Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%

The Use of Pictures as Media to Improve Students’ Speaking Ability. Cries Tia NofiaDewi NRP. 107010008

This final paper is an action research. The aim of the study is to know the use of pictures in improving seventh grade students’speaking ability. There is no doubt that speaking is one of the most difficult skill for second language. The seventh grade of SMP Kemah Indonesia 4 Bandung also faced the same problem. Based on the observation, it was found that the students’ speaking ability in descriptive study was still far from expectation. This research was conducted in a classroom action research in order to improve students’ speaking ability. This study used classroom action research (PTK), It consists of four steps in conducting the action research : planning, acting, observing and reflecting. This action research was done in two cycles. The results of this research indicated that using pictures can improve students’ speaking ability in descriptive study especially in describing a picture. Morover, the students were able to improve their ability in speaking. Based on the result of classroom action research, the conclusion shows that pictures media are appropriate to help and guide the students’ speaking in descriptive study. Teaching descriptive by using pictures media is an affective way to solve the problem faced by students in terms of developing ideas to speaking in descriptive study

Optimization of microwave-assisted extraction in the purification of triglycerides from non-edible crude Calophyllum inophyllum oil as biodiesel feedstock using artificial intelligence

Crude nyamplung Calophyllum inophyllum is a potential non-edible feedstock for biodiesel production. Calophyllum Inophyllum oil (CCIO) is a non-edible oil that has a high content of triglyceride (TG) and free fatty acid (FFA). This study aims to optimize microwave-assisted power and extraction time of triglyceride purification from C. inophyllum crude oil for biodiesel. This work deployed Artificial Intelligence (AI) algorithms consisting of eight Machine Learning (ML) algorithms and found the most accurate model, then optimized using the Particle Swarm Optimization (PSO) algorithm.The result of machine learning modelling Random Forest achieved higher accuracy in R-Square and lower Mean Square Error (MSE) than any other models. Overall, in R-Square average across all variables was 0.949 ± 0.026 and the MSE average of 0.097 ± 0.068. This result can be interpreted as a mean deviation between the predicted value and an accurate value of less than 0.1 for all variables. The optimum of the TG compound resulted in the power of 462.3 W and time of 39.12 min that equalled at 84.02% and FFA equalled at 6.92%. The TG have increased by 11% from the reference range, which states conventional methods from crude oil. Comparison with the MAE method has a minimum fitness value difference of 0.0006 but has a smaller accuracy of less than 1%. Implementing this prediction and optimization method can shorten the extraction time by 5.8 min and reduce energy consumption or system work by 130 kJ. This method can be used for input parameter model prediction and parameter optimization in purification for biodiesel feedstock. Further research can be carried out using other artificial intelligence methods to optimize biodiesel production

MITNet: Supplementary Dataset

Feature extracted dataset from aggregated epitope-TCR databases (McPAS-TCR, VDJdb, IEDB) that were used in the MITNet paper

A Genetic Algorithm approach for optimization of geothermal power plant production: Case studies of direct steam cycle in Kamojang

Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia