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1st International Conference on Data Engineering and Machine Intelligence, ICDEMI 2023

作者： Mohan, Ramya Kadry, Seifedine Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates

ISBN: (纸本)9789819776153

In addition to traditional approaches, several computerized techniques have been developed to enhance the results. The automation in the medical domain considerably reduced the burden and improved disease diagnosis, treatment planning, and implementation. With the advent of automation, the medical domain has greatly reduced the burden of diagnosing diseases, developing treatment plans, and implementing them. This research aims to develop image-processing assisted detection of Diabetic Foot Ulcer (DF) from digital photographs. Digital photographs are used to assist in diagnosing Diabetic Foot Ulcer (DF). As part of the developed tool, (i) images are collected and resized, (ii) Otsu/Kapur and Hummingbird-optimizer thresholding is supported, (iii) level-set segmentation is used to extract the DF region, and (iv) the developed scheme is compared with the binary mask and its performance is verified. A total of 300 images are examined in this work, and MATLAB software is used to conduct the experiments. Comparing Otsu + LS with Kapur + LS, the overall result achieved with Kapur’s enhanced image is better than that of Otsu + LS. Authentic clinical DF images can be used to verify the proposed scheme in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MATLAB

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Mapping Very-High Resolution Evapotranspiration Data Using a Dual GAN Model 15

Mapping Very-High Resolution Evapotranspiration Data Using a...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Zhang, Ting Yang, Yun McDaniel College Department of Mathematics and Computer Science WestminsterMD United States School of Integrative Plant Science Cornell University Soil and Crop Sciences Section IthacaNY United States

ISBN: (纸本)9798331507695

Evapotranspiration (ET) is a crucial process in understanding plant water use and is essential for effective agricultural management, particularly in optimizing irrigation and detecting crop stress. Remote sensing retrieved ET relying on Landsat observations can provide 30-meter water use data, which is relatively coarse for specialty crops, such as vineyards and orchards. To overcome this limitation, we propose a dual GAN-based super-resolution model (DuGAN-ETSR) to enhance the spatial resolution of the available 30-m ET product. However, a major challenge remains in this approach: the scarcity of paired high- and low-resolution ET images needed for training. To address this, we utilize 4-band reflectance data, a key parameter for ET estimation, as a substitute for training our model. Experimental results show that DuGAN-ETSR outperforms traditional interpolation methods, reconstructing high-resolution ET images while preserving essential fine-scale details. This approach provides a cost-effective and accurate solution for improving ET monitoring in agriculture, with a strong potential to enhance the management of specialty crops. © 2025 IEEE.

关键词： Generative adversarial networks

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EXAMINATION OF UNREMITTING KIDNEY ILLNESS BY UTILIZING MACHINE LEARNING CLASSIFIERS 16

EXAMINATION OF UNREMITTING KIDNEY ILLNESS BY UTILIZING MACHI...

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16th International Conference on ICT, Society and Human Beings, ICT 2023, the 15th International Conference on e-Health, EH 2023, the 9th International Conference on Connected Smart Cities, CSC 2023 and 8th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2023

作者： Sarwar, Fareeha Garrido, Nuno Sebastião, Pedro Rehan, Akmal Ed. Armadas Lisbon1649-026 Portugal Faculty of Science Department of Computer Science University of Agriculture Faisalabad Pakistan

ISBN: (纸本)9789898704504

Chronic kidney disease is a rising health issue that affects millions of people worldwide. Early detection and characterization of this disease is essential for effective management and control. This disease is associated with several serious health risks, such as cardiovascular disease, increased risk of stroke, and end-stage renal disease, which can be effectively prevented by early detection and treatment. Medical scientists rely on machine learning algorithms to diagnose the disease accurately at its outset. Recently, adding value to healthcare is being accomplished through the integration of machine learning algorithms into mobile health solution. Considering this, this paper proposes a predictive model of three machine learning classifiers, including Support Vector Machine, Decision Tree, and Multilayer Perceptron for chronic kidney disease prediction. The performance of the model was assessed using confusion matrix and executed in popular machine learning software tools such as WEKA and Rapid Minor. The study found that support vector machine yielded the highest accuracy rate of 98% in predicting chronic kidney disease in WEKA among other standard classifiers by using 10-fold cross validation. In addition, the proposed prediction model has been compared with existing models in terms of accuracy, sensitivity, and specificity. The experimental results indicate that the proposed predictive model shows promising results. These findings could integrate with the development of mobile health solution and other innovative approaches to prevent and treat this debilitating condition. © ICT *** rights reserved.

