scholarly journals Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

2020 ◽  
Vol 12 (4) ◽  
pp. 646 ◽  
Author(s):  
Jamie Barwick ◽  
David William Lamb ◽  
Robin Dobos ◽  
Mitchell Welch ◽  
Derek Schneider ◽  
...  
Keyword(s):  
Visual Observation ◽  
Classification Model ◽  
Moving Window ◽  
State Classification ◽  
Sensor Orientation ◽  
Data Segment ◽  
Axial Acceleration ◽  
Widespread Agreement ◽  
Significant Difference ◽  
Continuous Stream

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.

MONITORING AND CLASSIFYING THE STATE OF HARD DISKS USING RECURRENT NEURAL NETWORKS

2021 ◽  
pp. 36-43
Author(s):  
L. A. Demidova ◽  
A. V. Filatov
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Dense Layer ◽  
Hard Disks ◽  
Destructive Testing ◽  
Data Set ◽  
State Classification

The article considers an approach to solving the problem of monitoring and classifying the states of hard disks, which is solved on a regular basis, within the framework of the concept of non-destructive testing. It is proposed to solve this problem by developing a classification model using machine learning algorithms, in particular, using recurrent neural networks with Simple RNN, LSTM and GRU architectures. To develop a classification model, a data set based on the values of SMART sensors installed on hard disks it used. It represents a group of multidimensional time series. At the same time, the structure of the classification model contains two layers of a neural network with one of the recurrent architectures, as well as a Dropout layer and a Dense layer. The results of experimental studies confirming the advantages of LSTM and GRU architectures as part of hard disk state classification models are presented.

Study on the syndrome characteristics and classification model of non-small cell lung cancer based on tongue and pulse data (Preprint)

2021 ◽  
Author(s):  
Yulin Shi ◽  
Jiayi Liu ◽  
Xiaojuan Hu ◽  
Liping Tu ◽  
Ji Cui ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Deficiency Syndrome ◽  
Small Cell ◽  
Classification Model ◽  
Syndrome Differentiation ◽  
Small Cell Lung ◽  
Yin Deficiency ◽  
Significant Difference

BACKGROUND Lung cancer is a common malignant tumor that affects people's health seriously. Traditional Chinese medicine (TCM) is one of the effective methods for the treatment of advanced lung cancer, accurate TCM syndrome differentiation is essential to treatment. When the symptoms are not obvious, the traditional symptom-based syndrome differentiation cannot be carried out. There is a close relationship between syndrom and index of western medicine, the combination of micro index and macro symptom can assist syndrome differentiation effectively. OBJECTIVE To explore the characteristics of tongue and pulse data of non-small cell lung cancer (NSCLC) with Qi deficiency syndrome and Yin deficiency syndrome, and to establish syndromes classification model based on tongue and pulse data by using machine learning method, and to evaluate the feasibility of the model. METHODS Tongue and pulse data of non-small cell lung cancer (NSCLC) patients with Qi deficiency syndrome (n=163), patients with Yin deficiency syndrome (n=174) and healthy controls (n=185) were collected by using intelligent Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument, respectively. The characteristics of tongue and pulse data were analyzed, the correlation analysis was also made on tongue and pulse data. And four machine learning methods, namely Random Forest, Logistic Regression, Support Vector Machine and Neural Network, were used to establish the classification models based on symptoms, tongue & pulse data, and symptoms & tongue & pulse data, respectively. RESULTS Significant difference indexes of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll and the tongue coating texture indexes including TC-Con, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indexes of pulse diagnosis were t4 and t5. The classification performance of each model based on different data sets was as follows: model of tongue & pulse data <model of symptom < model of symptom & tongue & pulse data. The Neural Network model had a better classification performance for the symptom & tongue & pulse data, with an area under the ROC curve and accuracy rate were 0.9401 and 0.8806. CONCLUSIONS This study explored the characteristics of tongue and pulse data of NSCLC with Qi deficiency syndrome and Yin deficiency syndrome, and established syndromes classification model. It was feasible to use tongue and pulse data as one of the objective diagnostic indexes in Qi deficiency syndrome and Yin deficiency syndrome of NSCLC. CLINICALTRIAL Trial registration number: ChiCTR1900026008; ChiCTR-IOR-15006502 Date of registration: Jun. 04th, 2015 URL of trial registry record: http://www.chictr.org.cn/showprojen.aspx?proj=11119; http://www.chictr.org.cn/edit.aspx?pid=38828&htm=4 (This is a retrospective registration)

The role of radiomics in the preoperative diagnosis of renal cell carcinoma with sarcomatoid dedifferentiation (sRCC).

