scholarly journals 249 Predicting body weight of finishing pigs using machine and deep learning algorithms

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 176-176
Author(s):  
Yuqing He ◽  
Francesco Tiezzi ◽  
Christian Maltecca
Keyword(s):  
Body Weight ◽  
Deep Learning ◽  
Feed Intake ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Occupation Time ◽  
Feed Consumption ◽  
Lasso Regression ◽  
Recording Equipment

Abstract Understanding and exploiting feeding patterns in swine could allow a reduced feed waste and minimized sorting losses. The objectives of this study were to evaluate the ability to predict whether a pig reached a target weight at finishing by using several algorithms and to compare the prediction using varying amounts of data during the growing period. Data were collected on 655 pigs from 75 to 166 days of age. Pigs were housed with 8 to 15 pigs and a Feed Intake Recording Equipment in each pen. Feed consumption, occupation time, and body weight per visit were recorded when a pig visited the feeder. Lasso Regression (LS), a machine learning algorithm: Random Forest (RF), and a deep learning algorithm: Long-short Term Memory (LSTM) network, were used to forecast whether pigs can reach 129 kg at the finishing stage (159–166 d). Times of visits, a sum of feed consumption, a sum of occupation time in the feeder every day, and age were used as predictors. Data were split into 6 slices by 14 days and used to calibrate the models and their predictive ability was tested with data corresponding to the last 8 days of the study period. The greatest correlation coefficients were 0.799, 0.828, and 0.868 using slice 6 (145–158 d) to train the LS, RF, and LSTM, respectively. The LS and LSTM algorithms had a smaller root mean squared error, 0.863 and 0.895 compared to the RF with 1.375 in the prediction. Overall, LS and LSTM performed best. Predictions using data closest to the finishing stage proved better. This study connects the dynamics of feeding behavior and feed intake data to growth using prediction methods that will hopefully accelerate the mainstream application of electronic feeders in pig production systems.

scholarly journals Geo-Assessment of Chemical Composition and Nutritional Evaluation of Moringa oleifera Seeds in Nutrition of Broilers

2014 ◽  
Vol 6 (4) ◽  
pp. 119 ◽  
Author(s):  
A. Annongu ◽  
O. R. Karim ◽  
A. A. Toye ◽  
F. E. Sola-Ojo ◽  
R. M. O. Kayode ◽  
...  
Keyword(s):  
Body Weight ◽  
Weight Gain ◽  
Chemical Composition ◽  
Feed Intake ◽  
Moringa Oleifera ◽  
The Body ◽  
Feed Consumption ◽  
Seed Meal ◽  
Mineral Matter ◽  
Inclusion Level

Chemical composition of Moringa oleifera seeds obtained from the middle belt of Nigeria, Benue State, was determined and the seed was blended to form a seed meal. The Moringa oleifera Seed Meal, MOSM was included in diets at graded levels of 2.50, 5.00 and 7.50% and the dietary performance of the broiler chicks on the test diets was compared with that of a corn-soy reference diet. Results on the chemical/nutritional composition of MOSM showed that the full-fat seeds contained (%) on proximate basis, reasonable concentration of 90.38 dry matter, 25.37 crude protein, 14.16 crude fat, 4.03 mineral matter, 30.64 crude fiber, 25.80 soluble carbohydrate and 5.79 kcal/g gross energy. Analyses also gave appreciable quantities of the water and fat soluble vitamins, macro - and micro-minerals. Feeding chicks with the seed meal at graded levels in diets resulted in decrease in feed intake and body weight gain as the inclusion level increased in diets relative to the conventional diet (p < 0.05). Reduction in feed consumption could be attributed to the full-fat nature of the seed meal used which might have imparted extra-caloric effect in the test diets and slowed digestion and absorption as the analyzed nutrients content of diets. A higher ether extract value on Moringa based diets relative to the control diet was obtained. Phytochemical composition of Moringa namely phenols including tannins, saponins, phytate, cyanogenic glucoside, glucosinolates and other numerous chemical constituents affected the body weight of the chicks negatively with increasing dietary MOSM. Decrease in weight gain following increase in dietary seed meal could also be due to decrease in feed intake as a result of the bitter taste of alkaloids, saponins, acting in concert with the other Moringa phytotoxins in test diets. Survival rate (100%) was not affected indicating that the level of highest inclusion in this study (7.50%) was not fatal to the experimental animal models. Further research is progressing to ascertain the highest inclusion level possible to elicit fatality and attempts to detoxify or treat the seed meal before feeding to animals.

