KYOTO – Shimadzu Corp. launched on Monday a near-infrared camera system to help diagnose metastatic breast cancer, making a foray into the Japanese medical camera market. Lightvision, the new product, allows surgeons to identify easily the positions of lymph nodes to be removed for diagnosis, by creating a real-time visualization that makes lymph nodes appear to glow blue or green following the injection of a special medical agent. With the system, which has 10-times zoom and auto-focus functions, surgeons will be able to perform operations while confirming the images on the monitor, the precision equipment maker said. In the future, Shimadzu hopes to use the technique to develop products for angiography.

NAGOYA – A passenger jet being developed by Mitsubishi Aircraft Corp. aborted its second attempt to head to the United States on Sunday when an air conditioning problem that thwarted its first bid reoccurred. The Mitsubishi Regional Jet, which has been mired in a series of development delays, left Nagoya at around 1 p.m. for Hokkaido, its first planned stop. But it was forced to turn around two hours later, according to Mitsubishi Aircraft, a subsidiary of Mitsubishi Heavy Industries Ltd. The company said it has not decided when the jet will make the next attempt to fly to the U.S. as it needs to identify the cause of the problem. Mitsubishi Heavy aimed to take the MRJ to the U.S. for certification testing by the end of this month.

Hi friends,I was struggling for Decision tree visualization in python.Sometimes there is error due to pydot and sometimes due to graphviz....even though I have installed both in my windows machine but still no luck... please let me know if you know any easy method for this visualization in ipython notebook

Machine learning tools are commonly used in modern high energy physics (HEP) experiments. Different models, such as boosted decision trees (BDT) and artificial neural networks (ANN), are widely used in analyses and even in the software triggers. In most cases, these are classification models used to select the "signal" events from data. Monte Carlo simulated events typically take part in training of these models. While the results of the simulation are expected to be close to real data, in practical cases there is notable disagreement between simulated and observed data. In order to use available simulation in training, corrections must be introduced to generated data. One common approach is reweighting - assigning weights to the simulated events. We present a novel method of event reweighting based on boosted decision trees. The problem of checking the quality of reweighting step in analyses is also discussed.

High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametric imaging features are combined to produce a CAD system for differentiating cancerous and non-cancerous lesions. The complete system uses a rule-extraction algorithm to present lesion classification results in an easy to understand graph visualization.

I'm implementing a machine learning structure to try and predict fraud on financial systems like banks, etc... This means that there is a lot of different data that can be used to train the model eg. I'm having trouble deciding which structure is the best for this problem. I have some experience with decision trees but currently I have started to question if a neural network would be better for this kind of problem. Also if any other method would be best please feel free to enlighten me.

Conventional intrusion and detection methods to evaluate network anomalies have several impasses for large-scale datasets over ultra-blazing speed networks with disparate sources of data coming in with high-velocity and high-volume. Machine learning and artificial intelligence techniques mine the massive network datasets with IP size distribution can perform dichotomy of flow-based network traffic to diagnose the network anomalies as an effective solution. The simplex and similar size of the IP distribution with same attributes hitting the flow-based analysis on regular time intervals display the symptoms of network anomalies. Various flow-based monitoring tools such as nProbe and FlowMon Probe detect these intrusions on gigabit-sized networks. Two key detection techniques of NetFlow-based on large-scale and high-speed networks are: a) the misuse intrusion method; b) network anomaly detection method.

We propose using five data-driven community detection approaches from social networks to partition the label space for the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector, infomap, walktrap and label propagation algorithms. We construct a label co-occurence graph (both weighted an unweighted versions) based on training data and perform community detection to partition the label set. We include Binary Relevance and Label Powerset classification methods for comparison. We use gini-index based Decision Trees as the base classifier. We compare educated approaches to label space divisions against random baselines on 12 benchmark data sets over five evaluation measures. We show that in almost all cases seven educated guess approaches are more likely to outperform RAkELd than otherwise in all measures, but Hamming Loss. We show that fastgreedy and walktrap community detection methods on weighted label co-occurence graphs are 85-92% more likely to yield better F1 scores than random partitioning. Infomap on the unweighted label co-occurence graphs is on average 90% of the times better than random paritioning in terms of Subset Accuracy and 89% when it comes to Jaccard similarity. Weighted fastgreedy is better on average than RAkELd when it comes to Hamming Loss.

Data mining has useful classification methods for the data analysis and prediction. One of them is decision tree induction, which is the learning of decision trees from the class-labeled dataset. It can provide an easy way to understand the data and view the relationship among attributes because it has a flowchart-like tree structure. When I applied the decision tree algorithm with parameters (criterion: gain_ratio and minimal gain: 0.03) to MSD dataset using the RapidMiner tool, the "start_of_fade_out" attribute is the best one to partition the data, as shown in Figure 1. Only 2 Rock and 1 New Age songs have start_of_fade_out that is greater than 547.698 seconds.

Whatever its name, it's the same thing – machines recognizing clinical problems in digital images ahead of the radiologists charged with making the diagnosis. The artificial intelligence (AI) trend is new, but it's gaining ground quickly, according to industry experts. The advent of these technologies and radiology's growing interest in and dependence on them has been discussed at national and international meetings, including the RSNA, HIMSS, and SIIM annual meetings, during the past year. But, there's still a long way to go. "We're just barely scratching the surface of using artificial intelligence in the last few years," said Eliot Siegel, MD, professor and vice chair of research information systems for the University of Maryland Department of Diagnostic Radiology and Nuclear Medicine. "There's an emergence of increasing interest in the largest companies in the world, including Google, Microsoft, Apple, and IBM, in actually starting to use these technologies for data extraction and evaluation."