Use of sensor-determined behaviours to develop algorithms for pasture intake by individual grazing cattle

2017 ◽  
Vol 68 (12) ◽  
pp. 1091 ◽  
Author(s):  
P. L. Greenwood ◽  
D. R. Paull ◽  
J. McNally ◽  
T. Kalinowski ◽  
D. Ebert ◽  
...  
Keyword(s):  
Simulation Models ◽  
Classification Model ◽  
Pasture Management ◽  
Concentrate Supplementation ◽  
Grazing Cattle ◽  
Sensor Devices ◽  
Measurement Units ◽  
Using Data ◽  
Prediction And Simulation ◽  
Further Development

Practical and reliable measurement of pasture intake by individual animals will enable improved precision in livestock and pasture management, provide input data for prediction and simulation models, and allow animals to be ranked on grazing efficiency for genetic improvement. In this study, we assessed whether pasture intake of individual grazing cattle could be estimated from time spent exhibiting behaviours as determined from data generated by on-animal sensor devices. Variation in pasture intake was created by providing Angus steers (n = 10, mean ± s.d. liveweight 650 ± 77 kg) with differing amounts of concentrate supplementation during grazing within individual ryegrass plots (≤0.22 ha). Pasture dry matter intake (DMI) for the steers was estimated from the slope (kg DM day–1) of the regression of total pasture DM per plot on intake over an 11-day period. Pasture DM in each plot, commencing with ≤2 t DM ha–1, was determined by using repeatedly calibrated pasture height and electronic rising plate meters. The amounts of time spent grazing, ruminating, walking and resting were determined for the 10 steers by using data from collar-mounted, inertial measurement units and a previously developed, highly accurate, behaviour classification model. An initial pasture intake algorithm was established for time spent grazing: pasture DMI (kg day–1) = –4.13 + 2.325 × hours spent grazing (P = 0.010, r2 = 0.53, RSD = 1.65 kg DM day–1). Intake algorithms require further development, validation and refinement under varying pasture conditions by using sensor devices to determine specific pasture intake behaviours coupled with established methods for measuring pasture characteristics and grazing intake and selectivity.

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

2018 ◽  
Vol 7 (2.4) ◽  
pp. 10
Author(s):  
V Mala ◽  
K Meena
Keyword(s):  
Data Mining ◽  
Learning Algorithm ◽  
Detection System ◽  
Classification Model ◽  
Detection Methods ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Intruder Detection ◽  
Hybrid Classification ◽  
Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

scholarly journals Text Mining Drug-Protein Interactions using an Ensemble of BERT, Sentence BERT and T5 models

2021 ◽  
Author(s):  
Xin Sui ◽  
Wanjing Wang ◽  
Jinfeng Zhang
Keyword(s):  
Protein Interactions ◽  
Clustering Algorithm ◽  
Data Augmentation ◽  
Majority Vote ◽  
Classification Model ◽  
Ensemble Model ◽  
K Nearest Neighbors ◽  
Test Dataset ◽  
Improved Performance ◽  
Using Data

In this work, we trained an ensemble model for predicting drug-protein interactions within a sentence based on only its semantics. Our ensembled model was built using three separate models: 1) a classification model using a fine-tuned BERT model; 2) a fine-tuned sentence BERT model that embeds every sentence into a vector; and 3) another classification model using a fine-tuned T5 model. In all models, we further improved performance using data augmentation. For model 2, we predicted the label of a sentence using k-nearest neighbors with its embedded vector. We also explored ways to ensemble these 3 models: a) we used the majority vote method to ensemble these 3 models; and b) based on the HDBSCAN clustering algorithm, we trained another ensemble model using features from all the models to make decisions. Our best model achieved an F-1 score of 0.753 on the BioCreative VII Track 1 test dataset.