scholarly journals An Improved Deep Learning Algorithm for Risk Prediction of Corporate Internet Reporting

2020 ◽  
Vol 34 (4) ◽  
pp. 437-444
Author(s):  
Lingyan Ou ◽  
Ling Chen
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Moving Average ◽  
Arma Model ◽  
Evaluation Index System ◽  
Online Information ◽  
Deep Learning Algorithm ◽  
Research Findings

Corporate internet reporting (CIR) has such advantages as the strong timeliness, large amount, and wide coverage of financial information. However, the CIR, like any other online information, faces various risks. With the aid of the increasingly sophisticated artificial intelligence (AI) technology, this paper proposes an improved deep learning algorithm for the prediction of CIR risks, aiming to improve the accuracy of CIR risk prediction. After building a reasonable evaluation index system (EIS) for CIR risks, the data involved in risk rating and the prediction of risk transmission effect (RTE) were subject to structured feature extraction and time series construction. Next, a combinatory CIR risk prediction model was established by combining the autoregressive moving average (ARMA) model with long short-term memory (LSTM). The former is good at depicting linear series, and the latter excels in describing nonlinear series. Experimental results demonstrate the effectiveness of the ARMA-LSTM model. The research findings provide a good reference for applying AI technology in risk prediction of other areas.

scholarly journals A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

2021 ◽  
Vol 25 (11) ◽  
pp. 6041-6066
Author(s):  
Jiancong Chen ◽  
Baptiste Dafflon ◽  
Anh Phuong Tran ◽  
Nicola Falco ◽  
Susan S. Hubbard
Keyword(s):  
Deep Learning ◽  
Predictive Modeling ◽  
Vegetation Index ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Model Simulation ◽  
Ecosystem Respiration ◽  
Ecosystem Dynamics ◽  
Accurate Estimation ◽  
East River

Abstract. Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (Reco). However, accurate estimation of ET and Reco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and Reco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and Reco variables, as well as providing reliable estimation of ET and Reco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and Reco and enhances process understanding at sparsely monitored watersheds.

Seasonal levels of serum parathyroid hormone, calcitonin and alkaline phosphatase in relation to antler cycles in white-tailed deer

1984 ◽  
Vol 106 (2) ◽  
pp. 234-240 ◽  
Author(s):  
Chun Chin Chao ◽  
Robert D. Brown ◽  
Leonard J. Deftos
Keyword(s):  
Alkaline Phosphatase ◽  
Body Weight ◽  
Parathyroid Hormone ◽  
Feed Intake ◽  
Feed Consumption ◽  
Late Winter ◽  
Serum Parathyroid Hormone ◽  
Serum Samples ◽  
Antler Growth ◽  
Serum Pth

Abstract. Seasonal levels of serum parathyroid hormone (PTH), calcitonin (CT), and alkaline phosphatase (AP) were studied in relation to antler growth cycles in 8 male (2.5–6 years old) white-tailed deer. Feed consumption was recorded weekly, whereas body weight was recorded biweekly. Antler length was measured from the pedicle to the tip after velvet growth was initiated. Serum samples were obtained biweekly while animals were tranquilized with xylazine hydrochloride. Serum Ca was significantly (P < 0.05) elevated during the summer. Serum P was significantly (P < 0.05) elevated only during early fall. There was an increase in serum PTH during velvet initiation in April–May, but not thereafter. CT increased during the rapid antler growth period. Serum PTH levels were significantly (P < 0.05) elevated (0.628 vs 0.884 ng/ml) during post-velvet shedding and decreased (0.602 vs 0.346 ng/ml, P < 0.05) during postantler casting. Serum AP activity was highest during rapid velvet antler growth. Feed intake was lowest in early winter, but a compensatory increase was found in late winter. Feed intake peaked in May, then gradually decreased. Body weight was maximum in November and minimum in March. It is concluded that increased PTH during velvet initiation is responsible for Ca absorption and/or mobilization. Increasing PTH levels are related to final mineralization of antlers post-velvet shedding. Higher levels of serum Ca in June–July inhibit continued increase in PTH. Increased CT during rapid antler growth may have prevented excessive bone resorption.

