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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Mohamed Abdul Karim Sadiq Thirumurugan Shanmugam Nasser Mohammed Al-Fannah Firdous Kausar College of Computing and Information Science University of Technology and Applied Sciences Suhar Oman Department of Mathematics and Computer Science Fisk University Nashville Tennessee USA

Named Entity Recognition in low resource languages such as Arabic is relatively less accurate. Despite this, many applications attempt query-document matching, which necessitates entity resolution. We explore certain possible solutions in this regard to enhance the matching process. A system is proposed to build a sub graph of terms in the Knowledge Graph. These terms specifically deal with skills in the Arabic documents related to the Human Resources domain. The accuracy measures of skills extraction using two different Language Models viz., SparkNLP and Hatmi are presented. The results are promising and have scope for improvement in entity resolution.

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An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc Network Using Mine Blast Algorithm

An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc ...

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Advances in Power, Signal, and Information Technology (APSIT), International Conference in

作者： Subhrananda Goswami Sukumar Mondal Subhankar Johardar Chandan Bikash Das Department of Computer Science Research Centre of Natural and Applied Sciences Raja Narendra Lal Khan Women's College Department of Mathematics Raja Narendra Lal Khan Women's College Department of Computer Science Haldia Institute of Technology Department of Mathematics Tamralipta Mahavidyalaya

The collection of wireless mobile nodes dynamically in a temporary network is termed a Mobile Ad Hoc Network (MANET). Here, energy consumption is an important complication in MANET because the mobile node doesn't offer a permanent power supply to the network, and they highly depend on batteries. The lifetime of the batteries is exhausted quickly as the nodes are traveled and change their positions frequently in the MANET network. The key factor for MANET is efficient energy to improve the network lifetime. Moreover, the efficacies of MANET are increased by different criteria like residual energy and link stability, which are always required to optimize. The major goal of multi-objective optimization issues is to reduce the energy consumption rate and also enhance the lifetime of the network. Based on the battery-dependent nodes, the efficacy of MANET is reduced. So, it is more need to select multiple optimal node disjoint paths among the destination and source to transfer the data. Further, multi-objective functions are utilized to choose the optimal path that offers the optimum outcome regarding traffic load, hop and energy consumption. So, efficient routing protocols for energy efficiency improvement in MANET are developed by utilizing the Mine Blast Algorithm (MBA). All the sensor nodes of the MANET are taken for experimentation purposes. The shortest paths in the MANET model are optimized by implementing the MBA. The parameters optimized by MBA are link cost, path loss, consumption of energy, an energy that is still available in the battery, throughput, packet energy cost, end-to-delay, routing overhead ratio, and packet delivery ratio to evaluate the overall efficiency of the network. Parametric studies of the proposed MBA are done. The developed energy-efficient MBA-MANET systems are compared with various existing techniques to verify the improved energy efficiency of the developed MBA-MANET model.

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Study of fuzzy expert system for the diagnosis of various types of cancer

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Materials Today: Proceedings 2022年 56卷 298-307页

作者： Boadh, Rahul Grover, Reena Dahiya, Mamta Kumar, Ajay Rathee, Rakesh Rajoria, Yogendra Kumar Rawat, Meena Rani, Sangeeta Department of the Mathematics School of Basic and Applied Science K.R. Mangalam University Sohna Road Gurugram Haryana 122103 India Department of Mathematics Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus U.P. Ghaziabad India Department of Computer Science and Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122505 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122505 India University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak124001 India Department of Computer Science and Engineering World College of Technology and Management Gurgaon122506 India

Early exposure of critical diseases is the main necessity for the patients and a difficult errand for the oncologist. In the event that it is distinguished early, it makes the patient bound to live. Lately, the fuzzy rationale has shown up as a dynamical strategy in the acknowledgment of illnesses like various sorts of malignant growths. For the most part, the identification of malignant growths illnesses is taken care of with vagary, mistake, and unclearness yet very much worked by utilizing fuzzy rationale-based models. This paper surveys a few techniques for the early identification of diseases to sum things up. By using fuzzy artificial intelligent programme, created calculation;patient information alongside master specialist's recommendation, any quiet's infections can be analyzed early which lessen mortality and grimness. This review gives a proposal that a fuzzy approach running smoothly in multiple regions to the diagnosis of cancers. © 2022

