Cattle Identification Using Muzzle Images and Deep Learning Techniques

Traditional animal identification methods such as ear-tagging, ear notching, and branding have been effective but pose risks to the animal and have scalability issues. Electrical methods offer better tracking and monitoring but require specialized equipment and are susceptible to attacks. Biometric identification using time-immutable dermatoglyphic features such as muzzle prints and iris patterns is a promising solution. This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. Two deep learning classification models are implemented - wide ResNet50 and VGG16\_BN and image compression is done to lower the image quality and adapt the models to work for the African context. From the experiments run, a maximum accuracy of 99.5\% is achieved while using the wide ResNet50 model with a compression retaining 25\% of the original image. From the study, it is noted that the time required by the models to train and converge as well as recognition time are dependent on the machine used to run the model.Comment: 8 pages, 4 figures, 2 table

FORECASTING STOCK MARKET INDEX BASED ON PATTERN-DRIVEN LONG SHORT-TERM MEMORY

Stock trend prediction is an important area of study for researchers and practitioners. In recent years, along with traditional statistical prediction models, machine learning and deep learning techniques have been increasingly adopted in various financial studies. Long Short-Term Memory (LSTM) is one of the deep learning models for predicting time -series data. In the case of vanilla LSTM, shared weights are learned based on all available data; hence, it is difficult to accurately learn patterns and predict the future value from a subset of data. In this paper, a pattern -driven hybrid model that combines an LSTM with an unsupervised learning algorithm is proposed for precise prediction of stock prices. The performance of the hybrid model is evaluated using Korea stock index data. The results demonstrate that the proposed model outperforms traditional recurrent neural network (RNN) and LSTM models

Real-time quality monitoring and control system using an integrated cost effective support vector machine

The quality monitoring and control (QMC) has been an essential process in the manufacturing industries. With the advancements in data analytics, machine-learning based QMC has become popular in various manufacturing industries. At the same time, the cost effectiveness (CE) of the QMC is perceived as a main decision criterion that explicitly accounts for inspection efforts and has a direct relationship with the QMC capability. In this paper, the cost-effective support vector machine (CESVM)-based automated QMC system (QMCS) is proposed. Unlike existing models, the proposed CESVM explicitly incorporates inspection-related expenses and error types in the SVM algorithm. The proposed automated QMCS is verified and validated using an automotive door-trim manufacturing process. Next, we perform a design of experiment to assess the sensitivity analysis of the proposed framework. The proposed model is found to be effective and could be viewed as an alternative or complementary tool for the traditional quality inspection system