IBM IBMSPSSSTATL1P - IBM SPSS

Retinal microvasculature and cerebral small vessel sickness in the Lothian start Cohort 1936 and gentle Stroke examine | killexams.com Real Questions and Pass4sure dumps

The LBC193629 comprised group-living, in most cases in shape older adults, of mean age about 70 years when first recruited in older age. All were born in 1936. The information analysed for the current look at, together with digital retinal pictures and structural brain imaging, have been got at a 2d wave of trying out when the participants had been approximately 73 years historical (N = 866). The recruitment and trying out of the LBC1936 has been described in element previously29,30,31.

The light Stroke analyze (MSS)5 is a prospective examine of patients with fresh (inside 3 months) clinical lacunar or gentle cortical ischaemic stroke. All sufferers had been assessed by means of an skilled stroke general practitioner. The recruitment, testing and imaging of those patients has been described previously5,eleven.

each reviews have been accepted by means of Lothian analysis Ethics (LBC1936: REC 07/MRE00/58; MSS: 2002/eight/64). The LBC1936 study become additionally authorised by means of the Scottish Multicentre (MREC/01/0/fifty eight) research Ethics. Written suggested consent for participation in each stories was acquired from all contributors. The analysis changed into performed in compliance with the Helsinki assertion.

In each organizations, digital retinal fundus photos had been captured the use of the same non-mydriatic digital camera at 45° field of view (CRDGi; Canon united states of america, Lake Success, big apple, usa). 814 LBC1936 (from Wave 2 of trying out) and 190 MSS members offered retinal images of each eyes. photographs had been centred about on the optic disc. For the present evaluation, the retinal images have been reanalysed for retinal vascular characteristics using the same semi-automated utility equipment, VAMPIRE with the aid of an experienced operator. VAMPIRE photograph processing and evaluation has been described in detail previously32,33,34. in brief, the boundaries of the optic disc (OD) and place of the fovea in a retinal photo are first detected and the commonplace set of OD-centred round dimension zones dependent. Zone B is a ring 0.5 to 1 OD diameters far from the centre, and Zone C is a hoop extending from OD boundary to 2 OD diameters away. next, the software detects the retinal blood vessels present within the image and classifies them as arterioles or venules. The observer, when fundamental followed a standardised measurement protocol to preform manual interventions to correct computer-generated labelling of photograph facets, blind to all prior retinal evaluation, brain and VRF statistics. there have been finished retinal measurements from each eyes for 603 LBC1936 and one hundred fifty five MSS participants. Rejections were due to terrible pleasant photos, eyelashes causing streaks throughout the photo, out-of-focal point photos, and overexposure (in both eye); these befell in about 16% of LBC1936 photos, and eight% of MSS photos, with an further four% of MSS photos excluded as a result of appreciable differences in graphic decision (coming up from deviations from regular operation of the device when imaging).

Sixteen retinal vascular parameters had been measured from every picture in both cohorts: measures of vessel calibre—principal retinal artery equivalent (CRAE), critical retinal vein equivalent (CRVE), and the version in calibre—the common deviation of arteriolar and venular widths (BSTDa, BSTDv); the gradient of the width of the leading arteriolar and venular vessel paths (GRADa, GRADv); measures of branching complexity—arteriolar and venular fractal dimension (FDa, FDv); measures of vessel tortuosity—arteriolar and venular tortuosity (TORTa, TORTv); and measures of arteriolar and venular branching geometry—branching coefficient (BCa, BCv), length-diameter ratio (LDRa, LDRv) and asymmetry aspect (AFa, AFv). A lowercase ‘a’ or ‘v’ following the variable name indicates a measurement of arteriolar or venular vessels respectively. See Supplementary cloth for particulars on all retinal measurements and the way retinal variables had been selected for evaluation. To in the reduction of the number of variables, in the reduction of multicollinearity and enhance reliability, the above-outlined measurements from each eyes of every participant had been averaged to give an average dimension for all variables.

