Ergonomics For People With Disabilities

Multi-Criteria Analysis for Priority-setting in Mine Action

2nd Edition of the Multidiscipinary Doctoral Days (IMDD23)

3RD INTERNATIONAL CONFERENCE ON BIG DATA AND MACHINE LEARNING (BML'22)

IMDIS

Smart Analysis of Tourism Policy Efficiency in Bulgaria for the Period 1980-2017

International Symposium on Technikpsychologie (TecPsy) 2023

Python Data Structures Pocket Primer

C Programming

Angular and Deep Learning Pocket Primer

Pandas Basics

Data Cleaning Pocket Primer

Computer Concepts and Management Information Systems

Microsoft Excel Programming Pocket Primer

Working with grep, sed, and awk Pocket Primer

C Programming Pocket Primer

AutoCAD 2016

Practical Heat Transfer

Software Testing

Data Science Tools

<DIV align=justify> The 3rd edition of the International Conference on Big Data and Machine Learning　(BML’22)　is a major gathering of researchers, engineers, and practitioners within the fields of Big Data, Machine Learning, and Artificial Intelligence, from both theoretical and practical perspectives. We strongly encourage contributions that describe real-world problems, interdisciplinary research, and experimental and/or theoretical studies that yield new insights to advance Big Data and Machine Learning. Submitted papers should be original and relevant to any of the areas listed below. All accepted papers will be presented at the conference by one of the authors and published in the proceedings with an ISBN. The acceptance will be based on their quality, relevance, and originality. Presentation options include both oral and poster sessions. We have planned special sessions for case studies and commercial presentations, as well as tutorials focusing on technical or scientific matters. Companies interested in presenting their products or methodologies, as well as researchers wishing to demonstrate a demo or present a tutorial are encouraged to contact the conference secretariat. We cordially invite all speakers and participants to present their research and/or experiences at BML’22concerning the subjects listed below. While each of the main topic areas is further elaborated below, the sub-topics list provided is not exhaustive. Authors may address one or more of the listed sub-topics, while also being free to explore additional ones that align with the following main topic areas: <ul> <li> BIG DATA </li> <li> MACHINE LEARNING </li> <li> ARTIFICIAL INTELLIGENCE </li> </ul> Registration Fees <img src="https://sciendo-storage-bucket.s3.eu-central-1.amazonaws.com/wp-content/uploads/2023/04/03080231/FEES.jpg" alt='' /> Open Access Licence These conference proceedings provide immediate open access to its content under the CC-BY https://creativecommons.org/licenses/by/4.0/. Authors who will publish with these proceedings retain all copyrights and agree to the terms of the above-mentioned CC-BY 4.0 licence Open Access Statement This conference proceedings is an Open Access proceedings that allows a free unlimited access to all its contents without any restrictions upon publication to all users. <a href="https://sciendo-storage-bucket.s3.eu-central-1.amazonaws.com/wp-content/uploads/2023/04/03091333/Editorial_Policy-4.pdf">Editorial Policy</a> <a href="https://sciendo-storage-bucket.s3.eu-central-1.amazonaws.com/wp-content/uploads/2023/04/03091336/Instructions_for_authors-4.pdf">Instructions for authors</a> <a href="https://sciendo-storage-bucket.s3.eu-central-1.amazonaws.com/wp-content/uploads/2023/04/03094032/National_Scientific_Committee_978-83-67405-23-2.pdf">National Scientific Committee</a> <a href="https://sciendo-storage-bucket.s3.eu-central-1.amazonaws.com/wp-content/uploads/2023/04/03094035/International_Scientific_Committee_978-83-67405-23-2.pdf">International Scientific Committee</a>

23-24 May, 2022  Istanbul, Turkey (Hybrid)

A Hybrid Machine Learning-Based Malware Detection Model

A Hybrid Machine Learning-Based Malware Detection Model

Frontmatter

{"title":{"xml:lang":"en","content":"Frontmatter"}}

Contents

{"title":{"xml:lang":"en","content":"Contents"}}

Automatic Filtering of LiDAR Building Point Cloud Using Multilayer Perceptron Neuron Network

