Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks

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 experimentally 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

BAGGING BEHAVIOUR OF EXTENSIBILE SHIRT FABRICS

Fabric bagging is a type of three dimensional permanent deformations of garments that occurs at positions such as elbow and knee. When a prolonged compression force is exerted on a garment during wear, the three dimensional deformation may involve complex inelastic behaviour in the garment, including viscoelastic behaviour of the fibers and plastic behaviour due to frictional movements between fibers and between yarns in the fabric. The aim of this stduy is to engineer extensibilty values of shirt fabrics along weft direction and hence to analyze and interpret the bagging behavior of extensible shirt fabrics in terms of fabric mechanical preoperties. In this study, finished extensible plain woven shirt fabrics with changing the core-spun extensible yarn layout along weft direction were produced. The bagging behavior of samples tested at a speed of 7 (mm/min) and initial bagging height 7(mm) in 5 successive cycles using an Instron tensile tester equipped with 4 circular clamps in 56, 61, 66 and 71 (mm) diameter. The results were then statistically analysed using ANOVA test method. The statistical analysis results show that fabric extensibility along weft direction and sample diameter has a statistical significant effect on bagging behaviour of extensible shirt fabrics. It is indicated with the increase in fabric extensibility and sample diameter the bagging parameters are significantly decreased

Diabetes Prediction by Optimizing the Nearest Neighbor Algorithm Using Genetic Algorithm

Introduction: Diabetes or diabetes mellitus is a metabolic disorder in body when the body does not produce insulin, and produced insulin cannot function normally. The presence of various signs and symptoms of this disease makes it difficult for doctors to diagnose. Data mining allows analysis of patients’ clinical data for medical decision making. The aim of this study was to provide a model for increasing the accuracy of diabetes prediction. Method: In this study, the medical records of 1151 patients with diabetes were studied, with 19 features. Patients’ information were collected from the UCI standard database. Each patient has been followed for at least one year. Genetic Algorithm (GA) and the nearest neighbor algorithm were used to provide diabetes prediction model. Results: It was revealed that the prediction accuracy of the proposed model equals 0.76. Also, for the methods of Naïve Bayes, Multi-layer perceptron (MLP) neural network, and support vector machine (SVM), the prediction accuracy was 0.62, 0.65, and 0.75, respectively. Conclusion: In predicting diabetes, the proposed model has the lowest error rate and the highest accuracy compared to the other models. Naïve Bayes method has the highest error rate and the lowest accuracy

Tropisetron suppresses colitis-associated cancer in a mouse model in the remission stage

Patients with inflammatory bowel disease (IBD) have a high risk for development of colitis-associated cancer (CAC). Serotonin is a neurotransmitter produced by enterochromaffin cells of the intestine. Serotonin and its receptors, mainly 5-HT3 receptor, are overexpressed in IBD and promote development of CAC through production of inflammatory cytokines. In the present study, we demonstrated the in vivo activity of tropisetron, a 5-HT3 receptor antagonist, against experimental CAC. CAC was induced by azoxymethane (AOM)/dextran sodium sulfate (DDS) in BALB/c mice. The histopathology of colon tissue was performed. Beta-catenin and Cox-2 expression was evaluated by immunohistochemistry as well as quantitative reverse transcription-PCR (qRT-PCR). Alterations in the expression of 5-HT3 receptor and inflammatory-associated genes such as Il-1β, Tnf-α, Tlr4 and Myd88 were determined by qRT-PCR. Our results showed that tumor development in tropisetron-treated CAC group was significantly lower than the controls. The qRT-PCR analysis demonstrated that the expression of 5-HT3 receptor was significantly increased following CAC induction. In addition, tropisetron reduced expression of β-catenin and Cox-2 in the CAC experimental group. The levels of Il-1β, Tnf-α, Tlr4 and Myd88 were significantly decreased upon tropisetron treatment in the AOM/DSS group. Taken together, our data show that tropisetron inhibits development of CAC probably by attenuation of inflammatory reactions in the colitis

