scholarly journals Deep learning versus traditional methods for parking lots occupancy classification

2020 ◽  
Vol 19 (2) ◽  
pp. 964 ◽  
Author(s):  
Mohamed Sayed Farag ◽  
Mostafa Mohamed Mohie El Din ◽  
Hassan Ahmed Elshenbary
Keyword(s):  
Deep Learning ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Training Time ◽  
Parking Lot ◽  
Smart Parking ◽  
The Mean ◽  
Deep Learning Network ◽  
Deep Learning Neural Network

<span>Due to the increase in number of cars and slow city developments, there is a need for smart parking system. One of the main issues in smart parking systems is parking lot occupancy status classification, so this paper introduce two methods for parking lot classification. The first method uses the mean, after converting the colored image to grayscale, then to black/white. If the mean is greater than a given threshold it is classified as occupied, otherwise it is empty. This method gave 90% correct classification rate on cnrall database. It overcome the alexnet deep learning method trained and tested on the same database (the mean method has no training time). The second method, which depends on deep learning is a deep learning neural network consists of 11 layers, trained and tested on the same database. It gave 93% correct classification rate, when trained on cnrall and tested on the same database. As shown, this method overcome the alexnet deep learning and the mean methods on the same database. On the Pklot database the alexnet and our deep learning network have a close resutls, overcome <br /> the mean method (greater than 95%).</span>

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Fatty Acid Composition of Cosmetic Argan Oil: Provenience and Authenticity Criteria

Molecules ◽  
2020 ◽  
Vol 25 (18) ◽  
pp. 4080
Author(s):  
Milena Bučar Miklavčič ◽  
Fouad Taous ◽  
Vasilij Valenčič ◽  
Tibari Elghali ◽  
Maja Podgornik ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Discriminant Analysis ◽  
Trans Fatty Acids ◽  
Correct Classification ◽  
Fatty Acid Profiles ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Argan Oil ◽  
Geographical Regions

In this work, fatty-acid profiles, including trans fatty acids, in combination with chemometric tools, were applied as a determinant of purity (i.e., adulteration) and provenance (i.e., geographical origin) of cosmetic grade argan oil collected from different regions of Morocco in 2017. The fatty acid profiles obtained by gas chromatography (GC) showed that oleic acid (C18:1) is the most abundant fatty acid, followed by linoleic acid (C18:2) and palmitic acid (C16:0). The content of trans-oleic and trans-linoleic isomers was between 0.02% and 0.03%, while trans-linolenic isomers were between 0.06% and 0.09%. Discriminant analysis (DA) and orthogonal projection to latent structure—discriminant analysis (OPLS-DA) were performed to discriminate between argan oils from Essaouira, Taroudant, Tiznit, Chtouka-Aït Baha and Sidi Ifni. The correct classification rate was highest for argan oil from the Chtouka-Aït Baha province (90.0%) and the lowest for oils from the Sidi Ifni province (14.3%), with an overall correct classification rate of 51.6%. Pairwise comparison using OPLS-DA could predictably differentiate (≥0.92) between the geographical regions with the levels of stearic (C18:0) and arachidic (C20:0) fatty acids accounting for most of the variance. This study shows the feasibility of implementing authenticity criteria for argan oils by including limit values for trans-fatty acids and the ability to discern provenance using fatty acid profiling.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals BatScope manages acoustic recordings, analyses calls, and classifies bat species automatically

2018 ◽  
Vol 96 (9) ◽  
pp. 939-954 ◽  
Author(s):  
M.K. Obrist ◽  
R. Boesch
Keyword(s):  
High Frequency ◽  
Reference Database ◽  
Correct Classification Rate ◽  
Statistical Parameters ◽  
Classification Rate ◽  
Echolocation Calls ◽  
Portable Software ◽  
Automated Processing ◽  
The Mean ◽  
Species Assignment

BatScope is a free application for processing acoustic high-frequency recordings of bats. It can import data, including meta-data information, from recorders such as Batlogger. The resulting content can be filtered visually as spectrograms or according to data fields and can be displayed. Automated processing includes detecting and extracting of echolocation calls, filtering noise, and measuring statistical parameters. Calls are classified to species by statistically matching to a reference database. A weighted list of classifiers helps to assign the most likely species per call. Classifiers were trained on 19 636 echolocation calls of 27 European bat species. When classifiers all agree on a species (76.4% of all cases), the mean correct classification rate reaches 95.7%. A sequence’s summary statistic indicates the most likely species occurring therein. Classifications can be verified visually, by filtering, and by acoustic comparison with reference calls. Procedures are available for, e.g., excluding dubious cutouts from the statistics and for accepting or overriding the proposed species assignment. Acoustic recordings can be exported and exchanged with other users. Finally, the verified results can be exported to spreadsheets for further analyses and reporting. We currently reprogram BatScope using Java, PostgreSQL, and R to reach a unified and portable software architecture.

scholarly journals Near infrared spectroscopy in the supply chain monitoring of Annurca apple

2019 ◽  
Vol 27 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Marina Buccheri ◽  
Maurizio Grassi ◽  
Fabio Lovati ◽  
Milena Petriccione ◽  
Pietro Rega ◽  
...  
Keyword(s):  
Supply Chain ◽  
Infrared Spectroscopy ◽  
Shelf Life ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correct Classification ◽  
Geographical Indication ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Campania Region

Annurca is the most cultivated apple variety in the Campania region (Italy). It is an Italian protected geographical indication product and its management must follow a strict product specification which requires a typical postharvest treatment: the fruit must be subjected to a reddening process in air (‘melaio’) that improves the red colour and the flavour of the fruit but is very expensive and time consuming. For this reason there is sometimes a tendency to skip the ‘melaio’ process, but in this case the fruit cannot be labelled as ‘Melannurca Campana PGI’. The purpose of this work was to discriminate ‘melaio’ treated fruit from untreated fruit using near infrared spectroscopy. A further objective of the work was the non-destructive evaluation of the apple storage conditions which can affect the product quality. Fruit of Annurca ‘Rossa del Sud’ subjected or not subjected to the reddening treatment in ‘melaio’ were stored at 0.5℃ in air (Air) or in controlled atmosphere (1%O2, 0.7% CO2) for eight-month duration. Following storage, fruit were analysed for standard maturity indices (flesh firmness, soluble solids, acidity) and the near infrared spectrum of each fruit was collected. The spectral data, subjected to various pre-treatments, were used to calculate a calibration model by applying partial least squares-discriminant analysis. The best model allowed discrimination of fruit immediately after storage under different conditions, but with 0 days of shelf life, to be classified with a 93.3% correct classification rate for the prediction set. However, after seven days of shelf life at 20℃, post-storage, correct classification rate dropped to 70%, but it was always possible to discriminate the two treatments (96.6% correct classification rate). The results of this preliminary work suggest a possible use of the portable near infrared instrument in the monitoring of the Annurca (protected geographical indication) supply chain.

scholarly journals Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks

1997 ◽  
Vol 14 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Roger A. Kemp ◽  
Calum MacAulay ◽  
David Garner ◽  
Branko Palcic
Keyword(s):  
Neural Networks ◽  
Discriminant Function ◽  
Severe Dysplasia ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Normal Cells ◽  
Feed Forward ◽  
Linear Discriminant ◽  
Feed Forward Neural Networks

Normal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs). Using statistical models such as neural networks and discriminant functions it is possible to design classifiers that can separate these objects from truly normal cells. The correct classification rate using feed‐forward neural networks is compared to linear discriminant analysis when applied to detecting MACs. Classifiers were designed using 53 nuclear features calculated from images for each of 25,360 normal appearing cells taken from 344 slides diagnosed as normal or containing severe dysplasia. A linear discriminant function achieved a correct classification rate of 61.6% on the test data while neural networks scored as high as 72.5% on a cell‐by‐cell basis. The cell classifiers were applied to a library of 93,494 cells from 395 slides, and the results were jackknifed using a single slide feature. The discriminant function achieved a correct classification rate of 67.6% while the neural networks managed as high as 76.2%.

The Recognition Memory Test, Digit Span, and Knox Cube Test as Markers of Malingered Memory Impairment

Assessment ◽  
1994 ◽  
Vol 1 (4) ◽  
pp. 323-334 ◽  
Author(s):  
Grant L. Iverson ◽  
Michael D. Franzen
Keyword(s):  
Recognition Memory ◽  
Memory Impairment ◽  
Digit Span ◽  
Head Injuries ◽  
Memory Test ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Recognition Memory Test ◽  
General Populations

The purpose of this investigation was to examine the efficacy of using the Recognition Memory Test (RMT), Digit Span subtest (WAIS-R), and Knox Cube Test as markers for malingered memory deficits. Participants were 100 subjects from three general populations: university students, federal inmates, and patients with head injuries. Twenty students, 20 inmates, and 20 patients with head injuries resulting in memory impairment were instructed to try their best on the assessment procedures. The remaining 20 students and 20 inmates were instructed to malinger memory impairment on the procedures. The experimental-malingerers (i.e., students and inmates) performed more poorly than the patients with head injuries on nearly every score derived from the three tests. Discriminant function analyses using the age-corrected Digit Span scale score, the Knox Cube Test total score, and the RMT raw scores for words and faces as predictors of group membership resulted in an overall 98% correct classification rate and 100% correct on cross-validation. Simultaneously applying two empirically-derived RMT cutoff scores resulted in an overall correct classification rate of 100%. The extraordinarily high classification rates in this study were likely influenced by the experimental design and procedures.

