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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Malathi, P. Legapriyadharshini, N. Nair, Shruthi S Sujatha, M.P. Sadaieswaran, R. Thirumalaikumari, T. Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Department of Computer Science and Engineering Chennai India Tagore College of Arts and Science Department of Computer Applications Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350309140

The purpose of the study is to address the essential need for a more efficient and objective technique for detecting language disorders in children, overcoming the constraints of standard manual evaluation systems. Existing systems, which utilize subjective evaluations and manual scoring, frequently result in lengthy processes and possible errors. To solve these issues, the proposed system automates the identification process by combining Natural Language Processing (NLP) and Machine Learning (ML). The system achieves a breakthrough in accuracy by normalizing text and extracting a comprehensive collection of linguistic features using advanced NLP techniques. The implementation involves difficult data preprocessing, extensive feature extraction, and the application of a cutting-edge Support Vector Machine (SVM) classifier. The proposed system achieves a 15% increase in accuracy, a 12% increase in precision, a 10% increase in recall, and an 11% increase in F1 score over existing systems. The confusion matrices show improved prediction skills, while accuracy across varied linguistic samples demonstrates the system's adaptability. Finally, the proposed system not only speeds up the detection of language disorders but also provides the groundwork for deeper insights into linguistic development, with revolutionary potential in educational and therapeutic settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic Histopathological Image Classification using Multi-Deep Features with Handcrafted Features and Shallow Machine Learning Models

Automatic Histopathological Image Classification using Multi...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Sunrit Banerjee Sibarama Panigrahi Department Of Computer Science and Engineering Intelligent Data Analytics Laboratory National Institute Of Technology Rourkela Odisha India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Invasive ductal carcinoma (IDC) is one of the most common variant of breast cancer affecting women more frequently than any other cancer. One crucial clinical duty is to correctly detect and classify breast cancer subtypes. Automated techniques can help to reduce errors and save time. However, determining the malignancy from tissue biopsies is difficult and dependent on the subjectivity of the observer. To diagnose breast cancer, a novel classification model using fusion of deep features VGG16 and VGG19 with handcrafted feature GLCM alongside the Medium Gaussian SVM classifier is developed in this paper. Additionally, thirteen hybrid classifiers are used to study the classification of breast cancer. These classifiers use four deep features, three handcrafted features, and a further fusion of deep features to produce six more combinations. Resnet50, VGG16, VGG19 and GoogleNet (Inception v3) are the pre-trained networks used to extract the deep features. Similarly, the handcrafted feature extraction techniques used are HOG, LBP and GLCM. Following evaluation, the classification model is framed by considering the best-performing deep features (VGG16 and VGG19 fusion model), handcrafted feature (GLCM) and classifier (Medium Gaussian SVM). Area Under Curve (AUC) of 0.91, Accuracy of 87.8% and Positive Likelihood Ratio (LR+) of 12.2 were attained by the Medium Gaussian SVM based classification model that combined the features of GLCM with the fusion model of VGG16 and VGG19. According to STARD guidelines, the recommended diagnostic technique is Excellent (AUC is between 0.9 and 1.0 and LR+ is above 10.0). The achieved results also outperformed the recently published articles on the same domain tackling similar problems.

关键词： Support vector machines Accuracy Observers Feature extraction Breast cancer Robustness Optimization Image classification Guidelines Residual neural networks

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Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost

arXiv

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arXiv 2024年

作者： Attota, Dinesh Chowdary Yarmohammadtoosky, Sahar Department of Data Science & Analytics College of Computer Science and Software Engineering Kennesaw State University MariettaGA United States

As several studies have shown, predicting credit risk is still a major concern for the financial services industry and is receiving a lot of scholarly interest. This area of study is crucial because it aids financial organizations in determining the probability that borrowers would default, which has a direct bearing on lending choices and risk management tactics. Despite the progress made in this domain, there is still a substantial knowledge gap concerning consumer actions that take place prior to the filing of credit card applications. The objective of this study is to predict customer responses to mail campaigns and assess the likelihood of default among those who engage. This research employs advanced machine learning techniques, specifically logistic regression and XGBoost, to analyze consumer behavior and predict responses to direct mail campaigns. By integrating different data preprocessing strategies, including imputation and binning, we enhance the robustness and accuracy of our predictive models. The results indicate that XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation. These findings suggest that XGBoost is particularly effective in handling complex data structures and provides a strong predictive capability in assessing credit risk. Copyright © 2024, The Authors. All rights reserved.

关键词： Logistic regression

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A Comparison of the Instructor-Trainee Dance dataset Using Cosine similarity, Euclidean distance, and Angular difference 26

A Comparison of the Instructor-Trainee Dance Dataset Using C...

