Profile Details

Create Project

★★★★★

☆☆☆☆☆

★★★★★

☆☆☆☆☆

Hire Dr. Mohammad M.

Finland

Expert in Machine Vision, Deep Learning, NLP, and Data Analysis | 10+ Years of Research Excellence

Profile Summary

I am a self-motivated researcher with ten years' experience, focused on text and image classification, object detection, and segmentation algorithms using Python and MATLAB. In my doctoral studies, I developed a noise-robust convolutional neural network (CNN) for image classification. In a joint project between Simon Fraser University and Esfarayen Faculty of Medical Science, I contributed to the development of a data augmentation strategy by determining the type and noise density value to enhance the robustness and generalization of deep CNNs for COVID-19 detection. While remotely collaborating with the University of Leicester in the UK, I applied greedy AutoAugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy. Additionally, I collaborated remotely with the University of Florida in the US to introduce a novel method for detecting citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks. Since 2023, I have been a postdoctoral researcher at the University of Helsinki, contributing to a multimodal learning project aimed at detecting wildlife trade on social media.

Subject Matter Expertise

• ☆ Data Analysis
• ☆ Machine Learning
• ☆ Natural Language Processing
• ☆ Neural Networks
• ☆ Artificial Intelligence
• ☆ Deep Learning
• ☆ Computer Vision

Services

• Research
• Data & AI

Research User Research, Meta-Research, Scientific and Technical Research

Data & AI Predictive Modeling, Image Processing, Algorithm Design-ML, Data Visualization, Text Mining & Analytics, Data Mining, Data Processing

Work Experience

University of Helsinki

- Present

Education

Ph.D. (Computer Engineering )

Yazd University

2014 - 2020

M.Sc. (Faculty of Engineering)

University of Mohaghegh Ardabili

2011 - 2013

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

Mohammad Momeny, Mahmoud Marhamati, Behnam Dorry, Shima Imannezhad, Mohammad Arafat Hussain, Ali Asghar Neshat, Abulfazl Kalmishi (2024). Patient's airway monitoring during cardiopulmonary resuscitation using deep networks . Medical Engineering & Physics.

Patient's airway monitoring during cardiopulmonary resuscitation using deep networks @article{a9e87891ca32422897ca0a42008e7c32, title = "Patient's airway monitoring during cardiopulmonary resuscitation using deep networks", abstract = "Cardiopulmonary resuscitation (CPR) is a crucial life-saving technique commonly administered to individuals experiencing cardiac arrest. Among the important aspects of CPR is ensuring the correct airway position of the patient, which is typically monitored by human tutors or supervisors. This study aims to utilize deep transfer learning for the detection of the patient's correct and incorrect airway position during cardiopulmonary resuscitation. To address the challenge of identifying the airway position, we curated a dataset consisting of 198 recorded video sequences, each lasting 6–8 s, showcasing both correct and incorrect airway positions during mouth-to-mouth breathing and breathing with an Ambu Bag. We employed six cutting-edge deep networks, namely DarkNet19, EfficientNetB0, GoogleNet, MobileNet-v2, ResNet50, and NasnetMobile. These networks were initially pre-trained on computer vision data and subsequently fine-tuned using the CPR dataset. The validation of the fine-tuned networks in detecting the patient's correct airway position during mouth-to-mouth breathing achieved impressive results, with the best sensitivity (98.8 %), specificity (100 %), and F-measure (97.2 %). Similarly, the detection of the patient's correct airway position during breathing with an Ambu Bag exhibited excellent performance, with the best sensitivity (100 %), specificity (99.8 %), and F-measure (99.7 %).", keywords = "Artificial intelligence, Cardiopulmonary resuscitation, CPR, Deep learning, Transfer learning, 113 Computer and information sciences, 3111 Biomedicine", author = "Mahmoud Marhamati and Behnam Dorry and Shima Imannezhad and Hussain, {Mohammad Arafat} and Neshat, {Ali Asghar} and Abulfazl Kalmishi and Mohammad Momeny", note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)", year = "2024", month = jul, doi = "10.1016/j.medengphy.2024.104179", language = "English", volume = "129", journal = "Medical Engineering and Physics", issn = "1350-4533", publisher = "ELSEVIER SCI IRELAND LTD", } . Medical Engineering and Physics.

