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Validation and Implementation of Customer classification System using Machine Learning

MEASUREMENT-INTERDISCIPLINARY RESEARCH AND PERSPECTIVES 2024年第2期22卷 131-140页

作者： Yoon, Hyemin Kim, HyunJin Kim, Sangjin Dong A Univ Dept Management Informat Syst Busan South Korea Dong A Univ Grad Sch Business Adm Dept Digital Finance Busan South Korea

We have maintained the customer grade system that is being implemented to customers with excellent performance through customer segmentation for years. Currently, financial institutions that operate the customer grade system provide similar services based on the score calculation criteria, but the score calculation criteria vary from the financial institution to financial institution. In this study, we create a machine learning prediction model using items and added items that are based on the current customer grade of our bank,- and the purpose is an optimal model that considers the adequacy of existing variables and the validity of additional variables through comparison between models. Using Lasso, Elastic net and Multinomial Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, we propose that the best model be found and gradually applied to customer grade calculation criteria.

关键词： Customer grade variable selection algorithm classification algorithm machine learning lasso elastic net multinomial logistic regression decision tree random forest support vector machine

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A Layered Software Architecture for the Development of Smart Mobile Distributed Systems Oriented to the Management of Emergency Cases

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3664-3664页

作者： Contreras Rivas, Lizbeth Yesenia Lopez Dominguez, Eduardo Hernandez Velazquez, Yesenia Dominguez Isidro, Saul Medina Nieto, Maria Auxilio de la Calleja, Jorge Lab Nacl Informat Avanzada Xalapa 91100 Veracruz Mexico Inst Politecn Nacl Ctr Invest & Estudios Avanzados Dept Comp Mexico City 07360 Mexico Univ Veracruzana Fac Stat & Informat Veracruz 91020 Mexico Univ Politecn Puebla Postgrad Dept Puebla 72640 Mexico

Smart mobile distributed systems (SMDSs) oriented to managing emergency cases (ECs) have become a helpful support tool in case of earthquakes at government and higher education institutions (GHEIs). This fact has motivated various works to propose SMDSs that are focused on managing ECs;however, many fail to address all essential requirements for effective managing and streamlining of the activities that must be carried out before, during, and after an earthquake occurs at GHEIs, resulting in suboptimal design decisions. In addition, none of these works proposes a reference software architecture that guides the development of these systems. That has given rise to issues, such as lack of standardization and low quality of the developed systems. This work presents a software architecture to develop SMDSs focused on managing the activities of people and members of the brigades in case of earthquakes at GHEIs. The proposed software architecture was evaluated based on four main aspects: correctness and coverage of desirable requirements, functional suitability, usability assessment, and validation and verification of the reference architecture elements. The results show that our software architecture provides suitable architectural support for developing SMDSs oriented to managing ECs, which meets the needed requirements.

关键词： software architecture indoor location classification algorithm brigade activities web system mobile application

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Thermoacoustic stability prediction using classification algorithms

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DATA-CENTRIC ENGINEERING 2022年第12期3卷 e17-e17页

作者： Gaudron, Renaud Morgans, Aimee S. Imperial Coll London Dept Mech Engn London England

Predicting the occurrence of thermoacoustic instabilities is of major interest in a variety of engineering applications such as aircraft propulsion, power generation, and industrial heating. Predictive methodologies based on a physical approach have been developed in the past decades, but have a moderate-to-high computational cost when exploring a large number of designs. In this study, the stability prediction capabilities and computational cost of four wellestablished classification algorithms-the K-Nearest Neighbors, Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP) algorithms-are investigated. These algorithms are trained using an in-house physicsbased low-order network model tool called OSCILOS. All four algorithms are able to predict which configurations are thermoacoustically unstable with a very high accuracy and a very low runtime. Furthermore, the frequency intervals containing unstable modes for a given configuration are also accurately predicted using multilabel classification. The RF algorithm correctly predicts the overall stability and finds all frequency intervals containing unstable modes for 99.6 and 98.3% of all configurations, respectively, which makes it the most accurate algorithm when a large number of training examples is available. For smaller training sets, the MLP algorithm becomes the most accurate algorithm. The DTalgorithm is found to be slightly less accurate, but can be trained extremely quickly and runs about a million times faster than a traditional physics-based low-order network model tool. These findings could be used to devise a new generation of combustor optimization tools that would run much faster than existing codes while retaining a similar accuracy.

关键词： classification algorithm combustion instability machine learning thermoacoustics

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Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence

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IMAGING SCIENCE JOURNAL 2024年第7期72卷 828-839页

作者： Sumathi, M. Chitra, P. Sheela, S. Ishwarya, C. Sathyabama Inst Sci & Technol Chennai India Sathyabama Inst Sci & Technol Jeppiaar NagarRajiv Gandhi Salai 119 Chennai 600119 India

Artificial Intelligence (AI), is a field of science and engineering that deals with intelligent behaviour which has the potential of improved access and the cost of healthcare *** Ovarian Syndrome (PCOS) is characterised by a protracted menstrual cycle and frequent excess androgen levels, which often affect many women of reproductive age. There are now no trustworthy objective tests that might fully confirm the diagnosis and comprehension of PCOS. Effective image processing steps have been utilized in this work for the automation of PCOS diagnosis and the classification algorithms used are DarkNet-19, AlexNet, SqueezeNet, and SVM. As a result of the classifier's accelerated PCOS diagnosis and improved performance analysis, there will be fewer instances of potentially deadly consequences that can arise from a delayed diagnosis. The system produced metrics demonstrating improved performance, such as accuracy to provide a comprehensive ultrasound picture diagnosis using the classifier DarkNet-19 with 99%.

关键词： AI classification algorithm detection of PCOS ovary of ultrasound scan images performance metrics segmentation feature extraction graphical user interface >

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An examination of thematic research,development,and trends in remote sensing applied to conservation agriculture

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International Soil and Water Conservation Research 2024年第1期12卷 77-95页

作者： Zobaer Ahmed Aaron Shew Lawton Nalley Michael Popp V.Steven Green Kristofor Brye Center for Advanced Spatial Technologies University of ArkansasFayettevilleARUSA Environmental Dynamics Program University of ArkansasFayettevilleARUSA Department of Agricultural Economics and Agribusiness University of ArkansasFayettevilleARUSA College of Agriculture Arkansas State UniversityJonesboroARUSA University of Arkansas System Division of Agriculture Little RockARUSA Department of Crop Soiland Environmental SciencesUniversity of ArkansasFayettevilleARUSA

Conservation agriculture seeks to reduce environmental degradation through sustainable management of agricultural *** the 1990s,agricultural research has been conducted using remote sensing technologies;however,few previous reviews have been conducted focused on different conservation management *** of the previous literature has focused on the application of remote sensing in agriculture without focusing exclusively on conservation practices,with some only providing a narrative review,others using biophysical remote sensing for quantitative estimates of the bio-geo-chemical-physical properties of soils and crops,and few others focused on single agricultural management *** paper used the preferred reporting items for systematic review(PRISMA)methodology to examine the last 30 years of thematic research,development,and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales.A set of predefined key concepts and keywords were applied in three databases:Scopus,Web of Science,and Google Scholar.A total of 188 articles were compiled for initial examination,where 68 articles were selected for final analysis and grouped into cover crops,crop residue,crop rotation,mulching,and tillage *** on conservation agriculture research using remote sensing have been increasing since 1991 and peaked at 10 publications in *** the 68 articles,94%used a pixel-based,while only 6%used an object-based classification *** to 2005,tillage practices were abundantly studied,then crop residue was a focused theme between 2004 and *** 2012 to 2020,the focus shifted again to cover *** spectral indices were used in 76%of the 68 *** examination offered a summary of the new potential and identifies crucial future research needs and directions that could improve the contribution of remote sensing to the provision of long-term operat

关键词： Remote sensing Conservation agriculture classification algorithm Spatial resolution Satellite Spectral indices PRISMA

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Machine learning framework for selective and sensitive metal ion sensing with nitrogen-doped graphene quantum dots heterostructure

