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3rd international conference on machine intelligence and signal processing, misp 2021

3rd International Conference on Machine Intelligence and Sig...

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3rd international conference on machine intelligence and signal processing, misp 2021

ISBN: (纸本)9789811908392

The proceedings contain 67 papers. The special focus in this conference is on machine intelligence and signal processing. The topics include: Internet of Things: A Survey on Fused machine Learning-Based Intrusion Detection Approaches;Image Fusion-Based Watermarking in IWT-SVD Domain;on Twin Bounded Support Vector machine with Pinball Loss;traffic Rule Violation Detection System: Deep Learning Approach;template-Based Thinning Method for Handwritten Gujarati Character’s Strokes and its Classification for Writer-Dependent Gujarati Font Synthesis;a Model on Intrusion Detection Using Firefly Algorithm and Classical machine Learning;deep Learning Framework Based on Audio–Visual Features for Video Summarization;a Comparative Study of Deep Learning Models for Word-Sense Disambiguation;MultiYOLO: Learning New YOLO Categories Without Full Retraining;Analysis of Fifteen Approaches to Automated COVID-19 Detection Using Radiography Images;vehicle Routing Problem Using Reinforcement Learning: Recent Advancements;disease Detection of Plant Leaves with the Aid of Region Growing and Neural Network: A Comparative Analysis;a Web Application for Early Prediction of Diabetes Using Artificial Neural Network: A Comparative Study;machine Learning Equipped Web-Based Disease Prediction and Recommender System;comparing the Predictive Accuracy of machine Learning Algorithms for Neonatal Mortality Risk Classification;Extraction of Waterbody Using Object-Based Image Analysis and XGBoost;Deep Learning-Based Image Retrieval in the JPEG Compressed Domain;fake News Detection Using Genetic Algorithm-Based Feature Selection and Ensemble Learning;Application of SPSS for Forecasting of Renewable Energy as Future Energy in India;robust Multi-task Least Squares Twin Support Vector machines for Classification;human Emotion Classification Based on Speech Enhancement Using Neural Networks.

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A Survey on Image processing and machine Learning Techniques for Detection of Pulmonary Diseases Based on CT Images 3rd

A Survey on Image Processing and Machine Learning Techniques...

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Sawant, Priya Sreemathy, R. SCTR’s Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789811908392

In spite of advancements in medical technology, lung diseases continue to cause death and substantial morbidity worldwide as these diseases directly affect the respiratory systems and many a times create a layer of lesions on the lungs. A wide gamut of organisms is known to cause various pulmonary infections and newer ones are also emerging. Computer-aided diagnosis (CAD) empowered with image processing and machine learning techniques is an important diagnostic tool for early diagnosis of a disease. These techniques have exhibited a high degree of accuracy. The primary objective of this paper is to present a comprehensive survey of the numerous works carried out to detect lung diseases from CT images using machine learning and deep learning techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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On Twin Bounded Support Vector machine with Pinball Loss 1

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Anagha, P. Balasundaram, S. School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi110067 India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

With the introduction of pinball loss for misclassification, we present a novel pinball loss-based twin bounded support vector machine (Pin-TBSVM) having regularization term, scatter and misclassification loss term to enhance noise robustness. Pin-TBSVM is a non-parallel classifier where two kernel generated surfaces are constructed by solving quadratic programming problems. Experimental results on fourteen benchmark datasets show that the proposed method achieves improved accuracy performance than the popular traditional methods considered for comparison clearly indicating the advantage of the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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3rd international conference on machine Learning, Image processing, Network Security and Data Sciences, MIND 2021

3rd International Conference on Machine Learning, Image Proc...

