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LightGBM empowered by whale optimization for thyroid disease detection

International Journal of Information Technology (Singapore) 2023年第4期15卷 2053-2062页

作者： Sinha, Bam Bahadur Ahsan, Mohammad Dhanalakshmi, R. Department of Computer Science Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi 834010 India Informatics Cluster School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Department of Computer Science Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620009 India

Thyroid disorder is a significant source of formulation in medical classification and prognosis, with onset being a challenging assumption in medical study. The thyroid gland is a vital organ of our body. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are two of the most prevalent thyroid disorders that affect the thyroid’s ability to regulate the pace of the body’s metabolism. Data cleansing procedures were used to prepare the data to the point where analytics could be performed to determine the risk of individuals developing thyroid disease. In this paper, an optimal framework design employing LightGBM (Light Gradient Boosting Machine), Sequential Backward Selection (SBS), and a metaheuristic technique termed Whale Optimization (WO) plays a decisive role in detecting Thyroid disease. The objective of this research is to provide an impeccably clean and rational way to detecting thyroid disorder in humans. Numerous processes have been applied to thyroid data sets with astounding results, yet the data used for thyroid disease detection is redundant, inconsistent and contains missing feature values, as per our literature survey. The proposed effort would develop an expert advisory system based on the Opti-LightGBM architecture for the purpose of detecting thyroid disorders and initiating necessary medication promptly. The proposed Opti-LightGBM model yields an accuracy of 99.75% on Thyroid dataset and outperforms several state-of-art comparative models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Classification Feature selection LightGBM Optimization Thyroid

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Classification of Tuna Meat Grade Quality Based on Color Space Using Wavelet and k-Nearest Neighbor Algorithm

Classification of Tuna Meat Grade Quality Based on Color Spa...

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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Gede Sujana Eka Putra, I. Ketut Gede Darma Putra, I. Sudarma, Made Sudana, Anak Agung Kompiang Oka Institute of Business and Technology Indonesia Department of Computer Informatics Denpasar Indonesia Udayana University Department of Information Technology Denpasar Indonesia Udayana University Department of Electrical and Computer Engineering Denpasar Indonesia

ISBN: (纸本)9798350382822

Tuna products are one of Indonesia's leading export commodity products. Accuracy in determining the quality grade of tuna is necessary to ensure food safety and product quality. Several cases of rejection of Indonesian fishery products by the United States and cases of food poisoning show a lack of food safety management in Indonesia. Determination of fish quality grade was done manually by identifying the eyes condition, gills, and meat color, taking a sample of tuna meat using a spike, which is the manual grading process causing human error. Previous studies had been carried out to identify the freshness and quality of fish using odor profiles, and color profiles from color sensors, measuring fish freshness and quality based on fish eye images. According to Robert DiGregorio, five parameters determine the grade quality of tuna, namely freshness, fish size and shape, meat color, texture, and fat content. Fish grades are grouped into grade 1, grade 2+, grade 2, and grade 3. This study aims to determine the grade quality of tuna meat based on color space through image preprocessing, dataset training, and classification. Image preprocessing consists of image cropping, converting images from RGB to HSV, and feature extraction using a wavelet. The training phase uses the k-NN algorithm using k=4 based on the number of classes. The result shows the correlation coefficient between grades of feature extraction using wavelet Symlet better than Haar. Classification of 65 images test dataset using Symlet wavelet and k-NN has a better accuracy of 81.8% compared to the Haar wavelet k-NN with an accuracy of 80.3%. © 2023 IEEE.

关键词： Feature extraction

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A Comparative Study of Text Classification using Artificial Intelligence Models 4

A Comparative Study of Text Classification using Artificial ...

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4th International Conference on Current Research in engineering and Science Applications, ICCRESA 2022

作者： Abdulameer, Maysaa H. Abdullah, Mahmood Z. Informatics Institute for Post-Graduation Studies Iraqi Commission for Computer & informatics Baghdad Iraq College of Engineering Al-Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9798350334944

The presented study provides experiments for carrying out empirical studies related to deep learning, especially convolution networks, concerning novel dataset classification (video and audio). The study will utilize the architecture of the text classification problem in the document domain with the datasets related to documents collected from many projects on real document cases. The process of building a new dataset and the existence of such data followed the urgent need that is specialized in educational lectures. Test and evaluate the two new datasets (Audio and Video) with the published dataset Book dataset by using deep learning methods in classification and machine learning algorithms, then compare the Accuracy results for each dataset with both classification methods, which present that new datasets bring better results than Book dataset which are (95% and 90%) for new datasets and 83% for Book dataset by using Deep learning, in machine learning algorithms the results are (91%, 77%, and 46%). Finally, the two classification techniques are used to maximize the benefits of the classification process. In the selection process, the system will choose deep learning for classifying the datasets than using machine learning algorithms. © 2022 IEEE.