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CARBON 2025年 232卷

作者： Das, Ruma Paria, Abhirup Giri, P. K. Haldia Inst Technol Dept Appl Sci Phys Haldia 721657 India Haldia Inst Technol Dept Comp Sci & Engn Artificial Intelligence & Mac Haldia 721657 India Indian Inst Technol Guwahati Dept Phys Gauhati 781039 India Indian Inst Technol Guwahati Ctr Nanotechnol Gauhati 781039 India

This study introduces a machine learning (ML) framework to optimize photodetector performance for sensor applications. Using the data from the fabricated photodetector with the heterostructure of nitrogen-doped graphene quantum dot and gold nanoparticles (Au@N-GQDs), various supervised ML models (more than 20 models) are trained and tested for the selection and refinement of the most effective algorithm for our work. Depending on the best-performed ML model, the optimized working wavelength of the photodetector is found for the detection of metal ions. Remarkably, the ML-based sensor shows a high level of selectivity and sensitivity in nM level towards Fe3+ ions in Brahmaputra river water. A strong alignment between model predictions and experimental outcomes validates the efficacy of the proposed ML-based framework. Moreover, data visualization techniques such as heatmaps, classification algorithms, and confusion matrices are introduced to identify the trends in the database. The mechanistic insight of the sensor performance towards Fe3+ ion sensing is further explained with heatmap analysis and experimental verification, which emphasizes the role of photo-induced charge transfer and Fe-O bond formation between metal ions and Au@N-GQDs due to the high electron affinity of Fe3+ ions.

关键词： Supervised machine learning Heatmap matrix classification algorithm Confusion matrix Metal ion sensing in river water

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Application of Big Data Technology to Assessments of Female Ovarian Reserve Dysfunction

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IEEE ACCESS 2025年 13卷 19408-19419页

作者： Xia, Ji'An Ma, Yunfei Wu, Yiyun Zhao, Youlin Ni, Haorang Liu, Xinyan Nanjing Vocat Univ Ind Technol Sch Comp & Software Nanjing 210023 Jiangsu Peoples R China Jiangsu Prov Hosp Chinese Med Dept Ultrasound Nanjing 210029 Jiangsu Peoples R China

This paper evaluated the potential application of big data technology to assessments of diminished ovarian reserve (DOR). The study enrolled 162 patients who underwent ovarian reserve function assessment for the first time in the Department of Ultrasound, Jiangsu Province Hospital of Chinese Medicine from January 2023 to December 2023. Patients were divided into normal ovarian reserve function (n = 68), early-stage DOR (n = 66), mid-stage DOR (n = 12), and late-stage DOR (n = 16) groups. Hadoop and Spark frameworks were used to build a big data platform, and the MLlib parallel machine learning library was used to implement three multivariate classification models-multilayer perceptron, one-vs-rest, and random forest classifiers-to classify and analyse the ovarian reserve function dataset and evaluate the platform's performance. In the big data platform, the random forest algorithm achieved the highest classification accuracy (89.47%), followed by the neural network (81.06%) and support vector machine (72.91%) methods. The random forest algorithm had the least time overhead for datasets smaller than 50 MB;for datasets exceeding 50 MB, the support vector machine algorithm had the least time overhead, followed by the random forest and neural network algorithms. The neural network algorithm's speedup ratio was lower than that of the other two algorithms for small datasets, but with increasing dataset size, its speedup ratio significantly exceeded those of the other two algorithms. The random forest algorithm showed substantial growth for large datasets.

关键词： Medical diagnostic imaging Big Data Machine learning algorithms Sparks classification algorithms Indexes Data models Random forests Biochemistry Support vector machines Diminished ovarian reserve three-dimensional power Doppler ultrasound medical big data machine learning classification algorithm performance evaluation

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A Review of Machine Learning in Organic Solar Cells

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PROCESSES 2025年第2期13卷 393-393页

作者： Ahmed, Darya Rasul Muhammadsharif, Fahmi F. Koya Univ Fac Sci & Hlth Dept Phys Koya KOY45 Koy Sanjaq Iraq