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

scholarly journals Garment Categorization Using Data Mining Techniques

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 984
Author(s):  
Sheenam Jain ◽  
Vijay Kumar
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Random Forest ◽  
Good Deal ◽  
Classification Model ◽  
Upper Body ◽  
Whole Body ◽  
Product Data ◽  
Soft Classification ◽  
Using Data

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Email Worm Detection Using Data Mining

2011 ◽  
pp. 20-34
Author(s):  
Mohammad M. Masud ◽  
Latifur Khan ◽  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Two Phase ◽  
Feature Selection Technique ◽  
Worm Detection ◽  
Phase Selection ◽  
Using Data

This chapter applies data mining techniques to detect email worms. Email messages contain a number of different features such as the total number of words in message body/subject, presence/absence of binary attachments, type of attachments, and so on. The goal is to obtain an efficient classification model based on these features. The solution consists of several steps. First, the number of features is reduced using two different approaches: feature-selection and dimension-reduction. This step is necessary to reduce noise and redundancy from the data. The feature-selection technique is called Two-phase Selection (TPS), which is a novel combination of decision tree and greedy selection algorithm. The dimensionreduction is performed by Principal Component Analysis. Second, the reduced data is used to train a classifier. Different classification techniques have been used, such as Support Vector Machine (SVM), Naïve Bayes and their combination. Finally, the trained classifiers are tested on a dataset containing both known and unknown types of worms. These results have been compared with published results. It is found that the proposed TPS selection along with SVM classification achieves the best accuracy in detecting both known and unknown types of worms.

scholarly journals Translational Roadmap for the Organs-on-a-Chip Industry toward Broad Adoption

2020 ◽  
Vol 7 (3) ◽  
pp. 112 ◽  
Author(s):  
Vanessa Allwardt ◽  
Alexander J. Ainscough ◽  
Priyalakshmi Viswanathan ◽  
Stacy D. Sherrod ◽  
John A. McLean ◽  
...  
Keyword(s):  
Technology Development ◽  
Technology Readiness ◽  
Disruptive Technology ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Commercial Viability ◽  
Performance Benchmarks ◽  
Using Data ◽  
Further Development

Organs-on-a-Chip (OOAC) is a disruptive technology with widely recognized potential to change the efficiency, effectiveness, and costs of the drug discovery process; to advance insights into human biology; to enable clinical research where human trials are not feasible. However, further development is needed for the successful adoption and acceptance of this technology. Areas for improvement include technological maturity, more robust validation of translational and predictive in vivo-like biology, and requirements of tighter quality standards for commercial viability. In this review, we reported on the consensus around existing challenges and necessary performance benchmarks that are required toward the broader adoption of OOACs in the next five years, and we defined a potential roadmap for future translational development of OOAC technology. We provided a clear snapshot of the current developmental stage of OOAC commercialization, including existing platforms, ancillary technologies, and tools required for the use of OOAC devices, and analyze their technology readiness levels. Using data gathered from OOAC developers and end-users, we identified prevalent challenges faced by the community, strategic trends and requirements driving OOAC technology development, and existing technological bottlenecks that could be outsourced or leveraged by active collaborations with academia.

A new approach for determining rice critical nitrogen concentration

2011 ◽  
Vol 149 (5) ◽  
pp. 633-638 ◽  
Author(s):  
R. CONFALONIERI ◽  
C. DEBELLINI ◽  
M. PIRONDINI ◽  
P. POSSENTI ◽  
L. BERGAMINI ◽  
...  
Keyword(s):  
Nutritional Status ◽  
Cropping Systems ◽  
Nitrogen Concentration ◽  
Simulation Models ◽  
Crop Models ◽  
N Concentration ◽  
Alternative Approach ◽  
Using Data ◽  
The University ◽  
Critical N Concentration

SUMMARYA reliable evaluation of crop nutritional status is crucial for supporting fertilization aiming at maximizing qualitative and quantitative aspects of production and reducing the environmental impact of cropping systems. Most of the available simulation models evaluate crop nutritional status according to the nitrogen (N) dilution law, which derives critical N concentration as a function of above-ground biomass. An alternative approach, developed during a project carried out with students of the Cropping Systems Masters course at the University of Milan, was tested and compared with existing models (N dilution law and approaches implemented in EPIC and DAISY models). The new model (MAZINGA) reproduces the effect of leaf self-shading in lowering plant N concentration (PNC) through an inverse of the fraction of radiation intercepted by the canopy. The models were tested using data collected in four rice (Oryza sativaL.) experiments carried out in Northern Italy under potential and N-limited conditions. MAZINGA was the most accurate in identifying the critical N concentration, and therefore in discriminating PNC of plants growing under N-limited and non-limited conditions, respectively. In addition, the present work proved the effectiveness of crop models when used as tools for supporting education.

