Abdallah Abarda (0000-0002-9408-4991)

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Abdallah Abarda is a lecturer-researcher at the Faculty of Economics and Management of Settat, specializing in statistics and data analysis. He holds a Ph.D. in Applied Mathematics and Computer Science, with particular expertise in applying machine learning methods to complex problems.

Throughout his career, Abdallah Abarda has contributed to several research areas, including lung disease diagnosis, community detection in social networks, and large-scale data analysis using advanced statistical methods. His work has been published in renowned scientific journals such as Current Medical Imaging, Computing, and the International Journal of Data Analysis Techniques and Strategies.

As a prolific author, Abdallah Abarda has also presented his research at numerous international conferences and contributed to several collective works, particularly in the fields of statistic, computer science and artificial intelligence.

Thanks to his expertise and significant contributions to research, Abdallah Abarda continues to be recognized as a leader in his field, making valuable contributions to the understanding and application of statistical methods and machine learning in various scientific domains.

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Université Hassan 1er: Maroc, MA

Research Professor

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Abdallah Abarda

https://orcid.org/0000-0002-9408-4991

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Employment (1)

Université Hassan 1er: Maroc, MA

Education and qualifications (2)

Institut National de Statistique et d'Economie Appliquée: Rabat, Rabat-Salé-Kénitra, MA

faculty of science kenitra: Kenitra, Rabat-Salé-Kénitra, MA

Works (33)

A Deep Learning and Resampling Approach to Credit Card Fraud Detection

Advancements in detecting, preventing, and mitigating DDoS attacks in cloud environments: A comprehensive systematic review of state-of-the-art approaches

An Optimal Model Combining SqueezeNet and Machine Learning Methods for Lung Disease Diagnosis

Applying k-nearest neighbors to time series forecasting: Two new approaches

DeepDefend: A comprehensive framework for DDoS attack detection and prevention in cloud computing

Interpretable Deep Learning for DDoS Defense: A SHAP-based Approach in Cloud Computing

Latent class analysis: A review and recommendations for future applications in health sciences

The impact of feature extraction techniques on the performance of text data classification models

Arabic Fraudulent Online Advertisements Dataset

Arabic Fraudulent Online Advertisements Dataset

DeepDefend: A comprehensive framework for DDoS attack detection and prevention in cloud computing

DeepDefend: A comprehensive framework for DDoS attack detection and prevention in cloud computing

A New Hybrid Artificial Intelligence Model for Diseases Identification

Using Machine Learning with Pyspark for Solving a Big Data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS)

An efficient local algorithm for overlapping community detection in social networks

The greedy coupled-seeds expansion method for the overlapping community detection in social networks

Applying K-nearest neighbors to time series forecasting: Two new approaches

Evaluating information criteria in latent class analysis: Application to identify classes of breast cancer dataset

Using Machine Learning with PySpark and MLib for Solving a Binary Classification Problem: Case of Searching for Exotic Particles

Evaluating information criteria in latent class analysis: application to identify classes of breast cancer dataset

A Comparison of Random Forest Methods for Solving the Problem of Pulsar Search

Detection of Pulsar Candidates using Bagging Method

Grey-Markov Model for the Prediction of the Electricity Production and Consumption

Higgs Boson Discovery using Machine Learning Methods with Pyspark

Latent Transition Analysis (LTA): A Method for Identifying Differences in Longitudinal Change among Unobserved Groups

Using Ensemble Methods to Solve the Problem of Pulsar Search

A study of the application of statistical methods for big data

Asymptotic ruin probabilities in a discrete-time risk model with heavy-tailed random sums

Solving the problem of latent class selection

Exploring heterogeneity in clinical trials with latent class analysis.

A Divided Latent Class analysis for Big Data

Application of latent class analysis to identify the youth population who risk being cybercrime victim on social networks

Probabilistic approach to estimate the risk of being a cybercrime victim