MRI brain classification using support vector machine

The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research for studying brain images. Classification is an important part in retrieval system in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. In this paper, we have obtained the feature related to MRI images using discrete wavelet transformation. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed

Catastrophic forgetting: still a problem for DNNs

We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrophic forgetting (CF) behavior is measured using a new evaluation procedure that aims at an application-oriented view of incremental learning. In particular, it imposes that model selection must be performed on the initial dataset alone, as well as demanding that retraining control be performed only using the retraining dataset, as initial dataset is usually too large to be kept. Experiments are conducted on class-incremental problems derived from MNIST, using a variety of different DNN models, some of them recently proposed to avoid catastrophic forgetting. When comparing our new evaluation procedure to previous approaches for assessing CF, we find their findings are completely negated, and that none of the tested methods can avoid CF in all experiments. This stresses the importance of a realistic empirical measurement procedure for catastrophic forgetting, and the need for further research in incremental learning for DNNs.Comment: 10 pages, 11 figures, Artificial Neural Networks and Machine Learning - ICANN 201

Distortion of Infall Regions in Redshift Space-I

We show that spherical infall models (SIMs) can better describe some galaxy clusters in redshift slice space than in traditional axially-convolved projection space. This is because in SIM, the presence of transverse motion between cluster and observer, and/or shear flow about the cluster (such as rotation), causes the infall artifact to tilt, obscuring the characteristic two-trumpet profile; and some clusters resemble such tilted artifacts. We illustrate the disadvantages of applying SIM to convolved data and, as an alternative, introduce a method fitting a tilted 2D envelope to determine a 3D envelope. We also introduce a fitting algorithm and test it on toy SIM simulations as well as three clusters (Virgo, A1459, and A1066). We derive relations useful for using the tilt and width-to-length ratio of the fitted envelopes to analyze peculiar velocities. We apply them to our three clusters as a demonstration. We find that transverse motion between cluster and observer can be ruled out as sole cause of the observed tilts, and that a multi-cluster study could be a feasible way to find our infall toward Virgo cluster

The Use of Antihypertensive and Antiplatelet Drugs on Hospital Stroke Patients

Medicine is one of the most important part of the healing process, the restoration of health and prevention of disease. This study aims to describe the use of drugs, particularly antihypertensive and antiplatelet drugs in stroke patients hospitalized in PKU Muhammadiyah Hospital Bantul during December 2014-April 2015. This research is observational descriptive study. Data collection was done prospectively with a survey of stroke patients in inpatient Al-Insan and al-A'raaf wards in PKU Muhammadiyah Hospital Bantul during the specified period. During the study there were 61 stroke patients sampled in the study, of which 41 (67.2%) were geriatrics and 20 (32.8%) were not geriatrc. From the data, 28 (45.9%) patients did not receive antihypertensive drugs, only 33 (54,1%) patients received antihypertensive drug.The antihypertensive drugs type were given to patients were ACEI 9 (14.8%) and CCB 6 (9.8%), as well as combinations ACEI and CCB 7 (11.5%). Most patients did not receive antiplatelet 43 (70.5%), whereas patients receiving antiplatelet drugs most was the kind of aspirin 17 (27.9%). From the above data it can be concluded that the use of antihypertensive drugs in stroke patients in the inpatient ward in PKU Muhamaddiyah Hospital Bantul quite frequently used, while the use of antiplatelet drugs in these cases rarely used

Konstitusi Pendidikan Islam dalam Membangun Masyarakat Madani

This journal discuss how to Islamic education constituation in Indonesia and how are principles and implementation of islamic education in creating madani society.Islamic education constitution in Indonesia be included in UU RI No 20 2003 it significant is developing ability and creating character it aims to develop students potential to be faithful and piety to god, good character, healthy, skillful, learned, creative, stand alone, and to be democratis citizen and responsible. Principles of Islamic education in building madani society are freely and individual freedom, freedom and individual activity must be balance and be control, free thingking and individual activity. Madani is an adoration value of fair, democrasy, freedom, prosperous

Pengembangan Media Pembelajaran Kimia Berbasis Autoplay Media Studio 8 pada Pokok Bahasan Struktur Atom

Research development of chemical-based learning media Autoplay Media Studio 8 is intended to produce a decent learning media on the subject of the atomic structure is developed based on the feasibility aspect of the display media, programming, content, presentation material content and language in Chemical Education Program. Beside that, it also to know the response of chemistry teachers and learners to media-based learning Autoplay Media Studio 8 has been developed on the subject of atomic structure. This media development research using ADDIE model of which has 5 stages of development, namely analysis, design, development, implementation and evaluation. Results of research-based instructional media development AutoPlay Media Studio 8 on the subject of atomic structures that have been validated by a validator to be feasible with an average percentage of 96.38%. Response chemistry teachers and learners expressed either by the average percentage of 98.2% for chemistry teachers and 91.89% for the learners. This indicates that the learning generated media fit for use as a medium of learning

Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks

Metode Accelarated Shelf Life Test (Aslt) dengan Pendekatan Arrhenius dalam Pendugaan Umur Simpan Sari Buah Nanas, Pepaya dan Cempedak

Pineapple, papaya and cempedak are horticultural commodities that are perishable, require large space, and are usually consumed in fresh form. Based on that, technologies are required for processing them, and one alternative is juice technology. To ensure that the juice is still suitable for consumption and unspoiled, information on shelf life is necessary. Method of estimating shelf life used is ASLT (Accelarated Shelf Life Test). This research was conducted at the Laboratory of The Indonesian Center for Postharvest Agricultural Research and Development between September 2012 – January 2013. Results showed that kinetics reaction in the deterioration of vitamin C in pineapple and pineapple-Cempedak juice followed order one reaction. At storage temperature ranging between 30 °C to -5 °C pineapple-cempedak juice showed a shelf life longer than the shelf life of pineapple and pineapple-papaya juices. The estimated shelf life of pineapple-cempedak juice at a temperature of -5 °C was 197.85 days. Shelf life of pineapple-papaya juice at a temperature of -5 °C was 172.39 days. Shelf life of pineapple juice at a temperature of -5 °C was 156.85 days