World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

Football is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s readiness. Most prediction methods use large match datasets. Since every national team has a different squad for every world cup and the FIFA World Cup is held every four years, the usage of a large match dataset is irrelevant. Therefore, there is a need for a prediction method based on the relevant data. We applied the Poisson distribution model for predicting the FIFA World Cup 2022 knockout stage match results. We calculate the probability of winning and losing based on their average goal scores and goal conceded and evaluate the difference by the actual result using de Finetti distance. The successful prediction is 8 out of 15 matches, with six inside the round of 16 games. Thus, the new data attributes need to reformulate Poisson’s lambda. Further studies need to add the 3-4 prior world cup matches data to increase the acceptance of prediction

Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network

 Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems

Classification Methods Performance On Logistic Package State Recognition

 In the distribution sector, logistic package experience activities, such as transport, distribution, storage, packaging, and handling. Even though those processes have reasonable operational procedures, sometimes the package experience mishandling. The mishandling is hard to identify because many packages run simultaneously, and not all processes are monitored. An Inertial Measurement Unit (IMU) is installed inside a package to collect three acceleration and rotation data. The data is then labeled manually into four classes: correct handling, vertical fall, and thrown and rotating fall. Then, using cross-validation, ten classifiers were used to generate a model to classify the logistic package status and evaluate the accuracy score. It is hard to differentiate between free-fall and thrown. The classification only uses the accelerometer data to minimize the running time. The correct handling classification gives a good result because the data pattern has few variations. However, the thrown, free-fall and rotating data give a lower result because the pattern resembles each other. The average accuracy of the ten classifications is 78.15, with a mean deviation of 4.31. The best classifier for this research is the Gaussian Process, with a mean accuracy of 94.4 % and a deviation of 3.5 %

Smart GreenGrocer: Automatic Vegetable Type Classification Using the CNN Algorithm

In the food industry, separating vegetables is done by visually trained professionals. However, because it takes plenty of time to sort a large number of different types of vegetables, human errors might arise at any time, and using human resources is not always effective. Thus, automation is needed to minimize process time and errors. Computer vision helps reduce the need for human resources by automatizing the classification. Vegetables come in various colors and shapes; thus, vegetable classification becomes a challenging multiclass classification due to intraspecies variety and interspecies similarity of these main distinguishing characteristics. Consequently, much research is made to automatically discover effective methods to group each type of vegetable using computers. To answer this challenge, we proposed a solution utilizing deep learning with a Convolutional Neural Network (CNN) to perform multi-label classification on some types of vegetables. We experimented with the modification of batch size and optimizer type. In the training process, the learning rate is 0.01, and it adapts on arrival in the local minimum for result optimization. This classification is performed on 15 types of vegetables and produces 98.1% accuracy on testing data with 25 minutes and 45 seconds of training time

Hierarchical Neural Network Implementation: Emotion Recognition for Food Security Comments on Twitter

Modern Hierarchical Neural Network (HNN) implementation combines several deep learning algorithms working together, connected in a hierarchy layer. For this HNN architecture to work well, the problem and the data must be in a hierarchical format. Emotion recognition is the best example of a layered problem where each emotion is attached to a sentiment. This research proposes an HNN model to solve the emotion recognition problem with three deep learning, one for the sentiment in the first layer and two models for the emotion prediction in the second layer. There are two combinations to be compared, full-LSTM and full-CNN. Surprisingly, the overall HNN performance for both combinations is similar, and both are below a control model without HNN architecture. However, solving the emotion recognition problems in the food security domain was still possible despite poor performance. The application result creates a rough estimation of what people feel about the current food security trend