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Showing 3 results for Fardin
Construction and Training of Artificial Neural Networks using Evolution Strategy with Parallel Populations
Dr. Fardin Ahmadizar, Khabat Soltanian, Dr. Fardin Akhlaghiantab,
Volume 13, Issue 1 (6-2016)
Abstract

Application of artificial neural networks (ANN) in areas such as classification of images and audio signals shows the ability of this artificial intelligence technique for solving practical problems. Construction and training of ANNs is usually a time-consuming and hard process. A suitable neural model must be able to learn the training data and also have the generalization ability. In this paper, multiple parallel populations are used for construction of ANN and evolution strategy for its training, so that in each population a particular ANN architecture is evolved. By using a bi-criteria selection method based on error and complexity of ANNs, the proposed algorithm can produce simple ANNs that have high generalization ability. To assess the performance of the algorithm, 7 benchmark classification problems have been used. It has then been compared against the existing evolutionary algorithms that train and/or construct ANNs. Experimental results show the efficiency and robustness of the proposed algorithm compared to the other methods. In this paper, the impact of parallel populations, the bi-criteria selection method, and the crossover operator on the algorithm performance has been analyzed. A key advantage of the proposed algorithm is the use of parallel computing by means of multiple populations.


Outlier Detection on Data Streams Using a QLattice-based Model and Online Learning
Sahar Fardin, Mahdi Hashemzadeh,
Volume 20, Issue 2 (9-2023)
Abstract
With the advancement of computer science, the dramatic developments in data mining area and their increasing applications, the identification of outlier or anomaly data has also become one of the most important research topics. In most applications, the outlier data contain beneficial information that can be used to gain useful knowledge. Today, there are a large number of applications on data streams, in the vast majority of which the discovery of outlier/anomaly data is very important and in some cases vital. Detection of anomalies is an important way for detecting frauds, network intrusion detection, detection of abnormal behaviors in monitoring systems, and other rare events that are always of great importance; but they are often difficult to identify. Most of the existing efficient outlier detection algorithms have been designed for the static data. While outlier detection is more challenging in data streams, where data are generating continuously and has especial properties such as infinity and transience. In this research, we introduce an approach based on the QLattice classification model, which works based on the quantum computing and performs better in the intended application than other classification methods. Given the possibility of changing the distribution of data over time in streaming data, a scheme to take advantage of online incremental learning is also applied in the proposed method. Considering the unlimited data flow and limited processing memory, the detection process is applied to a window of data that is constantly updated with data sampled from previous windows. A function is also designed to solve the problem of data imbalance, which uses the random sampling technique to solve this issue. The results of experiments obtained on benchmark datasets show that the proposed approach has better performance than other methods.

Diagnosing Children's Developmental Disorders By A Transfer Learning Based Architecture Using Knowledge Distillation
Mrs Homeyra Sarabi, Dr. Fardin Abdali Mohammadi,
Volume 21, Issue 4 (3-2025)
Abstract
Enhancing medical devices with Internet of Things (IOT) and diagnostic artificial intelligence technology, while considering the constraints of these systems, has the potential to modernize and enhance the diagnostic approach of future generations of Internet of Things systems in healthcare. The radiography device, commonly used in paraclinical settings, is widely used in various hospital departments. The automated assessment of abnormalities and bone age from radiographic images of the left hand assists radiologists, pediatricians, and forensic experts in determining the developmental stage of young individuals. The IoT devices in medicine are unable to process large amounts of data due to limited resources. This article uses a teacher-student network for bone age classification, using the decomposed knowledge distillation model of convolutional neural networks. This approach minimizes the computational resources needed for edge devices. The proposed method is comprised of two sequential steps.  In the preprocessing step, the initial phase involves the elimination of non-clinical data and artifacts. This is followed by the extraction of region of interest (ROI). In this phase of the procedure, only the hand portion of the patient's X-ray remains for further evaluation. The subsequent phase involves the delineation of the boundaries of the region of interest. This is necessary because, in certain age groups, some bones are not ossified. Consequently, reliance on bones as landmarks is precluded.  In the second step, The extracted ROI from the preceding step is utilized to train the teacher model. The student model utilizes the teacher model's knowledge to learn how to predict patient age. Therefore, the present study puts forth transfer learning methodologies founded on the distillation of knowledge, with the aim of facilitating the transference of knowledge between teacher and student models.
 The proposed method is based on the data set of the Digital Hand Atlas (DHA) database. The evaluation criteria used in this work are Accuracy, recall, permission and mean absolute error (MAE). The proposed model achieves 96/47% test accuracy for bone age classification.

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