关键词： Diseases

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Unsteady squeezing flow and heat transport of SiO_(2)/kerosene oil nanofluid around radially stretchable parallel rotating disks with upper disk oscillating

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Propulsion and Power Research 2024年第1期13卷 64-79页

作者： Folarin Oluwaseun Sicelo Goqo Hiranmoy Mondal School of Mathematics Statistics and Computer ScienceUniversity of KwaZulu-NatalPrivate Bag X01ScottsvilePietermaritzburg 3209South Africa Department of Applied Mathematics Maulana Abul Kalam Azad University of TechnologyWest BengalKolkata 700064India

In this study,we have analyzedfluid mobility and thermal transport of the SiO_(2)/kerosene nanofluid within two rotating stretchable *** top disk is simulated to be oscil-lating with a periodic velocity and squeezing continuously the nanofluid within a porous me-dium and making thefluid toflow perpendicularly to the situated *** radiation effects are considered in the heat transfer *** non-linear(NL)PDEs that describe the nanofluid mobility structure and thermal transport are transformed into system of NL-ODEs by introducing adequately suitable non-dimensional variables after which the NL-ODEs were numerically solved via spectral quasi-linearization method(SQLM)on over-lapping *** consequences of several pertinent parameters of the model on pressure,tem-perature,velocity,skin drag coefficient and thermal transport rate are examined and elucidated in detail with the aid offigures and *** was found that theflow structure with prescribing conditions develops negative pressure situation which has vast applications in modern day medical engineering,especially in the construction of air pressure stabilizers used in medical isolation and wound therapy physiology.

关键词： Nanofluids Unsteady Squeezing Stretchable Overlapping spectral quasi-linearization SiO_(2) Kerosene Rotating disks

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Ontology-Driven Semantic Web Framework with Sentiment Analysis for Risk Assessment and Diagnosis of Diabetes Mellitus 4

Ontology-Driven Semantic Web Framework with Sentiment Analys...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Velmurugan, R. Kumar, R. Rajan, M. Sundar Sivaraj, V. Karnataka Bengaluru India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798331523923

This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous medical history. However, during personal interactions, medical staff often fail to gather all the necessary details for an accurate diagnosis, and patients may lose their previous medical records, which hinders doctors' ability to gain a full understanding of the problem. To address this issue, ontology techniques are employed, and additional details are collected from patients, such as their food habits, smoking history, alcohol consumption, sexual activity, and cardiovascular health. Using the collected data, ontology and semantic web technologies are applied to predict the risk analysis of diabetic patients. The results are visualized in a graph, providing a more comprehensive and accurate assessment of the patients' health risks. © 2025 IEEE.

关键词： Electronic health record

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K-Means Method for Clustering Groundwater Chemical Parameters of Konya Closed Basin 4

K-Means Method for Clustering Groundwater Chemical Parameter...

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4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Maftei, Carmen Serban, Cristina Kucuk, Kubra Demir, Vahdettin Muntean, Radu Transilvania University of Brasov Civil Engineering Faculty Brasov Romania Ovidius University of Constanta Mathematics and Computer Science Faculty Constanta Romania Kto Karatay University Civil Engineering Faculty Konya Turkey

ISBN: (纸本)9798350391183

Cluster analysis, respectively K-Means method, is used to classify groundwater from Konya Closed Basin, located in Central Anatolia Region of Turkey. Datasets include the beginning (MB) and end (MS) of the season values for pH, Na and HCO3 spanning 2015-2022 period. There are 163 water wells (stations) in Konya Closed Basin where chemical properties were measured. Additionally, an EDA and statistical analysis was performed. The international standards for pH, Na and HCO3 in drinking water and irrigation water are generally respected. To evaluate the success of cluster separation using the K-Means technique, we estimated the silhouette coefficient. The results reveal that, generally, 2 to 4 clusters were determined for all models proposed. This approach can help to identify those wells that exceed the standard values of the investigated chemical parameters for drinking and irrigation water. In this way, the water management department could take improvement measures over time. © 2024 IEEE.

