دانشکده دانشکده فنی و مهندسی | سامانه جامع پژوهشی دانشگاه اصفهان

نمای کلی

دانشکده یکی از بخش‌های اصلی دانشگاه است که با هدف آموزش، پژوهش و توسعه علمی فعالیت می‌کند. این مجموعه با فراهم آوردن محیطی مناسب برای یادگیری، بستری برای رشد فکری، فرهنگی و تخصصی دانشجویان فراهم می‌سازد. در دانشکده‌ها، کلاس‌های آموزشی، کارگاه‌ها، آزمایشگاه‌ها و فضاهای پژوهشی در اختیار دانشجویان و استادان قرار دارد تا بتوانند به صورت نظری و عملی به ارتقاء دانش خود بپردازند. اعضای هیئت علمی با تجربه، نقش مهمی در هدایت علمی دانشجویان ایفا می‌کنند و با بهره‌گیری از روش‌های نوین آموزشی، زمینه‌ساز پیشرفت علمی و حرفه‌ای آنان هستند. دانشجویان در این فضا فرصت دارند تا مهارت‌های فردی و گروهی خود را تقویت کرده و در مسیر تخصصی خود گام بردارند. همچنین، فعالیت‌های فرهنگی، اجتماعی و ورزشی در کنار برنامه‌های آموزشی، به ایجاد تعادل در زندگی دانشجویی کمک می‌کند. دانشکده‌ها با برگزاری همایش‌ها، نشست‌های علمی و همکاری با مراکز تحقیقاتی و صنعتی، نقش مؤثری در ارتباط دانشگاه با جامعه دارند. این ارتباط موجب می‌شود تا دانشجویان با مسائل واقعی و کاربردی آشنا شده و آمادگی لازم برای ورود به بازار کار را کسب کنند. امکانات آموزشی و پژوهشی دانشکده‌ها به گونه‌ای طراحی شده‌اند که پاسخگوی نیازهای علمی روز باشند و زمینه‌ساز نوآوری و خلاقیت در حوزه‌های مختلف شوند. در نهایت، دانشکده مکانی است که در آن علم، تجربه و انگیزه گرد هم می‌آیند تا نسل آینده‌ای توانمند، آگاه و مسئولیت‌پذیر تربیت شود

گروه ها

برونداد‌های پژوهشی

2948

همه موارد

2948

مقالات

مقالات

RSM optimization of dibenzothiophene biodesulfurization by newly isolated strain of Rhodococcus erythropolis PD1 in aqueous and biphasic systems

( Article )

Derikvand, P., Etemadifar, Z., Biria, D.

Publication Date: 2015

Microbiology (Russian Federation) (16083237)84(1)pp. 65-72

Taguchi optimization of dibenzothiophene biodesulfurization by Rhodococcus erythropolis R1 immobilized cells in a biphasic system

( Article )

Derikvand, P., Etemadifar, Z., Biria, D.

Publication Date: 2014

International Biodeterioration and Biodegradation (09648305)86pp. 343-348

A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection

( Article )

Alamatsaz, N., Tabatabaei, L., Yazdchi, M., Payan, H., Alamatsaz, N., Nasimi, F.

Publication Date: 2024

Biomedical Signal Processing and Control (17468108)90

Objective: Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper introduces a light Deep Learning (DL) approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythms. To employ DL techniques, the ECG signals were preprocessed using resampling and baseline wander removal techniques. The classification was performed using an 11-layer network employing a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Results: In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed DL framework based on the combination of CNN and LSTM showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. Conclusion: A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. Significance: This study presents a lightweight classification technique with high diagnostic accuracy compared to other notable methods, making it a potential candidate for implementation in Holter monitor devices for arrhythmia detection. Finally, we used SHapley Additive exPlanations (SHAP), the most popular Explainable Artificial Intelligence (XAI) method to understand how our model make predictions. The results indicate that those features (ECG samples) that have contributed the most to a prediction are consonant with clinicians’ decisions. Therefore, the use of interpretable models increases the trust of clinicians in AI and thus leads to decreasing the number of misdiagnoses of cardiovascular diseases. © 2023 Elsevier Ltd

An emotion recognition embedded system using a lightweight deep learning model

( Article . Gold • Green )

Bazargani, M., Tahmasebi, A., Yazdchi, M., Baharlouei, Z.

Publication Date: 2023

Journal Of Medical Signals And Sensors (22287477)13(4)pp. 272-279

Ocular condition prognosis in Keratoconus patients after corneal ring implantation using artificial neural networks

( Article . Green • Gold )

Mehdizadeh dastjerdi, O., Bakhtiarnia, M., Yazdchi, M., Maghooli, K., Farokhi, F., Jadidi, K.

Publication Date: 2023

Heliyon (24058440)9(9)

The common disorder, Keratoconus (KC), is distinguished by cumulative corneal slimming and steepening. The corneal ring implantation has become a successful surgical procedure to correct the KC patient's vision. The determination of suitable patients for the surgery alternative is among the paramount concerns of ophthalmologists. To reduce the burden on them and enhance the treatment, this research aims to previse the ocular condition of KC patients after the corneal ring implantation. It focuses on predicting post-surgical corneal topographic indices and visual characteristics. This study applied an efficacious artificial neural network approach to foretell the aforementioned ocular features of KC subjects 6 and 12 months after implanting KeraRing and MyoRing based on the accumulated data. The datasets are composed of sufficient numbers of corneal topographic maps and visual characteristics recorded from KC patients before and after implanting the rings. The visual characteristics under study are uncorrected visual acuity (UCVA), sphere (SPH), astigmatism (Ast), astigmatism orientation (Axe), and best corrected visual acuity (BCVA). In addition, the statistical data of multiple KC subjects were registered, including three effective indices of corneal topography (i.e., Ast, K-reading, and pachymetry) pre- and post-ring embedding. The outcomes represent the contribution of practical training of the introduced models to the estimation of ocular features of KC subjects following the implantation. The corneal topographic indices and visual characteristics were estimated with mean errors of 7.29% and 8.60%, respectively. Further, the errors of 6.82% and 7.65% were respectively realized for the visual characteristics and corneal topographic indices while assessing the predictions by the leave-one-out cross-validation (LOOCV) procedure. The results confirm the great potential of neural networks to guide ophthalmologists in choosing appropriate surgical candidates and their specific intracorneal rings by predicting post-implantation ocular features. © 2023 The Authors