik Informatika Institut Teknologi Sumatera Lampung Indonesia Robotics and Artificial Intelligence Engineering Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya Indonesia Sains Data Institut Teknologi Sumatera Lampung Indonesia

Dengue hemorrhagic fever (DHF) is an infectious disease caused by the dengue virus. This disease mostly affects children and adults, and also the number of sufferers and the extent of this disease are increasing. In grouping patient data based on the parameters of the symptoms of dengue fever, we will use the clustering method. In this study, a comparison of two clustering algorithms, namely K-Means and K-Medoids, uses a distance measure, namely Manhattan Distance, in both algorithms to determine the most optimal algorithm for grouping dengue patient data. The results of clustering carried out with a total of 3 k in the K-means clustering algorithm have members, namely in cluster 0 there are 32 members, cluster 1 there are 15 members, and cluster 2 there are 26 members. While the K-Medoids clustering algorithm has members, namely in cluster 0 there are 46 members, cluster 1 there are 24 members, and cluster 2 there are 3 members. Based on the results of the cluster obtained, the K-Medoids algorithm has a placement error in the data to 27, 34, 48, and 70 to the right cluster. While in the K-Means algorithm there is no error in the placement of data in the cluster. The results of the cluster analysis performed based on the Silhouette Coefficient value on the dengue patient data resulted that the K-Means algorithm was the optimal clustering method with the highest Silhouette Coefficient value of 0.25362003217634904 compared to the K-Medoids method of 0.1821645521976126. So, it can be concluded that the clustering method with the K-Means algorithm has a good cluster quality compared to the K-Medoids algorithm.

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Combining Hammett σ constants for ∆-machine learning and catalyst discovery

arXiv

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arXiv 2024年

作者： Rakotonirina, V. Diana Bragato, Marco Heinen, Stefan von Lilienfeld, O. Anatole Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Faculty of Physics University of Vienna Kolingasse 1416 WienAT1090 Austria Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada ML Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Physics University of Toronto St. George Campus TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada

We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning σ and ρ to ligands and metals, respectively. Implementing an additive combination (c) rule for obtaining σ constants for any ligand pair combination results in a cHIP model that enhances data efficiency in computational ligand tuning. We show its usefulness (i) as a baseline for ∆-machine learning (ML), and (ii) to identify novel catalyst candidates via volcano plots. After testing the combination rule on Hammett constants previously published in the literature, we have generated numerical evidence for the Suzuki-Miyaura (SM) C-C cross-coupling reaction using two synthetic datasets of metallic catalysts (including (10) and (11)-metals Ni, Pd, Pt, and Cu, Ag, Au as well as 96 ligands such as N-heterocyclic carbenes, phosphines, or pyridines). When used as a baseline, ∆-ML prediction errors of relative binding decrease systematically with training set size and reach chemical accuracy (∼1 kcal/mol) for 20k training instances. Employing the individual ligand constants obtained from cHIP, we report relative substrate binding for a novel dataset consisting of 720 catalysts (not part of training data), of which 145 fall into the most promising range on the volcano plot accounting for oxidative addition, transmetalation, and reductive elimination steps. Multiple Ni-based catalysts, e.g. Aphos-Ni-P(t-Bu)3, are included among these promising candidates, potentially offering dramatic cost savings in experimental applications. © 2024, CC BY.

关键词： Ligands

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Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning

arXiv

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arXiv 2022年

作者： Li, Yun Liu, Zhe Chang, Xiaojun McAuley, Julian Yao, Lina The School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China The School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The School of Computer Science and Engineering University of California San Diego San DiegoCA United States CSIRO’s Data61 University of New South Wales SydneyNSW2052 Australia

Zero-Shot Learning (ZSL) aims to transfer classification capability from seen to unseen classes. Recent methods have proved that generalization and specialization are two essential abilities to achieve good performance in ZSL. However, focusing on only one of the abilities may result in models that are either too general with degraded classification ability or too specialized to generalize to unseen classes. In this paper, we propose an end-to-end network, termed as BGSNet, which equips and balances generalization and specialization abilities at the instance and dataset level. Specifically, BGSNet consists of two branches: the Generalization Network (GNet), which applies episodic meta-learning to learn generalized knowledge, and the Balanced Specialization Network (BSNet), which adopts multiple attentive extractors to extract discriminative features and achieve instance-level balance. A novel self-adjusted diversity loss is designed to optimize BSNet with redundancy reduced and diversity boosted. We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved. Experiments on four benchmark datasets demonstrate our model’s effectiveness. Sufficient component ablations prove the necessity of integrating and balancing generalization and specialization abilities. Copyright © 2022, The Authors. All rights reserved.

关键词： Zero-shot learning

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Training superbot with learning automata and multi kernel SVM

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AIP Conference Proceedings 2023年第1期2782卷

作者： G. Revathy E. Gurumoorthi C. Sasikala T. M. Latha School of Computing SASTRA Deemed University Thanjavur Tamilnadu India Department of IT CMR College of Engineering and Technology Hyderabad Telangana India Department of Computer science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science M.A.M College of Engineering Trichy India

Websites are increasingly being used for a variety of applications, including education, healthcare, government, semi-government, public sector units, and others. The employment of a chat bot incorporated in each website assists visitors who are unfamiliar with the site. It even receives their enquiry, records the message, and a customer service representative will respond to the individuals. The existing process wastes a lot of time. As a result, a revolutionary approach of introducing Superbot termed “HANA,” which has already been trained using Learning Automata and Multikernel SVM, will immediately take the user to the appropriate response and assist in the resolution of inquiries. The produced Superbot “HANA” is swift in adaptive learning with the help of automata, and its usage is transparent thanks to computational intelligence. When compared to prior Bots, it provides the best solution of more than 80%.

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