Home › Researcher directory › Achraf Benba
Achraf Benba
Affiliation : Mohammed V University
- 60Publications (affiliation in Morocco)
- 75Total publications
- 707Citations
- 13h-index
Research profile
Achraf Benba has 60 indexed publications (2014–2026). Their work appears mainly in “Lecture notes in networks and systems”, “International Journal of Speech Technology” and “Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska”. Their most cited article, “Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease” (2015), has been cited 102 times. 33 of these publications date from the last three years of activity. Their work is co-authored with at least 12 researchers, including institutions in France.
Is this you? Get your research seen
Create your free DzCademia profile and add your articles: they will show up more easily on Google and in AI assistants like ChatGPT, so more people can read and cite your work.
Create my free profile Add my publicationsResearch areas
- 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
Publication venues
- 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 per year
- 202617
- 202513
- 20243
- 20235
- 20212
- 20202
- 20192
- 20174
- 20165
- 20156
Collaborations
Partner institutions : 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
Co-authors’ countries : France
Co-authors
- 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
Bibliographic data from OpenAlex (open database).
Why choose Dzcademia?
Profiles, articles and conference contributions: give your research a structured public presence. Integrated metadata help search engines and AI assistants identify your work and understand its references.
Comment with Google
Sign in with Google to comment directly on this page.