ALI MANSOUR MGAMMAL ALMADANI

Verified @gmail.com

Department of computer Science and Information Technology
Dr. Babasaheb Ambedkar Marathwada University Aurangabad

ALI MANSOUR MGAMMAL ALMADANI


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https://researchid.co/almadani1992
I received the B.Sc degree from the Department of Computer Science, Faculty of Computer Science & IT, Sana'a University, Yemen in 2015/2016. I received the degree from Ambedkar Marathwada University, Aurangabad, India in 2018. Currently, I'm research scholar at Department of CS and IT, Ambedkar Marathwada University, Aurangabad, India, and My area of interest Internet of Things(IoT), Blockchain Technology,Computer Vision, and Deep Learning.

EDUCATION

I received the B.Sc degree from the Department of Computer Science, Faculty of Computer Science & IT, Sana'a University, Yemen in 2015/2016. I received the degree from Ambedkar Marathwada University, Aurangabad, India in 2018. Currently, I'm research scholar at Department of CS and IT, Ambedkar Marathwada University, Aurangabad, India, and My area of interest Internet of Things(IoT), Blockchain Technology,Computer Vision, and Deep Learning.

RESEARCH INTERESTS

The Internet of Things(IoT), Blockchain Technology, Computer Vision, and Deep Learning.
11

