Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
8
Scopus Publications
Scopus Publications
AI-Driven F2N-ECC Framework for Secure, Real-Time E-Commerce Transactions Fatma Mallouli, Rimah Amami, Eman Jaradat, Hassan Ahmad Eleraky, Ahmad Abas, Rim Amami, Najla Frih Procedia Computer Science, 2025 As e-commerce continues to expand rapidly, ensuring secure real-time transactions has become increasingly important. This paper introduces an innovative cryptographic framework powered by artificial intelligence, integrating a Long Short-Term Memory (LSTM) based prediction engine with a Field-to-Number enhanced Elliptic Curve Cryptography (F2N-ECC) module. Designed for adaptive and secure processing, the system dynamically adjusts encryption strength based on real-time fraud risk assessments, while blockchain-based logging ensures tamper-proof transaction verification. Tests using a labeled e-commerce dataset show promising results: 96.4% accuracy in fraud detection, a 50% reduction in latency thanks to F2N-ECC, and strong scalability for Internet of Things (IoT) environments. Additionally, a case study on anomaly detection in financial time-series data highlights the framework’s flexibility across different domains. Overall, this architecture paves the way for AI-driven, post-quantum resilient security solutions tailored for the future of digital commerce.
Efficient Classification of Multiple Sclerosis and Idiopathic Transverse Myelitis Using CNN Feature Extraction and Walrus Optimizer on MRI Scans Mohamed G. Khattap, Mohamed Abd Elaziz, Mohamed Bekheet, Mohamed Zakaria El-Sayed, Abdelghani Dahou, Hassan Ahmed Eleraky, Hend Galal Eldeen Mohamed Ali Hassan 2024 International Conference on Smart Digital Green Technologies and Artificial Intelligence Sciences Csdgais 2024, 2024 Acute myelopathies, including Multiple Sclerosis (MS) and Idiopathic Transverse Myelitis (ITM), present significant diagnostic challenges due to overlapping clinical and imaging features. Accurate differentiation between these conditions and normal individuals is crucial for timely and appropriate treatment. In this study, we propose a method that utilizes Convolutional Neural Networks (CNNs), specifically ResNet50 and DenseNet201, to extract features from MRI images, followed by feature selection using the Walrus Optimizer for classification of MS, ITM, and healthy controls. A dataset of 2,746 MR images, including 128 MS patients, 131 ITM patients, and 150 healthy controls, was used for training and validation. The dataset, consisting of sagittal and axial views, captures the unique lesion characteristics of each condition, such as the length and location of spinal lesions. Our approach achieved an accuracy of over 90%, demonstrating the effectiveness of CNN-based feature extraction combined with advanced optimization techniques. This AI-driven method offers a significant advancement in non-invasive diagnostics, potentially reducing the need for additional procedures and enabling earlier and more precise clinical interventions. Our findings highlight the potential of combining CNNs with feature selection algorithms in the field of medical imaging, providing a reliable tool for the differentiation of complex neurological disorders.
NLP-Based Bi-Directional Recommendation System: Towards Recommending Jobs to Job Seekers and Resumes to Recruiters Suleiman Ali Alsaif, Minyar Sassi Hidri, Imen Ferjani, Hassan Ahmed Eleraky, Adel Hidri Big Data and Cognitive Computing, 2022 For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be appropriate for both job seekers and employers. The reason is that the number of produced results for job seekers may be enormous. Therefore, they are required to spend time reading and reviewing their finding criteria. Reciprocally, recruitment is a crucial process for every organization. Identifying potential candidates and matching them with job offers requires a wide range of expertise and knowledge. This article proposes a reciprocal recommendation based on bi-directional correspondence as a way to support both recruiters’ and job seekers’ work. Recruiters can find the best-fit candidates for every job position in their job postings, and job seekers can find the best-match jobs to match their resumes. We show how machine learning can solve problems in natural language processing of text content and similarity scores depending on job offers in major Saudi cities scraped from Indeed. For bi-directional matching, a similarity calculation based on the integration of explicit and implicit job information from two sides (recruiters and job seekers) has been used. The proposed system is evaluated using a resume/job offer dataset. The performance of generated recommendations is evaluated using decision support measures. Obtained results confirm that the proposed system can not only solve the problem of bi-directional recommendation, but also improve the prediction accuracy.
Learning-Based Matched Representation System for Job Recommendation Suleiman Ali Alsaif, Minyar Sassi Hidri, Hassan Ahmed Eleraky, Imen Ferjani, Rimah Amami Computers, 2022 Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.
Using capsule networks for android malware detection through orientation-based features Sohail Khan, Mohammad Nauman, Suleiman Ali Alsaif, Toqeer Ali Syed, Hassan Ahmad Eleraky Computers Materials and Continua, 2022 Mobile phones are an essential part of modern life. The two popular mobile phone platforms, Android and iPhone Operating System (iOS), have an immense impact on the lives of millions of people. Among these two, Android currently boasts more than 84% market share. Thus, any personal data put on it are at great risk if not properly protected. On the other hand, more than a million pieces of malware have been reported on Android in just 2021 till date. Detecting and mitigating all this malware is extremely difficult for any set of human experts. Due to this reason, machine learning–and specifically deep learning–has been utilized in the recent past to resolve this issue. However, deep learning models have primarily been designed for image analysis. While this line of research has shown promising results, it has been difficult to really understand what the features extracted by deep learning models are in the domain of malware. Moreover, due to the translation invariance property of popular models based on Convolutional Neural Network (CNN), the true potential of deep learning for malware analysis is yet to be realized. To resolve this issue, we envision the use of Capsule Networks (CapsNets), a state-of-the-art model in deep learning. We argue that since CapsNets are orientation-based in terms of images, they can potentially be used to capture spatial relationships between different features at different locations within a sequence of opcodes. We design a deep learning-based architecture that efficiently and effectively handles very large scale malware datasets to detect Android malware without resorting to very deep networks. This leads to much faster detection as well as increased accuracy. We achieve state-of-the-art F1 score of 0.987 with an FPR of just 0.002 for three very large, real-world malware datasets. Our code is made available as open source and can be used to further enhance our work with minimal effort.
The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images Mendel, 2022
Bouncer: A resource-aware admission control scheme for cloud services Aaqif Afzaal Abbasi, Mohammed A. A. Al-qaness, Mohamed Abd Elaziz, Hassan A. Khalil, Sunghwan Kim Electronics Switzerland, 2019 Cloud computing is a paradigm that ensures the flexible, convenient and on-demand provisioning of a shared pool of configurable network and computing resources. Its services can be offered by either private or public infrastructures, depending on who owns the operational infrastructure. Much research has been conducted to improve a cloud’s resource provisioning techniques. Unfortunately, sometimes an abrupt increase in the demand for cloud services results in resource shortages affecting both providers and consumers. This uncertainty of resource demands by users can lead to catastrophic failures of cloud systems, thus reducing the number of accepted service requests. In this paper, we present Bouncer—a workload admission control scheme for cloud services. Bouncer works by ensuring that cloud services do not exceed the cloud infrastructure’s threshold capacity. By adopting an application-aware approach, we implemented Bouncer on software-defined network (SDN) infrastructure. Furthermore, we conduct an extensive study to evaluate our framework’s performance. Our evaluation shows that Bouncer significantly outperforms the conventional service admission control schemes, which are still state of the art.