LBC1936 and MSS participants (at time of presentation) underwent mind MRI on the identical 1.5-Tesla GE Signa Horizon HDx scientific scanner (well-known electric, Milwaukee, WI) with T1-, T2- and T2*- weighted and fluid-attenuated inversion healing (flair) axial complete-mind imaging. Full details of the mind imaging scanning protocol for the LBC1936 and MSS had been described previously11,31. All analyses were performed blinded to all different statistics. The SVD lesions in both experiences were assessed qualitatively and quantitatively using validated strategies based on a precursor to the try criteria35. WMH have been visually scored using flair-, T1 and T2-weighted sequences on the Fazekas score36 in each the deep (0–3) and periventricular (0–three) white remember. acceptable sequences had been also rated for the presence of microbleeds (location and number), lacunes (location and number), and perivascular areas (in basal ganglia and centrum semiovale, 0–four aspect rating each and every) according to an established ranking protocol37. brain atrophy become coded using a validated template38, with superficial and deep atrophy coded one at a time.

We mixed the visual lesion rankings into an ordinal ‘complete SVD rating’ of 0–4, described previously39. in brief, a scale aspect turned into awarded for the presence of (early) confluent deep (2–3) WMH and/or irregular periventricular WMH extended into the deep white matter (3); one or more lacunes; one or greater microbleeds; and average to extreme grading (2–4) of basal ganglia perivascular spaces. These confirmed face-validity each as an ordinal ranking and as a latent variable in outdated analyses each in the latest cohorts and in different studies39,40. All score was carried out by means of a expert neuroradiologist educated and skilled in SVD facets and use of the visible ratings. first-class manage of photographs has been described previously17,40.

Quantitative measures of WMH, brain and intracranial quantity were obtained the use of T2*-weighted and aptitude sequences with a validated semi-automatic multispectral photo processing tool, MCMxxxVI41. This device turned into used to measure intracranial volume (ICV, smooth tissue buildings in the cranial cavity together with brain, cerebrospinal fluid, dural and venous sinuses), brain tissue quantity (BTV, intracranial volume except for the ventricular cerebrospinal fluid) and WMH. The constitution volumes have been measured as absolute values in cubic millimetres (BTV mm3, ICV mm3). Quantitative measures of WMH were expressed as percentage of WMH volume in ICV (WMH % ICV) and percentage of WMH volume in BTV (WMH % BTV).

Age and intercourse were included as covariates in each the LBC1936 and MSS samples. Measures of vascular chance have been covered as covariates in both samples. VRFs have been assessed within the LBC1936 topics at age ~73 years, at the same session because the retinal photography, and a median (SD) of 9 (5) weeks ahead of mind imaging; they were assessed on presentation within the MSS, at the same time as brain imaging, and about four weeks ahead of retinal photography. a mixture of medical background variables (medically clinically determined hypertension, diabetes, smoking, and hypercholesterolemia), and measured variables (blood force [BP], haemoglobin A1c, and plasma cholesterol) had been used. The commonplace of three sitting BP measurements have been used to derive mean systolic and imply diastolic BP variables in LBC1936 and one BP analyzing was used for MSS subjects. The above measures had been recorded for MSS subjects aside from haemoglobin A1c. All measures have been carried out blinded to all different facts. Variables had been selected in accordance with a collection of measures of vascular possibility that we had recognized contributed to vascular risk of WMH in outdated LBC1936 and MSS analysis17.

Age- and intercourse-adjusted linear regression was used to analyse the association between the sixteen retinal vascular characteristics and the structural brain imaging-derived measurements in both cohorts. To minimise the expertise for classification I error, p values had been adjusted based on the false discovery fee (FDR) method42. LBC1936 contributors with a background of stroke (n = 84, 14%; in line with scientific background and/or mind imaging appearances) had been eliminated in a sensitivity analysis. due to the small measurement and insufficient stroke classification this community couldn't be divided into stroke subtypes. VRFs have been proven as possible explanatory variables for any tremendous associations between retinal and brain imaging variables, due to the fact each retinal vascular abnormalities and SVD points are typical to be associated with average VRFs reminiscent of hypertension, smoking, diabetes, and so forth.; this was examined the use of SEM in LBC1936, and multivariable regression fashions in the MSS cohort (which we judged to be too small for SEM). See Penke and Deary (2010) for an obtainable description of SEM as utilized in neuroscience43.

The fundamental questions in the analyses have been no matter if retinal vessel measures have been associated with brain imaging measures, and whether these associations have been co-linked to VRFs. The LBC1936 is each giant in dimension and has diverse measures of brain white remember health and VRFs. for this reason, in checking out the questions above, we were capable of form multi-variable ‘latent traits’ (unobservable constructs underlying a combination of correlated individual measured variables) for retinal points, white count health, and vascular chance. outcomes from the regression analyses within the LBC1936 had been used to inspire the hypothesized relationships consequently to be confirmed by using SEM.

