The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that...The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.展开更多
Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision loss.Traditional methods of diagnosing and treating the disease are time-consuming and expensive.However,machine learning and dee...Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision loss.Traditional methods of diagnosing and treating the disease are time-consuming and expensive.However,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic retinopathy.These advanced techniques offer higher accuracy and performance.ComputerAided Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease diagnoses.Overall,these technological advancements hold great potential for improving the management of diabetic retinopathy.The study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these classes.The robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its robustness.The outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance understanding.This study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of DR.The manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.展开更多
To confirm phytoplasma infection,samples of pomegranate(Punica granatum L.)plants showing symptoms of fasciation were collected from an orchard located in Tai’an,Shandong Province,China.A fragment of approximately 1....To confirm phytoplasma infection,samples of pomegranate(Punica granatum L.)plants showing symptoms of fasciation were collected from an orchard located in Tai’an,Shandong Province,China.A fragment of approximately 1.2 kb was amplified with universal primers targeting the phytoplasma 16S r RNA gene from symptomatic pomegranate plants,while no fragment was obtained from healthy plants.The phytoplasma associated with the disease was designated as pomegranate fasciation(Po F).Two representative phytoplasma 16S r DNA gene sequences(Po F-Ch01 and Po F-Ch02)had 100%nucleotide sequence identity.The 16S r DNA sequence of Po F-Ch01 and Po F-Ch02 showed the highest similarity(99.6%)to that of‘P.granatum’phytoplasma isolate AY-PG,which belong to 16Sr I-B.Further phylogenetic analysis showed that Po F-Ch01 and Po F-Ch02 belonged to a cluster of 16Sr I subgroup members.In silico RFLP analysis indicated that Po F-Ch01 shared the highest similarity coefficient of 0.97 with reference strains of 16Sr I-B,M and N.Actual RFLP analysis of both enzymes Bst U I and Bfa I confirmed that of the virtual RFLP analysis.Combining these results,we concluded that Po F was a member of the‘Candidatus Phytoplasma asteris’group(16Sr I),and has very close relationship with 16Sr I-B subgroup.展开更多
A formal synthesis of betamethasone from 5α-pregnane-3β,16β,20S-triol is described.Key transformations are a bromination-acetylation of triol,an SN2 reaction of the resulting C16α-bromide with dimethylcopperlithiu...A formal synthesis of betamethasone from 5α-pregnane-3β,16β,20S-triol is described.Key transformations are a bromination-acetylation of triol,an SN2 reaction of the resulting C16α-bromide with dimethylcopperlithium to get the required C16β-methyl group,and a double hydroxylation to prepare the dihydroxyacetone side chain.展开更多
文摘The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.
文摘Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision loss.Traditional methods of diagnosing and treating the disease are time-consuming and expensive.However,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic retinopathy.These advanced techniques offer higher accuracy and performance.ComputerAided Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease diagnoses.Overall,these technological advancements hold great potential for improving the management of diabetic retinopathy.The study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these classes.The robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its robustness.The outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance understanding.This study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of DR.The manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.
基金supported by National Natural Science Foundation of China (Grant No. 31401718 )the Foundation for Excellent Young Scientists of Shandong Province (Grant No. BS2013NY012 )+1 种基金Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. SRFDP, 20123702110013 BR0801 )
文摘To confirm phytoplasma infection,samples of pomegranate(Punica granatum L.)plants showing symptoms of fasciation were collected from an orchard located in Tai’an,Shandong Province,China.A fragment of approximately 1.2 kb was amplified with universal primers targeting the phytoplasma 16S r RNA gene from symptomatic pomegranate plants,while no fragment was obtained from healthy plants.The phytoplasma associated with the disease was designated as pomegranate fasciation(Po F).Two representative phytoplasma 16S r DNA gene sequences(Po F-Ch01 and Po F-Ch02)had 100%nucleotide sequence identity.The 16S r DNA sequence of Po F-Ch01 and Po F-Ch02 showed the highest similarity(99.6%)to that of‘P.granatum’phytoplasma isolate AY-PG,which belong to 16Sr I-B.Further phylogenetic analysis showed that Po F-Ch01 and Po F-Ch02 belonged to a cluster of 16Sr I subgroup members.In silico RFLP analysis indicated that Po F-Ch01 shared the highest similarity coefficient of 0.97 with reference strains of 16Sr I-B,M and N.Actual RFLP analysis of both enzymes Bst U I and Bfa I confirmed that of the virtual RFLP analysis.Combining these results,we concluded that Po F was a member of the‘Candidatus Phytoplasma asteris’group(16Sr I),and has very close relationship with 16Sr I-B subgroup.
文摘A formal synthesis of betamethasone from 5α-pregnane-3β,16β,20S-triol is described.Key transformations are a bromination-acetylation of triol,an SN2 reaction of the resulting C16α-bromide with dimethylcopperlithium to get the required C16β-methyl group,and a double hydroxylation to prepare the dihydroxyacetone side chain.