scholarly journals Brief history and future of animal simulation models for science and application

2014 ◽  
Vol 54 (12) ◽  
pp. 1883 ◽  
Author(s):  
J. L. Black
Keyword(s):  
Animal Models ◽  
Real Time ◽  
Dynamic Models ◽  
Management Strategies ◽  
Simulation Models ◽  
Real Time Control ◽  
Livestock Farming ◽  
Animal Metabolism ◽  
The Impact ◽  
Further Development

Mathematical equations have been used to add quantitative rigour to the description of animal systems for the last 100 years. Initially, simple equations were used to describe the growth of animals or their parts and to predict nutrient requirements for different livestock species. The advent of computers led to development of complex multi-equation, dynamic models of animal metabolism and of the interaction between animals and their environment. An understanding was developed about how animal systems could be integrated in models to obtain the most realistic prediction of observations and allow accurate predictions of as yet unobserved events. Animal models have been used to illustrate how well animal systems are understood and to identify areas requiring further research. Many animal models have been developed with the aim of evaluating alternative management strategies within animal enterprises. Several important gaps in current animal models requiring further development are identified: including a more mechanistic representation of the control of feed intake; inclusion of methyl-donor requirements and simulation of the methionine cycle; plus a more mechanistic representation of disease and the impact of microbial loads under production environments. Reasons are identified why few animal models have been used for day-to-day decision making on farm. In the future, animal simulation models are envisaged to function as real-time control of systems within animal enterprises to optimise animal productivity, carcass quality, health, welfare and to maximise profit. Further development will be required for the integration of models that run real time in enterprise management systems adopting precision livestock farming technologies.

scholarly journals Monitoring lidia cattle with GPS-GPRS technology; a study on grazing behaviour and spatial distribution

2017 ◽  
Vol 4 (4) ◽  
Author(s):  
Juan Manuel Lomillos Pérez ◽  
Marta Elena Alonso de la Varga ◽  
Juan José García ◽  
Vicente Ramiro Gaudioso Lacasa
Keyword(s):  
Spatial Distribution ◽  
Home Range ◽  
Cattle Breed ◽  
General Packet Radio Service ◽  
Pasture Management ◽  
Grazing Cattle ◽  
Position Data ◽  
Grazing Area ◽  
Different Dimensions ◽  
Grazing Behaviour

Veterinaria México OA ISSN: 2448-6760Cite this as:Lomillos Pérez JM, Alonso de la Varga ME, García JJ, Gaudioso Lacasa VR. Monitoring lidia cattle with GPS-GPRS technology; a study on grazing behaviour and spatial distribution. Veterinaria México OA. 2017;4(4). doi:10.21753/vmoa.4.4.405.The behavior of grazing cattle has not been studied as much as farmed animals. In certain breeds, reared in extensive systems, human presence can cause an interruption or modification in their ethological patterns moving away from the person watching them. The use of technologies like a Global Position System and a General Packet Radio Service (GPS-GPRS) allows monitoring bovine animals exploited in extensive systems, providing information in real time about distances traveled, home range grazing areas, frequented territories, behavior patterns, etc. In the present work, GPS-GPRS collars were used to monitor 21 cows of to the lidia cattle breed, with different ages, and from three different herds in the Salamanca province (Spain). The study lasted 8 months, the animals being distributed in enclosures of different dimensions and orographic characteristics, geographic position data being collected every 15 minutes. The proper functioning of the GPS-GPRS devices was proven and home range grazing area for each animal has been calculated, with an average of 56 hectares. A graph of animals’ circadian rhythm with the distances traveled for hours has been developed. A trend was observed to start daily activity hours before dawn, diminishing its activity with the evening and overnight, with a night’s rest phase of about 7 hours. We also report daily distance traveled (3.15 km on average), finding differences depending on age, available space allowance/animal, daylight and theseason. Our results could be of relevance for a better pasture management using enclosures of size that increase the use of all the surface available.Figure 3. Image of fencing No. 5 positions of the 3 animals monitored. Red, Yellow, Blue.

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