Esmat Rashedi

In an effort to transform memristive systems, this work presents design and simulation of a silicon nanowire (SiNW) with memristor characteristics. The study introduces fundamental principles of memristors and semiconductor properties, followed by an investigation of mimicking memristor performance within n-type SiNW with a doping concentration (N d ) of 10 16 cm -3 . A heuristic search algorithm, namely the Particle Swarm Optimization (PSO) technique, is employed to optimize both the geometrical features of the SiNW (such as length and diameter) and the input signal parameters (amplitude and frequency). Simulation results reveal that the proposed device exhibits the characteristic memristive I-V hysteresis curve and distinct high/low resistance states (HRS/LRS). Importantly, after establishing the memristive behavior through optimization, it is observed that by varying the input frequency while keeping other parameters fixed, the SiNW demonstrates a specific switching characteristic, indicating its potential to function as a nanoscale resistive switch. These results demonstrate the favorable characteristics of the memristive SiNW at the device level, highlighting its promise as a fundamental building block for future silicon-based resistive switching devices.

Cutaneous leishmaniasis (CL) remains a significant global public health disease, with the critical distinction and exact detection between responsive and unresponsive cases dictating treatment strategies and patient outcomes. However, image‐based methods for differentiating these groups are unexplored. This study addresses this gap by developing a deep learning (DL) model utilizing transfer learning to automatically identify responses in CL lesions. A dataset of 102 lesion images (51 per class; equally distributed across train, test, and validation sets) is employed. The DenseNet161, VGG16, and ResNet18 networks, pretrained on a massive image dataset, are fine‐tuned for our specific task. The models achieved an accuracy of 76.47%, 73.53%, and 55.88% on the test data, respectively, with a sensitivity of 80%, 75%, and 100% and specificity of 73.68%, 72.22%, and 53.12%, individually. Transfer learning successfully addressed the limited sample size challenge, demonstrating the models’ potential for real‐world application. This work underscores the significance of automated response detection in CL, paving the way for treatment and improved patient outcomes. While acknowledging limitations like the sample size, the need for collaborative efforts is emphasized to expand datasets and further refine the model. This approach stands as a beacon of hope in the contest against CL, illuminating the path toward a future where data‐driven diagnostics guide effective treatment and alleviate the suffering of countless patients. Moreover, the study could be a turning point in eliminating this important global public health and widespread disease.

Fast and relatively accurate determination of the fragment size distribution of a muck-pile is still a challenge in mining operations and the existing measurement methods are inefficient. In this research, a new algorithm to determine fragment size d...

Background subtraction is a crucial task in computer vision that involves segmenting video frames into foreground and background regions. While deep learning techniques have shown promise in this field, existing approaches typically rely on supervised learning and have limited generalization capabilities for unseen video data. Moreover, many of these methods are not suitable for real-time applications due to their offline or partially online nature. This paper introduces ORGRU, an unsupervised, online, and robust deep learning-based framework for background subtraction. ORGRU utilizes a robust version of Gated Recurrent Units (GRUs) to simultaneously estimate and maintain the background model as the low-rank component while calculating the sparse section as the foreground in a fully online manner. The model is iteratively updated in real time with an unsupervised learning algorithm utilizing only the current frame. To evaluate the effectiveness of the proposed approach, we conduct experiments on the LASIESTA dataset, which is a comprehensive, fully-labeled dataset for change detection covering various background subtraction challenges. The experimental results provide both qualitative and quantitative assessments, demonstrating the robustness and superiority of the proposed approach compared to the state-of-the-art methods.

Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful in terms of patient’s survival and response to therapy, protein-protein interaction network analysis has been applied on the genes. An interesting pattern was emerged when examining the genetic network. HSP90AA1, PTK2 and SRC genes were amongst the top-rated bottleneck genes, and DNA damage, cell adhesion and migration pathways are highly enriched in the network.

An effective method for optimal design of water distribution network (WDN) can significantly benefit to develop commercial software for component sizing. This research investigates Gravitational Search Algorithm (GSA) for pipe cost optimization model problems. GSA is a meta-heuristic (MH) algorithm which makes ease of its applicability to the design of WDNs due to its minimum number of algorithm parameters and requiring least effort in fine-tuning the parameters. Three well-known benchmark networks (Hanoi network, Two-Reservoir network and New York tunnels network) and a real-world WDN located Khorramshahr city in Iran were used. The GSA results were compared with the solutions obtained through various Evolutionary Algorithms. Experimental results show success of GSA in arriving minimum cost solution. GSA achieved to the best so far solution reported for one case (Hanoi network), and it could find the least cost for three other networks compared to the best results of other optimization algorithms. In addition, for two case studies (Hanoi network and Khorramshahr city network) number of function evaluations were less than other algorithms. Further, the study reveals that GSA achieved the maximum number of times the best so far solution and confirming rapid convergence without struck up at local optimum.