Organic solar cells (OSCs) are a promising renewable energy technology due to their flexibility, lightweight nature, and cost-effectiveness. However, challenges such as inconsistent efficiency and low stability limit their widespread application. Addressing these issues requires extensive experimentation to optimize device performance, a process hindered by the complexity of OSC molecular structures and device architectures. Machine learning (ML) offers a solution by accelerating material discovery and optimizing performance through the analysis of large datasets and prediction of outcomes. This review explores the application of ML in advancing OSC technologies, focusing on predicting critical parameters such as power conversion efficiency (PCE), energy levels, and absorption spectra. It emphasizes the importance of supervised, unsupervised, and reinforcement learning techniques in analyzing molecular descriptors, processing data, and streamlining experimental workflows. Concludingly, integrating ML with quantum chemical simulations, alongside high-quality datasets and effective feature engineering, enables accurate predictions that expedite the discovery of efficient and stable OSC materials. By synthesizing advancements in ML-driven OSC research, the gap between theoretical potential and practical implementation can be bridged. ML can viably accelerate the transition of OSCs from laboratory research to commercial adoption, contributing to the global shift toward sustainable energy solutions.

关键词： machine learning organic solar cell feature selection photovoltaic parameter classification algorithm

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Development of a GUI for Automated classification of Scientific Journal Articles using clustering 5

Development of a GUI for Automated Classification of Scienti...

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5th International Conference on Innovative Trends in Information Technology (ICITIIT)

作者： Sateesh, Nayani Kaur, Kuljeet Lakshminarayana, M. Vekariya, Vipul Patil, Harshal Maranan, Ramya CVR Coll Engn Dept Comp Sci & Engn Hyderabad 501510 Telangana India Graph Era Deemed Be Univ Dept Management Dehra Dun 248002 Uttarakhand India MS Ramaiah Inst Technol Dept Med Elect Engn Bengaluru 560054 Karnataka India Parul Univ Parul Inst Engn & Technol Dept Comp Sci & Engn Waghodia 391760 Gujarat India Symbiosis Int Deemed Univ Symbiosis Inst Technol Dept Comp Sci & Engn Pune 412115 Maharashtra India SIMATS Saveetha Sch Engn Dept Res & Innovat Chennai 602105 Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

The ever-expanding volume of scientific literature necessitates innovative solutions for efficient information organization and retrieval. This final qualifying work focuses on the development of a robust algorithm for the automated categorization of articles in a scientific journal. The primary objective is to streamline the process of classifying diverse research contributions, thereby enhancing accessibility and knowledge discovery within scholarly domains. The goal of the final qualifying work is to develop an algorithm for automated categorization of articles in a scientific journal. To achieve this goal, an application is developed for the administrator of a scientific journal, allowing for the preparation, classification and visualization of data.

关键词： Automated Categorization Scientific Journal Articles Natural Language Processing Machine Learning Textual Analysis Information Retrieval Knowledge Management classification algorithm

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An Approach to Recognize and Classify Arm Activities Using Wearable Ultra Wide Band Antennas

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WIRELESS PERSONAL COMMUNICATIONS 2024年第2期139卷 1303-1318页

作者： Tiwari, Bhawna Gupta, Sindhu Hak Balyan, Vipin Amity Univ Dept Elect & Commun Noida Uttar Pradesh India GL Bajaj Inst Technol & Management Greater Noida Uttar Pradesh India Cape Peninsula Univ Technol Dept Elect Elect & Comp Engn Cape Town South Africa

Continuous observation, recognition and classification of various body movements and activities is essential for the implementation of Wireless Body Area Networks (WBAN) to discern the status of body parts functionalities or abnormalities if any. WBAN can continuously observe various movements of human body parts and human body activities. Recognition and classification of arm activities plays an important role in fitness monitoring, assisted living, and sports tracking, etc. In the presented work, Ultra Wide Band antennas are designed and employed on human body to observe datasets of antenna performance parameters associated with various arm movements and activities. The classification of three arm activities i.e. boxing, rowing, and clapping are implemented using Support Vector Machine, K-Nearest Neighbor, Random Forest and Decision Tree machine learning algorithms. Performance of classification depends on accuracy of implemented algorithm. The highest classification accuracies are found to be 99% in case of Decision Tree algorithm.

关键词： Arm activity recognition and classification Health monitoring WBAN UWB antenna S-11 S-21 VSWR Machine learning classification algorithm

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