Accueil › Annuaire des chercheurs › Achraf Benba
Achraf Benba
Affiliation : Mohammed V University
- 60Publications (affiliation marocaine)
- 75Publications au total
- 707Citations
- 13Indice h
Profil de recherche
Achraf Benba compte 60 publications référencées (2014–2026). Ses travaux paraissent principalement dans « Lecture notes in networks and systems », « International Journal of Speech Technology » et « Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska ». Son article le plus cité, « Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease » (2015), a été cité 102 fois. 33 de ces publications datent des trois dernières années d’activité. Ses travaux sont cosignés avec au moins 12 chercheurs, notamment dans des établissements de France.
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Créer mon profil gratuit Ajouter mes publicationsDomaines de recherche
- Artificial Intelligence in Healthcare
- Voice and Speech Disorders
- Emotion and Mood Recognition
- Speech Recognition and Synthesis
- AI in cancer detection
Publications (60)
- Efficient CNN-based classification of white blood cells: a comparative study of model performance
- Explainable Parkinson’s Disease Detection Using Temporal Attention Mechanism on Voice Analysis
- Bridging Clinical Prediction and Operational Action: An AI-Driven Framework with a Translation Layer for Enhanced Hospital Information systems
- Bio-Inspired Biomedical System Integrating Selective Sensors and Neural AI for Patient Monitoring and Early Cancer Detection
- An Overview of Wearable Sensor-Based Machine and Deep Learning Approaches in Parkinson’s Disease Diagnosis
- Parkinson’s Disease Classification from Smartwatch Non-Motor Symptom Questionnaires in the PADS Dataset
- Enhanced Temporal-Spectral Fusion Network for Parkinson’s Disease Detection from Voice Recordings
- Quality Audit in Hospital Pharmacy: An Essential Tool for Excellence in Patient Care
- Algorithms and Cybersecurity in Hospital Pharmacy Quality Audits: A Comprehensive Review of Emerging Technologies and Challenges
- A Comprehensive Review of Deep Learning for COVID-19 and Pneumonia Classification Using Chest X-Ray
- AI and Signal Processing Baseline for Multi-Pathology Chest X-Ray Detection toward Smart Health Sensor Networks
- Performance of Machine Learning and Deep Learning Algorithms for Lung Cancer Prediction
- Selective Sensor Networks with AI for Head and Neck Cancer Biomarker Detection Using SERS Spectroscopy
- Handwriting-Based Parkinson's Disease Detection: A Comparative Study of Classical CNN Architectures with Advanced Image Preprocessing
- Hybrid deep learning for dysarthric speech recognition: a benchmark study using the UASPEECH preprocessed dataset
- Resource-aware continuous ECG monitoring with selective cloud escalation and exact decision reuse
- Detecting Tuberculosis Disease from Chest X-Ray Using Deep Learning
- Non-invasive detection of Parkinson’s disease using voice analysis and machine learning techniques
- Optimized Deep Learning Model for Early Parkinson’s Disease Detection Using Speech Spectrograms
- Hybrid models for handwriting-based diagnosis of Parkinson's disease
- Motion detection for patient safety in CT scanner
- Machine learning prediction of histological types of breast cancer: a case study in Morocco
- Machine learning-based voice analysis for differentiating Parkinson’s disease and neurological disorders
- Enhancing early Parkinson’s disease diagnosis through handwriting analysis
- Developing Predictive Models for Breast Cancer Metastasis Using Machine Learning: A Study from the Hassan II Regional Oncology Center, Agadir, Morocco
- DR-MUSIC: Deep Learning-Based Reconstruction of ECG from Millimeter-Wave Radar for Contactless Cardiac Monitoring
- Design and implementation of a vein detection system for improved accuracy in blood sampling
- Automatic Classification of Respiratory Sounds for Wheeze Detection Using Artificial Intelligence
- Conception and Study of Medical Devices for Knee Treatment
- Early Detection of Parkinson’s Disease Through Voice Analysis Using Classification Algorithms and an Embedded Arduino System
- Alzheimer’s disease classification: a comprehensive study
- Comparative Classification of Alzheimer’s Disease
- Detecting hemorrhagic stroke from computed tomographic scans using machine learning models comparison
- COMPREHENSIVE MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR PARKINSON'S DISEASE CLASSIFICATION AND SEVERITY ASSESSMENT
- CLASSIFICATION OF PARKINSON’S DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES
- IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED ECG ACQUISITION SYSTEM FOR THE DETECTION OF CARDIAC ABNORMALITIES
- AUTOMATIC DETECTION OF ALZHEIMER'S DISEASE BASED ON ARTIFICIAL INTELLIGENCE
- THE APPLICATION OF MACHINE LEARNING ON THE SENSORS OF SMARTPHONES TO DETECT FALLS IN REAL-TIME
- Using novel method: Real Cepstral Discrete Cosine Transform, for detecting Parkinson from multiple system atrophy, other neurological diseases and healthy cases using voice analysis
- PlanTech: Early Detection of Plant Disease Based on HWSN Using Deep Learning
- Machine learning‐based edge‐computing on a multi‐level architecture of WSN and IoT for real‐time fall detection
- A hybrid method for the diagnosis and classifying parkinson's patients based on time–frequency domain properties and K-nearest neighbor
- Novel PCG Analysis Method for Discriminating Between Abnormal and Normal Heart Sounds
- Voice signal processing for detecting possible early signs of Parkinson’s disease in patients with rapid eye movement sleep behavior disorder
- Using Human Factor Cepstral Coefficient on Multiple Types of Voice Recordings for Detecting Patients with Parkinson's Disease
- Voice assessments for detecting patients with neurological diseases using PCA and NPCA
- Quantification system of Parkinson’s disease
- Detecting multiple system atrophy, Parkinson and other neurological disorders using voice analysis
- Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people
- Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA
- Using RASTA-PLP for discriminating between different Neurological diseases
- A Review of the Assessment Methods of Voice Disorders in the Context of Parkinson's Disease
- Novel extraction and tumour detection method using histogram study and SVM classification
- Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease
- Detecting Patients with Parkin son’s disease using Mel Frequency Cepstral Coefficients a nd Support Vector Machines
- Detecting patients with Parkinson's disease using PLP and VQ
- New method of tumor extraction using a histogram study
- New tumor detection method using Nl-means filter and histogram study
- New Method of Extraction and Tumor Detection Based on a Histogram Study and Support Vector Machine
- Hybridization of best acoustic cues for detecting persons with Parkinson's disease
Revues de publication
- Lecture notes in networks and systems × 11
- International Journal of Speech Technology × 10
- Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska × 9
- International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) × 6
- International Symposium on Signal, Image, Video and Communications (ISIVC) × 2
- IRBM × 2
Publications par année
- 202617
- 202513
- 20243
- 20235
- 20212
- 20202
- 20192
- 20174
- 20165
- 20156
Collaborations
Établissements partenaires : University of Hassan II Casablanca · Arts et Métiers · École Normale Supérieure de l'Enseignement Technique de Mohammedia · Université Ibn-Tofail · Université Internationale Abulcasis des Sciences de la Santé
Pays des co-auteurs : France
Co-auteurs
- Ahmed Hammouch
- Abdelilah Jilbab
- Latifa Doudach
- Sara Sandabad
- Samir Belfkih
- Ouafaa Ikhzmane
- Asma Ouabd
- Oumaima Majdoubi
- Wajih Rhalem
- Kenza Belkhou
- Nour Akalai
- Fatima Ezahra Mouas
Données bibliographiques issues d’OpenAlex (base ouverte).
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