Saeed Meshgini

Bio: Saeed Meshgini is an academic researcher from University of Tabriz. The author has contributed to research in topics: Artificial intelligence & Computer science. The author has an hindex of 6, co-authored 34 publications receiving 153 citations.

Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

Abstract: This work was supported by Research and Innovation Management Center (PPPI) and Faculty of Engineering, Universiti Malaysia Sabah (UMS) under VOT (TBP0002).

Facial expression recognition based on Local Binary Patterns

Abstract: Extraction methods of facial expression characteristics have disadvantages according to Classical LBP such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. In this paper, we introduce an improved LBP algorithm to solve these problems that utilizes Fast PCA algorithm for reduction of vector dimensions of extracted features. In other words, proffer method (Fast PCA+LBP) is an improved LBP algorithm that is extracted from classical LBP operator. In this method, first circular neighbor operator is used for features extraction of facial expression. Then, an algorithm of Fast PCA is used for reduction of feature vector dimensions. Simulation results show that the proposed method in this paper in terms of accuracy and speed of recognition, has had a better performance compared with the same algorithm.

Face Recognition Using Gabor Filter Bank, Kernel Principle Component Analysis and Support Vector Machine

Abstract: This paper presents a novel face recognition method based on the Gabor filter bank, Kernel Principle Component Analysis (KPCA) and Support Vector Machine (SVM). At first, the Gabor filter bank with 5 frequencies and 8 orientations is applied on each face image to extract robust features against local distortions caused by variance of illumination, facial expression and pose. Then, the feature reduction technique of KPCA is performed on the outputs of the filter bank to form the new low-dimensional feature vectors. Finally, SVM is used for classification of the extracted features. The proposed method is tested on the ORL face database. The experimental results reveal that the proposed method has a maximum recognition rate of 98.5% which is higher than the other related algorithms applied on the ORL database.

CloudID: Trustworthy cloud-based and cross-enterprise biometric identification

Abstract: In biometric identification systems, the biometric database is typically stored in a trusted server, which is also responsible for performing the identification process. However, a standalone server may not be able to provide enough storage and processing power for large databases. Nowadays, cloud computing and storage solutions have provided users and enterprises with various capabilities to store and process their data in third-party data centers. However, maintenance of the confidentiality and integrity of sensitive data requires trustworthy solutions for storage and processing of data with proven zero information leakage. In this paper, we present CloudID, a privacy-preserving cloud-based and cross-enterprise biometric identification solution. It links the confidential information of the users to their biometrics and stores it in an encrypted fashion. Making use of a searchable encryption technique, biometric identification is performed in encrypted domain to make sure that the cloud provider or potential attackers do not gain access to any sensitive data or even the contents of the individual queries. In order to create encrypted search queries, we propose a k-d tree structure in the core of the searchable encryption. This helps not only in handling the biometrics variations in encrypted domain, but also in improving the overall performance of the system. Our proposed approach is the first cloud-based biometric identification system with a proven zero data disclosure possibility. It allows different enterprises to perform biometric identification on a single database without revealing any sensitive information. Our experimental results show that CloudID performs the identification of clients with high accuracy and minimal overhead and proven zero data disclosure.

Identification Using Encrypted Biometrics

Abstract: Biometric identification is a challenging subject among computer vision scientists. The idea of substituting biometrics for passwords has become more attractive after powerful identification algorithms have emerged. However, in this regard, the confidentiality of the biometric data becomes of a serious concern. Biometric data needs to be securely stored and processed to guarantee that the user privacy and confidentiality is preserved. In this paper, a method for biometric identification using encrypted biometrics is presented, where a method of search over encrypted data is applied to manage the identification. Our experiments of facial identification demonstrate the effective performance of the system with a proven zero information leakage.

Pathology of the Kidney.

Abstract: This is a splendid book. It will join Arthur C. Allen's The Kidney and M. B. Strauss and L. G. Welt's Diseases of The Kidney as one of the most useful textbooks on kidney disease for the nephrologist, internist, and pathologist. It derives strength by drawing on a huge experience with renal biopsy material as the basis of understanding the early stages and progression of renal diseases. The author properly emphasizes the more frequent and significant kidney diseases, yet he also includes definitive sections on less common entities such as the glomerular lesions of cyanotic congenital heart disease and the renal lesion of Fabry's disease, indicating the completeness of the volume. Upholding the standard of excellence of the rest of the book are three chapters contributed by collaborating authors, "Development of the Kidney" and "Congenital Malformations" by Dr. John M. Kissane and "Renal Transplantation" by Dr. Kendrick A. Porter. Sharply

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Abstract: During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.