Scopus Publications

518

Scholar Citations

9

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • MOFL-CyberNet: A Novel Multi-Objective Federated Learning Framework for Simultaneous Cybersecurity Intrusion Detection and Network Performance Optimization
    Ali Mansour Al‐madani, Xu Ying
    Computational Intelligence, 2026
    Federated learning enables collaborative intrusion detection without centralizing sensitive network data, but existing approaches optimize solely for detection accuracy while neglecting critical performance and privacy constraints. This paper presents MOFL‐CyberNet, a novel multi‐objective federated learning framework that simultaneously optimizes three competing objectives: detection accuracy, network efficiency, and privacy preservation. The framework integrates three key innovations: (1) a lightweight Cross‐Attention Transformer architecture (5.0 M parameters) specifically designed for distributed intrusion detection, capturing complex attack patterns with minimal computational overhead; (2) an adaptive Pareto‐optimal aggregation mechanism using NSGA‐II that dynamically balances objectives based on real‐time network conditions; and (3) comprehensive privacy‐preserving mechanisms including differential privacy and secure aggregation. Through rigorous evaluation on four major data sets totaling over 27 million samples (NSL‐KDD, EDGE‐IIoTset, CICIDS‐2018, and CIC‐IoT‐2023), MOFL‐CyberNet demonstrates exceptional performance: 97.86% detection accuracy coupled with 47% latency reduction and 36% energy savings compared to state‐of‐the‐art baselines. These results demonstrate that multi‐objective optimization can achieve favorable performance across all evaluated metrics concurrently making federated intrusion detection practical for resource‐constrained IoT deployments.
  • Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm
    Hussein Ridha Sayegh, Wang Dong, Bahaa Hussein Taher, Muhanad Mohammed Kadum, Ali Mansour Al-madani
    Peerj Computer Science, 2025
    As the number of connected devices and Internet of Things (IoT) devices grows, it is becoming more and more important to develop efficient security mechanisms to manage risks and vulnerabilities in IoT networks. Intrusion detection systems (IDSs) have been developed and implemented in IoT networks to discern between regular network traffic and potential malicious attacks. This article proposes a new IDS based on a hybrid method of metaheuristic and deep learning techniques, namely, the flower pollination algorithm (FPA) and deep neural network (DNN), with an ensemble learning paradigm. To handle the problem of imbalance class distribution in intrusion datasets, a roughly-balanced (RB) Bagging strategy is utilized, where DNN models trained by FPA on a cost-sensitive fitness function are used as base learners. The RB Bagging strategy derives multiple RB training subsets from the original dataset and proper class weights are incorporated into the fitness function to attain unbiased DNN models. The performance of our IDS is evaluated using four commonly utilized public datasets, NSL-KDD, UNSW NB-15, CIC-IDS-2017, and BoT-IoT, in terms of different metrics, i.e., accuracy, precision, recall, and F1-score. The results demonstrate that our IDS outperforms existing ones in accurately detecting network intrusions with effective handling of class imbalance problem.
  • Railway Track Faults Detection Using Ensemble Deep Transfer Learning Models
    Ali Almadani, Vivek Mahale, Ashok T. Gaikwad
    Statistics Optimization and Information Computing, 2024
    Railway track fault detection is an essential task for ensuring the safety and reliability of railway systems, particularly in the summer and rainy seasons when train wheels may slide due to fractures in the track or corrosion may cause track fractures. In this study, we propose a novel approach for the automated detection of railway track faults using deep transfer learning models. The proposed method combines image processing techniques and the training of three pretrained models: InceptionV3, ResNet50V2, and VGG16, on a dataset of railway track images. We evaluated the performance of our proposed method by measuring its accuracy on a test set of railway track images. The individual training accuracies for InceptionV3, ResNet50V2, and VGG16 were 94.30%, 96.79%, and 94.64%, respectively. We then combined these models using an ensemble approach, which achieved an impressive accuracy of 98.57% on the test set. Our results demonstrate the effectiveness of using deep ensemble transfer learning for railway track fault detection. Moreover, our proposed method can be used as a valuable tool for railway track maintenance and monitoring, which can ultimately lead to the improvement of the safety and reliability of railway systems.our proposed approach for railway track fault detection using ensemble deep transfer learning models shows promising results, indicating that it has great potential for detecting track faults accurately and efficiently. The proposed method can be used in various railway systems worldwide, ultimately leading to improved safety and reliability for passengers and cargo transportation.
  • Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data
    Hussein Ridha Sayegh, Wang Dong, Ali Mansour Al-madani
    Applied Sciences Switzerland, 2024
    This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.
  • Multi-features Extraction for Automating Covid-19 Detection from Cough Sound using Deep Neural Networks
    Mohammed Tawfik, Sunil Nimbhore, Nasser M. Al-Zidi, Zeyad A. T. Ahmed, Ali Mansour Almadani
    Proceedings 4th International Conference on Smart Systems and Inventive Technology Icssit 2022, 2022
