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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Divya Nimma Mathi Kannaiyan Deepak Gupta Manidipa Roy Mohammed Saleh Al Ansari A. Balakumar PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Mechanical Engineering University College of Kancheepuram Anna University Kancheepuram Campus Kanchipuram Tamil Nadu India Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of ECE ABES Engineering College Ghaziabad Uttar Pradesh India Department of Chemical Engineering College of Engineering University of Bahrain Bahrain Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Predictive maintenance (PdM) is essential to make sure the protection and reliability of the technology. This paper affords a remarkable technique that mixes hybrid deep reinforcement getting to know (DRL) with transformer networks to enhance predictive protection strategies for protection in material safety engineering. In most cases, inefficient and costly implementation strategies are based on predefined guidelines or ad hoc practices in traditional management. A system that integrates DRL and Transformer Networks to quickly identify and predict operational needs and respond effectively to various conditions. Hybrid DRL approach has the capability of non-modeling and reinforcing model-based learning for maintenance a it is best through communication with the system. enabling the study of systems Environmental and historical data. The recognized efficiency of transformer networks in sequential data processing will enhances the ability of the model to process and interpret the time series sensor data, helps to improves the prediction performanceThis method uses dual stage architecture which extract the time based features from sensor data. This approach agrees real-time optimization of emerging patterns and anomalies, This will improves prediction capabilities and reduces false positives. This model was tested on various industrial datasets, indicating significant developments in prediction accuracy and maintenance scheduling efficiency compared to traditional methods. By integrating these advanced techniques, The approach provides a more vigorous and adaptive solution for ensuring product safety and reliability in complex industrial environments. Proposed model achieved a notable reduction in Mean Absolute Error (MAE) 0.15 compared to traditional predictive maintenance methods.

关键词： Adaptation models Time series analysis Predictive models Transformers Feature extraction data models Real-time systems Maintenance Protection Predictive maintenance

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Enhancing Customer Churn Prediction in Telecommunication with CNN-Gradient Boosting Machine

Enhancing Customer Churn Prediction in Telecommunication wit...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Sls Raajavinayaga Subaash Divya Nimma B Kiran Bala Shamim Ahmad Khan Manidipa Roy Vuda Sreenivasa Rao Saveetha School of Law (SIMATS) Chennai India Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of AI & DS K. Ramakrishnan College of Engineering Trichy India Glocal School of Science & Technology Glocal University U.P India Department of ECE ABES Engineering College Ghaziabad Uttar Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP INDIA

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

The telecommunications industry faces a constant challenge in retaining customers amidst fierce competition, making accurate churn prediction a critical aspect of business strategy. In this paper, we propose a novel hybrid model, implemented in Python, that combines Convolutional Neural Networks (CNNs) with Gradient Boosting Machines (GBMs) to enhance customer churn prediction accuracy. Leveraging a comprehensive dataset encompassing demographic details, service usage, payment information, and customer support interactions, our model extracts intricate patterns from the data using CNNs and integrates them with traditional features for robust prediction. Through extensive experimentation and evaluation, we demonstrate that our proposed CNN-GBM model achieves an outstanding accuracy of 99%, surpassing other existing approaches by $\mathbf{2.6 \%}$. The model’s performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-Score, showcasing its superiority over traditional methods like Random Forest and Support Vector Machine. The CNN-GBM model’s remarkable accuracy and superior performance highlight its efficacy in accurately identifying churned customers while minimizing false positives, thus providing a valuable tool for telecom companies to implement targeted retention strategies. Moreover, our methodology offers scalability and generalizability, making it applicable across industries facing similar churn challenges. Overall, our proposed hybrid CNNGBM model presents a significant advancement in customer churn prediction, offering telecom companies a reliable solution to improve customer retention and competitiveness in the market.