We confirmed in the past that VRFs, WMH measures and SVD facets formed latent variables within the LBC193617,40,44. hence, we used the equal dimension models to derive the latent variables. Vascular risk became measured as a single latent element from eight variables; hypertension, diabetes, hypercholesterolemia, smoking, (treated) systolic and diastolic BP, haemoglobin A1c, and plasma ldl cholesterol, as previously17. The extent of WMH as a percentage of ICV, and Fazekas rankings in periventricular and deep white be counted were used to derive a latent variable of ‘WMH load’ as previously44. ‘SVD burden’ changed into measured the use of a single latent ingredient with five indications, particularly, Fazekas scores for both periventricular and deep areas, lacunes, microbleeds, and basal ganglia perivascular areas, as previously40. This become undertaken to verify no matter if together with three additional imaging markers of SVD may boost the potential to find tremendous associations. A single latent ‘calibre-complexity’ element become derived from four retinal indications; two measures of vessel width (CRAE, CRVE), and two measures of branching complexity; arteriolar and venular fractal dimension. The derivation of this latent variable is described wholly in the Supplementary material. All models were estimated the use of R’s lavaan SEM package, edition 0.5–2245.

models had been estimated using the powerful (suggest and variance adjusted) weighted least squares (WLSMV) estimator. WLSMV is strong to non-normality and is applicable for model estimation with specific records. Standardised regression coefficients (parameter weights, comparable to standardised partial beta weights) were computed for each route within the models. model fit was assessed the use of cut-off facets of >0.06 for the foundation suggest rectangular error of approximation (RMSEA), and ≥0.90 for the comparative fit index (CFI) and Tucker-Lewis index (TLI). dimension models for latent features are shown in Supplementary Figs S1–S3.

We confirmed the equal two questions within the MSS as above for the LBC1936. although, as a result of the smaller pattern dimension, latent variables had been no longer shaped in MSS and we did not use SEM to test hypotheses. instead, multivariable regression models have been applied within the MSS to look at various for associations between retinal and mind imaging-derived measurements, and the controls had been applied for age, sex, and VRFs. To in the reduction of the variety of vascular risk parameters and the probability of type I mistakes, essential accessories analysis (PCA) was applied to the eight measured VRF variables in MSS. the first unrotated important part accounted for a considerable percent of the typical variance in VRF variables (26%), with loadings ranging between 0.18 and zero.77, and become used to generate a generic VRF rating. To validate using a foremost component score, component ratings for VRFs were derived the use of the equal PCA components within the LBC1936 pattern. The correlation between the VRF predominant part score from PCA evaluation and the VRF latent trait bought the use of SEM within the LBC1936 changed into very strong (r = 0.89). Multivariable ordinal regression analysis become used for WMH and SVD scores in MSS. results are offered as odds ratio (OR) with ninety five% confidence interval (CI). Predictors had been transformed to z-scores, such that the ensuing ORs mirror the odds of getting bigger pathology rankings for each and every regular unit enhance within the predictor variable. Regression analyses were carried out with SPSS statistics version 22 (IBM Corp., Armonk, big apple).

sure, IBM i shops Have AI alternatives, Too | killexams.com Real Questions and Pass4sure dumps

April 8, 2019 Alex Woodie

organizations of all sizes and shapes are inspired to adopt synthetic intelligence these days. Most of these days’s AI tech, although, become developed to run in open methods and X86 environments. but there are a growing number of AI alternate options from IBM and its partners for customers that wish to keep their statistics resident on the vigor programs platform.

There’s no denying there’s loads of hype around AI today. possible scarcely switch on the television or open a journal or net page with out being inundated with claims of how leading companies are the use of AI to gain a aggressive side, make or save lots of cash, and make clients happier. (AI apparently can’t make us younger or more advantageous-searching yet, but supply it time.)

I’m detecting conflicted emotions. Why are you me like that, pricey IT Jungle reader?”

while some businesses are making headway with AI, the fact is the majority of corporations are nevertheless in the starting phases with AI. The web giants are actually the usage of AI – and establishing and open sourcing lots of the tools to construct AI – however they’re also investing billions of greenbacks to do it. And the entire AI use situations as much as this point are what’s called “slender AI,” now not the “everyday AI” HAL 9000 that doomed Discovery One.