scholarly journals Physicochemical, Spectroscopic and Chromatographic Analyses in Combination with Chemometrics for the Discrimination of Four Sweet Cherry Cultivars Grown in Northern Greece

Foods ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 442 ◽  
Author(s):  
Spyridon Papapetros ◽  
Artemis Louppis ◽  
Ioanna Kosma ◽  
Stavros Kontakos ◽  
Anastasia Badeka ◽  
...  
Keyword(s):  
Volatile Compounds ◽  
Sweet Cherry ◽  
Statistical Treatment ◽  
Quality Parameters ◽  
Total Phenolic ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Northern Greece ◽  
Classification Rate ◽  
Linear Discriminant

A total of 56 sweet cherry samples belonging to four cultivars (Ferrovia, Canada Giant, Lapins, and Germersdorfer) grown in northern Greece were characterized and differentiated according to botanical origin. For the above purpose, the following parameters were determined: conventional quality parameters (titratable acidity (TA), pH, total soluble solids (TSS), total phenolic content (TPC), mechanical properties and sensory evaluation, sugars by High Performance Liquid Chromatography (HPLC), volatile compounds by GC/MS, and minerals by ICP-OES. Statistical treatment of the data was carried out using Multivariate Analysis of Variance (MANOVA) and Linear Discriminant Analysis (LDA). The results showed that the combination of volatile compounds and conventional quality parameters provided a correct classification rate of 84.1%, the combination of minerals and conventional quality parameters 86.4%, and the combination of minerals, conventional quality parameters and sugars provided the highest correct classification rate of 88.6%. When the above four cherry cultivars were combined with previously studied Kordia, Regina, Skeena and Mpakirtzeika cultivars, collected from the same regions during the same seasons, the respective values for the differentiation of all eight cultivars were: 85.5% for the combination of conventional quality parameters, volatiles and minerals; and 91.3% for the combination of conventional quality parameters, volatiles, minerals, and sugars.

The Diagnostic Efficacy of Three-Dimensional Reconstruction System-Based Methods for Predicting Axillary Lymph Node Metastasis in Breast Cancer Patients: An Exploratory Study

2021 ◽  
Author(s):  
Limeng Qu ◽  
Wenjun Yi ◽  
Na Luo ◽  
Piao Zhao ◽  
Qiongyan Zou ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
3D Reconstruction ◽  
Axillary Lymph Node ◽  
Axillary Lymph ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Diagnostic Efficacy ◽  
Node Metastasis

Abstract Background: The status of axillary lymph node metastases determines the treatment and overall survival of breast cancer (BC) patients. Three-dimensional (3D) assessment methods have advantages for spatial localization and are more responsive to morphological changes in lymph nodes than two-dimensional (2D) assessment methods, and we speculate that methods developed using 3D reconstruction systems have high diagnostic efficacy.Methods: This exploratory study included 43 patients with histologically confirmed BC diagnosed at Second Xiangya Hospital of Central South University between July 2017 and August 2020, all of whom underwent preoperative CT scans. Patients were divided into a training cohort to train the model and a validation cohort to validate the model. A 3D axillary lymph node atlas was constructed on a 3D reconstruction system to create various methods of assessing lymph node metastases for a comparison of diagnostic efficacy. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic values of these methods.Results: A total of 43 patients (mean [SD] age, 47 [10] years) met the eligibility criteria and completed 3D reconstruction. An axillary lymph node atlas was established, and a correlation between lymph node sphericity and lymph node metastasis was revealed. By continuously fitting the size and characteristics of axillary lymph nodes on the 3D reconstruction system, formulas and models were established to determine the presence or absence of lymph node metastasis, and the 3D method had better sensitivity for axillary lymph node assessment than the 2D method, with a statistically significant difference in the correct classification rate. The combined diagnostic method was superior to a single diagnostic method, with a 92.3% correct classification rate for the 3D method combined with ultrasound. In addition, in patients who received neoadjuvant chemotherapy (NAC), the correct classification rate of the 3D method (72.7%) was significantly higher than that of ultrasound (45.5%) and CT (54.5%).Conclusion: By establishing an axillary lymph node atlas, the sphericity formula and model developed with the 3D reconstruction system achieve a high correct classification rate when combined with ultrasound or CT and can also be applied to patients receiving NAC.

scholarly journals Feature Set Fusion for Spoof Iris Detection

2018 ◽  
Vol 8 (2) ◽  
pp. 2859-2863 ◽  
Author(s):  
P. V. L. Suvarchala ◽  
S. Srinivas Kumar
Keyword(s):  
Iris Recognition ◽  
Large Scale ◽  
Transform Domain ◽  
Correct Classification ◽  
Gray Level ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Low Contrast ◽  
Biometric Systems ◽  
Imaging Conditions

Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database is 100% while it is 95.63% and 88.83% with IITD spoof iris databases respectively.

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