关键词： Industries Accuracy Companies Predictive models Boosting Feature extraction Telecommunications Convolutional neural networks Telecommunication network reliability Churn

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INVESTIGATING REPRESENTATION SCHEMES FOR SURROGATE MODELING OF HIGH ENTROPY ALLOYS

arXiv

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arXiv 2022年

作者： Debnath, Arindam Reinhart, Wesley F. Department of Materials Science and Engineering Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Institute for Computational and Data Sciences Pennsylvania State University University ParkPA16802 United States

The design of new High Entropy Alloys that can achieve exceptional mechanical properties is presently of great interest to the materials science community. However, due to the difficulty of designing these alloys using traditional methods, machine learning has recently emerged as an essential tool. Particularly, the screening of candidate alloy compositions using surrogate models has become a mainstay of materials design in recent years. Many of these models use the atomic fractions of the alloying elements as inputs. However, there are many possible representation schemes for encoding alloy compositions, including both unstructured and structured variants. As the input features play a critical role in determining surrogate model performance, we have systematically compared these representation schemes on the basis of their performance in single-task deep learning models and in transfer learning scenarios. The results from these tests indicate that compared to the unstructured and randomly ordered schemes, chemically meaningful arrangements of elements within spatial representation schemes generally lead to better models. However, we also observed that tree-based models using only the atomic fractions as input were able to outperform these models in transfer learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Alloying elements

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MultiCalib4DEB: A toolbox exploiting multimodal optimisation in Dynamic Energy Budget parameters calibration

arXiv

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arXiv 2023年

作者： Robles, Juan Francisco Chica, Manuel Filgueira, Ramón Agüera, Antonio Damas, Sergio Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Benthic resources Norwegian Institute of Marine Research BergenNO-5817 Norway Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Marine Affairs Program Life Sciences Centre Dalhousie University 1459 Oxford Street HalifaxNSB3H 4R2 Canada

1. Calibration is a crucial step for the validation of computational models and a challenging task to accomplish. 2. Dynamic Energy Budget (DEB) theory has experienced an exponential rise in the number of published papers, which in large part has been made possible by the DEBtool toolbox. Multimodal evolutionary optimisation could provide DEBtool with new capabilities, particularly relevant on the provisioning of equally optimal and diverse solutions. 3. In this paper we present MultiCalib4DEB, a MATLAB toolbox directly integrated into the existing DEBtool toolbox, which uses multimodal evolutionary optimisation algorithms to find multiple global and local optimal and diverse calibration solutions for DEB models. 4. MultiCalib4DEB adds powerful calibration mechanisms, statistical analysis, and visualisation methods to the DEBtool toolbox and provides a wide range of outputs, different calibration alternatives, and specific tools to strengthen the DEBtool calibration module and to aid DEBtool users to evaluate the performance of the calibration results. © 2023, CC BY-NC-ND.

关键词： Calibration

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Fixation probability in evolutionary dynamics on switching temporal networks

arXiv

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arXiv 2023年

作者： Bhaumik, Jnanajyoti Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. Research in the past two decades has found various families of amplifiers while suppressors still remain somewhat elusive. It has also been discovered that most networks are amplifiers of selection under the birth-death updating combined with uniform initialization, which is a standard condition assumed widely in the literature. In the present study, we extend the birth-death processes to temporal (i.e., time-varying) networks. For the sake of tractability, we restrict ourselves to switching temporal networks, in which the network structure deterministically alternates between two static networks at constant time intervals or stochastically in a Markovian manner. We show that, in a majority of cases, switching networks are less amplifying than both of the two static networks constituting the switching networks. Furthermore, most small switching networks, i.e., networks on six nodes or less, are suppressors, which contrasts to the case of static networks. © 2023, CC BY.

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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International Conference on data engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

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Exploring Cutting-Edge Machine Learning and data Mining Techniques for Enhancing Big data Management with Advanced Algorithmic Strategies for Optimal data Processing and Analysis

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

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International Conference on Advances in Information Technology (ICAIT)

作者： M. Anitha V Sheeja Kumari N. Muthuvairavan Pillai P. Jesu Jayarin D. Beulah David Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India Department of Computer Science and O. Business Systems R.M.D Engineering College Thiruvallur Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting.

关键词： Support vector machines Machine learning algorithms computational modeling Scalability Software algorithms Big data Prediction algorithms Boosting data models Ensemble learning

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Entanglement and coherence in pure and doped Posner molecules

arXiv

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arXiv 2023年

作者： Adams, Betony Sinayskiy, Ilya Agarwal, Shivang Petruccione, Francesco Quantum Research Group University of KwaZulu-Natal South Africa National Institute for Theoretical and Computational Sciences South Africa The Guy Foundation Dorset United Kingdom The Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States School of Data Science and Computational Thinking Department of Physics Stellenbosch University South Africa