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 621-621
Author(s):  
Chalairat Suk-Ouichai ◽  
Aikaterini Kotrotsou ◽  
Tagwa Idris ◽  
Srishti Abrol ◽  
Eric Umbreit ◽  
...  
Keyword(s):  
Tumor Size ◽  
Clear Cell ◽  
Clinical Care ◽  
External Validation ◽  
Second Order ◽  
Response To Therapy ◽  
Classification Model ◽  
Experienced Radiologist ◽  
Contrast Enhanced Ct ◽  
Significant Difference

621 Background: sRCC is an aggressive renal malignancy, with poor survival and limited response to therapy. Preoperative identification of sRCC would be helpful for counselling patients, and clinical trial enrollment. This study aims at assessing the potential of radiomics to discriminate clear cell sRCC from non-sarcomatoid clear cell RCC (nsRCC). Methods: The study included 49 sRCC and 41 nsRCC patients treated with surgery between 2007-2016, who had contrast-enhanced CT available. An experienced radiologist delineated the entire tumor using 3D Slicer ( http://www.slicer.org ). The extracted 3D region of interest was imported in our in-house radiomic pipeline. A total of 310 features (10 histogram-based and 300 second-order features) were calculated. Second-order radiomic features were calculated using the Grey Level Cooccurrence Matrix (GLCM) and 20 Haralick features were obtained from the GLCM. To account for directionality, the mean, variance and range of the features across different directions were calculated. Finally, different number of gray levels were also considered in the analysis (N = 8, 16, 32, 64, 256). Core features were obtained using a feature selection based on Least Absolute Shrinkage and Selection Operator (LASSO). Selected features were used to build a classification model for prediction of sRCC versus nsRCC (XGboost). To evaluate the robustness of the estimates, Leave One Out Cross-Validation (LOOCV) was conducted on the patient set. Results: Overall, median tumor size was 10.0 cm and most patients had pT3a (68%). There was no significant difference of age, gender, race, tumor size and stages between sRCC and nsRCC cohorts. The prediction of sRCC using LOOCV was significant with p-value < 0.0001. Area under the curve, sensitivity, and specificity for identification of sRCC were 96.8%, 92.6% and 93.8% respectively. Conclusions: This study demonstrates that CT radiomic features can accurately discriminate between sRCC and nsRCC. The proposed tool has the potential to advance clinical management strategies. In addition to being noninvasive, this methodology can be applied to scans obtained during routine clinical care. Further external validation is warranted.

scholarly journals Evaluation of two estrus synchronization protocols in dairy cattle at North Shoa zone Ethiopia

2018 ◽  
Vol 19 (2) ◽  
pp. 93
Author(s):  
Yeshimebet Chanyalew ◽  
Tesfaye Zewde ◽  
Hulunim Gatew ◽  
Lina Girma ◽  
Getachew Kassa ◽  
...  
Keyword(s):  
Reproductive Tract ◽  
Visual Observation ◽  
Estrus Synchronization ◽  
Livestock Management ◽  
Significant Difference ◽  
Remarkable Result ◽  
Calving Rate ◽  
Estrus Response ◽  
Rectal Palpation ◽  
Small Holder

This study was initiated to change the hesitation of the farmer on the effectiveness of estrus synchronization under their (Ethiopian small holder) livestock management system using two synchronization protocols. Non-pregnant animals with normal reproductive tract and that fulfilled the preconditions for estrus synchronization were considered for treatment & assigned into two synchronization protocols (single PGF2α injection; and double PGF2α injection). Among 94 (27 heifer and 67 cows) synchronized cows using one and two injections of PGF2α protocols 26 heifers and 63 cows (89/94.7%) were exhibited estrus by visual observation and rectal palpation the remaining 5 (5.3%) did not illustrate heat. The overall pregnancy was 59.6 % with overall birth 94.3 %. High pregnancy was obtained in the double injection of PGF2α treatment group (63.1 %) than animals treated with one shot protocol 55.8 % there were statistically significant difference between treatments (p<0.05). Higher pregnancy was obtained from cross breed animals than local breeds. More over most of the animals come to estrus greater than 96 hrs. There was also significant difference between technicians on detecting the CL and conception. The estrus response, conception rate, pregnancy rate and calving rate was higher in both protocols so producers or farmers can use either the two protocols to achieve remarkable result but tight follow-ups and more resources are need to be exploited at farmer level.

scholarly journals Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.

Revista Vitae ◽  
2019 ◽  
Vol 26 (2) ◽  
pp. 94-103
Author(s):  
Hugo Italo Romero ◽  
Ivan RAMÍREZ-MORALES ◽  
Cinthia ROMERO FLORES
Keyword(s):  
Water Samples ◽  
Cross Validation ◽  
Tap Water ◽  
Automatic Classification ◽  
Chloride Ions ◽  
Bottled Water ◽  
Classification Model ◽  
Final Decision ◽  
Significant Difference ◽  
Fold Cross Validation

Background: concern about the quality of the water for human consumption has become widespread among the population. The taste and some problems associated with drinking water have been the cause of increased demand for bottled water. Due to this, day to day, a large number of companies has manifested their interest in the production of bottled water. Objective: to evaluate a novel automatic classification model that differentiates bottled water from tap water. Methods: the voltammetric technique consisted of three electrode setup. The output current has been considered for data analysis. From the results of grid search, six pairs of values were pre-selected for the parameters of σ and C whose results were similar. High values of accuracy, specificity and sensitivity were achieved in test dataset. The final decision was made after performing an ANOVA test of 100 repetitions of 5-fold cross-validation, 3000 models were evaluated with the parameter combinations described above for the SVM. Results: the oxidation and reduction peaks of the water samples have been observed to be prominent. Absolute values of current (I) increased in the case of public water samples, possibly due to the largest concentration of chloride ions which have higher contributions to the conductivity. 5-fold cross-validation test mean specificity resulted in C parameters values greater than 0 and between 0 and 30; a σ value greater than 10 and between 0 and 15 were found for tap water and bottled water, respectively. The combination (σ = 10, C = 30) presented best results in accuracy 0.988 ± 0.037, specificity 0.973 ± 0.085 and sensitivity 1 ± 0.09. Conclusions: results of this research work have shown that voltammograms for values of current increased for tap water samples, 9.94e-6μA, compared to 7.99e-6μA due to higher chloride ions concentration in the former. The parameters combination (σ = 10, C = 20) was selected as optimal parameters since there were no significant difference between this and the former.

Comparison of micropuncture and microcatheterization in papillary collecting duct

1980 ◽  
Vol 239 (1) ◽  
pp. F92-F95 ◽  
Author(s):  
H. Sonnenberg ◽  
C. Chong
Keyword(s):  
Volume Expansion ◽  
Collecting Duct ◽  
Extracellular Fluid ◽  
Sampling Technique ◽  
Ringer Solution ◽  
Visual Observation ◽  
Terminal Segment ◽  
Significant Difference ◽  
Papillary Collecting Duct ◽  
Sodium And Potassium

Collecting duct transport of fluid, sodium, and potassium was studied in rats infused with Ringer solution (5 ml·100 g body wt-1·h-1). A terminal segment of surface collecting duct in the exposed papilla was catheterized as far upstream as possible under visual observation. After fluid sampling the same duct was punctured at the same site with a glass micropipette and a second sample was taken. Samples were then obtained from the opening of the duct at the papilla tip by both catheter and micropipette. No significant difference between the two collection sites was found in the fraction of filtered sodium, potassium, or fluid remaining in the tubule, independent of the sampling technique used, indicating that volume expansion inhibited salt and water reabsorption. Although fractional fluid and sodium remainders were slightly higher and potassium remainder lower in upstream micropuncture samples compared to catheterization samples, the good correlation between collections obtained with both techniques suggests that both are equally valid as indicators of transport in terminal collecting ducts. Ringers infusion; extracellular fluid volume expansion; fluid reabsorption; sodium and potassium transport Submitted on May 7, 1979 Accepted on January 22, 1980

scholarly journals A Comparison of Two Types of Personal Protective Equipment (PPE) Doffing Process: Frequency and Sites of Contamination

2019 ◽  
Vol 34 (s1) ◽  
pp. s110-s110
Author(s):  
You Jian-ping ◽  
Yang Sha ◽  
Luo Hong-Xia ◽  
Zhang Hui-Lan
Keyword(s):  
Personal Protective Equipment ◽  
Ebola Virus ◽  
Visual Observation ◽  
World Health ◽  
Protective Equipment ◽  
Left Chest ◽  
Left Hand ◽  
Significant Difference ◽  
Health Organization ◽  
Left Abdomen