Suffice it to say, you’re no longer too late to the AI party. if you’re a mid-sized business in an established true world enterprise that basically makes, moves, or manages tangible belongings (i.e. you’re now not a digital native relocating bytes for income), there remains time to harness AI to supply your company an talents.

if you’re a digital native, you probably have already implemented AI (and also you wouldn’t be studying this newsletter, anyway). but when you’re an IBM i shop, your AI experience may still probably delivery with IBM.

huge Blue is making an important effort to bolster its line of AI solutions. That includes setting up AI-certain types of the vigor systems server designed to crush computing device getting to know jobs hungry for CPUs and GPUs. big iron, either on-prem or in the cloud, is a requisite for many computer gaining knowledge of workloads.

but an awful lot of the innovation is happening in AI revolves around software, which conjures IBM’s sprawling Watson company. Watson once observed the energy-based mostly supercomputer that beat Ken Jennings at Jeopardy! again in 2011. but today Watson is the umbrella time period for all of IBM’s AI offerings, which contains over one hundred diverse items and capabilities (it truly is, APIs).

The core IDE in the Watson lineup is known as Watson Studio, which became formerly referred to as facts Science event. This product offers a notebook-vogue interface for statistics scientists to write down machine studying code in a lot of languages, including R and Python.

Watson is IBM’s company for all of its AI application products.

IBM’s product for deploying computing device studying into construction is called Watson computing device discovering. IBM presents two types, including WML group edition, a free product that comes loaded with the latest deep studying utility like TensorFlow and Caffe, in addition to IBM’s own SnapML, which is a souped-up edition of the commonplace Scikit be trained product.

IBM additionally sells a more advanced version known as WML Accelerator (WMLA), which turned into formerly known as PowerAI. This providing is designed to handle basically huge computer studying fashions that need to scale throughout a cluster of machines.

while most Watson choices will now run on X86 apart from energy (which IBM announced at its fresh IBM consider 2019 conference), WMLA remains an influence-best affair, because of the quickly NVLink connections that IBM constructed into the Power9 chip and its power AC922 equipment to hyperlink those energy CPUs with Nvidia Tesla GPU accelerators.

IBM has committed to preserving Watson as open as viable. a whole lot of the utility that underpins Watson, including the quick in-reminiscence Apache Spark processing framework, is open source, and it’s IBM’s plan to leverage the open supply neighborhood to retain Watson significant as expertise inevitably improves.

for instance, WMLA may also be used to control fashions developed in other data science environments, together with H2O.ai, Anaconda, and SAS, in response to Sumit Gupta, the vice president of IBM’s AI, machine learning, and HPC efforts. “we can use Watson desktop researching Accelerator to control an Anaconda job,” Gupta noted. “in case you’re the use of SAS or you’re the usage of some other analytics framework, we work with them.”

IBM has encouraged its IBM i purchasers to begin using Watson to process data originating in IBM i Montreal, Quebec-based mostly Fresche options these days launched a collection of lessons to help instruct IBM i developers the way to use the various Watson APIs that are available on the cloud.

however IBM i shops aren’t confined to operating within the cloud. really, lots of these other options can run on vigour, too. H2O.ai and Anaconda each guide vigor with their desktop getting to know automation tools. really, one IBM i store from South the us, vision Banco, lately mentioned its use of H2O.ai with IT Jungle.

in line with vision Banco’s head facts scientist, Ruben Diaz, the Paraguay bank started out the usage of SPSS statistical tools to calculate key variables within the business equation, together with credit score rankings, fraud risk, and odds of defaulting on a personal loan. The business developed the statistical equations in SPSS, after which applied them as kept processes within the DB2 database powering its core IBM i banking applications, Diaz spoke of.

The business multiplied its statistical work several years lower back and adopted other tools like KNIME and R. The business all started using extra advanced fashions, akin to random forests and gradient boosting machines (GBMs), and exported them using predictive mannequin markup language (PMML). it might then call the routines from the core IBM i banking device by way of a leisure-based web provider, Diaz explains.

About three years in the past, the enterprise embarked upon the third era of its records science setup, which covered H2O’s customary suite of machine gaining knowledge of algorithms. Diaz and his colleagues all started the usage of more advanced algorithms, together with XGBoost, neural networks, and superior collections of algorithms referred to as ensembles.