Anticancer Effects of ZnO/CNT@Fe3O4 in AML-Derived KG1 Cells: Shedding Light on Promising Potential of Metal Nanoparticles in Acute Leukemia

Background: Therapeutic approaches for acute myeloid leukemia (AML) have remained largely unchanged for over 40 years and cytarabine and an anthracycline (e.g., daunorubicin) backbone is the main induction therapy for these patients. Resistance to chemotherapy is the major clinical challenge and contributes to short-term survival with a high rate of disease recurrence. Given the established efficacy of nanoparticles in cancer treatment, this study was designed to evaluate the anticancer property of our novel nanocomposite in the AML-derived KG1 cells.Materials and Methods: To assess the anti-leukemic effects of our nanocomposite on AML cells, we used MTT and trypan blue assays. Flow cytometric analysis and q-RT-PCR were also applied to evaluate the impact of nanocomposite on cell cycle and apoptosis.Results: Our results outlined that ZnO/CNT@Fe3O4 decreased viability and metabolic activity of KG1 cells through induction of G1 arrest by increasing the expression of p21 and p27 cyclin-dependent kinase inhibitors and decreasing c-Myc transcription. Moreover, ZnO/CNT@Fe3O4 markedly elevated the percentage of apoptotic cells which was coupled with a significant alteration of Bax and Bcl-2 expressions. Synergistic experiments showed that ZnO/CNT@Fe3O4 enhances the cytotoxic effects of Vincristine on KG1 cells.Conclusion: In conclusion, this study sheds light on the potent anti-leukemic effects of ZnO/CNT@Fe3O4 and provides evidence for the application of this agent in the treatment of acute myeloid leukemia.</p

Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks

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.Peer reviewe

Vision-based strawberry classification using generalized and robust deep networks

Grading of agricultural products such as fruits and vegetables based on ripeness level and visual defects for the purpose of export, storage and waste control is a process of special importance. Various methods have been used to detect levels of ripeness and the quality of agricultural products, some of which are destructive and some non-destructive. The machine vision system is one of the non-destructive and accurate systems in the field of detecting the quality of agricultural products. In this study, we propose a robust and generalized model via fine-tuning the pre-trained networks for the classification of strawberry fruit. A dataset containing 800 confirmed strawberry images in four classes (unripe, half-ripe, ripe, and damaged) was used. Instead of using fundamental data augmentation (FDA) techniques to prevent overfitting problem and increase the robustness of the model, we employed a novel learning-to-augment strategy (LAS) using noisy images that creates new noisy variant of data via original images. By using the Bayesian optimization algorithm, controllers were used to select the optimal noise parameters of Gaussian and speckle noise to generate new noise images. The best policies of data augmentation based on LAS was used to fine-tune pre-trained cutting-edge models (GoogleNet, ResNet18, and ShuffleNet). The results show that in all the proposed scenarios (i.e. using original data without data augmentation, employing FDA, and applying LAS) the GoogleNet model was able to achieve 96.88 %, 97.50 %, and 98.85 % accuracy, respectively

A noise robust convolutional neural network for image classification

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.Peer reviewe

The DNA Methylation in Neurological Diseases

DNA methylation is critical for the normal development and functioning of the human brain, such as the proliferation and differentiation of neural stem cells, synaptic plasticity, neuronal reparation, learning, and memory. Despite the physical stability of DNA and methylated DNA compared to other epigenetic modifications, some DNA methylation-based biomarkers have translated into clinical practice. Increasing reports indicate a strong association between DNA methylation profiles and various clinical outcomes in neurological diseases, making DNA methylation profiles valuable as novel clinical markers. In this review, we aim to discuss the latest evidence concerning DNA methylation alterations in the development of neurodegenerative, neurodevelopmental, and neuropsychiatric diseases. We also highlighted the relationship of DNA methylation alterations with the disease progression and outcome in many neurological diseases such as Alzheimer&rsquo;s disease, Parkinson&rsquo;s disease, amyotrophic lateral sclerosis, frontotemporal dementia, and autism

Patient's airway monitoring during cardiopulmonary resuscitation using deep networks

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 %).Peer reviewe