Introduction:Personal protective equipment (PPE) is a necessary item in the period of unknown and high-risk emerging infectious disease. It is not only the necessary requirement of strict isolation, but also the last line of defense to protect medical staff.Aim:Compare the differences between contaminated frequency and sites under two types of PPE doffing.Methods:Recruited 56 health care workers (HCWs) who worked in clinical to follow the different PPE removal guidelines issued by the Chinese Center for Disease Control (CDC) and the World Health Organization (WHO) final resolution for preventing Ebola virus. Eight batches of HCWs were divided to conduct simulations of contaminated PPE removal using fluorescent lotion (Glitter Bug Potion, On Solution Pty Lt). Then we recorded the frequency and sites of contamination of personnel after removal of contaminated PPE by the method of visual observation.Results:According to China’s CDC process, the parts that are easily contaminated during PPE removal are: left hand and wrist (7 times), left calf (7 times), front chest center and left and right chest (6 times each) and left abdomen (5 times). Contaminated parts of the PPE process in accordance with the WHO process from high to low were: right hand and wrist (13 times), left hand and wrist (12 times), middle of the abdomen (10 times), left chest (9 times), and left abdomen (6 Times). There was no statistical difference between the two kinds of PPE piercing and removal (Z=1.177, P > 0. 05).Discussion:Under the guidance of the two processes recommended by China CDC and WHO, there was no significant difference in the frequency of pollution after removing PPE. It is speculated that the PPE recommendation processes issued by WHO and China CDC are effective for personal protection against fulminating infectious diseases.

scholarly journals Hierarchical Diagnostic Classification Modeling of Reading Comprehension

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402093106
Author(s):  
Mona Tabatabaee-Yazdi
Keyword(s):  
Reading Comprehension ◽  
Gender Gap ◽  
Classification Model ◽  
Diagnostic Classification ◽  
Entrance Examination ◽  
Comprehension Test ◽  
Classification Consistency ◽  
Significant Difference ◽  
Accuracy Indices ◽  
University Entrance Examination

The Hierarchical Diagnostic Classification Model (HDCM) reflects on the sequences of the presentation of the essential materials and attributes to answer the items of a test correctly. In this study, a foreign language reading comprehension test was analyzed employing HDCM and the generalized deterministic-input, noisy and gate (G-DINA) model to determine and compare respondents’ mastery profiles in the test’s predefined skills and to illustrate the relationships among the attributes involved in the test to capture the influence of sequential teaching of materials on increasing the probability of getting an item a correct answer. Furthermore, Differential Item Functioning (DIF) analysis was applied to detect whether the test functions as a reason for the gender gap in participants’ achievement. Finally, classification consistency and accuracy indices are studied. The results showed that the G-DINA and one of the HDCMs fit the data well. However, although the results of HDCM showed the existence of attribute dependencies in the reading comprehension test, the relative fit indices highlight a significant difference between the G-DINA and HDCM, favoring G-DINA. Moreover, results indicate that there is a significant difference between males and females in six items in favor of females. Besides, classification consistency and accuracy indices specify that the Iranian University Entrance Examination holds a 71% chance of categorizing a randomly selected test taker consistently on two distinct test settings and a 78% likelihood of accurately classifying any randomly selected student into the true latent classes. As a result, it can be concluded that the Iranian University Entrance Examination can be considered as a valid and reliable test.

156 THE USE OF THE DG29™ ENZYME-LINKED IMMUNOSORBENT ASSAY KIT TO PREDICT PREGNANCY PRIOR TO EMBRYO TRANSFER IN LACTATING HOLSTEIN COWS

2013 ◽  
Vol 25 (1) ◽  
pp. 226
Author(s):  
Y. Nakamura ◽  
A. Ideta ◽  
A. Shirasawa ◽  
K. Hayama ◽  
S. Sakai ◽  
...  
Keyword(s):  
Immunosorbent Assay ◽  
Statistical Significance ◽  
Visual Observation ◽  
Holstein Cows ◽  
Enzyme Linked Immunosorbent Assay ◽  
Animal Reproduction ◽  
Array Transducer ◽  
Significant Difference ◽  
Chi Squared ◽  
Student’S T

