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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Suchi, Tania Alam Rabbi, Md. Anayt Layek, Md. Abu Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

An unnecessarily increased blood glucose level is a symptom of the metabolic disorder diabetes mellitus (DM). Early diabetes detection is reduce the risk factor of kidney disease, heart failure, liver cirrhosis, it might be hazardous. Our motivation is to developed a machine learning models to forecast diabetes in the future. We applied efficient data cleaning, label encoding, normalization and pre-processing methods for better models accuracy. Prediction with synthetic minority over-sampling (SMOTE) implies the equilibrium of an unstable dataset. Different feature selection techniques such as Chi-square test, Information gain attribute evaluator, Extra trees classifier, SelectKbest, and correlation-based approach to finding the key variables. Machine learning algorithms including Random Forest(RF), K-nearest neighbor(KNN), Naive Bayes (NB), Support Vector Machine (SVM), Gradient Boosting (GB), Decision Tree (DT), and Logistic Regression (LR), have been applied in the initial phase. Random Forest accuracy of 95.03% after normalizing the dataset with synthetic minority over-sampling (SMOTE) techniques. We using a voting classifier for ensemble all machine learning models in the second phase. The models achieved soft voting highest accuracy 96.69%. The findings of our models comparing with some current research indicate that it can offer greater accuracy. Now, we can accurately forecast diabetes. © 2023 IEEE.

关键词： Feature Selection

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Fig Leaf Disease Prediction and Severity Estimation UsingDeep Learning

Fig Leaf Disease Prediction and Severity Estimation UsingDee...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Prasanna Kumar, K.R. Gunasekar, M. Logeswaran, K. Dhanya Sree, P. Malathi, P. Vignesh, V. Dept. of Computer Science and Design Kongu Engineering College Tamil Nadu Erode India School of Computer Science and Engineering VIT Tamil Nadu Vellore India Dept. of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331510299

Fig leaf diseases are among the major causes of crop loss. Better methods for detection and further increased accuracy in the estimation of the severity of the disease are demanded. In basis of what is previously researched, this project continues with not just the identification of the disease but also the identification of the level of infections. Having a large dataset of 1, 871 images that comprises 971 healthy samples and 900 infected samples, the analysis will be more elaborate to determine the disease in comparison with previous estimates. We have used two deep learning models - GoogLeNet and AlexNet with outstanding performance in preceding research to make reliable predictions on leaf diseases. The novel contribution of our work is estimating the disease severity with the help of Convolutional Neural Networks that could extract features and classify disease. For the estimation of the disease severity, we will use the VGG16 model to be able to categorize the infection severity into three classes- healthy, mild, and high. The diseases addressed in this paper are Cerotelium fici (fig rust) and Fig mosaic *** goal is to offer a comprehensive tool designed to identify diseases in fig crops and assess their severity for fig farmers and the researcher so that timely disease management strategies could be undertaken. This integrated approach not only aids in taking prompt intervention but also contributes significantly toward sustainable agriculture by reducing adverse impacts on fig leaf diseases. © 2024 IEEE.

关键词： AlexNet Deep Learning GoogLeNet Plant Disease prediction Severity Estimation VGG16

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Deep Learning-Assisted MRI Image Segmentation and Classification for Precise Brain Tumor Analysis 6

Deep Learning-Assisted MRI Image Segmentation and Classifica...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Das, Shuvro Chandra Sarder, Md.Ahiya Das, Srejon Tanvir, Deluwar Hussen Aziz, Sheikh Taraque Islam, Ashraful Sylhet Engineering College Dept. of Electrical and Electronic Engineering Sylhet Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350346961

For determining the appropriate treatment for brain tumors, an accurate diagnosis is necessary. Many studies have focused on the deep learning-based classification of brain tumors. This study employed a comprehensive approach using deep learning techniques to improve diagnostic accuracy and treatment planning for brain tumors. The Random Forest algorithm, SVM, YOLOv5, and Neural Network classifier were utilized to classify brain tumors and different planes of brain MRI images. To segment the tumors, the U-Net architecture with various neural network backbones (VGG16, Resnet34, and InceptionV3) was implemented, and metrics such as intersection over union (IOU) and dice coefficient were used to assess the segmentation outcome. The U-Net model achieved an IOU of 0.73 and a dice coefficient of 0.84. The Random Forest algorithm achieved 99 percent and 95 percent classification accuracy in image planes and tumor types, respectively. The SVM, YOLOv5, and Neural Network Classifier provided classification accuracies of 91 percent, 95 percent, and 96 percent, respectively, in classifying brain tumors. The ensemble models predicted the final result, demonstrating the potential of this approach to improve brain tumor diagnosis and treatment planning.. © 2023 IEEE.