关键词： Fuzzy logic

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Regression and classification of Windkessel parameters from non-invasive cardiovascular quantities using a fully connected neural network

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Informatics in Medicine Unlocked 2025年 53卷

作者： Gdoura, Ahmed Bernhard, Stefan Departement of Life Science Engineering Technische Hochschule Mittelhessen University of Applied Sciences Giessen Germany Department of Mathematics and Computer Science Free University of Berlin Berlin Germany

Despite their simplicity, three-element Windkessel models (WK-3) provide an effective and straightforward representation of the aortic input impedance. The WK-3 model not only captures valuable information about the mechanical and structural characteristics of the aortic arch but also generates reliable estimations of the central blood pressure (cBP) wave, a significant cardiovascular risk indicator. However, fitting the parameters of the WK-3 model typically requires invasively collected data, which carries substantial risk and high cost for patients. This study aims to enable non-invasive impedance estimation of the WK-3 model using cardiovascular signals. As a proof of concept, we developed and trained a fully connected neural network (FCNN) on an in-silico dataset to predict the WK-3 parameters: characteristic impedance, peripheral arterial resistance, and arterial compliance. These predictions are based on non-invasive parameters, including zero-flow pressure intercept, heart rate, stroke volume, and left ventricular ejection time. The proposed model achieved an overall accuracy of 80% with an average area under the curve (AUC) of 0.91±0.11. The implementation and best-fitting model are available for download from this link. © 2025 The Authors

关键词： Asymptotic pressure BEC Classification Fully connected NN Heart rate LVET Regression Stroke volume WGM Windkessel τ

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Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

arXiv

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arXiv 2024年

作者： Alam, Md. Zehan Roy, Tonmoy Kawsar, H.M. Nahid Rimi, Iffat Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Data Analytics and Information Systems Utah State University LoganUT United States Department of Applied Mathematics Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL—using pre-trained models on the ImageNet dataset—varies due to domain-specific challenges. We evaluate five pre-trained models—MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201—on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with resampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Effect of radiofrequency coil and primary magnetic field on radiolucent composite plates

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Noise and Vibration Worldwide 2023年第1期54卷 44-58页

作者： Gawande, Atul S Mukka Ramachanra, Shreyas Kamyab, Hesam Trung, Nguyen-Thoi Kattimani, Subhaschandra Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Laboratory for Applied and Industrial Mathematics Institute of Computational Science and Artificial Intelligence Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

Magnetic Resonance Imaging (MRI) systems need a material compatible with the imaging technique with lesser attenuation and provide accurate images without distortion. Carbon fibers are the best-suited materials for x-ray applications because of their radiolucent properties and reduced attenuation characteristics. However, carbon fiber produces images with distortion in the MRI system by absorbing electromagnetic energy because of its conductive nature. In the present study, five different fiber-reinforced radiolucent composite plates are analyzed to predict their suitability for a radio frequency coil and the effect of a primary magnetic field of 1.5 T and 3.0 T on mechanical responses. Simulation models are built to explore the impact of electromagnetic waves and birdcage coil configurations on composite material and quantify the temperature changes caused due to energy absorption. A multiphysics coupling simulation is being used to understand the effect of stacking sequence, ply orientation, and boundary conditions on the response of composite plates under an electro-magneto-mechanical environment. © The Author(s) 2022.