Automatic Filtering of LiDAR Building Point Cloud Using Multilayer Perceptron Neuron Network

The Profitable Gates Location on the Highway of the Dynamic Wireless Charging Transmitter for the Electric Vehicles

The Profitable Gates Location on the Highway of the Dynamic Wireless Charging Transmitter for the Electric Vehicles

Metadata modeling for data lakes based on dynamic ontologies

Metadata modeling for data lakes based on dynamic ontologies

{"title":{"xml:lang":"en","content":"Metadata modeling for data lakes based on dynamic ontologies"}}

A Novel hybrid model for the detection of drivers' drowsiness based on EEG signals and personalized extra-trees classification

A Novel hybrid model for the detection of drivers' drowsiness based on EEG signals and personalized extra-trees classification

Optimal power flow using Machine Learning: A survey

Optimal power flow using Machine Learning: A survey

{"title":{"xml:lang":"en","content":"Optimal power flow using Machine Learning: A survey"}}

Cartography for the training of Deep Learning algorithms on fire forest risk areas in the TAZEKKA park of MOROCCO: Geostatistical approach

Cartography for the training of Deep Learning algorithms on fire forest risk areas in the TAZEKKA park of MOROCCO: Geostatistical approach

Waste collection by compartmentalized vehicles in smart cities

Waste collection by compartmentalized vehicles in smart cities

Brain Tumor Semantic Segmentation Using Convolutional Neural Networks in 2D MRI Images

Brain Tumor Semantic Segmentation Using Convolutional Neural Networks in 2D MRI Images

The Truncated M-Fractional Ordinary Differential Equations: Lie Symmetry Analysis

The Truncated M-Fractional Ordinary Differential Equations: Lie Symmetry Analysis

{"title":{"xml:lang":"en","content":"A Hybrid Machine Learning-Based Malware Detection Model"}}

High efficacy of handling imbalanced data to predict bankruptcy

High efficacy of handling imbalanced data to predict bankruptcy

Objective weighting methods combined with TOPSIS and their effects on ranking nodes in social networks

Objective weighting methods combined with TOPSIS and their effects on ranking nodes in social networks

Big Data: Concept and Security and Privacy Challenges

Big Data: Concept and Security and Privacy Challenges

{"title":{"xml:lang":"en","content":"Big Data: Concept and Security and Privacy Challenges"}}

Big Data in Precision Agriculture: A comprehensive Review

Big Data in Precision Agriculture: A comprehensive Review

{"title":{"xml:lang":"en","content":"Big Data in Precision Agriculture: A comprehensive Review"}}

Assessment in e-learning systems Feedback on the adoption of peer assessment for the correction of continuous control in algorithmic and programming

Assessment in e-learning systems Feedback on the adoption of peer assessment for the correction of continuous control in algorithmic and programming

Exploring Big Data Analytics: Challenges and Infrastructures

Exploring Big Data Analytics: Challenges and Infrastructures

{"title":{"xml:lang":"en","content":"Exploring Big Data Analytics: Challenges and Infrastructures"}}

LSTM Recurrent Neural Network for Daily Forecast of PM10 Concentrations in Tangier

LSTM Recurrent Neural Network for Daily Forecast of PM10 Concentrations in Tangier

 The 3rd edition of the International Conference on Big Data and Machine Learning　(BML’22)　is a major gathering of researchers, engineers, and practitioners within the fields of Big Data, Machine Learning, and Artificial Intelligence, from both theoretical and practical perspectives.   We strongly encourage contributions that describe real-world problems, interdisciplinary research, and experimental and/or theoretical studies that yield new insights to advance Big Data and Machine Learning.   Submitted papers should be original and relevant to any of the areas listed below.

 The 3rd edition of the International Conference on Big Data and Machine Learning　(BML’22)　is a major gathering of researchers,...

A Hybrid Machine Learning-Based Malware Detection Model

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