    This research paper proposed a smart system based on deep learning to detect COVID-19 patient's using the cough sound. The deep neural networks are used to distinguish between different types of cough COVID-19 positive or negative coughs. The proposed system is segmented into three stages: Audio pre-processing by noise reduction, segmentation, feature extraction, classification, and model deployment. Eight features have been extracted from 1635 sound subjects: 573 COVID-19 positive and 1062 negative coughs. The feature's extracted data have trained using two models; first model Cough detection based on ANN used to distinguish if there is cough or not, the second model to detect the covid-19 using Convolutional Neural Network. The overall accuracy for both models is 98.1% for the Cough model and 98.5% for the Covid-19 model. The models were compiled after deployment to work together as a web service based on flask. Cough model receives cough sound from the mobile app or web interface and discriminates if there is cough then passe it coivd1-9 model that will analyze if cough is positive or negative. and send the result back to the mobile app.
  • RETRACTED: Facial Features Detection System to Identify Children with Autism Spectrum Disorder: Deep Learning Models
    Zeyad A. T. Ahmed, Theyazn H. H. Aldhyani, Mukti E. Jadhav, Mohammed Y. Alzahrani, Mohammad Eid Alzahrani, Maha M. Althobaiti, Fawaz Alassery, Ahmed Alshaflut, Nouf Matar Alzahrani, Ali Mansour Al-madani
    Computational and Mathematical Methods in Medicine, 2022
    Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.
  • Overview of Biometric Traits
    Belal Alsellami, Prapti D. Deshmukh, Zeyad A.T. Ahmed, Mohammed Tawfik, Ali Mansour Al-madani
    Proceedings of the 3rd International Conference on Inventive Research in Computing Applications Icirca 2021, 2021
    In the present era of the technological advancement revolution, biometrics has become of paramount importance in the field of identification of physiological characteristics of individuals such as the face, fingerprint, iris, and palmprint or behavioral traits like voice, and signature for the identification and verification of an individual. As a more reliable alternative to password-based protection measures, biometric authentication is growing in popularity as it is reasonably hard to forget, hack, or guess compared to PIN, ID cards in authentication. This paper is an overview of many previous studies on biometrics traits that aim at improving the potentiality of systems to handle weak quality and missing data of users, obtaining scalability to manage a huge database of users, ensuring interoperability, and defending user privacy against attacks. The focus here is on the models where a combination of more than one biometric, i.e., a fusion of biometrics or multimodal biometric traits, are utilized to achieve a high robust authentication of the system during the feature matching.
  • Applying blockchain technology to secure object detection data
    Ahmed Abdullah A. Shareef, Pravin L. Yannawar, Zeyad A.T. Ahmed, Ali Mansour Al-madani
    Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks Icicv 2021, 2021
    Recently, blockchain, deep learning, and computer vision are considered the most fundamental technologies in the world. The researchers have been paying much attention to use them. The centralized database system’s problem has low security because it must be transferred through a trusted third party, while the data transmitted might be hacked. However, blockchain provides a distributed database that makes the network secure, flexible, and capable of supporting real-time services. This article proposes a blockchain-based object detection model. The blockchain system makes files secure using the Interplanetary Files System (IPFS) that receives our files, stores them on the decentralized application, and sends the hash to Ethereum to be stored. A YOLOv3 object detection model receives our encrypted data as a hash value and detects objects from images that consist of a huge dataset of different objects.
  • Real-time Driver Drowsiness Detection based on Eye Movement and Yawning using Facial Landmark
    Ali Mansour Al-madani, Ashok T. Gaikwad, Vivek Mahale, Zeyad A.T. Ahmed, Ahmed Abdullah A. Shareef
    2021 International Conference on Computer Communication and Informatics Iccci 2021, 2021
    The drowsiness of the drivers might increase road accidents. Nowadays, computer vision and image processing technology can solve the problem and decrease the number of accidents by detecting the driver's drowsiness and gives an alert to beware of sleep that leads to an accident. This study has developed a real-time driver drowsiness detection based on eye movement and yawning using facial landmarks and dlib. This system helps to avoid accidents caused by drowsiness by detecting eye movements and yawning of the driver. The advantages of this system are low cost and minimized the requires the resource. The behavioral analysis method monitor results from the driver's facial landmark while driving without the need to place sensors in the driver's body.
  • Decentralized E-voting system based on Smart Contract by using Blockchain Technology
    Ali Mansour Al-madani, Ashok T. Gaikwad, Vivek Mahale, Zeyad A.T. Ahmed
    Proceedings of the 2020 International Conference on Smart Innovations in Design Environment Management Planning and Computing Icsidempc 2020, 2020
    Nowadays the use of the Internet is growing; E-voting system has been used by different countries because it reduces the cost and the time which used to consumed by using traditional voting. When the voter wants to access the E-voting system through the web application, there are requirements such as a web browser and a server. The voter uses the web browser to reach to a centralized database. The use of a centralized database for the voting system has some security issues such as Data modification through the third party in the network due to the use of the central database system as well as the result of the voting is not shown in real-time. However, this paper aims to provide an E-voting system with high security by using blockchain. Blockchain provides a decentralized model that makes the network Reliable, safe, flexible, and able to support real-time services.
  • IoT Data Security Via Blockchain Technology and Service-Centric Networking
    Ali Mansour Al-madani, Ashok T. Gaikwad
    Proceedings of the 5th International Conference on Inventive Computation Technologies Icict 2020, 2020