关键词： Magnetic resonance imaging

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia computer science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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An Effective Exploration of Accessing 5G Mobile Communication That Affects E-Commerce Using IoT 8th

An Effective Exploration of Accessing 5G Mobile Communicatio...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Ljubimova, Elena Shichiyakh, Rustem Zakieva, Rafina Lydia, E. Laxmi Kumar, K. Vijaya Department of Mathematics and Applied Computer Science Kazan Federal University Elabuga Institute of KFU Yelabuga Russia Economic Sciences Department of Management Kuban State Agrarian University named after I.T. Trubilin Krasnodar Russia Pedagogical Sciences Department of Industrial Electronics and Lighting Engineering Kazan State Power Engineering University Kazan Russia Vijayawada520007 India Visakhapatnam India

ISBN: (纸本)9789819748945

To effectively answer these questions, your application's design needs to make a new user of your service feel at ease and not disoriented. This website is one of the most important parts of your marketing plan. If you have a fully functional, expert, and visually appealing website, it will be easier for consumers to do business with you. A user-friendly website will increase your revenue dramatically. It will be possible for your visitors to find the products on your website even more quickly than before. Many businesses have switched to selling their goods online, increasing the number of e-commerce platforms, thanks to COVID. In addition to retail and literature, e-commerce today supports a number of industries, such as books, home products, and cosmetics. Users will benefit from new features like augmented reality, chat capabilities, and recommended engines as 5G develops. Your business will get an immediate competitive edge over competitors by using these new features. Notwithstanding geographical disparities and the Covid pandemic, the business's target market has grown thanks to its offerings of groceries, essentials, and goods for the aged. These modifications suggest to people that e-commerce purchases now involve more commonplace needs than luxuries. By providing the best customer care possible at every point of contact, we at Clever Data prioritize the needs of our clients. With a team of dedicated developers and industry knowledge, it can provide you with the greatest solution for your clients’ comfort. They are thrilled to become a part of the intelligent Data family. 5G has a lot of potential and promises to help with these issues. Data processing and transmission speeds will soar to new heights with 5G. Thus, 5G will aid in the development and delivery of efficient online video advertising that can grab consumers’ interest and yield the most outcomes. Similarly, 5G networks guarantee that gadgets maintain their Internet connections while moving from o

关键词： Marketplaces

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Reducing the Percentage of Defective Castings Using the Neural Network Modeling Method

Reducing the Percentage of Defective Castings Using the Neur...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Yasnitsky, Leonid N. Rabchevsky, Andrey N. Martynenko, Sergey V. Perm State University Department of Applied Mathematics and Informatics United States National Research University 'Higher School of Economics' Department of Information Technologies Perm Russia Computer Science and Technology Institute Perm State University Perm Russia Ural Federal University Named After. B.N YeItsin Branch in Nizhny Tagil Department of Metallurgical Technologies Nizhny Tagil Russia

ISBN: (纸本)9798350395044

The article is devoted to the current problem of reducing the percentage of defective products manufactured at serial steelmaking plants. To study the patterns of defect formation, a neural network was created that predicts the formation of defects such as 'Cracks' in castings produced by the open-hearth method. To train the neural network, statistical data taken under the conditions of the current production process was used, which resulted in a high degree of information noise. Outliers of statistical information were detected and removed with the original author's neural network technique. By conducting virtual computer experiments on a neural network model, some important dependences of the probability of the defect formation on the content of manganese, phosphorus, silicon, chromium, and sulfur were identified. Based on that, practical recommendations have been developed to reduce defects by adjusting the chemical composition of the metal. Despite the relatively low accuracy of the created neural network model, the application of the practical recommendations made it possible to reduce the percentage of defective products manufactured at a serial plant by half. © 2024 IEEE.

关键词： Statistics

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Machine learning of microstructure–property relationships in materials with robust features from foundational vision transformers

arXiv

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arXiv 2025年

作者： Whitman, Sheila E. Latypov, Marat I. Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States

Machine learning of microstructure–property relationships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure–property relationship. We propose utilizing pre-trained foundational vision transformers for the extraction of task-agnostic microstructure features and subsequent light-weight machine learning of a microstructure-dependent property. We demonstrate our approach with pre-trained state-of-the-art vision transformers (CLIP, DINOV2, SAM) in two case studies on machine-learning: (i) elastic modulus of two-phase microstructures based on simulations data;and (ii) Vicker’s hardness of Ni-base and Co-base superalloys based on experimental data published in literature. Our results show the potential of foundational vision transformers for robust microstructure representation and efficient machine learning of microstructure–property relationships without the need for expensive task-specific training or fine-tuning of bespoke deep learning models. © 2025, CC BY.

关键词： Elastic moduli

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