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26th International computer science and Engineering Conference, ICSEC 2022

作者： Srikaewsiew, Thanawat Khianchainat, Khatadet Tharatipyakul, Atima Pongnumkul, Suporn Kanjanawattana, Sarunya Institute of Engineering Suranaree University of Technology Department of Computer Engineering Nakhon Ratchasima Thailand Data Science and Analytics Research Group Pathumthani Thailand

ISBN: (纸本)9781665491983

The COVID-19 outbreak has restricted most outdoor activities, leads to increasing interest in exercise at home with online trainers. One issue of online exercise technology is the safety since improper motion might result in injury. As a basis to prevent improper motion, methods for evaluating the motion similarity between an instructor and a trainee are essential. Cosine similarity, Angular difference, and Euclidean distance are three general ways for the motion evaluation. This study aimed to determine the most effective way for analyzing the similarity of human motion on the dataset of instructor-led dances. We first experimented with the data to find the appropriate cut-off value for classifying posture into two classes based on the similarity score. Confusion matrix, precision, recall, F1-score, accuracy of the results were then used to compare the efficiency. We discovered that Cosine similarity had the highest accuracy, 82.77 percent at cut-off 93. © 2022 IEEE.

关键词： COVID-19

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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Optimization Study of Renal CT Image Classification Using Convolution Neural Networks 14

Optimization Study of Renal CT Image Classification Using Co...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Afnaan, K. Nagaraja, K.V. Singh, Tripty Bhanu Prakash, K.N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Bengaluru India A∗STAR Clinical Data Analytics and Radiomics Singapore

ISBN: (纸本)9798350335095

computer tomography (CT), also known as computed tomography, is a medical imaging method that generates detailed and precise horizontal or axial images of targeted regions of the body for diagnostic purposes. In this particular study, the focus was on performing image classification of CT scan images of the Kidney. This organ plays an essential role in detoxification, fluid balance, and maintaining electrolyte levels in the human body. The dataset used for the study contained 12,446 images into four labeled classes - Cyst, Tumor, Stone, and Normal. Convolution Neural Network (CNN) model is built for binary (Normal and Tumor) and multi-image Classification to classify the Renal CT images into four classes. CNN uses ReLU as an activation function for classifying these images. The Model is evaluated and compared based on Accuracy and Loss performance metrics by compiling the CNN model with different deep learning TensorFlow Keras optimizers for Version 2.11.0 for Binary Image Classification at 10,20, and 50 epochs. The CNN model is assembled with Adam Optimizer and Follows the Regularized Leader (FTRL) Optimizer. The Testing and training Accuracy and Losses achieved are evaluated with a learning rate of 0.001 and 0.0001. Adam Optimizer outperforms to be the best Optimizer for Binary and Multi-Image Classification. © 2023 IEEE.

关键词： Image classification

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases 6

Hybrid Convolutional Neural Networks for Enhanced Detection ...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Porna, Shweta Bhattacharjee Kabir, Md. Firoz Rana, Md. Imran Chowdhury Sajol, Md. Saiful Islam Roy, Tonmoy Khan, Mohammad Aman Ullah Adnan, Md. Al Bhavani, Girigula Durga Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Master's in Information Technology University of the Cumberlands United States Bachelor of Business Administration International American university Los Angeles CA United States Louisiana State University Department of Computer Science and Engineering LA United States Data Analytics & Information Systems Utah State University Logan United States Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka1212 Bangladesh Noakhali Science And Technology University Bsc in software engineering Bangladesh Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798331505790

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model's performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of 100 % on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture. © 2024 IEEE.

关键词： Fruits

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Thai Conversational Chatbot Classification Using BiLSTM and data Augmentation 1st

Thai Conversational Chatbot Classification Using BiLSTM and ...

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1st International Conference on data science and Artificial Intelligence, DSAI 2023

作者： Lhasiw, Nunthawat Tanantong, Tanatorn Sanglerdsinlapachai, Nuttapong Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks with Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9789819979684

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep Learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Classification (of information)

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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

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Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial Network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial Network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial Networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Thalaseemia Prediction Using Deep Neural Networks: A Step Towards Personalized Medicine

Thalaseemia Prediction Using Deep Neural Networks: A Step To...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： V. Pream Sudha S. Karpagavalli K. Sathiyakumari A.C. Sountharraj Department of Computer Science with Data Analytics Dr.N. G.P Arts and Science College Coimbatore India Department of Computer Science PSGR Krishnammal College for Women Coimbatore India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Thalaseemia is a series of genetic hemolytic diseases brought on by defective hemoglobin synthesis. It is prevalent in many Asian, African, and Mediterranean nations. Four million Indians are Thalaseemia carriers, and more than 100,000 are actively combating the condition, according to the World Health Organization. It is specifically taken under investigation because cases of Thalassemia that have clinical, hematologic characteristics cannot be effectively separated from cases of sickle cell anemia and are misdiagnosed as such. The precise profiles of the genetic and pathophysiological mechanisms of patients with Thalassemia are not yet well defined, despite the fact that they are clinically treated identically. Even among people with the same genotype, there are differences in the severity of the clinical phenotype. The frequency of complications changes over time, both within and between individuals. Patients display a striking variation in severity, from a milder clinical course to severe transfusion dependency and progressive organ destruction. This study focuses on the creation of a discriminative model for Thalaseemia classification. In this study, the prediction of Thalassemia is modeled as a pattern classification issue, and the models are constructed using deep learning approaches. The hybrid CNN-Long Short Term Memory (LSTM) based Thalaseemia prediction model has demonstrated improved performance, according to the performance evaluation of these models.

关键词： Deep learning Time-frequency analysis Phenotypes Precision medicine Predictive models Feature extraction data models Convolutional neural networks Long short term memory Blood

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