RECENT SCHOLAR PUBLICATIONS

  • MOFL‐CyberNet: A Novel Multi‐Objective Federated Learning Framework for Simultaneous Cybersecurity Intrusion Detection and Network Performance Optimization
    AM Al‐madani, X Ying
    Computational Intelligence 42 (3), e70236 , 2026
    2026
  • Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm
    HR Sayegh, W Dong, BH Taher, MM Kadum, AM Al-Madani
    PeerJ Computer Science 11, e2745 , 2025
    2025
    Citations: 6
  • An Optimized Blockchain Model for Secure and Efficient Data Management in Internet of Things
    AM Al-madni, X Ying, M Tawfik, ZAT Ahmed
    2024 IEEE International Conference on Information Technology, Electronics … , 2024
    2024
    Citations: 9
  • Railway track faults detection using ensemble deep transfer learning models
    AM Al-Madani, V Mahale, AT Gaikwad
    Statistics, Optimization & Information Computing 12 (6), 1886-1911 , 2024
    2024
    Citations: 5
  • A Framework for Epileptic Seizure Monitoring Based on IoT and Machine Learning Technologies
    A Alharbi, M Dhopeshwarkar, ZAT Ahmed, E Mahyoub, M Tawfik, ...
    2024 3rd International Conference for Innovation in Technology (INOCON), 1-8 , 2024
    2024
    Citations: 2
  • Enhanced intrusion detection with LSTM-based model, feature selection, and SMOTE for imbalanced data
    HR Sayegh, W Dong, AM Al-madani
    Applied Sciences 14 (2), 479 , 2024
    2024
    Citations: 129
  • Toward Accurate and Flexible Arabic Speech Recognition: A Comprehensive Framework
    EM Naji, AA Maslekar, ZAT Ahmed, AM Almadani, M Tawfik, A Alharbi
    2023 Global Conference on Information Technologies and Communications (GCITC … , 2023
    2023
    Citations: 3
  • Real-time detection of crime and violence in video surveillance using deep learning
    AM Al-Madani, V Mahale, AT Gaikwad
    First International Conference on Advances in Computer Vision and Artificial … , 2023
    2023
    Citations: 7
  • COVID-19 Detection and Remote Tracking System Using IoT-Based Wearable Bracelet
    NM Al-Zidi, M Tawfik, TA Aldhaheri, AM Almadani, ZAT Ahmed, AM Al-Zidi
    Proceedings of the International Conference on Cognitive and Intelligent … , 2023
    2023
  • Web Application Based on Deep Learning for Detecting COVID-19 Using Chest X-Ray Images
    AM Al-Madani, AT Gaikwad, ZAT Ahmed, V Mahale, SN Alsubari, ...
    Telemedicine: The Computer Transformation of Healthcare, 283-294 , 2022
    2022
    Citations: 3
  • Identifying traffic signs in a hazy environment using a vehicle's viewing distance
    AM Al-Madani, DA Gaikwad, V Mahale, DAT Gaikwad
    NeuroQuantology 20 (7), 4160-4172 , 2022
    2022
  • Multi-features extraction for automating COVID-19 detection from cough sound using deep neural networks
    M Tawfik, S Nimbhore, NM Al-Zidi, ZAT Ahmed, AM Almadani
    2022 4th International Conference on Smart Systems and Inventive Technology … , 2022
    2022
    Citations: 18
  • [retracted] facial features detection system to identify children with autism spectrum disorder: Deep learning models
    ZAT Ahmed, THH Aldhyani, ME Jadhav, MY Alzahrani, ME Alzahrani, ...
    Computational and Mathematical Methods in Medicine 2022 (1), 3941049 , 2022
    2022
    Citations: 103
  • Overview of biometric traits
    B Alsellami, PD Deshmukh, ZAT Ahmed, M Tawfik, AM Al-madani
    2021 Third International Conference on Inventive Research in Computing … , 2021
    2021
    Citations: 3
  • Real-time detection of student engagement: Deep learning-based system
    ZAT Ahmed, ME Jadhav, AM Al-madani, M Tawfik, SN Alsubari, ...
    International Conference on Innovative Computing and Communications … , 2021
    2021
    Citations: 19
  • Applying blockchain technology to secure object detection data
    AAA Shareef, PL Yannawar, ZAT Ahmed, AM Al-madani
    2021 Third International Conference on Intelligent Communication … , 2021
    2021
    Citations: 9
  • Real-time driver drowsiness detection based on eye movement and yawning using facial landmark
    AM Al-Madani, AT Gaikwad, V Mahale, ZAT Ahmed, AAA Shareef
    2021 International Conference on Computer Communication and Informatics … , 2021
    2021
    Citations: 48
  • Decentralized E-voting system based on Smart Contract by using Blockchain Technology
    AM Al-Madani, AT Gaikwad, V Mahale, ZAT Ahmed
    2020 international conference on smart innovations in design, environment … , 2020
    2020
    Citations: 111
  • 5th International Conference on Inventive Computation Technologies (ICICT-2020) 26-28 February 2020
    MG Kambalimath, MS Kakkasageri, BA Patel, A Parikh, Z Wu, S Qi, ...
    2020
  • IoT data security via blockchain technology and service-centric networking
    AM Al-madani, AT Gaikwad
    2020 International Conference on Inventive Computation Technologies (ICICT … , 2020
    2020
    Citations: 27

