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Ensemble-Stacking machine learning Model for Multi-Class Skin Diseases Identification 5

Ensemble-Stacking Machine Learning Model for Multi-Class Ski...

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5th international conference on Biomedical Engineering, IBIOMED 2024

作者： Tandelon, Dennis Christiono, David Lawrence Andryani, Nur Afny Catur Gondokaryono, Srie P. Rahardja, July I. Danarti, Retno School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Department Jakarta Indonesia Indonesian Society of Dermatology & Venereology Padjadjaran University Faculty of Medicine Indonesia Dermatovenereology Department Indonesia Indonesian Society of Dermatology & Venereology Gadjah Mada University Faculty of Medicine Indonesia Dermatovenereology Department Indonesia

ISBN: (纸本)9798350389265

Algorithm development on skin diseases identification with high cardinality classification is still very challenging. In addition, the high similarity appearance among diseases makes the computer vision approach meet high difficulties on providing clear distinct feature representation. In general, some literature shows that the performance of single machine learning or single deep learning techniques can be further improved when merged by ensemble mechanism. this approach has been applied on binary skin's cancer diseases diagnosis prediction and successfully improve the prediction ability. this paper elaborates the potential of ensemble machine learning approaches using stacking techniques on conducting feature extraction and classification model for identifying more general skin diseases based on dermoscopy images and mobile phone images. the proposed model combines three different CNN architectures, ResNet50, Xception, and VGG16 to extract features from the image then stacked as input for the model trained using five different algorithms, which are logistic regression, support vector machines, multi-layer perceptron, k-nearest neighbors, random forests and the combination of these algorithm in stacking classifier as the base estimators and final estimator. the simulation results present the proposed ensemble - stacking model succeeds to improve the identification ability in all utilized datasets. In addition, the proposed ensemble - stacking models show better recall which indicates good sensitivity on handling false negative. It is beneficial for medical application. © 2024 IEEE.

关键词： Federated learning

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machine learning techniques for identifying Cyberbullying on digital networks 4

Machine Learning techniques for identifying Cyberbullying on...

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4th international conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Gayathri, N. Prawin, R. Ranjith Kumar, A. Manikandan, R. Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9781665494144

With the proliferation of virtual entertainment platforms around the world, particularly among young people, digital taunting and enmity have become real and annoying problems that networks must address. threats can use these levels to attack and weaken others in their networks. To combat digital tormenting, various strategies and tactics have been used or proposed, including early detection and alarms that both detect and protect victims from such attacks. machine learning techniques with Artificial Intelligence Framework are being widely used to identify specific linguistic patterns that danger uses to hunt for their victims. the Feeling Analysis (FA) of virtual entertainment content is one of the expanding fields of research in AI. FA makes it possible to gradually identify online harassment and continuously recognize cyberbullying. this study recommends a SA model to identify cyberbullying messages in Facebook web-based entertainment. SVM and MAXENT classifier, controlled AI arrangement tools, are employed in this model. When a higher n-grams language model is applied to such texts in correlation with analogous prior research, the findings of the investigations carried out using this model showed encouraging results. Similar patterns in the results showed that these classifiers preferred execution measures over other classifiers on such remarks. © 2023 IEEE.

关键词： learning algorithms

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the Ensemble Learners: machine learning Classification of Diabetic Patient Readmission 4

The Ensemble Learners: Machine Learning Classification of Di...

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4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Xu, Qianwen Ariel Chang, Victor Eniola, Rahman Olamide Ganatra, Meghana Ashok Tung, Wong Siu Department of Operations and Information Management Aston Business School Aston University Birmingham United Kingdom School of Computing and Digital Technologies Teesside University Middlesbrough United Kingdom Department of Physics King's College London London United Kingdom

ISBN: (纸本)9798350341690

Diabetes is a major health problem that affects a large number of people worldwide. We proposed an effective and reliable method to diagnose diabetic readmission in this research based on machine learning models. the model was developed on tree-based ensemble classification algorithms, such as Decision Tree, Extreme Gradient Boost, Ada Boost, and CatBoost. In addition, to increase accuracy, we stacked the models with Stack Classifier using Catboost classifier as the final estimator. We carried out the experiments on the diabetic readmission dataset obtained from the UCI machine learning repository. We used the Grid Search technique to learn the best practices for model evaluation and hyperparameter tuning. the performances of all six algorithms are measured using different metrics such as AUC, Accuracy, and Recall. AUC measures a classifier's ability to distinguish between classes. CatBoost Classifier outperforms other algorithms in terms of AUC, Recall, and Accuracy, with 68 percent, 58.7 percent, and 63.2 percent, respectively. these findings are validated using ROC (Receiver Operating Characteristic) curves correctly and systematically. © 2023 IEEE.