关键词： Tumors

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A Proposed Model for Machine Learning-Based Early Chronic Kidney Disease Detection 3

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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Field Demonstration of Blockchain-based Security for a Solar Farm

Field Demonstration of Blockchain-based Security for a Solar...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Ahn, BoHyun Nakka, Kalyan Handke, Nathanial R. Reyna, Trevor J. Kim, Taesic Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science KingsvilleTX78363 United States Texas A&m University Dept. of Computer Science and Engineering College StationTX77843 United States University of Missouri-Columbia Dept. of Electrical Engineering and Computer Science ColumbiaMO65211 United States

ISBN: (纸本)9798350376067

This paper introduces a Blockchain (BC)-based security model for a solar farm, providing security functions such as firmware patching management, role-based access control, public key infrastructure, and man-in-the-middle attacks detection (MITM), malware file detection. In particular, this paper provides detailed field-testing methods at a solar fam to demonstrate the feasibility and effectiveness of cyber-attack detection methods. Practical cyberattacks targeting solar farms are designed and conducted. It is demonstrated that the proposed BC-based system proactively detects MITM attacks, firmware modification attack, and malware attack, while ensuring the continuous operation of the solar farm during attack events. © 2024 IEEE.

关键词： Cyber attacks

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Partial Safety Factors for Hydraulic Conductivity Requirements of Granular and Geotextile Filters

Partial Safety Factors for Hydraulic Conductivity Requiremen...

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2023 Geo-Congress: Sustainable Infrastructure Solutions from the Ground Up - Foundations, Retaining Structures, and Geosynthetics

作者： Shubham, Kalore A. G.L., Sivakumar Babu Dept. of Civil Engineering Indian Institute of Science Bengaluru India

ISBN: (数字)9780784484685

ISBN: (纸本)9780784484708

The presence of a filter media impedes water flow in terms of generating excess pore pressure and decreasing the flow rate of the soil-filter system compared to the flow rate in soil. Therefore, the hydraulic conductivity requirement includes excess pore pressure and flow rate requirements. Different criteria for granular and geotextile filters are currently developed empirically based on experimental investigations. This paper proposes an analytically derived design criterion for the hydraulic conductivity requirements for granular and geotextile filters. The derivation has shown that the hydraulic conductivity requirements of pressure head and flow rate are satisfied with a sole condition of hydraulic conductivity of filter greater than or equal to the hydraulic conductivity of soil times the hydraulic gradient in soil. The coefficient of variation of saturated hydraulic conductivity is often greater than 50%, reflecting a need to quantify the uncertainty in the design criterion. Therefore, partial safety factors are developed for the proposed criterion to quantify the variabilities of hydraulic conductivities of soil and filter such that the requirement is satisfied at targeted reliability. © 2023 American Society of civil Engineers (ASCE). All rights reserved.

关键词： Hydraulic conductivity

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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An ensemble learning-based machine learning with voting mechanism for chronic disease prediction

An ensemble learning-based machine learning with voting mech...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

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LeXNet++: Layer-wise eXplainable ResUNet++ framework for segmentation of colorectal polyp cancer images

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Neural Computing and Applications 2025年第1期37卷 213-229页

作者： Das, Surajit Khan, Soumya Suvra Sengupta, Diganta Debashis, De Dept. of Information Technology Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Heritage Institute of Technology Chowbaga Road West Bengal Anandapur Kolkata700107 India Dept. of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal NH-12 Simhat Haringhata West Bengal Nadia741249 India

Colorectal polyps are benign lesions that develop in the colon and can progress to cancer if left untreated. Clinical observations from medical images are often preferred over computational results due to the lack of trust in the machine learning models, thereby posing serious challenge for the explainability of the results. In order to computationally diagnose colorectal polyps from cancerous images and explain the results, we propose a Layer-wise eXplainable ResUNet++ (LeXNet++) framework for segmentation of the cancerous images, followed by layer-wise explanation of the results. We utilize a publicly accessible dataset that contains of 612 raw images with a resolution of 256×256×3 and an additional 612 clinically annotated and labeled images with a resolution of 256×256×1, which includes the infected region. The LeXNet++ framework comprises of three components—encoder, decoder and the bridge. The encoder and the decoder components each comprise of four layers. Each of the four layers in the encoder and the decoder comprises of 14 and 11 internal sub-layers, respectively. Among the sub-layers of the encoder and the decoder, there are three 3×3 convolutional layers with an additional 3×3 convolution-transpose layer in the decoder. The output of each of the sub-layers has been explained through heatmap generation after each iteration which have been further explained. The encoder and the decoder are connected by the bridge which comprises of three sub-layers. The results obtained from these three sub-layers have also been explained to inculcate trust in the findings. In this study, we have used three models to segment the images, namely UNet, ResUNet, and proposed LeXNet++. LeXNet++ exhibited the best result among the three models in terms of performance;hence, only LeXNet++ was explained layer-wise. Apart from explanation of the results fetched in this study, the performance of the proposed explainable model has been observed to be 2% greater than the existing poly

关键词： Image segmentation

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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