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3rd international conference on machine Learning, Image processing, Network Security and Data Sciences, MIND 2021

ISBN: (纸本)9789811958670

The proceedings contain 68 papers. The special focus in this conference is on machine Learning, Image processing, Network Security and Data Sciences. The topics include: Analyzing Wearable Data for Diagnosing COVID-19 Using machine Learning Model;comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy Data;a Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print;region Classification for Air Quality Estimation Using Deep Learning and machine Learning Approach;neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning;detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization;traditional Indian Textile Designs Classification Using Transfer Learning;classification of Electrocardiogram signal Using Hybrid Deep Learning Techniques;automated Detection of Type 2 Diabetes with Imbalanced and machine Learning Methods;fault Diagnosis in Wind Turbine Blades Using machine Learning Techniques;real-Time Detection of Vehicles on South Asian Roads;stock Market Prediction Using Ensemble Learning and Sentimental Analysis;Multiple Feature-Based Tomato Plant Leaf Disease Classification Using SVM Classifier;a Methodological Review of Time Series Forecasting with Deep Learning Model: A Case Study on Electricity Load and Price Prediction;unexpected Alliance of Cardiovascular Diseases and Artificial intelligence in Health Care;a Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care;An Enhanced Deep Learning Approach for Smartphone-Based Human Activity Recognition in IoHT;classification of Indoor–Outdoor Scene Using Deep Learning Techniques;prediction of the Reference Evapotranspiration Data from Raipur Weather Station in Chhattisgarh using Decision Tree-Based machine Learning Techniques;deep Transfer Learning and Intelligent Item Packing in Retail Management;preface.

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Feature Extraction from Plant Leaves and Classification of Plant Health Using machine Learning 3rd

Feature Extraction from Plant Leaves and Classification of P...

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Abisha, A. Bharathi, N. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus No.1 Jawaharlal Nehru Rd Tamil Nadu Vadapalani India

ISBN: (纸本)9789811908392

Feature extraction (FE) is an important method that contributes dimensionality reduction especially when extracting the features from images. It is the method of converting the input images into a set of features understandable by the system or software. While processing images, it is desirable to extract features that are oriented toward discernment between two or more classes. In this proposed method, two classes are considered as infected and healthy, and data is distributed into six cases. Features are extracted from the plant images using feature descriptors Hu moments, Haralick texture and color histogram. After feature extraction, the images are classified as infected or healthy based on the feature descriptors. The extracted data are trained with the various classifier models such as LR, LDA, KNN, CART and RF, and validation is performed. 10 k cross-validation is used, and the three best algorithms are chosen to classify and predict the model. Among these models, RF, CART and KNN have given better performance, respectively. Finally, accuracy score above 95% and r2 score above 85% is found for various cases, and the results are visualized for all six cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Comparing the Predictive Accuracy of machine Learning Algorithms for Neonatal Mortality Risk Classification 3rd

Comparing the Predictive Accuracy of Machine Learning Algori...

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Sivarajan, A. Bala Aditya, A. Sivasankar, E. Department of CSE National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9789811908392

Neonatal mortality is a major concern in developing countries and is a crude indicator of the prevalence of poverty in a society. Neonatal risk analysis is an important decision-making tool for gauging progress and penetration of medical services in a specific region, especially in developing countries. This research compares the accuracy of the predictions of various machine learning algorithms and the impact of different sampling techniques, when attempting to classify neonatal mortality. We have conducted different rounds of experiments with six sampling techniques and seven machine learning algorithms on a neonatal death dataset. Further, groups of three base classifiers chosen among the seven machine learning algorithms were made into six different ensemble combinations and trained to improve the classification accuracy. The proposed diagnostic support system with an ensemble of random forest classifier, Gaussian NB, and AdaBoost with pipeline sampling achieved an accuracy and ROC AUC scores above 98% and 96%, respectively. This decision support tool will facilitate medical practitioner to identify infants at risk of dying before 28 days of life. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Developing countries

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A Model on Intrusion Detection Using Firefly Algorithm and Classical machine Learning 1

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Ranjan, Prabhu Singh, Sunil Kumar Department of Computer Science and Information Technology Mahatma Gandhi Central University Motihari Bihar India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

Effective intrusion detection is one of the key concerns in increasing the sizes of the network. A network intrusion detection system is commonly used as a tool to detect and protect various networks. False-positive detection is one of the key issues because it can distract various organizations from legitimate securities. In this work, we have applied a firefly algorithm-based machine learning model to improve intrusion detection. Firefly-DT-based proposed model giving better classification accuracy in comparison to other KNN, random forest, and neural network models. Further, the firefly algorithm is used to find the optimal structure of the decision tree (DT) by minimizing the false positive and maximizing the true-positive intrusion detection rate. The NSL-KDD dataset is used as a benchmark to observe the outcomes of the proposed model. The performance analysis shows the usefulness of the model, and the comparative study also concludes the same. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer 1

