scholarly journals Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 575
Author(s):  
Prabhjot Kaur ◽  
Shilpi Harnal ◽  
Rajeev Tiwari ◽  
Shuchi Upadhyay ◽  
Surbhi Bhatia ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Deep Architecture ◽  
Agriculture Sustainability ◽  
Time Limitations ◽  
Considerable Loss

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

scholarly journals Characterization of Highly Irradiated ALPIDE Silicon Sensors

Universe ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 91
Author(s):  
Valentina Raskina ◽  
Filip Křížek
Keyword(s):  
Nuclear Physics ◽  
Detection Efficiency ◽  
Tracking System ◽  
Physics Institute ◽  
Total Ionization ◽  
Silicon Sensors ◽  
Active Pixel ◽  
Academy Of Sciences

The ALICE (A Large Ion Collider Experiment) experiment at CERN will upgrade its Inner Tracking System (ITS) detector. The new ITS will consist of seven coaxial cylindrical layers of ALPIDE silicon sensors which are based on Monolithic Active Pixel Sensor (MAPS) technology. We have studied the radiation hardness of ALPIDE sensors using a 30 MeV proton beam provided by the cyclotron U-120M of the Nuclear Physics Institute of the Czech Academy of Sciences in Řež. In this paper, these long-term measurements will be described. After being irradiated up to the total ionization dose 2.7 Mrad and non-ionizing energy loss 2.7 × 10 13 1 MeV n eq · cm - 2 , ALPIDE sensors fulfill ITS upgrade project technical design requirements in terms of detection efficiency and fake-hit rate.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

Post-Operative Dysphagia in Anterior Cervical Discectomy and Fusion

2021 ◽  
pp. 000348942110155
Author(s):  
Leonard Haller ◽  
Khush Mehul Kharidia ◽  
Caitlin Bertelsen ◽  
Jeffrey Wang ◽  
Karla O’Dell
Keyword(s):  
Risk Factors ◽  
Medical Records ◽  
Assessment Tool ◽  
Demographic Data ◽  
Anterior Cervical Discectomy ◽  
Cervical Discectomy ◽  
Long Term Follow Up

Objective: We sought to identify risk factors associated with long-term dysphagia, characterize changes in dysphagia over time, and evaluate the incidence of otolaryngology referrals for patients with long-term dysphagia following anterior cervical discectomy with fusion (ACDF). Methods: About 56 patients who underwent ACDF between May 2017 to February 2019 were included in the study. All patients were assessed for dysphagia using the Eating Assessment Tool (EAT-10) survey preoperatively and late postoperatively (≥1 year). Additionally, 28 patients were assessed for dysphagia early postoperatively (2 weeks—3 months). Demographic data, medical comorbidities, intraoperative details, and post-operative otolaryngology referral rates were collected from electronic medical records. Results: Of the 56 patients enrolled, 21 patients (38%) had EAT-10 scores of 3 or more at long-term follow-up. None of the demographics, comorbidities, or surgical factors assessed were associated with long-term dysphagia. Patients who reported no long-term dysphagia had a mean EAT-10 score of 6.9 early postoperatively, while patients with long-term symptoms had a mean score of 18.1 ( P = .006). Of the 21 patients who reported persistent dysphagia symptoms, 3 (14%) received dysphagia testing or otolaryngology referrals post-operatively. Conclusion: Dysphagia is a notable side effect of ACDF surgery, but there are no significant demographics, comorbidities, or surgical risk factors that predict long-term dysphagia. Early postoperative characterization of dysphagia using the EAT-10 questionnaire can help predict long-term symptoms. There is inadequate screening and otolaryngology follow-up for patients with post-ACDF dysphagia.

scholarly journals Clinical characterization of 266 patients and family members with cleft lip and/or palate with associated malformations and syndromes