MOST CITED SCHOLAR PUBLICATIONS

  • Enhanced intrusion detection with LSTM-based model, feature selection, and SMOTE for imbalanced data
    HR Sayegh, W Dong, AM Al-madani
    Applied Sciences 14 (2), 479 , 2024
    2024
    Citations: 129
  • Decentralized E-voting system based on Smart Contract by using Blockchain Technology
    AM Al-Madani, AT Gaikwad, V Mahale, ZAT Ahmed
    2020 international conference on smart innovations in design, environment … , 2020
    2020
    Citations: 111
  • [retracted] facial features detection system to identify children with autism spectrum disorder: Deep learning models
    ZAT Ahmed, THH Aldhyani, ME Jadhav, MY Alzahrani, ME Alzahrani, ...
    Computational and Mathematical Methods in Medicine 2022 (1), 3941049 , 2022
    2022
    Citations: 103
  • Real-time driver drowsiness detection based on eye movement and yawning using facial landmark
    AM Al-Madani, AT Gaikwad, V Mahale, ZAT Ahmed, AAA Shareef
    2021 International Conference on Computer Communication and Informatics … , 2021
    2021
    Citations: 48
  • IoT data security via blockchain technology and service-centric networking
    AM Al-madani, AT Gaikwad
    2020 International Conference on Inventive Computation Technologies (ICICT … , 2020
    2020
    Citations: 27
  • Real-time detection of student engagement: Deep learning-based system
    ZAT Ahmed, ME Jadhav, AM Al-madani, M Tawfik, SN Alsubari, ...
    International Conference on Innovative Computing and Communications … , 2021
    2021
    Citations: 19
  • Multi-features extraction for automating COVID-19 detection from cough sound using deep neural networks
    M Tawfik, S Nimbhore, NM Al-Zidi, ZAT Ahmed, AM Almadani
    2022 4th International Conference on Smart Systems and Inventive Technology … , 2022
    2022
    Citations: 18
  • A review: the risks and weakness security on the IoT
    AM Almadani, A Alharbi
    IOSR Journal of Computer Engineering (IOSR-JCE) , 2017
    2017
    Citations: 16
  • An Optimized Blockchain Model for Secure and Efficient Data Management in Internet of Things
    AM Al-madni, X Ying, M Tawfik, ZAT Ahmed
    2024 IEEE International Conference on Information Technology, Electronics … , 2024
    2024
    Citations: 9
  • Applying blockchain technology to secure object detection data
    AAA Shareef, PL Yannawar, ZAT Ahmed, AM Al-madani
    2021 Third International Conference on Intelligent Communication … , 2021
    2021
    Citations: 9
  • Real-time detection of crime and violence in video surveillance using deep learning
    AM Al-Madani, V Mahale, AT Gaikwad
    First International Conference on Advances in Computer Vision and Artificial … , 2023
    2023
    Citations: 7
  • Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm
    HR Sayegh, W Dong, BH Taher, MM Kadum, AM Al-Madani
    PeerJ Computer Science 11, e2745 , 2025
    2025
    Citations: 6
  • Railway track faults detection using ensemble deep transfer learning models
    AM Al-Madani, V Mahale, AT Gaikwad
    Statistics, Optimization & Information Computing 12 (6), 1886-1911 , 2024
    2024
    Citations: 5
  • Toward Accurate and Flexible Arabic Speech Recognition: A Comprehensive Framework
    EM Naji, AA Maslekar, ZAT Ahmed, AM Almadani, M Tawfik, A Alharbi
    2023 Global Conference on Information Technologies and Communications (GCITC … , 2023
    2023
    Citations: 3
  • Web Application Based on Deep Learning for Detecting COVID-19 Using Chest X-Ray Images
    AM Al-Madani, AT Gaikwad, ZAT Ahmed, V Mahale, SN Alsubari, ...
    Telemedicine: The Computer Transformation of Healthcare, 283-294 , 2022
    2022
    Citations: 3
  • Overview of biometric traits
    B Alsellami, PD Deshmukh, ZAT Ahmed, M Tawfik, AM Al-madani
    2021 Third International Conference on Inventive Research in Computing … , 2021
    2021
    Citations: 3
  • A Framework for Epileptic Seizure Monitoring Based on IoT and Machine Learning Technologies
    A Alharbi, M Dhopeshwarkar, ZAT Ahmed, E Mahyoub, M Tawfik, ...
    2024 3rd International Conference for Innovation in Technology (INOCON), 1-8 , 2024
    2024
    Citations: 2
  • MOFL‐CyberNet: A Novel Multi‐Objective Federated Learning Framework for Simultaneous Cybersecurity Intrusion Detection and Network Performance Optimization
    AM Al‐madani, X Ying
    Computational Intelligence 42 (3), e70236 , 2026
    2026
  • COVID-19 Detection and Remote Tracking System Using IoT-Based Wearable Bracelet
    NM Al-Zidi, M Tawfik, TA Aldhaheri, AM Almadani, ZAT Ahmed, AM Al-Zidi
    Proceedings of the International Conference on Cognitive and Intelligent … , 2023
    2023
  • Identifying traffic signs in a hazy environment using a vehicle's viewing distance
    AM Al-Madani, DA Gaikwad, V Mahale, DAT Gaikwad
    NeuroQuantology 20 (7), 4160-4172 , 2022
    2022