The potential role of spin in biological systems is a primary topic in quantum biology. However, much of this research focuses on electron spin. A recent hypothesis suggests that nuclear spin may be better suited to biological processes, being less sensitive to decoherence. The hypothesis details how phosphorus nuclei might be prepared in a spin entangled state, how this entanglement is protected by assembly into calcium phosphate (Posner) molecules, and how this entanglement might modulate calcium ion production and concomitant neural activation. In this paper we investigate the robustness of quantum effects such as coherence and entanglement in Posner molecules. We investigate how these effects are directly dependent on specific parameters such as spin-spin coupling strengths and Posner molecule symmetry. We also investigate how lithium isotope doped Posner molecules differentially modulate quantum resources such as coherence and entanglement and whether this is a viable explanation for lithium's mechanism of action in bipolar disease. Finally we illustrate how entanglement might possibly be preserved through exploitation of the biological environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Molecules

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Development and performance evaluation of a Bengali-adapted mHealth app for early detection of impaired visual acuity

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Heliyon 2024年第23期10卷 e40853页

作者： Haque, Monzurul Zaman, Marzia Islam, Ashraful Sarker, Farhana Ferdausi, Nahid Mamun, Khondaker A. AIMS Lab IRIIC United International University Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka Bangladesh Gopalganj Eye Hospital and Training Institute Gopalganj Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh CMED Health Limited Dhaka Bangladesh

This paper presents a comprehensive study on the development and performance evaluation of “Dristi,” a mobile health (mHealth) app designed to facilitate eye screening for impaired visual acuity (VA) in Bangladesh. Recognizing the critical importance of vision and the challenges posed by visual impairments, particularly in low-resource settings, this study explores an innovative solution to enhance eye care accessibility. The app, developed for Android devices, integrates features such as VA testing using adapted Bengali letters, color blindness tests, and eye health education, aiming to make eye care more user-friendly and accessible. Inspired by the Snellen chart, the app employs a simplified method for VA assessment, suitable for the constraints of mobile devices. The study involved a cross-sectional comparative design, conducted between June and September 2022, to clinically validate the app's feature of testing distant vision and near vision. Participants were recruited from one government eye hospital, one semi-private eye hospital, one high school, and a private clinic in Bangladesh, encompassing a diverse demographic. The validation study revealed promising results in terms of the app's accuracy, sensitivity, and specificity in both distant and near vision testing, although some variability was observed across different data sources. The study also identified a significant number of participants with diabetes and hypertension, common comorbidities affecting eye health. Overall, “Dristi” demonstrates potential as a valuable tool in the early detection and management of visual impairments in Bangladesh. Its development marks a significant step towards utilizing mHealth solutions to overcome healthcare barriers and improve eye care services in low-resource settings. Future directions include expanding the app's features, enhancing its integration with healthcare systems, and extending its reach to other similar contexts. © 2024 The Author(s)

关键词： Distant vision Eye mHealth Near vision Vision problem Visual acuity

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Fast sequence to graph alignment using the graph wavefront algorithm

arXiv

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arXiv 2022年

作者： Zhang, Haowen Wu, Shiqi Aluru, Srinivas Li, Heng School of Computational Science and Engineering Georgia Institute of Technology United States Department of Mathematics Nanjing University China Institute for Data Engineering and Science Georgia Institute of Technology United States Department of Data Sciences Dana-Farber Cancer Institute United States Department of Biomedical Informatics Harvard Medical School United States

Motivation: A pan-genome graph represents a collection of genomes and encodes sequence variations between them. It is a powerful data structure for studying multiple similar genomes. Sequence-to-graph alignment is an essential step for the construction and the analysis of pan-genome graphs. However, existing algorithms incur runtime proportional to the product of sequence length and graph size, making them inefficient for aligning long sequences against large graphs. Results: We propose the graph wavefront alignment algorithm (Gwfa), a new method for aligning a sequence to a sequence graph. Although the worst-case time complexity of Gwfa is the same as the existing algorithms, it is designed to run faster for closely matching sequences, and its runtime in practice often increases only moderately with the edit distance of the optimal alignment. On four real datasets, Gwfa is up to four orders of magnitude faster than other exact sequence-to-graph alignment algorithms. We also propose a graph pruning heuristic on top of Gwfa, which can achieve an additional ∼10-fold speedup on large graphs. © 2022, CC BY.

关键词： Wavefronts

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