Evaluation of postpartum fertility in cows is important for the efficient management of reproduction. DG29™ enzyme-linked immunosorbent assay (ELISA) kit (Conception, Animal Reproduction Technologies, Canada) measures the level of pregnancy–related glycoproteins in blood that are linked to pregnancy in the bovine species. The proteins are known to persist in the postpartum period. Here, we investigated whether the postpartum fertility in Holstein dairy cows can be evaluated through the use of the DG29 kit. We confirmed that genital organs of lactating Holstein cows (n = 119, from Days 56 to 688 postpartum) were normal by a 5.0/7.5-MHz linear array transducer (Tringa, Pie Medical Equipment B.V., Maastricht, The Netherlands), then a progesterone releasing intravaginal device (PRID; CEVA Sante Animale, Libourne, France) was inserted (Day 0) and maintained for 9 days. On Day 7, PGF2α was administered (2 mL Dalmazine, Kyoritsu Seiyaku, Tokyo, Japan). Blood samples were collected from the tail vein or artery into vacuum tubes at the time of PRID insertion. Serum was separated and stored at –30°C until the ELISA was performed. Oestrus (Day 0) was detected by visual observation. Fresh embryos recovered from Japanese Black cows were transferred to 119 recipient cows in various parities (primiparous = 70, biparous = 27, and multiparous = 22) on Days 6 to 8 of oestrous cycle. Pregnancy was diagnosed between Days 40 to 60 by transrectal ultrasonography. The statistical significance of any differences between various parities was assessed by chi-squared and Student’s t-tests. The pregnancy rate was higher for primiparous cows than for biparous and multiparous cows (64.3, 55.6, and 54.5%, respectively), while concentrations of the pregnancy-related glycoproteins in primiparous cows (135.0 ± 29.8 pg mL–1) were significantly lower than those of biparous (389.4 ± 175.9 pg mL–1) and multiparous cows (399.2 ± 203.1 pg mL–1, mean ± SEM; P < 0.05). In primiparous and multiparous cows, the concentrations of pregnancy-related glycoproteins were significantly lower in pregnant cows compared with nonpregnant cows (primiparous: 81.1 ± 29.9 v. 232.6 ± 59.8 pg mL–1; P < 0.05; multiparous: 20.8 ± 16.2 v. 853.4 ± 411.5 pg mL–1; P < 0.05). However, there was no significant difference between pregnant and nonpregnant biparous cows. In conclusion, the DG29 kit may be useful for the prediction of postpartum fertility in lactating Holstein cows. Further studies are needed to test the validity of this observation by using a greater number of various parties’ cows.

Machine learning analysis on American Gut Project microbiome data to identify subjects with cancer both with and without chemotherapy exposure.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14069-e14069
Author(s):  
Oguz Akbilgic ◽  
Ibrahim Karabayir ◽  
Hakan Gunturkun ◽  
Joseph F Pierre ◽  
Ashley C Rashe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Test Data ◽  
Gut Microbiome ◽  
Classification Model ◽  
Outcome Variable ◽  
Gradient Boosting ◽  
Healthy Controls ◽  
Extreme Gradient Boosting ◽  
Significant Difference

e14069 Background: There is growing interest in the links between cancer and the gut microbiome. However, the effect of chemotherapy upon the gut microbiome remains unknown. We studied whether machine learning can: 1) accurately classify subjects with cancer vs healthy controls and 2) whether this classification model is affected by chemotherapy exposure status. Methods: We used the American Gut Project data to build a extreme gradient boosting (XGBoost) model to distinguish between subjects with cancer vs healthy controls using data on simple demographics and published microbiome. We then further explore the selected features for cancer subjects based on chemotherapy exposure. Results: The cohort included 7,685 subjects consisting of 561 subjects with cancer, 52.5% female, 87.3% White, and average age of 44.7 (SD 17.7). The binary outcome variable represents cancer status. Among 561 subjects with cancer, 94 of them were treated with chemotherapy agents before sampling of microbiomes. As predictors, there were four demographic variables (sex, race, age, BMI) and 1,812 operational taxonomic units (OTUs) each found in at least 2 subjects via RNA sequencing. We randomly split data into 80% training and 20% hidden test. We then built an XGBoost model with 5-fold cross-validation using only training data yielding an AUC (with 95% CI) of 0.79 (0.77, 0.80) and obtained the almost the same AUC on the hidden test data. Based on feature importance analysis, we identified 12 most important features (Age, BMI and 12 OTUs; 4C0d-2, Brachyspirae, Methanosphaera, Geodermatophilaceae, Bifidobacteriaceae, Slackia, Staphylococcus, Acidaminoccus, Devosia, Proteus) and rebuilt a model using only these features and obtained AUC of 0.80 (0.77, 0.83) on the hidden test data. The average predicted probabilities for controls, cancer patients who were exposed to chemotherapy, and cancer patients who were not were 0.071 (0.070,0.073), 0.125 (0.110, 0.140), 0.156 (0.148, 0.164), respectively. There was no statistically significant difference on levels of these 12 OTUs between cancer subjects treated with and without chemotherapy. Conclusions: Machine learning achieved a moderately high accuracy identifying patients’ cancer status based on microbiome. Despite the literature on microbiome and chemotherapy interaction, the levels of 12 OTUs used in our model were not significantly different for cancer patients with or without chemotherapy exposure. Testing this model on other large population databases is needed for broader validation.

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