关键词： Decision trees

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Few-shot object detection based on multi-scale attention model 4

Few-shot object detection based on multi-scale attention mod...

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4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

作者： Hao, Zongbo Lu, Juncong Luo, Aoyu University of Electronic Science and Technology of China No. 4 Section 2 Jianshe North Road Chenghua District Sichuan Chengdu610054 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Conventional deep learning based object detection methods demand substantial annotated data for training, incurring considerable time and labor costs. Conversely, few-shot object detection necessitates only limited data from novel categories, emerging as a prominent research focus. this study proposes the Attention Contrastive Network (ACNet) to address few-shot object detection challenges. ACNet incorporates an attention mechanism architecture, extracting attention values and keys from image features in both support and query sets. It compares key attention across the sets and weights query set features with attention to augment local features. Additionally, multi-scale pooling layers enhance the network's capability to identify objects across varying scales. the introduction of an attract-repel mechanism in the loss function significantly amplifies inter-class differences, thereby improving classification accuracy. ACNet's efficacy is experimentally affirmed on the PASCAL VOC and COCO datasets, yielding commendable results in few-shot detection tasks. © 2024 SPIE.

关键词： Object detection

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Detection of Defects in Soybean Seeds by Extracting Deep Features with SqueezeNet 12

Detection of Defects in Soybean Seeds by Extracting Deep Fea...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications, IDAACS 2023

作者： Koklu, Murat Kursun, Ramazan Yasin, Elham Tahsin Taspinar, Yavuz Selim Selcuk University Department of Computer Engineering Konya Turkey Guneysinir Vocational School Selcuk University Konya Turkey Graduate School of Natural and Applied Sciences Selcuk University Konya Turkey Doganhisar Vocational School Selcuk University Konya Turkey

ISBN: (纸本)9798350358056

A vital part of ensuring the quality of soybean products is detecting defects. the current study presents a five-category classification of soybean seed defects: broken, immature, intact, skin-damaged, and spotted soybean seeds. Our goal is to improve the overall quality of soybean products through accurately identifying and categorizing defects. computer vision techniques and machine learning algorithms are combined comprehensively to achieve this goal. To begin with, soybean seed images are analyzed using the SqueezeNet model, a deep-learning architecture known for its efficiency in image analysis. Features extracted from soybeans indicate the types of defects they present and their key visual characteristics. then, we applied three widely used machine learning algorithms, including Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), to classify soybean seed images. A labeled dataset of soybean seed images with known defects is used to train and fine-tune each algorithm. An appropriate evaluation metric is used for assessing each algorithm. It provides valuable insights into the application of machine learning techniques in improving product quality by improving the detection and classification of soybean seed defects. © 2023 IEEE.

关键词： ANN classification feature exraction LR machine learning RF soybean

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Using machine learning Techniques for Outlier Detection application 4

Using Machine Learning Techniques for Outlier Detection Appl...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Chahar, Ravindra Kumar Singh, Ajay Shanker Galgotias University School of Computing Science & Engineering Greater Noida India

ISBN: (纸本)9781665474368

the aim of designing models that have the ability for adapting to their surroundings and have capability for learning from past data has gained attention from numerous spheres, comprising computing engineering, artificial intelligence, statistics, and mathematics. Numerous machine learning methods have evolved from research in the field. Supervised, unsupervised and reinforcement techniques have been applied for a variety of scientific and industrial applications. this paper introduces a description of a scheme for outlier detection application that is based on Isolation Forest machine learning technique. the scheme is an unsupervised learning method. the scheme also uses Random Forest machine learning technique for the application. this method is a supervised learning method. the learning techniques are tested on a sample system. A comparison of Isolation Forest technique with other unsupervised learning algorithms is presented, along with its comparison to supervised Random Forest Classifier algorithm. © 2022 IEEE.

关键词： Anomaly detection

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An Interpretable machine learning Model with Deep learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease 26th

An Interpretable Machine Learning Model with Deep Learning-B...