Effect of methionine and lysine supplementation of diets for laying hens, on production and feed intake

1976 ◽  
Vol 27 (5) ◽  
pp. 739 ◽  
Author(s):  
MW McDonald ◽  
IJ Bruce
Keyword(s):  
Body Weight ◽  
Feed Intake ◽  
Egg Production ◽  
Function Analysis ◽  
Basal Diet ◽  
Metabolizable Energy ◽  
Feed Consumption ◽  
Multiple Regression Equation ◽  
Weight Changes ◽  
Egg Weight

Six diets containing five levels of methionine and two of lysme were each fed to 12 Leghorn and 12 Australorp pullets for a period of 16 weeks Body weight changes, egg production, egg weight and feed intake were measured Although responses to the diets did not differ significantly In univariate analyses, multrvarrate analysis lndicated a srgnificant interaction between breeds and diets. Increased methionine produced small, significant Increases In egg weight, although responses were inconsistent at different levels Body weight gains were not significantly different between diets. Egg production and feed consumption were significantly affected by diet?, but the pattern of response was also inconsistent. A discriminate function analysis showed a consistent response to increased methionine, which indicated that the basal diet was deficient and that the pullets required a total of 0 275% methionine in their diet Lysine supplementation produced a 'significant decrease In feed intake but had no significant effect on any other variable.A multiple regression equation relating metabolizable energy intake to the variables measured in the experiment was calculated and compared with others reported In the literature This was used to predict the requirements of the pullets for a number of essential amino acids.

scholarly journals Performance Characteristics and Nutrient Utilization by Meat Type Chicken Fed Fusarium oxysporum Degraded Brewer Dried Grain

2020 ◽  
pp. 198-204
Author(s):  
T. E. Lawal ◽  
F. A. Aderemi ◽  
O. M. Alabi ◽  
O. A. Oguntunji ◽  
M. O. Ayoola ◽  
...  
Keyword(s):  
Body Weight ◽  
Weight Gain ◽  
Fusarium Oxysporum ◽  
Feed Intake ◽  
Body Weight Gain ◽  
Nutrient Utilization ◽  
Feed Consumption ◽  
Control Treatment ◽  
Feed Conversion ◽  
Crude Fibre

The objective of this study was to determine the effect of Fusarium oxysporum degraded Brewer dried grain (BDG) on the performance and nutrient utilization ofbroiler chicken at starter and finisher phases. Undegraded and degraded (BDG) werewas used to compound rations for broiler birds for 8 weeks. The undegraded BDG was used at 7% inclusion level and the degraded BDG was used at 3, 5, and 7%. A total of 150 day old chicks were randomly selected and allocated for 5 treatments. Thirty 30 birds were allocated to each treatment with three replicates each. Fusarium oxysporum was inoculated into BDG through Solid State Fermentation for a period of 7 days. This was used as degraded sample. There was improvement in the crude protein, ash, and gross energy after biodegradation. Biodegradation led to reduction in crude fibre, cellulose, hemicellulose, and detergent fibre content. At  starter phase, there were significant (P<0.05) (P=0.05) differences in feed consumption and body weight gain and the highest feed consumption (FC) and body weight gain  (BWG) were found in treatment 5, which contained 7% degraded BDG (DBDG) and the FC and BWG they were 88.93 and 41.07g/bird/day, respectively. At the finisher phase, there were significant (P=0.05) differences in both the average feed intake and the average body weight gain by the birds. The highest feed intake was found in treatment 5 (140 g/b/d) and the highest body weight gain was also observed in treatment 5 (78.21g/b/d). Significant differences (P=0.05) were also observed for the feed conversion ratio at the finisher phase. The best value (1.64) was recorded at the control treatment and this was followed by the value recorded for treatment 5 (1.79). The relative cost benefits revealed that it is profitable to feed broilers with F. oxysporum degraded BDG. The results showed that F. oxysporum was able to enhance the feeding value of BDG and this impacted positively on the feed consumption and body weight gain by the birds.

scholarly journals NanoReviser: An Error-correction Tool for Nanopore Sequencing Based on a Deep Learning Algorithm

2020 ◽  
Author(s):  
Luotong Wang ◽  
Li Qu ◽  
Longshu Yang ◽  
Yiying Wang ◽  
Huaiqiu Zhu
Keyword(s):  
Deep Learning ◽  
Error Rate ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Consensus Sequence ◽  
Sequencing Error ◽  
Nanopore Sequencing ◽  
Electrical Signals ◽  
E Coli ◽  
Deep Learning Algorithm

AbstractNanopore sequencing is regarded as one of the most promising third-generation sequencing (TGS) technologies. Since 2014, Oxford Nanopore Technologies (ONT) has developed a series of devices based on nanopore sequencing to produce very long reads, with an expected impact on genomics. However, the nanopore sequencing reads are susceptible to a fairly high error rate owing to the difficulty in identifying the DNA bases from the complex electrical signals. Although several basecalling tools have been developed for nanopore sequencing over the past years, it is still challenging to correct the sequences after applying the basecalling procedure. In this study, we developed an open-source DNA basecalling reviser, NanoReviser, based on a deep learning algorithm to correct the basecalling errors introduced by current basecallers provided by default. In our module, we re-segmented the raw electrical signals based on the basecalled sequences provided by the default basecallers. By employing convolution neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks, we took advantage of the information from the raw electrical signals and the basecalled sequences from the basecallers. Our results showed NanoReviser, as a post-basecalling reviser, significantly improving the basecalling quality. After being trained on standard ONT sequencing reads from public E. coli and human NA12878 datasets, NanoReviser reduced the sequencing error rate by over 5% for both the E. coli dataset and the human dataset. The performance of NanoReviser was found to be better than those of all current basecalling tools. Furthermore, we analyzed the modified bases of the E. coli dataset and added the methylation information to train our module. With the methylation annotation, NanoReviser reduced the error rate by 7% for the E. coli dataset and specifically reduced the error rate by over 10% for the regions of the sequence rich in methylated bases. To the best of our knowledge, NanoReviser is the first post-processing tool after basecalling to accurately correct the nanopore sequences without the time-consuming procedure of building the consensus sequence. The NanoReviser package is freely available at https://github.com/pkubioinformatics/NanoReviser.

scholarly journals Real Time LDR Data Prediction using IoT and Deep Learning Algorithm

2020 ◽  
pp. 158-161
Author(s):  
Chandraprabha S ◽  
Pradeepkumar G ◽  
Dineshkumar Ponnusamy ◽  
Saranya M D ◽  
Satheesh Kumar S ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Intensity ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Time Interval ◽  
Deep Learning Algorithm ◽  
Data Prediction ◽  
Solar Plant

This paper outfits artificial intelligence based real time LDR data which is implemented in various applications like indoor lightning, and places where enormous amount of heat is produced, agriculture to increase the crop yield, Solar plant for solar irradiance Tracking. For forecasting the LDR information. The system uses a sensor that can measure the light intensity by means of LDR. The data acquired from sensors are posted in an Adafruit cloud for every two seconds time interval using Node MCU ESP8266 module. The data is also presented on adafruit dashboard for observing sensor variables. A Long short-term memory is used for setting up the deep learning. LSTM module uses the recorded historical data from adafruit cloud which is paired with Node MCU in order to obtain the real-time long-term time series sensor variables that is measured in terms of light intensity. Data is extracted from the cloud for processing the data analytics later the deep learning model is implemented in order to predict future light intensity values.

scholarly journals The effects of Papain Crude Extract Addition in Diets on Broilers Production Performances

2016 ◽  
Vol 18 (1) ◽  
pp. 30 ◽  
Author(s):  
Laurentius Rumokoy ◽  
Endang Pudjihastuti ◽  
Ivonne Maria Untu ◽  
Wisje Lusia Toar
Keyword(s):  
Body Weight ◽  
Feed Intake ◽  
Crude Extract ◽  
Broiler Chickens ◽  
Production Performance ◽  
Feed Consumption ◽  
Feed Conversion ◽  
Protease Enzyme ◽  
Physical Form ◽  
Pellet Feed

This study was conducted to evaluate the effects of papain crude extract addition in mash and pellet feed forms on production performance of broiler chickens in order to obtain the best level of extract papain in mash or pellet form. This natural protease enzyme was extracted from unripe papaya. A complete random design was applied in this study and it was arranged with factorial 4 * 2 and three replications. The treatments were 4 levels of papain (0, 0.03, 0.05, and 0.07 %) and two physical forms of feed (mash and pellet). Broilers production parameters measured were: feed intake, body weight, feed conversion ratio (FCR) and carcass percentage. The results of analysis of variance showed that the interaction was highly significant (P <0.01) for feed intake, body weight, carcass percentage respectively while feed conversion showed significant interaction (P <0.05). The significant differences in the feed consumption described the role of papain enzyme through treatment of CEP and the physical form of feed. The results indicate that the all treatment of papain crude extract level  both in mash and pellet feed form were able to improve feed intake, body weight, FCR and carcass percentage of broiler chickens, whereas the best performance was obtained in the treatment of 0.05% papain crude extract in mash form of diets.

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