Mohammad Momeny, Hossein Azizi, Ezzatollah Askari Asli-Ardeh, Ahmad Jahanbakhshi(2024). Vision-based strawberry classification using generalized and robust deep networks . Journal of Agriculture and Food Research. 15.

Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN @article{8c9288de50fa4a478cfe372bd7e4a30e, title = "Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN", abstract = "Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU.", keywords = "Data augmentation, Deep learning, Fraud detection, Noise, Quality control, Saffron", author = "Mohammad Momeny and Neshat, {Ali Asghar} and Ahmad Jahanbakhshi and Majid Mahmoudi and Yiannis Ampatzidis and Petia Radeva", year = "2023", month = may, doi = "10.1016/j.foodcont.2022.109554", language = "English", volume = "147", journal = "Food Control", issn = "0956-7135", publisher = "ELSEVIER SCI IRELAND LTD", } . Food Control.

LAIU-Net @article{e8181eb47a344ed2836994684890b20b, title = "LAIU-Net: A learning-to-augment incorporated robust U-Net for depressed humans? tongue segmentation", abstract = "Computer-aided tongue diagnosis system requires segmentation of the tongue body. The frequent movement of the tongue due to its natural flexibility often causes shape variability in photographs across subjects, which makes segmenting the tongue challenging from non-tongue elements, such as the lips, teeth, and other objects in the background of the tongue. The flexibility of the tongue causes a further challenge in maintaining a similar shape and style when taking photos of many healthy subjects and patients. To address these challenges, we have built a tongue dataset, where the tongue of each subject has been scanned thrice with an interval of less than a second. We have collected 333 tongue images from 111 depressed humans, who have been diagnosed with depression by a psychiatrist. In addition, in this paper, we propose a learning-to-augment incorporated U-Net (LAIU-Net) for the segmentation of the depressed human tongue in photographic images. The best policies for data augmentation were automatically chosen with the proposed LAIU-Net. For this purpose, we corrupted photographic tongue images with the Gaussian, speckle, and Poisson noise. The proposed approach addresses the overfitting problem as well as increases the generalizability of a deep network. We have compared the performance of the proposed LAIU-Net with that of other state-of-the-art U-Net configurations. Our LAIU-Net approach achieved a mean boundary F1 score of 93.1%.", keywords = "Data augmentation, Deep learning, Learning-to-augment strategy, Tongue segmentation, U-Net, 113 Computer and information sciences", author = "Mahmoud Marhamati and Zadeh, {Ali Asghar Latifi} and Fard, {Masoud Mozhdehi} and Hussain, {Mohammad Arafat} and Khalegh Jafarnezhad and Ahad Jafarnezhad and Mahdi Bakhtoor and Mohammad Momeny", year = "2023", month = jan, doi = "10.1016/j.displa.2023.102371", language = "English", volume = "76", journal = "Displays", issn = "0141-9382", publisher = "Elsevier B.V.", } . Displays.

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation @article{38b69c5ba71e4229b8e5116d4f063cb4, title = "Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT", abstract = "Supervised deep learning has become a standard approach to solving medical image segmentation tasks. How-ever, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.", keywords = "Active learning, Covid-19, Deep learning, Noisy teacher, Pneumonia, Segmentation, Semi-supervised learning", author = "Hussain, {Mohammad Arafat} and Zahra Mirikharaji and Mohammad Momeny and Mahmoud Marhamati and Neshat, {Ali Asghar} and Rafeef Garbi and Ghassan Hamarneh", year = "2022", month = dec, doi = "10.1016/j.compmedimag.2022.102127", language = "English", volume = "102", journal = "Computerized Medical Imaging and Graphics", issn = "0895-6111", publisher = "PERGAMON", } . Computerized Medical Imaging and Graphics.