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26th international conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Kang, Wenjie Li, Bo Papma, Janne M. Jiskoot, Lize C. De Deyn, Peter Paul Biessels, Geert Jan Claassen, Jurgen A. H. R. Middelkoop, Huub A. M. van der Flier, Wiesje M. Ramakers, Inez H. G. B. Klein, Stefan Bron, Esther E. Erasmus MC Dept Radiol & Nucl Med Rotterdam Netherlands Erasmus MC Dept Neurol Rotterdam Netherlands Univ Med Ctr Groningen Dept Neurol Groningen Netherlands Univ Med Ctr Groningen Alzheimer Ctr Groningen Netherlands Univ Med Ctr Utrecht UMC Utrecht Brain Ctr Dept Neurol Utrecht Netherlands Radboud Univ Nijmegen Med Ctr Nijmegen Netherlands Leiden Univ Med Ctr Dept Neurol & Neuropsychol Leiden Netherlands Leiden Univ Inst Psychol Hlth Med & Neuropsychol Unit Leiden Netherlands Univ Amsterdam Med Ctr VUmc Amsterdam Netherlands Maastricht Univ Med Ctr Alzheimer Ctr Limburg Sch Mental Hlth & Neurosci MHeNS Maastricht Netherlands

ISBN: (纸本)9783031474002;9783031474019

machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. the proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. the proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.

关键词： Alzheimer's disease MRI Convolutional neural network Explainable boosting machine Interpretable AI

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Dual Autoencoder-based Framework for Image Compression and Decompression 15

Dual Autoencoder-based Framework for Image Compression and D...

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15th international conference on machine Vision (ICMV)

作者： Patel, Bhargav Ahmedabad Univ Ahmadabad Gujarat India

ISBN: (纸本)9781510666184;9781510666191

Recent development in deep learning has shown great possibilities in many computer vision tasks. Image compression is a crown field of computer vision, and deep learning is gradually being used for image compression & decompression tasks. the compression rate of the lossy compression algorithm is higher than lossless compression but the main disadvantage of lossy compression is the loss of data while compressing it. It is observed that a higher compression rate causes higher data loss. Recent advancements in deep learning technique for computer vision like image noise reduction and image super-resolution has shown great possibilities in the area of image enhancement. If these techniques can be utilized to mitigate the impact of a higher compression rate on the output of lossy compression, then the nearly same image quality can be achieved. In this paper, an image compression & decompression framework are proposed. this framework is based on two convolutional neural networks (CNN) based autoencoders. Images and videos are the main sources of unstructured data. Storing and transmitting these unstructured data in the cloud server can be costly and resource-consuming. If a lossy compression can compress the image to store in the cloud server then it can reduce the storage cost and space. We have achieved a 4x compression ratio which means an image will occupy only 25% space compared to the original image. the original image will be retrieved using a joint operation of deconvolution and image enhancement algorithms. the proposed framework receives state-of-the-art performance in terms of Peak Signal-to-Noise ratio (PSNR) and Structure Similarity Index Measure (SSIM) with 4x compression.

关键词： Auto-Encoder Convolutional neural network (CNN) Image compression & decompression Deep learning

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Image Analysis and machine learning in Malaria Parasite Detection: Recent Advances and Future Perspective 4th

Image Analysis and Machine Learning in Malaria Parasite Dete...

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4th international conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Aggarwal, Gunjan Goyal, Mayank Kumar Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819736898

Plasmodium parasites cause malaria, a deadly disease that continues to pose a significant global health burden, particularly in resource-limited regions. Detecting and classifying the parasite accurately and promptly are crucial for controlling the progression of the disease. Malaria detection and classification are most frequently performed using a single-class classification approach. Single-class classification approaches include image-based classification, feature-based classification, and machine learning-based classification. Data from multiple classes are used in multi-class classification, which is more complex than single-class classification. these methods are often more extensive than single-class techniques since they can identify and categorize several Plasmodium species and their life stages. Ensemble learning, fuzzy logic-based classification, and deep learning-based classification are some of the common multi-class classification techniques. In addition to their advantages, each approach has its own disadvantages. An in-depth review of existing single-class and multi-class approaches for malaria classification is provided in this paper to gain a better understanding of the different approaches. In the end, this study attempts to present a thorough analysis of the current approaches for classifying malaria. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive learning

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4th international conference on Emerging Research in Electronics, computer Science and Technology, ICERECT 2022

4th International Conference on Emerging Research in Electro...

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4th international conference on Emerging Research in Electronics, computer Science and Technology, ICERECT 2022

ISBN: (纸本)9781665456357

the proceedings contain 260 papers. the topics discussed include: blooms taxonomy based gradation of the question paper;software effort estimation using hard limiting techniques with special reference to small size technical & analytical projects;deep learning based handwritten arithmetic equation solver;anomaly detection in cardiac related datasets;comparison study of buck-boost and zeta converter for real time application;prediction of rainfall using machine learning;tea leaf disease classification and tea bud identification;model-checking-based automated test case generation for Z formal specification of an urban railway interlocking system;enhanced thyroid nodule classification adopting significant features selection;comparative study on filters with special reference to pterygium images;plant foliage recognition based on classification using artificial neural network;and parametric analysis of piezoelectric-sensor array in foot step power generation system.

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