2021 ◽  
Author(s):  
Theodosia Bartzela ◽  
Björn Theuerkauf ◽  
Elisabeth Reichardt ◽  
Malte Spielmann ◽  
Charlotte Opitz
Keyword(s):  
Early Identification ◽  
Congenital Malformations ◽  
Cleft Lip ◽  
Phenotypic Variability ◽  
Family Members ◽  
Pierre Robin Sequence ◽  
Associated Malformations ◽  
Pierre Robin

Abstract Objectives To clinically characterize patients and family members with cleft lip and/or palate (CL/P) and associated congenital malformations or syndromes and propose possible inheritance patterns. Materials and methods An observational study of patients with CL/P, including medical and family history and intra- and extra-oral examination of their family members, was performed. Results Two hundred sixty-six patients, 1257 family members, and 42 pedigrees were included in the study. The distribution of patients according to the cleft type was 57.9% with CLP, 25.2% with cleft palate (CPO), and 12.8% with cleft lip with/without alveolus (CL/A). Seventy-four (27.8%) patients had associated malformations, and 24 (9.2%) a syndrome. The skeletal (27.7%), cardiovascular (19.3%) systems, and eyes (22.9%) were most commonly affected. Pierre Robin Sequence (7 patients) and van der Woude (4) were the most common syndromes. The majority of patients with CPO (19/24) had an associate syndrome. The families had an average of 2.45 affected members. Conclusion Individual and interfamilial phenotypic variability in patients with CL/P makes the understanding of etiopathogenesis challenging. Clinical relevance The overall prevalence of individuals with CL/P and their pedigrees with associated malformations and syndromes emphasize the need for early identification, interdisciplinary, and long-term planning.

scholarly journals Characterization of Postharvest Fungicide-Resistant Botrytis cinerea Isolates From Commercially Stored Apple Fruit

2017 ◽  
Vol 107 (3) ◽  
pp. 362-368 ◽  
Author(s):  
Wayne M. Jurick ◽  
Otilia Macarisin ◽  
Verneta L. Gaskins ◽  
Eunhee Park ◽  
Jiujiang Yu ◽  
...  
Keyword(s):  
Botrytis Cinerea ◽  
Gray Mold ◽  
Apple Fruit ◽  
Sublethal Dose ◽  
Long Term Storage ◽  
Pose Control ◽  
First Time

Botrytis cinerea causes gray mold and is an economically important postharvest pathogen of fruit, vegetables, and ornamentals. Fludioxonil-sensitive B. cinerea isolates were collected in 2011 and 2013 from commercial storage in Pennsylvania. Eight isolates had values for effective concentrations for inhibiting 50% of mycelial growth of 0.0004 to 0.0038 μg/ml for fludioxonil and were dual resistant to pyrimethanil and thiabendazole. Resistance was generated in vitro, following exposure to a sublethal dose of fludioxonil, in seven of eight dual-resistant B. cinerea isolates. Three vigorously growing B. cinerea isolates with multiresistance to postharvest fungicides were further characterized and found to be osmosensitive and retained resistance in the absence of selection pressure. A representative multiresistant B. cinerea strain caused decay on apple fruit treated with postharvest fungicides, which confirmed the in vitro results. The R632I mutation in the Mrr1 gene, associated with fludioxonil resistance in B. cinerea, was not detected in multipostharvest fungicide-resistant B. cinerea isolates, suggesting that the fungus may be using additional mechanisms to mediate resistance. Results from this study show for the first time that B. cinerea with dual resistance to pyrimethanil and thiabendazole can also rapidly develop resistance to fludioxonil, which may pose control challenges in the packinghouse environment and during long-term storage.

Long term characterization of the vehicle stock in Turkey

2021 ◽  
Vol 99 ◽  
pp. 102988
Author(s):  
Merih Aydınalp Köksal ◽  
Ş. Elçin Tekeli ◽  
Shihomi Ara Aksoy ◽  
Anna Kızıltan ◽  
Mustafa Kızıltan ◽  
...  
Sign in / Sign up

Export Citation Format

Share Document