关键词： Classification (of information)

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Flexible Real-Time Emulation of Fading Channels on SoC-FPGA Devices

Flexible Real-Time Emulation of Fading Channels on SoC-FPGA ...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Ilias Papalamprou Giorgos Armeniakos Ioannis Stratakos George Lentaris Dimitrios Soudris School of Electrical and Computer Engineering National Technical University of Athens Department of Informatics and Computer Engineering University of West Attica

Modeling and replicating the channel effects between a transmitter and receiver, is crucial in any telecommunication system, in order to evaluate the impact on the transmitted data. Therefore the necessity of having access to a real time channel emulator is vital. In order to meet these constraints, in this work we present a flexible, high performance and resource efficient FPGA-based channel emulator, focusing on fading models. Additionally, we perform a trade-off analysis between bit accuracy, resources utilization and system’s accuracy in terms of Mean Relative Error and the Probability Density Function. Our study shows that by using a circuit with reduced bit-widths, we can achieve 24%–46% reduction in various resources, while preserving the algorithmic accuracy of the channel emulator and operating at a throughput of 500Msps.

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RCS Reduction and Shielding Effectiveness of 3-D Dielectric Bodies Coated with Thin Conductive Materials Using the Method of Auxiliary Sources

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IEEE Transactions on Antennas and Propagation 2025年

作者： Kouroublakis, Minas Zouros, Grigorios P. Anastassiu, Hristos T. Tsitsas, Nikolaos L. Aristotle University of Thessaloniki School of Informatics Thessaloniki54124 Greece National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece International Hellenic University Department of Computer Informatics and Telecommunications Engineering Serres62124 Greece

Electromagnetic (EM) wave scattering and shielding effectiveness (SE) of three-dimensional (3-D) dielectric bodies coated by ultra-thin conductive materials are investigated using the method of auxiliary sources (MAS). With the increasing demand for lightweight and stealthy structures in radar, telecommunications, and aerospace applications, this study presents a theoretical framework for applying MAS for dielectric bodies coated with indium tin oxide (ITO), graphene, and MXene, and discusses numerical results for radar cross section (RCS) and SE, providing insights for future research. The application of MAS, along with the impedance matrix boundary condition (IMBC), enables accurate predictions of the scattering behavior of smooth dielectric bodies of various shapes. Monolayer graphene (MLG) and MXene are evaluated as alternatives to ITO for RCS reduction and EM shielding. The MAS theory is fully validated with the HFSS commercial software. The study demonstrates the potential of these advanced material combinations in practical applications, such as military aircraft canopies and drone sensor casings. © 1963-2012 IEEE.

关键词： Conductive plastics

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Classification of Bird Species using K-Nearest Neighbor Algorithm 10

Classification of Bird Species using K-Nearest Neighbor Algo...

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10th International Conference on Cyber and IT Service Management, CITSM 2022

作者： Budiman, Ichsan Ramdania, Diena Rauda Gerhana, Yana Aditia Putra, Alif Rakasha Pratama Faizah, Nisairrizqy Nabilah Harika, Maisevli Uin Sunan Gunung Djati Bandung Department of Informatics Bandung Indonesia Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia

ISBN: (数字)9781665460743

ISBN: (纸本)9781665460743

Birds play an essential role in the functioning of the world's ecosystems by directly impacting human health, economy, and food production and benefiting millions of other species. The diversity of bird habitats shows that there are many types of bird species. This study aims to provide a new treasure for the ontology field by applying a machine learning algorithm to classify bird species based on K-Nearest Neighbor (KNN). A total of 400 species of bird images with a total of 58388 images of data were tested in this study. The test scenario was carried out in 3 stages: 400, 200, and 100 species. The results of testing the accuracy of the K-Nearest Neighbor model applied to the bird image dataset are 26.846%, with a value of K=1. The comparison of training data with test data is 95:5 percent (%). This result shows that the KNN algorithm can classify bird species. © 2022 IEEE.

关键词： Birds

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Classifying News Based on Indonesian News Using LightGBM

Classifying News Based on Indonesian News Using LightGBM

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Lubis, Arif Ridho Prayudani, Santi Fatmi, Yulia Nugroho, Okvi Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Kulkas IT Medan Indonesia

ISBN: (纸本)9781665476508

There were several news categories that present editors with challenges. Some news categories, such megapolitan, national, celebrity, news, lifestyle, and economics, used vocabulary that was quite similar to that of the other categories. International required an editor to be familiar with the article's contents in order for it to be uploaded and placed in the proper category. We had to first digest the news before we could label it compared to other kinds of data. The text mining approach, which attempts to make text or documents may be processed so that it will aid in the process of news classification, will be used to categorize and determine the type of news in this context. The Light Gradient Boosted Machine (LightGBM) model was used in this study to increase the gradient point with a learning stage and obtain the optimal value. This model's training process was intended to be quick while consuming less storage space and processing information more accurately. The accuracy of the classifications made using a confusion matrix to quantify the findings of this investigation, which were news type classifications, was 86%. © 2022 IEEE.

关键词： Digital storage

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Smart vegetable cutter for Smart Home 1

Smart vegetable cutter for Smart Home

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Singhal, Saurabh Mangal, Deepak Kumar, Rakesh Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications Mathura India Jain University Department of Computer Science and IT Bangalore India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time. © 2023 IEEE.

关键词： Real time systems

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Sound of Reason: Using AI to build a Digital Twin after the Kanon of Pythagoras in school settings 10

Sound of Reason: Using AI to build a Digital Twin after the ...

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10th IEEE Smart World Congress, SWC 2024

作者： Anthis, Zach Basiakoulis, Alexandros Efstathiou, Kyriakos Efstathiou, Marianna Department of Communication Culture and Media London United Kingdom Aristotle University of Thessaloniki Laboratory for Machine Tools and Manufacturing Engineering Department of Mechanical Engineering Thessaloniki Greece Cyprus University of Technology Digital Heritage Research Lab Department of Electrical Engineering Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331520861

This paper presents ongoing work on kanON, an AI-driven EdTech tool designed to foster interactive learning and support Digital Cultural Heritage (DCH) preservation. kanON facilitates the digital reconstruction of the infamous Pythagorean monochord, by allowing users to seamlessly manipulate parameters such as origin (location and epoch), materials, and shape/dimensions. Through the integration of Machine Learning (ML) algorithms, the tool predicts missing archaeological data and generates real-time 3D renderings based on user input, accompanied by corresponding confidence metrics. A mixed-methods (pilot) study was conducted in classroom environments to evaluate its educational impact via quantitative Likert scale assessments and qualitative content analysis of educator feedback. Preliminary results demonstrate high levels of student engagement and reflect positive educator responses, highlighting its potential to enhance the in-class accessibility and understanding of complex historical concepts, as well as to enable the preservation and exploration of cultural artifacts through digital means. Future work will focus on refining kanON and expanding its applicability within both educational and DCH contexts, by introducing the capability of outputting a fully operational digital twin, that integrates mixed reality functionalities. The idea is to enable users to later engage with their (personalized) model dynamically (e.g., readjusting string length and tension), thus allowing them to produce different soundscapes in real-time. This advancement aims to deepen the pedagogical impact by merging tactile interaction with visual and auditory feedback, further enhancing the educational experience via immersive, experiential learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on MRI Image Super-Resolution Reconstruction Algorithm Based on Information Distillation 4

Research on MRI Image Super-Resolution Reconstruction Algori...

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4th IEEE International Conference on Data Science and computer Application, ICDSCA 2024

作者： Zhu, Huazheng Tang, Ling Jia, Yuanyuan Liu, Yaping Zhang, Jun Jiang, Aiping Nie, Zicheng School of Computer Science and Engineering Chongqing University of Science & Technology Chongqing China School of Digital Economy Chongqing Vocational College of Art and Engineering Chongqing China College of Medical Informatics Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368239

In recent years, deep learning has demonstrated powerful capabilities in computer vision tasks and injected new energy into MRI image super-resolution reconstruction algorithms. However, existing MRI super-resolution methods based on 2D and 3D CNNs require interpolation to upsample low-resolution images, which increases the computational burden. To address this, this paper proposes an MRI image super-resolution algorithm based on information distillation, called DASID-SR. The algorithm uses information distillation to reduce network parameters and computational complexity while extracting deep features, and applies sub-pixel convolution for direct reconstruction and upsampling, thereby reducing the model's complexity. The core innovations include constructing a Mixed Information Distillation Module (M-IMDB), introducing a differentiable architecture search algorithm in the deep feature extraction module, and implementing dense connections between modules to enhance feature utilization efficiency. Experimental results show that on the Kirby21 and ALVIN-adult datasets, with a reconstruction factor of 3, the proposed method achieves peak signal-to-noise ratios (PSNR) of 40.21dB and 37.56dB, respectively, outperforming other networks by 0.02dB to 2.68dB and 0.02dB to 1.41dB. Moreover, the model's parameter size is only 0.45MB, demonstrating that the proposed algorithm can generate high-quality MRI images while maintaining a low parameter count. ©2024 IEEE.

关键词： Signal to noise ratio

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