Mohammad Momeny, Mohammad Arafat Hussain, Zahra Mirikharaji, Mahmoud Marhamati, Ali Asghar Neshat, Rafeef Garbi, Ghassan Hamarneh(2022). Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT . Computerized Medical Imaging and Graphics. 102. p. 102127. Elsevier {BV}

Mohammad Momeny, Ahmad Jahanbakhshi, Ali Asghar Neshat, Ramazan Hadipour-Rokni, Yu-Dong Zhang, Yiannis Ampatzidis(2022). Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks . Ecological Informatics. 71. p. 101829. Elsevier {BV}

Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks @article{be6ab962c40a4abbbd3b100432c661c4, title = "Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks", abstract = "Fruit infected by pests or diseases and fruit harvests with different levels of ripeness cause a lack of marketability, decrease in economic value, and increase in crop waste. In this study, we propose a robust and generalized deep convolutional neural network (CNN) model via fine-tuning the pre-trained models for detecting black spot disease and ripeness levels in orange fruit. A dataset containing 1896 confirmed orange images in the farm in four classes (unripe, half-ripe, ripe, and infected with black spot disease) was used. In order to prevent overfitting and increase the robustness and generalizability of the model, instead of using fundamental data augmentation techniques, a novel learning-to-augment strategy that creates new data using noisy and restored images was employed. Controllers using the Bayesian optimization algorithm were utilized to select the optimal noise pa-rameters of Gaussian, speckle, Poisson, and salt-and-pepper noise to generate new noisy images. A convolutional autoencoder model was developed to produce newly restored images affected by optimized noise density. The dataset augmented by the best policies of the learning-to-augment strategy was used to fine-tune several pre -trained models (GoogleNet, ResNet18, ResNet50, ShuffleNet, MobileNetv2, and DenseNet201). The results showed that the learning-to-augment strategy for the fine-tuned ResNet50 achieved the best performance with 99.5% accuracy, and 100% F-measure by assigning images infected with black spot disease as the positive class. The proposed automatic disease and fruit quality monitoring technique can be also used for the detection of other diseases in agriculture and forestry.", keywords = "Classification, Data augmentation, Deep learning, Machine learning, Orange fruit, Waste management", author = "Mohammad Momeny and Ahmad Jahanbakhshi and Neshat, {Ali Asghar} and Ramazan Hadipour-Rokni and Yu-Dong Zhang and Yiannis Ampatzidis", year = "2022", month = nov, doi = "10.1016/j.ecoinf.2022.101829", language = "English", volume = "71", journal = "Ecological Informatics", issn = "1574-9541", publisher = "Elsevier Scientific Publ. Co", } . Ecological Informatics.

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images @article{12e4863ca11645858d314a0acf0a87ad, title = "Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images", abstract = "Deep convolutional neural networks (CNNs) are used for the detection of COVID-19 in X-ray images. The detection performance of deep CNNs may be reduced by noisy X-ray images. To improve the robustness of a deep CNN against impulse noise, we propose a novel CNN approach using adaptive convolution, with the aim to ameliorate COVID-19 detection in noisy X-ray images without requiring any preprocessing for noise removal. This approach includes an impulse noise-map layer, an adaptive resizing layer, and an adaptive convolution layer to the conventional CNN framework. We also used a learning-to-augment strategy using noisy X-ray images to improve the generalization of a deep CNN. We have collected a dataset of 2093 chest X-ray images including COVID-19 (452 images), non-COVID pneumonia (621 images), and healthy ones (1020 images). The architecture of pre-trained networks such as SqueezeNet, GoogleNet, MobileNetv2, ResNet18, ResNet50, ShuffleNet, and EfficientNetb0 has been modified to increase their robustness to impulse noise. Validation on the noisy X-ray images using the proposed noise-robust layers and learning-to-augment strategy-incorporated ResNet50 showed 2% better classification accuracy compared with state-of-the-art method.", keywords = "Adaptive convolution, Adaptive resize, COVID-19 classification, Data augmentation, Noise", author = "Adel Akbarimajd and Nicolas Hoertel and Hussain, {Mohammad Arafat} and Neshat, {Ali Asghar} and Mahmoud Marhamati and Mahdi Bakhtoor and Mohammad Momeny", year = "2022", month = sep, doi = "10.1016/j.jocs.2022.101763", language = "English", volume = "63", journal = "Journal of Computational Science", issn = "1877-7503", publisher = "Elsevier", } . Journal of Computational Science.

