Dr. Abdussalam Elhanashi
Researcher at University of Pisa
Research Expertise
About
Publications
Real-time video fire/smoke detection based on CNN in antifire surveillance systems
Journal of Real-Time Image Processing / Nov 10, 2020
Saponara, S., Elhanashi, A., & Gagliardi, A. (2020). Real-time video fire/smoke detection based on CNN in antifire surveillance systems. Journal of Real-Time Image Processing, 18(3), 889–900. https://doi.org/10.1007/s11554-020-01044-0
Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19
Journal of Real-Time Image Processing / Jan 21, 2021
Saponara, S., Elhanashi, A., & Gagliardi, A. (2021). Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19. Journal of Real-Time Image Processing, 18(6), 1937–1947. https://doi.org/10.1007/s11554-021-01070-6
Fine-Grained Modulation Classification Using Multi-Scale Radio Transformer With Dual-Channel Representation
IEEE Communications Letters / Jun 01, 2022
Zheng, Q., Zhao, P., Wang, H., Elhanashi, A., & Saponara, S. (2022). Fine-Grained Modulation Classification Using Multi-Scale Radio Transformer With Dual-Channel Representation. IEEE Communications Letters, 26(6), 1298–1302. https://doi.org/10.1109/lcomm.2022.3145647
Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture
IEEE Access / Jan 01, 2021
Saponara, S., Elhanashi, A., & Zheng, Q. (2021). Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture. IEEE Access, 9, 147888–147899. https://doi.org/10.1109/access.2021.3124746
Impact of Image Resizing on Deep Learning Detectors for Training Time and Model Performance
Lecture Notes in Electrical Engineering / Jan 01, 2022
Saponara, S., & Elhanashi, A. (2022). Impact of Image Resizing on Deep Learning Detectors for Training Time and Model Performance. Applications in Electronics Pervading Industry, Environment and Society, 10–17. https://doi.org/10.1007/978-3-030-95498-7_2
Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19
Journal of Real-Time Image Processing / Feb 22, 2022
Saponara, S., Elhanashi, A., & Zheng, Q. (2022). Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19. Journal of Real-Time Image Processing, 19(3), 551–563. https://doi.org/10.1007/s11554-022-01203-5
An integrated and real-time social distancing, mask detection, and facial temperature video measurement system for pandemic monitoring
Journal of Real-Time Image Processing / Aug 16, 2023
Elhanashi, A., Saponara, S., Dini, P., Zheng, Q., Morita, D., & Raytchev, B. (2023). An integrated and real-time social distancing, mask detection, and facial temperature video measurement system for pandemic monitoring. Journal of Real-Time Image Processing, 20(5). https://doi.org/10.1007/s11554-023-01353-0
Classification and Localization of Multi-Type Abnormalities on Chest X-Rays Images
IEEE Access / Jan 01, 2023
Elhanashi, A., Saponara, S., & Zheng, Q. (2023). Classification and Localization of Multi-Type Abnormalities on Chest X-Rays Images. IEEE Access, 11, 83264–83277. https://doi.org/10.1109/access.2023.3302180
An automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19
Real-time Processing of Image, Depth and Video Information 2023 / Jun 07, 2023
Elhanashi, A., Saponara, S., & Zheng, Q. (2023). An automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19. Real-Time Processing of Image, Depth and Video Information 2023. https://doi.org/10.1117/12.2663754
An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data
Algorithms / Oct 07, 2020
Fedele, R., & Merenda, M. (2020). An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data. Algorithms, 13(10), 254. https://doi.org/10.3390/a13100254
Assembly of micro-/nano- materials with optoelectronic tweezers and freeze-drying
2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) / Jul 17, 2022
Li, F., Xu, B., & Zhang, S. (2022). Assembly of micro-/nano- materials with optoelectronic tweezers and freeze-drying. 2022 IEEE International Conference on Real-Time Computing and Robotics (RCAR). https://doi.org/10.1109/rcar54675.2022.9872290
MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems
Drones / Sep 22, 2023
Zheng, Q., Tian, X., Yu, Z., Ding, Y., Elhanashi, A., Saponara, S., & Kpalma, K. (2023). MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems. Drones, 7(10), 596. https://doi.org/10.3390/drones7100596
A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data
Big Data and Cognitive Computing / Jul 15, 2023
Tian, X., Zheng, Q., Yu, Z., Yang, M., Ding, Y., Elhanashi, A., Saponara, S., & Kpalma, K. (2023). A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data. Big Data and Cognitive Computing, 7(3), 131. https://doi.org/10.3390/bdcc7030131
Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity
Applied Sciences / Jun 25, 2023
Dini, P., Elhanashi, A., Begni, A., Saponara, S., Zheng, Q., & Gasmi, K. (2023). Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity. Applied Sciences, 13(13), 7507. https://doi.org/10.3390/app13137507
A real-time transformer discharge pattern recognition method based on CNN-LSTM driven by few-shot learning
Electric Power Systems Research / Jun 01, 2023
Zheng, Q., Wang, R., Tian, X., Yu, Z., Wang, H., Elhanashi, A., & Saponara, S. (2023). A real-time transformer discharge pattern recognition method based on CNN-LSTM driven by few-shot learning. Electric Power Systems Research, 219, 109241. https://doi.org/10.1016/j.epsr.2023.109241
DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization
Engineering Applications of Artificial Intelligence / Jun 01, 2023
Zheng, Q., Tian, X., Yu, Z., Wang, H., Elhanashi, A., & Saponara, S. (2023). DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization. Engineering Applications of Artificial Intelligence, 122, 106082. https://doi.org/10.1016/j.engappai.2023.106082
Application of wavelet-packet transform driven deep learning method in PM2.5 concentration prediction: A case study of Qingdao, China
Sustainable Cities and Society / May 01, 2023
Zheng, Q., Tian, X., Yu, Z., Jiang, N., Elhanashi, A., Saponara, S., & Yu, R. (2023). Application of wavelet-packet transform driven deep learning method in PM2.5 concentration prediction: A case study of Qingdao, China. Sustainable Cities and Society, 92, 104486. https://doi.org/10.1016/j.scs.2023.104486
A Blind Modulation Classification Method Based on Decision Tree and High Order Cumulants
Lecture Notes in Electrical Engineering / Jan 01, 2023
He, Y., Wu, H., Zheng, Q., Liu, Y., Elhanashi, A., & Saponara, S. (2023). A Blind Modulation Classification Method Based on Decision Tree and High Order Cumulants. Applications in Electronics Pervading Industry, Environment and Society, 312–319. https://doi.org/10.1007/978-3-031-30333-3_42
Digital Modulation Recognition Method Based on High-Order Cumulant Feature Learning
Lecture Notes in Electrical Engineering / Jan 01, 2023
Li, H., Wu, H., Zhen, Q., Liu, Y., Elhanash, A., & Saponara, S. (2023). Digital Modulation Recognition Method Based on High-Order Cumulant Feature Learning. Applications in Electronics Pervading Industry, Environment and Society, 287–293. https://doi.org/10.1007/978-3-031-30333-3_38
Machine Learning Techniques for Anomaly-Based Detection System on CSE-CIC-IDS2018 Dataset
Lecture Notes in Electrical Engineering / Jan 01, 2023
Elhanashi, A., Gasmi, K., Begni, A., Dini, P., Zheng, Q., & Saponara, S. (2023). Machine Learning Techniques for Anomaly-Based Detection System on CSE-CIC-IDS2018 Dataset. Applications in Electronics Pervading Industry, Environment and Society, 131–140. https://doi.org/10.1007/978-3-031-30333-3_17
Modulation Recognition Based on BP Neural Network
Lecture Notes in Electrical Engineering / Jan 01, 2023
Sun, Z., Wu, H., Zheng, Q., Liu, Y., Elhanashi, A., & Saponara, S. (2023). Modulation Recognition Based on BP Neural Network. Applications in Electronics Pervading Industry, Environment and Society, 339–345. https://doi.org/10.1007/978-3-031-30333-3_46
Deep learning techniques to identify and classify COVID-19 abnormalities on chest x-ray images
Real-Time Image Processing and Deep Learning 2022 / May 27, 2022
Elhanashi, A., Lowe, D., Saponara, S., & Moshfeghi, Y. (2022). Deep learning techniques to identify and classify COVID-19 abnormalities on chest x-ray images. Real-Time Image Processing and Deep Learning 2022. https://doi.org/10.1117/12.2618762
An Intelligent Non-cooperative Spectrum Sensing Method Based on Convolutional Auto-encoder (CAE)
Lecture Notes in Electrical Engineering / Jan 01, 2022
Zheng, Q., Wang, H., Elhanashi, A., Saponara, S., & Zhang, D. (2022). An Intelligent Non-cooperative Spectrum Sensing Method Based on Convolutional Auto-encoder (CAE). Applications in Electronics Pervading Industry, Environment and Society, 1–9. https://doi.org/10.1007/978-3-030-95498-7_1
Heat Conduction Plate Layout Optimization Using Physics-Driven Convolutional Neural Networks
Applied Sciences / Oct 30, 2022
Sun, Y., Elhanashi, A., Ma, H., & Chiarelli, M. R. (2022). Heat Conduction Plate Layout Optimization Using Physics-Driven Convolutional Neural Networks. Applied Sciences, 12(21), 10986. https://doi.org/10.3390/app122110986
Reconstruct fingerprint images using deep learning and sparse autoencoder algorithms
Real-Time Image Processing and Deep Learning 2021 / Apr 12, 2021
Saponara, S., Elhanashi, A., & Gagliardi, A. (2021). Reconstruct fingerprint images using deep learning and sparse autoencoder algorithms. Real-Time Image Processing and Deep Learning 2021. https://doi.org/10.1117/12.2585707
Enabling YOLOv2 Models to Monitor Fire and Smoke Detection Remotely in Smart Infrastructures
Lecture Notes in Electrical Engineering / Jan 01, 2021
Saponara, S., Elhanashi, A., & Gagliardi, A. (2021). Enabling YOLOv2 Models to Monitor Fire and Smoke Detection Remotely in Smart Infrastructures. Applications in Electronics Pervading Industry, Environment and Society, 30–38. https://doi.org/10.1007/978-3-030-66729-0_4
Exploiting R-CNN for video smoke/fire sensing in antifire surveillance indoor and outdoor systems for smart cities
2020 IEEE International Conference on Smart Computing (SMARTCOMP) / Sep 01, 2020
Saponara, S., Elhanashi, A., & Gagliardi, A. (2020). Exploiting R-CNN for video smoke/fire sensing in antifire surveillance indoor and outdoor systems for smart cities. 2020 IEEE International Conference on Smart Computing (SMARTCOMP). https://doi.org/10.1109/smartcomp50058.2020.00083
Education
University of Pisa
PhD, Department of Information Engineering / February, 2023
University of Nicosia
MBA, Department of Business Management / March, 2018
University of Glasgow
Master of Science, Department of Electronics and Electrical Engineering / January, 2018
Experience
University of Pisa
Researcher / July, 2019 — Present
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