Selection of Learning Algorithm for Musical Tone Stimulated Wavelet De-Noised EEG Signal Classification

The task of classifying EEG signals pose a challenge in the selection of which learning algorithm is best to provide higher classification accuracy. In this study, five wellknown learning algorithms used in data mining were utilized. The task is to classify musical tone stimulated wavelet de-noised EEG signals. Classification tasks include whether the EEG signal is tone stimulated or not, and whether the EEG signal is stimulated by either the C, F or G tone. Results show higher correct classification instances (CCI) percentages and accuracies in the first classification task using the J48 decision tree as the learning algorithm. For the second classification task, the k-nn learning algorithm outruns the other classifiers but gave low accuracy and low correct classification percentage. The possibility of increasing the performance was explored by increasing the k (number of neighbors). With the increment, its produced directly proportionate in accuracy and correct classification percentage within a certain value of k. A larger k value will reduce the accuracy and the correct classification percentages

Pre-Harvest Factors Optimization Using Genetic Algorithm for Lettuce

The agricultural sector is facing problems on crop development due to climate change and global warming. Crops such as rice, tomato, corn, lettuce, potato, wheat, soybeans and others are affected. Through analyzing the graphical representation of data, no optimum values are observed. In this study, the suitability of the genetic algorithm in finding the best condition for producing high quality lettuce crop was determined. The parameters that were optimized are the light intensity, temperature and CO2. These parameters were essential preharvest factors for lettuce. The system selected the 50 fittest individuals based on the fitness score and then proceeds to the recombination process. A mutation has been applied to test if the solution is the global one. When the iterations had reached the required number of generation, the system stopped and gave the best condition for lettuce. Critical design on GA was done and the best fitness plot was obtained. The GA results showed that the optimum conditions for a highquality lettuce crop needs a light intensity of 175.22296 μmol/m2/s, a temperature of 19.36228 ºC and a CO2 level of 803.01855 ppm

Real-Time Evaluation of Breast Self-Examination Using Computer Vision

Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents amethod for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance

Real-Time Evaluation of Breast Self-Examination Using Computer Vision

Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents amethod for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance

Female Voice Recognition Using Artificial Neural Networks and MATLAB Voicebox Toolbox

Voice and speaker recognition performances are measured based on the accuracy, speed and robustness. These three key performance indicators are primarily dependent on voice feature extraction method and voice recognition algorithm used. This paper aims to discuss various researches in speech recognition that has yielded high accuracy rates of 95% and above. The extracted MFCCs from MATLAB Voicebox toolbox were used as inputs to the multilayer Artificial Neural Networks (ANN) for female voice recognition algorithm. This study explored the recognition performance of the neural networks using variable number of hidden neurons and layers, and determine the architecture that would provide the optimum performance in terms of high recognition rate. MATLAB simulation resulted to a training and testing recognition rate of 100.00% when using 3-hidden-layer neural network from speech samples of a single-speaker, and highest training recognition rate of 98.11% and testing recognition rate of 87.20% when using 4-hidden-layer neural network from speech samples of several speakers. When tested with homonyms, the best recognition rate was 75.00% from a 3-hidden-layer neural network trained from a single-speaker, and 81.91% from a 4- hidden-layer neural network trained from multiple speakers. The deviation in recognition rates were primarily attributed to the variations made in the number of input neurons, hidden layers, and neurons of the speech recognition neural network

Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

Fira middle-league robot soccer game strategy

The thrust of this paper is to design and implement a hierarchical control for the cooperative behavior of autonomous mobile robots using fuzzy logic systems with 5 vs. 5 Mirosot as the platform for its investigation. Our strategy is to identify the state of the game, offense or defense, using a fuzzy logic main-task-identifier. During offense a fuzzy control system is used by the ball handler to decide whether to shoot, pass, or just dribble the ball. To ensure success for these tasks, they are preceded by lane checking activity to assign a lane free of obstacles. Experimental results show that the proposed strategy is effective and robust