Mohammad Momeny, Ali Asghar Neshat, Abdolmajid Gholizadeh, Ahad Jafarnezhad, Elham Rahmanzadeh, Mahmoud Marhamati, Bagher Moradi, Ali Ghafoorifar, Yu-Dong Zhang (2022). Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy . Computers in Biology and Medicine.

Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy @article{3d5cd471dd05405dbe2d4d01eaa72cae, title = "Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy", abstract = "Although tuberculosis (TB) is a disease whose cause, epidemiology and treatment are well known, some infected patients in many parts of the world are still not diagnosed by current methods, leading to further transmission in society. Creating an accurate image-based processing system for screening patients can help in the early diag-nosis of this disease. We provided a dataset containing1078 confirmed negative and 469 positive Mycobacterium tuberculosis instances. An effective method using an improved and generalized convolutional neural network (CNN) was proposed for classifying TB bacteria in microscopic images. In the preprocessing phase, the insig-nificant parts of microscopic images are excluded with an efficient algorithm based on the square rough entropy (SRE) thresholding. Top 10 policies of data augmentation were selected with the proposed model based on the Greedy AutoAugment algorithm to resolve the overfitting problem. In order to improve the generalization of CNN, mixed pooling was used instead of baseline one. The results showed that employing generalized pooling, batch normalization, Dropout, and PReLU have improved the classification of Mycobacterium tuberculosis im-ages. The output of classifiers such as Naive Bayes-LBP, KNN-LBP, GBT-LBP, Naive Bayes-HOG, KNN-HOG, SVM-HOG, GBT-HOG indicated that proposed CNN has the best results with an accuracy of 93.4%. The improvements of CNN based on the proposed model can yield promising results for diagnosing TB.", keywords = "Convolutional neural network, Deep learning, Dropout, Greedy Autoaugment, Mixed pooling, Tuberculosis", author = "Mohammad Momeny and Neshat, {Ali Asghar} and Abdolmajid Gholizadeh and Ahad Jafarnezhad and Elham Rahmanzadeh and Mahmoud Marhamati and Bagher Moradi and Ali Ghafoorifar and Yu-Dong Zhang", year = "2022", month = feb, doi = "10.1016/j.compbiomed.2021.105175", language = "English", volume = "141", journal = "Computers in Biology and Medicine", issn = "0010-4825", publisher = "PERGAMON", } . Computers in Biology and Medicine.

Momeny, Mohammad, Akbarimajd, Adel, Hoertel, Nicolas, Hussain, Mohammad Arafat, Neshat, Ali Asghar, Marhamati, Mahmoud, Bakhtoor, Mahdi (2022). Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images . Journal of Computational Science.

Momeny, Mohammad, Jahanbakhshi, Ahmad, Neshat, Ali Asghar, Hadipour-Rokni, Ramazan, Zhang, Yu-Dong, Ampatzidis, Yiannis (2022). Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks . Ecological Informatics.

Momeny, Mohammad, Hussain, Mohammad Arafat, Mirikharaji, Zahra, Marhamati, Mahmoud, Neshat, Ali Asghar, Garbi, Rafeef, Hamarneh, Ghassan (2022). Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT . Computerized Medical Imaging and Graphics.