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Kumar, Pullela Svvsr Neti, Praveen Kumar, Dirisala J. Nagendra Murthy, G.S.N. Lalitha, R.V.S. Kalyan Ram, Mylavarapu Aditya College of Engineering Surampalem India Vishnu Institute of Technology Bhimavaram India Aditya College of Engineering and Technology Surampalem India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

One of the most common diseases among the middle-aged women is breast cancer. The proposed system designed an efficient breast cancer detection and classification approach using an ensemble booster algorithm "XGBOOST" classifier. All the traditional approaches in machine learning have developed models with low variance or with high bias. So, the proposed system has evaluated the model with different evaluation metrics. In the world of machine learning, optimization has a great impact. To address this issue, the proposed system performed whirling of hyper-parameters during the classification process. Finally, the designed system is compared with conventional models. The major goal of the proposed system is to identify the breast cancer and classify the stage of cancer. So, the automated system can help the doctors to recommend the treatment or medicines and in turn the morality rate due to the breast cancer can be reduced. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Study of machine Learning Techniques to Mitigate Fraudulent Transaction in Credit Cards 3rd

Study of Machine Learning Techniques to Mitigate Fraudulent ...

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Sikder, Sayan Sarkar, Shubhasree Varghese, Eric G. Bhattacharjee, Pritam Department of CSE University of Engineering & Management West Bengal Kolkata700156 India Knowledge Sourcing Intelligence Uttar Pradesh Noida201301 India School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789811908392

Two very important aspects that associate with the growth of human civilization are consumerism and the urge of minimizing human efforts, not only physical but also intellectual, for regular functioning. Due to the substantial growth of consumerism, a significant increase in credit card transactions can be observed in recent years and that certainly comes with rampant credit card fraud issues as well. This study suggests popular machine learning methods to mitigate fraudulent credit card transactions and explores the effectiveness of it. Six strategically different and popular classifiers, namely—random forest, XGBoost, decision tree, K-nearest neighbors, Naïve Bayes approach, and logistic regression, are employed to study their applicability in the credit card fraud detection problem. Out of the 6 algorithms taken into consideration for this particular problem, random forest classifier has turned out to be the best one and Naive Bayes has turned out to be the worst one in prediction according to conventional parameters such as accuracy, recall, specificity, and precision. This paper also intends to present a basic framework to select the best among state-of-the-art models to address the credit card fraudulency problem. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Long-Term Average Spectral Feature-Based Parkinson’s Disease Detection from Speech 3rd

Long-Term Average Spectral Feature-Based Parkinson’s Diseas...

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3rd international conference on machine intelligence and signal processing, misp 2021

作者： Lahoti, Anshul Krishna, Gurugubelli Arroyave, Juan Rafel Orozco Vuppala, Anil Kumar Speech Processing Laboratory LTRC KCIS International Institute of Information Technology Hyderabad India Medellín Colombia

ISBN: (纸本)9789811908392

Parkinson’s disease (PD) is a speech disorder caused by the effect of neurological damage on motor control activity of speech production. Due to the noninvasive nature, detection of PD from the speech is remarkable. This study proposes the use of Long-Term Average Spectral (LTAS) feature to detect PD from speech using different filterbank techniques and classifiers on the PC-GITA dataset. The fact that PD can be perceived from long-temporal information has motivated us to explore the LTAS features for PD detection. Performance of LTAS features is compared to baseline features, namely statistical and openSMILE features (ComParE, GeMAPS, and eGeMAPS). The performance of these features is validated using five different classifiers. The LTAS features, which are obtained with auditory filterbanks (Constant Q Transform and Gammatone), showed better performance as compared to the statistical features (MFCC_Stat and LPCC_Stat) and openSMILE features (ComParE, GeMAPS, and eGeMAPS). The combination of the Gammatone-based LTAS feature with eGeMAPS further improved the detection accuracy of the PD detection system on the PC-GITA database. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Filter banks

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