Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing proce...Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.展开更多
In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structu...In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.展开更多
The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convol...The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convolutional Neural Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images, focusing on the impact of varying learning rates and optimization strategies. Despite the abundance of chest X-ray datasets from various institutions, the lack of a dedicated COVID-19 dataset for computational analysis presents a significant challenge. Our work introduces an empirical analysis across four distinct learning rate policies—Cyclic, Step Based, Time-Based, and Epoch Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The performance of these configurations was evaluated in terms of training and validation accuracy over 100 epochs. Our results demonstrate significant differences in model performance, with the Cyclic learning rate policy combined with SGD optimizer achieving the highest validation accuracy of 83.33%. This study contributes to the existing body of knowledge by outlining effective CNN configurations for COVID-19 image dataset analysis, offering insights into the optimization of machine learning models for the diagnosis of infectious diseases. Our findings underscore the potential of CNNs in supplementing traditional PCR tests, providing a computational approach to identify patterns in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the swift and accurate diagnosis of the virus.展开更多
The objective of this study was to formulate a niosomal in situ gel of sertraline hydrochloride for the treatment of depression.Sertraline hydrochloride is a Biopharmaceutical Classification System class II drug with ...The objective of this study was to formulate a niosomal in situ gel of sertraline hydrochloride for the treatment of depression.Sertraline hydrochloride is a Biopharmaceutical Classification System class II drug with low solubility and high permeability.This research aims to improve the bioavailability of drugs by increasing their solubility by encapsulation in niosomal vesicles incorporated into an in situ gel.Niosomes were prepared via the ether injection method using a 3^(2)full factorial design.Sertraline hydrochloride in situ gel was prepared via the cold method by using Poloxamer 407 and PVP K 30.One factor was evaluated at 3 levels,and the concentrations of surfactant Span 60(X_(1))and of cholesterol(X_(2))were selected as independent variables.The F8 batch of niosomes out of 9 formulations was found to be optimized.The average vesicle size of the niosomal suspension in the optimized F8 batch was 167.5 nm,with a zeta potential of-31.4 mV.The entrapment efficiency across the 9 batches varied from 79.6%to 90.5%,while the drug content ranged between 91.2%and 98.5%.The entrapment efficiency and drug content of the optimized batch were 90.5%and 98.5%,respectively.In vitro drug release for these batches was between 86.32%and 91.33%.The in vitro drug release and ex vivo permeation rates of the optimized batch were 88.03%and 81.74%,respectively.Thus,the application of niosomes has proven potential for intranasal delivery of sertraline hydrochloride over conventional gel formulations,and intranasal drug delivery for sertraline hydrochloride has been successfully developed.展开更多
文摘Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.
文摘In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.
文摘The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convolutional Neural Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images, focusing on the impact of varying learning rates and optimization strategies. Despite the abundance of chest X-ray datasets from various institutions, the lack of a dedicated COVID-19 dataset for computational analysis presents a significant challenge. Our work introduces an empirical analysis across four distinct learning rate policies—Cyclic, Step Based, Time-Based, and Epoch Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The performance of these configurations was evaluated in terms of training and validation accuracy over 100 epochs. Our results demonstrate significant differences in model performance, with the Cyclic learning rate policy combined with SGD optimizer achieving the highest validation accuracy of 83.33%. This study contributes to the existing body of knowledge by outlining effective CNN configurations for COVID-19 image dataset analysis, offering insights into the optimization of machine learning models for the diagnosis of infectious diseases. Our findings underscore the potential of CNNs in supplementing traditional PCR tests, providing a computational approach to identify patterns in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the swift and accurate diagnosis of the virus.
文摘The objective of this study was to formulate a niosomal in situ gel of sertraline hydrochloride for the treatment of depression.Sertraline hydrochloride is a Biopharmaceutical Classification System class II drug with low solubility and high permeability.This research aims to improve the bioavailability of drugs by increasing their solubility by encapsulation in niosomal vesicles incorporated into an in situ gel.Niosomes were prepared via the ether injection method using a 3^(2)full factorial design.Sertraline hydrochloride in situ gel was prepared via the cold method by using Poloxamer 407 and PVP K 30.One factor was evaluated at 3 levels,and the concentrations of surfactant Span 60(X_(1))and of cholesterol(X_(2))were selected as independent variables.The F8 batch of niosomes out of 9 formulations was found to be optimized.The average vesicle size of the niosomal suspension in the optimized F8 batch was 167.5 nm,with a zeta potential of-31.4 mV.The entrapment efficiency across the 9 batches varied from 79.6%to 90.5%,while the drug content ranged between 91.2%and 98.5%.The entrapment efficiency and drug content of the optimized batch were 90.5%and 98.5%,respectively.In vitro drug release for these batches was between 86.32%and 91.33%.The in vitro drug release and ex vivo permeation rates of the optimized batch were 88.03%and 81.74%,respectively.Thus,the application of niosomes has proven potential for intranasal delivery of sertraline hydrochloride over conventional gel formulations,and intranasal drug delivery for sertraline hydrochloride has been successfully developed.