Ahmad Jahanbakhshi and Mohammad Momeny and Majid Mahmoudi and Petia Radeva(2021). Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks . Energy Reports. 7. p. 5248--5256. Elsevier {BV}

Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks @article{45571fe157f647c8911ca58d73d8f641, title = "Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks", abstract = "In this study, we address the problem of classification of carrot fruit in order to manage and control their waste using improved deep neural networks. In this work, we perform a deep study of the problem of carrot classification and show that convolutional neural networks are a straightforward approach to solve the problem. Additionally, we improve the convolutional neural network (CNN) based on learning a pooling function by combining average pooling and max pooling. We experi-mentally show that the merging operation used increases the accuracy of the carrot classification compared to other merging methods. For this purpose, images of 878 carrot samples in various shapes (regular and irregular) were taken and after the preprocessing operation, they were classified by the improved deep CNN. To compare this method with the other methods, image features were extracted using Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) methods and they were classified by Multi-Layer Perceptron (MLP), Gradient Boosting Tree (GBT), and K-Nearest Neighbors (KNN) algorithms. Finally, the method proposed based on the improved CNN algorithm, was compared with other classification algorithms. The results showed 99.43% of accuracy for grading carrot through the CNN by configuring the proposed Batch Normalization (BN)-CNN method based on mixed pooling. Therefore, CNN can be effective in increasing marketability, controlling waste and improving traditional methods used for grading carrot fruit. (C) 2021 The Authors. Published by Elsevier Ltd.", keywords = "Carrot, Classification, Convolutional Neural Network, Data augmentation, Deep learning, Waste control", author = "Ahmad Jahanbakhshi and Mohammad Momeny and Majid Mahmoudi and Petia Radeva", year = "2021", month = nov, doi = "10.1016/j.egyr.2021.08.028", language = "English", volume = "7", pages = "5248--5256", journal = "Energy Reports", issn = "2352-4847", publisher = "Elsevier", } . Energy Reports.

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder @article{75996f1ce41543f7a74f8104987808da, title = "A novel method based on machine vision system and deep learning to detect fraud in turmeric powder", abstract = "Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-destructive technique in measuring the quality of food and spices has always been taken into consideration by researchers. Due to the high nutritional value of turmeric among the spices as well as the fraudulent motives to gain economic profit from the selling of this product, its quality assessment is very important. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with turmeric in powder form and sold in the market. In this study, an improved convolutional neural network (CNN) was used to classify turmeric powder images to detect fraud. CNN was improved through the use of gated pooling functions. We also show with a combined approach based on the integration of average pooling and max pooling that the accuracy and performance of the proposed CNN has increased. In this study, 6240 image samples were prepared in 13 categories (pure turmeric powder, chickpea powder, chickpea powder mixed with food coloring, 10, 20, 30, 40 and 50% fraud in turmeric). In the preprocessing step, unwanted parts of the image were removed. The data augmentation (DA) was used to reduce the overfitting problem on CNN. Also in this research, MLP, Fuzzy, SVM, GBT and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that prevention of the overfitting problem using gated pooling, the proposed CNN was able to grade the images of turmeric powder with 99.36% accuracy compared to other classifiers. The results of this study also showed that computer vision, especially when used with deep learning (DL), can be a valuable method in evaluating the quality and detecting fraud in turmeric powder.", keywords = "Data augmentation, Deep learning, Food quality, Image processing, Turmeric powder, 213 Electronic, automation and communications engineering, electronics, 113 Computer and information sciences", author = "Ahmad Jahanbakhshi and Yousef Abbaspour-Gilandeh and Kobra Heidarbeigi and Mohammad Momeny", year = "2021", month = sep, doi = "10.1016/j.compbiomed.2021.104728", language = "English", volume = "136", journal = "Computers in Biology and Medicine", issn = "0010-4825", publisher = "PERGAMON", } . Computers in Biology and Medicine.

Learning-to-augment strategy using noisy and denoised data @article{f6a6c04b5ec94516973304823c422d10, title = "Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images", abstract = "Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy.", keywords = "Covid-19, Classification, Data augmentation, Deep learning, Learning-to-augment, Noise, X-ray images", author = "Mohammad Momeny and Neshat, {Ali Asghar} and Hussain, {Mohammad Arafat} and Solmaz Kia and Mahmoud Marhamati and Ahmad Jahanbakhshi and Ghassan Hamarneh", year = "2021", month = sep, doi = "10.1016/j.compbiomed.2021.104704", language = "English", volume = "136", journal = "Computers in Biology and Medicine", issn = "0010-4825", publisher = "PERGAMON", } . Computers in Biology and Medicine.

Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning @article{b871c9c99d3f4bf49c0ede5994e250ff, title = "Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning", abstract = "Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with ginger in powder form and sold in the market. Demand for non-destructive methods of measuring food quality, such as machine vision and the growing need for food and spices, were the main motives to conduct this study. This study classified ginger powder images to detect fraud by improving convolutional neural networks (CNN) through a gated pooling function. The main approach to improving CNN is to use a pooling function that combines average pooling and max pooling. The Batch normalization (BN) technique is used in CNN to improve classification results. We show empirically that the combining operation used increases the accuracy of ginger powder classification compared to the baseline pooling method. For this purpose, 3360 image samples of ginger powder were prepared in 7 categories (pure ginger powder, chickpea powder, 10%, 20%, 30%, 40%, and 50% fraud in ginger powder). Moreover, MLP, Fuzzy, SVM, GBT, and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that using batch normalization based on gated pooling, the proposed CNN was able to grade the images of ginger powder with 99.70% accuracy compared to other classifiers. Therefore, it can be said that the CNN method and image processing technique effectively increase marketability, prevent ginger powder fraud, and promote traditional methods of ginger powder fraud detection.", keywords = "Convolutional neural networks, Deep learning, Food fraud, Ginger powder, Machine vision, 213 Electronic, automation and communications engineering, electronics, 113 Computer and information sciences", author = "Ahmad Jahanbakhshi and Yousef Abbaspour-Gilandeh and Kobra Heidarbeigi and Mohammad Momeny", year = "2021", month = sep, doi = "10.1016/j.compbiomed.2021.104764", language = "English", volume = "136", journal = "Computers in Biology and Medicine", issn = "0010-4825", publisher = "PERGAMON", } . Computers in Biology and Medicine.

Ahmad Jahanbakhshi and Yousef Abbaspour-Gilandeh and Kobra Heidarbeigi and Mohammad Momeny(2021). A novel method based on machine vision system and deep learning to detect fraud in turmeric powder . Computers in Biology and Medicine. 136. p. 104728. Elsevier {BV}

Mohammad Momeny, Ali Asghar Neshat, Mohammad Arafat Hussain, Solmaz Kia, Mahmoud Marhamati, Ahmad Jahanbakhshi, Ghassan Hamarneh (2021). Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images . Computers in Biology and Medicine.

Ahmad Jahanbakhshi and Yousef Abbaspour-Gilandeh and Kobra Heidarbeigi and Mohammad Momeny(2021). Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning . Computers in Biology and Medicine. p. 104764. Elsevier {BV}

A noise robust convolutional neural network for image classification @article{c5573738052b4c05b869c856dc126a1a, title = "A noise robust convolutional neural network for image classification", abstract = "Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks. In this paper, a Noise-Robust Convolutional Neural Network (NR-CNN) is proposed to classify the noisy images without any preprocessing for noise removal and improve the classification performance of noisy images in convolutional neural networks. In the proposed NR-CNN, a noise map layer and an adaptive resize layer are added to the architecture of convolutional neural network. Moreover, the noise problem is considered in different components of NR-CNN such that convolutional layer, pooling layer and loss function of the convolutional neural network are improved for robustness of CNN to noise. The adaptive data augmentation based on noise map are introduced to improve the classification performance of the proposed NR-CNN. Experimental results demonstrate that the proposed NR-CNN improves the noisy image classification and the network training speed.", keywords = "Adaptive convolution, Adaptive data augmentation, Adaptive pooling, Convolutional neural network, Image classification, Noise, 218 Environmental engineering", author = "Mohammad Momeny and Latif, {Ali Mohammad} and Sarram, {Mehdi Agha} and Razieh Sheikhpour and Zhang, {Yu Dong}", year = "2021", month = jun, doi = "10.1016/j.rineng.2021.100225", language = "English", volume = "10", journal = "Results in Engineering", issn = "2590-1230", publisher = "Elsevier", } . Results in Engineering.

Mohammad Momeny and Ali Mohammad Latif and Mehdi Agha Sarram and Razieh Sheikhpour and Yu Dong Zhang(2021). A noise robust convolutional neural network for image classification . Results in Engineering. 10. p. 100225. Elsevier {BV}

Momeny, Mohammad, Latif, Ali Mohammad, Sarram, Mehdi Agha, Sheikhpour, Razieh, Zhang, Yu Dong (2021). A noise robust convolutional neural network for image classification . Results in Engineering.

Influence of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer @article{c6b9add3d5754ca79f8f8c29dea33351, title = "Influence of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer", abstract = "Drying is one of the ways to reduce postharvest waste and processing in agricultural products. Drying with hot air is one of the most popular drying methods in the food industry. The purpose of this study is to investigate the effect of ultrasound and temperature on the quality and thermodynamic properties in the process of drying nectarine slices in a hot air dryer. The drying process was performed at four levels of ultrasonic pre-treatment of 0 min (control sample), 10, 20, and 40 min and three temperature levels of 50, 60, and 75 degrees C. Experiments were performed on 5 mm thick nectarine slices with 12 treatments and three replications. The obtained data were analyzed by a factorial test based on a completely randomized design. The moisture ratio change of nectarine samples was fitted with 12 thin-layer drying models. The results showed that by increasing the temperature and duration of the ultrasound treatment, drying time for nectarine slices decreased. The Page model was recognized as the best model for describing the drying behavior of nectarine slices through ultrasound. The highest amounts of shrinkage and color change were obtained as 31.35% and 25.03%, respectively, at the temperature of 75 degrees C and for the control samples. The use of ultrasound in the process of drying nectarine slices at different temperatures resulted in an increase in the effective moisture diffusion coefficient from 6.50 x 10(-10)to 2.11 x 10(-9) m(2)/s. The amount of specific energy consumption (SEC) in the process of drying nectarine slices was calculated to be 59.70 to 212.97 kwh/kg. Practical applications Postharvest drying of fruits is essential to increase shelf life and control waste. A certain level of a product's moisture can be reduced through extraction of water from it under controlled conditions in order to prevent the microbial growth that causes it to spoil. Moreover, the preservation of qualitative properties (such as color and shrinkage) and thermodynamic properties (such as reduced energy consumption) has attracted the attention of many researchers working on the drying process of agricultural products. In this study, the effect of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer were investigated. The results of this study can be useful in optimizing drying operations and improving the qualitative and thermodynamic properties. The results can also be applied as a technical basis for drying nectarine and designing the required equipment.", keywords = "Variable effective diffusivity, Energy-consumption, Borne ultrasound, Kinetics, Solar, Microwave, Intensification, Attributes, Shrinkage, Exergy", author = "Ahmad Jahanbakhshi and Reza Yeganeh and Mohammad Momeny", year = "2020", month = oct, doi = "10.1111/jfpp.14818", language = "English", volume = "44", journal = "Journal of Food Processing and Preservation", issn = "0145-8892", publisher = "Wiley Periodicals, Inc. ", number = "10", } . Journal of Food Processing and Preservation.

Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach @article{2020c70955d446b7870f4161e6db422d, title = "Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach", abstract = "The most important quality parameter of a product is its nutritional value, but marketability of agricultural products depends primarily on the overall appearance and shape of the products. This study was carried out with the aim of developing cherry fruit packing methods and thus reducing waste and increasing its exportability and marketability. Therefore, the purpose of research was to use the improved Convolutional Neural Network (CNN) algorithm to detect the appearance of cherries and provide an efficient system for their grading. In order to identify and classify images cherry on two classes (regular and irregular shaped) was prepared. After preprocessing the images, the proposed method utilized its ability to improve generalization in the CNN through a combination of max pooling and average pooling techniques, to grade cherries. In order to compare the proposed method (CNN) with HOG and LBP methods, the properties of the images extracted by KNN, ANN, Fuzzy and Ensemble Decision Trees (EDT) algorithms were categorized. The proposed method based on hybrid pooling is also compared with CNN with baseline pooling method such as average pooling. Comparisons based on the results of simulation demonstrate the superiority of the proposed improved CNN over other methods presenting an accuracy of 99.4 %. Therefore, the CNN and image processing methods are effective in managing the marketability and exportability of the cherry fruit and can replace the traditional methods applied for grading cherries.", keywords = "Cherry, Convulsion Neural Network, Deep learning, Grading, Image processing", author = "Mohammad Momeny and Ahmad Jahanbakhshi and Khalegh Jafarnezhad and Yu-Dong Zhang", year = "2020", month = aug, doi = "10.1016/j.postharvbio.2020.111204", language = "English", volume = "166", journal = "Postharvest Biology and Technology", issn = "0925-5214", publisher = "Elsevier Scientific Publ. Co", } . Postharvest Biology and Technology.

Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks @article{059e9a4e790e4c59bf3809b714642ac3, title = "Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks", abstract = "Quality assessment of agricultural products is one of the most important factors in promoting their marketability and waste control management. Image processing systems are new and non-destructive methods that have various applications in the agriculture sector, including product grading. The purpose of this study is to use an improved CNN algorithm to detect the apparent defects of sour lemon fruit, grade them and provide an efficient system to do so. In order to identify and categorize defects, sour lemon images were prepared and placed in two groups of healthy and damaged ones. After pre-processing, the images were categorized based on an improved algorithm (CNN). From the data augmentation and the stochastic pooling mechanism were used to improve CNN results. In addition, to compare the proposed model with other methods, feature extraction algorithms (histogram of oriented gradients (HOG) and local binary patterns (LBP)) and k-nearest neighbour (KNN), artifical neural network (ANN), Fuzzy, support vector machine (SVM) and decision tree (DT) classification algorithms were used. The results showed that the accuracy of the convolutional neural network (CNN) was 100 %. Therefore, it can be said that the CNN method and image processing are effective in managing waste and promoting the traditional method of sour lemon grading.", keywords = "Data augmentation, Deep learning, Fruit, Grading, Image processing, Waste management", author = "Ahmad Jahanbakhshi and Mohammad Momeny and Majid Mahmoudi and Yu-Dong Zhang", year = "2020", month = mar, day = "15", doi = "10.1016/j.scienta.2019.109133", language = "English", volume = "263", journal = "Scientia Horticulturae", issn = "0304-4238", publisher = "Elsevier Scientific Publ. Co", } . Scientia Horticulturae.

Mohammad Momeny, Ahmad Jahanbakhshi, Majid Mahmoudi, Yu-Dong Zhang(2020). Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks . Scientia Horticulturae. 263. p. 109133. Elsevier {BV}

Momeny, M., Jahanbakhshi, A., Jafarnezhad, K., Zhang, Y.-D.(2020). Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach . Postharvest Biology and Technology. 166.

Momeny, Mohammad, Jahanbakhshi, Ahmad, Jafarnezhad, Khalegh, Zhang, Yu-Dong (2020). Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach . Postharvest Biology and Technology.

Jahanbakhshi, A., Momeny, M., Mahmoudi, M., Zhang, Y.-D.(2020). Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks . Scientia Horticulturae. 263.

Momeny, Mohammad, Jahanbakhshi, Ahmad, Mahmoudi, Majid, Zhang, Yu-Dong (2020). Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks . Scientia Horticulturae.

Jahanbakhshi, A., Yeganeh, R., Momeny, M.(2020). Influence of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer . Journal of Food Processing and Preservation. 44. (10).

Momeny, Mohammad, Jahanbakhshi, Ahmad, Yeganeh, Reza (2020). Influence of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer . Journal of Food Processing and Preservation.

Nooshyar, Mahdi, Momeny, Mohamad (2013). Removal of high density impulse noise using a novel decision based adaptive weighted and trimmed median filter . 10TH IRANIAN CONFERENCE ON MACHINE VISION AND IMAGE PROCESSING (MVIP).

CONFERENCE PAPER

Nooshyar, M., Momeny, M.(2013). Removal of high density impulse noise using a novel decision based adaptive weighted and trimmed median filter . Iranian Conference on Machine Vision and Image Processing, MVIP. p. 387-391.