关键词： Groundwater geochemistry

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Comparative Analysis of Deep Learning and Machine Learning Analysis for Kidney Tumor Analysis 7

Comparative Analysis of Deep Learning and Machine Learning A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Yazhini, A. Kalpana Devi, M. Anitha, M. Praveena, R. Mol, Maheswari K Lawrence, Jinsha Anna University Faculty of Electrical Engineering College of Engineering Chennai600025 India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore10 India KIT - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India Jai Shriram Engineering College Department of Computer Science and Engineering Tirupur638660 India Veterinary College and Research Institute Thanjavur614625 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One of the prevalent ailments that has impacted our society is kidney disease. Around the world, kidney tumors (KT) rank tenth in terms of frequency in both men and women. The organization of cancer is a problem that is attracting increasing attention in the domains of bioinformatics and computational biology. This study presents a comparative analysis of the latest developments in deep learning (DL) and machine learning (ML). While several methods have been proposed to tackle the cancer classification challenge, new research indicates that supervised and deep learning-based methods are the most effective. Healthcare has advanced as a result of the creation of numerous machine learning techniques for awareness evaluation in cancer categorization. It seems that there is a high requirement for the ongoing development of efficient categorization algorithms in order to handle the growth in healthcare applications. The comparative analysis of the proposed classification shows the accuracy as 0.92%. © 2024 IEEE.

关键词： Adversarial machine learning

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Automated Diagnoses Glaucoma Approach in Retinal Fundus Images Using Support Vector Machine

Automated Diagnoses Glaucoma Approach in Retinal Fundus Imag...

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作者： Kamara, Osama M. Asad, Ahmed H. Hefny, Hesham A. Department of Computer Science Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt

Glaucoma is chronic disease steals sight slowly so causing blindness gradually. Although, it cannot be cured at all but only the early treatment can only slow down its progress. However, minority of doctors can diagnose it accurately, so it is very crucial issue to improve the performance of glaucoma diagnosis. This paper proposed an approach with two phases, phase (1) is the approach without feature selection and in phase (2) we apply feature selection algorithms and compare results. It is based on superpixel algorithm and supervised machine learning then in phase 2 we perform feature selection for selecting the most significant features to improve the performance, reduce data dimensionality and decrease elapsed time, then we performed the support vector machine (SVM) classifier. The proposed approach is evaluated on Rim-One r3 dataset, and the result is that the data dimensionality is decreased by 50% and elapsed time decreased by 42%, on the other hand, we get phase 1 accuracy 99.2% but it is decreased to 98.5% in phase 2 and mean CRD-error is increased by 0.02. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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AI-Driven Innovations in Healthcare: Bridging Imaging and Genomics for Advanced Disease Insights

AI-Driven Innovations in Healthcare: Bridging Imaging and Ge...

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Adornetto, Carlo Bruno, Pierangela Calimeri, Francesco De Rose, Edoardo Greco, Gianluigi Quarta, Alessandro Department of Mathematics and Computer Science University of Calabria Via Pietro Bucci Rende Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Rome Italy

The application of Artificial Intelligence (AI) techniques for analyzing medical images and omics data is revolutionizing the healthcare industry by offering profound insights into various diseases. Achieving precise diagnoses and formulating effective treatment plans, however, demands intricate and multimodal analysis of complex, sensitive, and diverse medical datasets. Recent advancements in Machine Learning and Deep Learning have proven to be formidable in identifying and classifying specific diseases. This paper outlines the current projects undertaken by our research group in this innovative domain. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Fused Transformers: Fused Information of Arabic long Article for Summarization 6

Fused Transformers: Fused Information of Arabic long Article...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Ezzat, Zeyad Khalfallah, Ayman Khoriba, Ghadad Center for Informatics Science Information Technology and Computer Science School Nile University Giza Egypt Computer and Systems Department Faculty of Engineering Alexandria University Alexandria Egypt Applied Innovation Centre Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

This paper introduces a novel approach for extending the applicability of pre-trained models to accommodate longer texts. Addressing the inherent limitation of quadratic performance in attention models within transformers, we propose the concept of fused transformers. By integrating new adaptor techniques, we enhance the encoding of lengthy text segments by breaking them into shorter spans. Subsequently, these segments are fused to increase the model's effectiveness in processing extended texts. This fusion mechanism serves to fortify the model's capacity for fine-tuning. Furthermore, we implement a length-based curriculum for expedited training Our experiments yielded 16 Rouge-2 points, representing a doubling of the score achieved by the vanilla fine-tuning method on the newly introduced "Mukhtasar" dataset for summarization. This highlights the effectiveness of managing complex relations among text segments and confirms that our method can outperform conventional training approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Distribution transformers

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