可塑的シナプスのイメージングによる解析と工学応用

神経系の最大の特徴は、環境に応じて学習や記憶する柔軟性(可塑性) と、記憶 したことを忘れない頑強性（安定性）という相反する性質を併せ持つことです。神経細胞が他の神経細 胞に信号を伝える部分はシナプスと呼ばれ、その信号伝達効率が適当な条件刺激で変化するものが可塑 的シナプスです。近年、可塑的シナプスが脳神経系各部で発見され、神経可塑性の少なくとも一部は、 これらに依存するとされています。また、ロボットや自動診断装置など機械学習の分野でも、可塑性と 安定性のバランスは極めて重要で、可塑性が高いと学習は早いが過去の記憶を忘却し、安定性が高いと 学習が進まないという問題が生じています。本研究では可塑的シナプスの特性を実際の生物で実測する と共に、機械学習に相応しい数理モデルやアルゴリズムを開発しました。福井大学平成２２年度重点研究「重点研究育成経費

Towards transparent machine learning models using feature sensitivity algorithm

Despite advances in health care, diabetic ketoacidosis (DKA) remains a potentially serious risk for diabetes. Directing diabetes patients to the appropriate unit of care is very critical for both lives and healthcare resources. Missing data occurs in almost all machine learning models, especially in production. Missing data can reduce the predictive power and produce biased estimates of models. Estimating a missing value around a 50 percent probability may lead to a completely different decision. The objective of this paper was to introduce a feature sensitivity score using the proposed feature sensitivity algorithm. The data were electronic health records contained 644 records and 28 attributes. We designed a model using a random forest classifier that predicts the likelihood of a developing patient DKA at the time of admission. The model achieved an accuracy of 80 percent using five attributes; this new model has fewer features than any model mentioned in the literature review. Also, Feature sensitivity score (FSS) was introduced, which identifies within feature sensitivity; the proposed algorithm enables physicians to make transparent, and accurate decisions at the time of admission. This method can be applied to different diseases and datasets

Similarity Based Entropy on Feature Selection for High Dimensional Data Classification

Curse of dimensionality is a major problem in most classification tasks. Feature transformation and feature selection as a feature reduction method can be applied to overcome this problem. Despite of its good performance, feature transformation is not easily interpretable because the physical meaning of the original features cannot be retrieved. On the other side, feature selection with its simple computational process is able to reduce unwanted features and visualize the data to facilitate data understanding. We propose a new feature selection method using similarity based entropy to overcome the high dimensional data problem. Using 6 datasets with high dimensional feature, we have computed the similarity between feature vector and class vector. Then we find the maximum similarity that can be used for calculating the entropy values of each feature. The selected features are features that having higher entropy than mean entropy of overall features. The fuzzy k-NN classifier was implemented to evaluate the selected features. The experiment result shows that proposed method is able to deal with high dimensional data problem with average accuracy of 80.5%

Seleksi Fitur dengan Artificial Bee Colony untuk Optimasi Klasifikasi Data Teh menggunakan Support Vector Machine

Teh dapat dikenal kualitasnya melalui aroma yang dihasilkan. Penelitian klasifikasi teh menggunakan e-nose umumnya hanya mendeteksi kualitas aroma menggunakan general sensor gas. Namun, adanya redundansi fitur sensor dapat menyebabkan penurunan performa sistem e-nose. Oleh karena itu diperlukan sebuah sistem yang dapat menyeleksi fitur sehingga performa klasifikasi menjadi lebih optimal. Pada penelitian ini dibentuk sistem perangkat lunak yang mampu menyeleksi fitur untuk mengoptimalkan performa klasifikasi. Data input untuk sistem adalah respon sensor e-nose terhadap 3 kualitas teh hitam dengan jumlah sampel 300. Fitur yang diseleksi berupa sensor-sensor pada instrumen e-nose. Proses seleksi fitur dilakukan dengan pendekatan wrapper, algoritma ABC digunakan untuk seleksi fitur, kemudian hasil fitur yang terpilih dievalusi dengan klasifikasi menggunakan SVM. Hasil sistem ABC-SVM kemudian dibandingkan dengan sistem SVM tanpa seleksi fitur. Hasil penelitian menunjukkan bahwa dari 12 sensor e-nose, sensor yang paling mencirikan teh hitam kualitas 1-3 yaitu sensor TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 dan TGS 822. Sedangkan untuk sensor MQ-7, TGS 826 dan TGS 2610 merupakan sensor yang redundant pada sistem dikarenakan gas yang dideteksi oleh 3 sensor tersebut dapat diwakili oleh sensor lainnya. Dengan berkurangnya fitur menjadi 9, performa akurasi klasifikasi meningkat 16,7%

Classification model and analysis on students’ performance

The purpose of this paper is to propose a classification model for classifying students’performance in SijilPelajaran Malaysia in order to help teachers plan suitable teachingactivities for their students based on the students’ performance. Five classifier algorithms have been used during the process which are Naïve Bayes, Random Tree, Multi Class Classifier, Conjunctive Rule and Nearest Neighbour. Data was collected from MaktabRendahSains MARA Kuala Berang, Terengganu, Malaysia starting May 2011 until December 2014. The students’ performance was evaluated based on the category of students according to their SPM Results. Parameters that contribute to students’ performance such as stream, state, gender and hometown are also investigated along with the examination data.This research shows that first semester results can be used to identify students’ performance.Keywords: educational data mining; classification model; feature selection

Providing an efficient framework for power theft detection based on combination of Raven roosting optimization algorithm and clustering and classification techniques

One of the main concerns of power generation systems around the world is electricity theft. One of the goals of the Advanced Measurement Infrastructure (AMI) is to reduce the risk of electricity theft in the electric smart grids. However, the use of smart meters and the addition of a security layer to the measurement system paved the way for electricity theft. Nowadays, machine learning and data mining technologies are used to find abnormal patterns of consumption. The lack of a comprehensive dataset about abnormal consumption patterns, the issue of choosing effective features, the balance between consumer\u27s normal and abnormal consumption patterns, and the choice of type and number of classifiers and how to combine them are the challenges of these technologies. Therefore, a detection system for electricity theft that is capable of effectively detecting theft attacks is needed. To this end, a framework including data preparation phases, feature selection, clustering, and combined modeling have been proposed to address the aforementioned challenges. In order to balance normal and abnormal data, 6 artificial attacks have been created. Moreover, with respect to the Chief element in the Raven optimization algorithm and its two-step search feature, this algorithm has been used in feature selection and clustering phases. Stacking as a two-step combined modeler has been used to strengthen the prediction of accuracy. In the second step of this modeler, the meta-Gaussian Processes algorithm is used due to the high accuracy of detection. The Irish Social Science Data Archive (ISSDA) dataset has been used to evaluate performance. The results show that the proposed method identifies dishonest customers with higher accurac

Evidence-based clinical engineering : machine learning algorithms for prediction of defibrillator performance

Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare. To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance