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Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods

Remote sensing (Basel, Switzerland), 2024-09, Vol.16 (17), p.3210

2024

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Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda

BMC pregnancy and childbirth, 2022-04, Vol.22 (1), p.348-19, Article 348

2022

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Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Bioengineering (Basel), 2022-07, Vol.9 (8), p.335

2022

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Exploring Machine Learning Algorithms to Find the Best Features for Predicting Modes of Childbirth

IEEE access, 2021, Vol.9, p.1680-1692

2021

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Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing

Horticulturae, 2024-02, Vol.10 (2), p.186

2024

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Image Processing and Support Vector Machine (SVM) for Classifying Environmental Stress Symptoms of Pepper Seedlings Grown in a Plant Factory

Agronomy (Basel), 2024-09, Vol.14 (9), p.2043

2024

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Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model

Agriculture (Basel), 2024-08, Vol.14 (8), p.1390

2024

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Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Horticulturae, 2023-12, Vol.9 (12), p.1346

2023

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Machine Learning Model to Analyze Telemonitoring Dyphosia Factors of Parkinson’s Disease

International journal of advanced computer science & applications, 2021, Vol.12 (8)

2021

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TL-GAN: Transfer Learning with Generative Adversarial Network Model for Satellite Image Resolution Enhancement

2023 26th International Conference on Computer and Information Technology (ICCIT), 2023, p.1-5

2023

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A Machine Learning-Based System to Recommend Appropriate Military Training Program for a Soldier

Machine Intelligence and Emerging Technologies, p.151-161

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Development of Trustable Deep Learning Model in Remote Sensing through Explainable-AI Method Selection

2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2023, p.0261-0267

2023

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An Intelligent Vaccine Distribution Process in COVID-19 Pandemic through Blockchain-SDN Framework from Bangladesh Perspective

2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 2021, p.1-4

2021

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Prediction of Cesarean Childbirth using Ensemble Machine Learning Methods

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services, 2020, p.331-339

2020

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An Image-based Digital Forensic Investigation Framework for Crime Analysis

2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), 2023, p.1-6

2023

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Forecasting PV Panel Output Using Prophet Time Series Machine Learning Model

2020 IEEE REGION 10 CONFERENCE (TENCON), 2020, p.1141-1144

2020

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WebGuardML: Safeguarding Users with Malicious URL Detection Using Machine Learning

2023 26th International Conference on Computer and Information Technology (ICCIT), 2023, p.1-6

2023

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"Can NLP techniques be utilized as a reliable tool for medical science?" - Building a NLP Framework to Classify Medical Reports

2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2020, p.0159-0166

2020

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Speaker Identification through Gender Detection

2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2022, p.181-188

2022

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COVID-19 Classification from X-Ray Images using 2D CNN

2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), 2022, p.13-18

2022

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Analyzing Malware from System Calls by Using Machine Learning

Proceedings of Eighth International Congress on Information and Communication Technology, 2023, Vol.696, p.1127-1136

2023

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Selectively Oversampling Difficult Positive Samples from Imbalanced Data for Preprocessing

2019 22nd International Conference on Computer and Information Technology (ICCIT), 2019, p.1-6

2019

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Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods

Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Exploring Machine Learning Algorithms to Find the Best Features for Predicting Modes of Childbirth

Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing

Image Processing and Support Vector Machine (SVM) for Classifying Environmental Stress Symptoms of Pepper Seedlings Grown in a Plant Factory

Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Machine Learning Model to Analyze Telemonitoring Dyphosia Factors of Parkinson’s Disease

TL-GAN: Transfer Learning with Generative Adversarial Network Model for Satellite Image Resolution Enhancement

A Machine Learning-Based System to Recommend Appropriate Military Training Program for a Soldier

Development of Trustable Deep Learning Model in Remote Sensing through Explainable-AI Method Selection

An Intelligent Vaccine Distribution Process in COVID-19 Pandemic through Blockchain-SDN Framework from Bangladesh Perspective

Prediction of Cesarean Childbirth using Ensemble Machine Learning Methods

An Image-based Digital Forensic Investigation Framework for Crime Analysis

Forecasting PV Panel Output Using Prophet Time Series Machine Learning Model

WebGuardML: Safeguarding Users with Malicious URL Detection Using Machine Learning

"Can NLP techniques be utilized as a reliable tool for medical science?" - Building a NLP Framework to Classify Medical Reports

Speaker Identification through Gender Detection

COVID-19 Classification from X-Ray Images using 2D CNN

Analyzing Malware from System Calls by Using Machine Learning

Selectively Oversampling Difficult Positive Samples from Imbalanced Data for Preprocessing

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