“H2O shocked us for the velocity to educate fashions,” Diaz says. “using R in practising a random woodland it may take hours. but with H2O that takes simply minutes. you could do greater fashions in the generation of the facts science technique.”

these days, the company moved up to DriverlessAI, a new suite of predictive equipment from H2O designed to automate lots greater of the statistics science manner. The business also purchased an IBM AC922 server geared up with the newest Tesla V100 GPU accelerators from Nvidia.

Diaz says he’s capable of crank through greater models in a sooner time with DriverlessAI operating on the quickly IBM vigour hardware. “As a data scientist, it makes my job less complicated, quicker, and improved fine,” he said. “in the facts science procedure, time is cash. in case you can construct a mannequin quicker, that you would be able to do more experiments.”

one of the crucial projects Diaz used DriverlessAI for changed into constructing a propensity to purchase mannequin for bank card offers for americans who name into the name core. “We doubled the response,” Diaz stated. “That was an outstanding outcome.”

sooner or later, Diaz hopes to advance extra statistics science use instances as vision Banco, including device that utilize time-collection datasets to discover cash laundering, and audio and video processing the use of NLP and the latest deep getting to know techniques.

imaginative and prescient Banco is one of the greatest banks in Paraguay, with about 1,800 employees and 800,000 customers. in the united states, it would be considered an exceptional medium-sized enterprise. With a team of simply seven facts scientists and analysts – not to point out the gunship of an AI server, the energy AC922 – Diaz is capable of make the most of statistics to make enhanced predictions about his enterprise, with a roadmap to enforcing probably the most most advanced neural networking ideas.

evidently, we’re at the start of a new era in computing, one pushed by statistical possibilities. If a solidly midsize IBM i store like vision Banco can put into effect these things, what’s protecting you back?

Taking A Fresche approach To IBM i-Watson education

Watson within the actual World

IBM i, Watson & Bluemix: The leisure Of The Story

Watson Apps able to change the area

update 5-IBM to buy analytics enterprise SPSS for $1.2 bln | killexams.com Real Questions and Pass4sure dumps

* IBM to pay $50 a share, a top class of about forty two %

* SPSS shares upward thrust pretty much forty one pct

* IBM exec sees double-digit increase in analytics business (adds interview with IBM executive, updates share circulation)

with the aid of Franklin Paul and Ritsuko Ando

big apple, July 28 (Reuters) - IBM (IBM.N) plans to purchase enterprise analytics business SPSS Inc SPSS.O for $1.2 billion in cash to enhanced compete with Oracle Corp ORCL.O and SAP AG (SAPG.DE) in the transforming into container of enterprise intelligence.

Shareholders of SPSS, which offers utility and services to support businesses analyze and forecast trends in customer behavior, would obtain $50 a share, a 42 % top class to Monday’s closing price.

The proposed acquisition, introduced on Tuesday, follows a spate of offers in fresh years within the company intelligence sector, comparable to Oracle’s purchase of Hyperion, SAP’s acquisition of enterprise Objects and foreign enterprise Machines Corp’s own deal for Cognos.

different names in the area consist of MicroStrategy Inc (MSTR.O), Actuate Corp ACTU.O and Datawatch Corp DWCH.O.

“We’re in a duration the place consolidation looks to be a rule of the online game,” said Charles King, an analyst with Pund-IT analysis. “SPSS changed into probably excellent by itself as an independent enterprise, but IBM provides the distribution and balance, the economics and expertise basis.”

Shares of Chicago-based mostly SPSS, a pioneer in enterprise intelligence, jumped $14.36 or forty.ninety two percent to $49.forty five. The inventory had already risen 30 % this yr.

usaanalyst Maynard Um estimates that SPSS could add three cents a share to IBM’s 2010 salary, however talked about the choicest profit may also lie in deeper penetration into the analytics market.

“We consider specific benefits may also prove superior because the deal adds to IBM’s enterprise and predictive analytics portfolio, which could be a necessary part of IBM’s smarter enterprise techniques and which the business has identified as a big boom chance over the following few years,” he pointed out.

DOUBLE-DIGIT boom

A senior IBM govt noted he expects double-digit boom in its analytics company regardless of a weak economy that has forced many corporations to reduce returned on spending.

“We’re driving a plan for double-digit growth,” Steve Mills, senior vp and group govt of IBM’s utility community, advised Reuters in an interview. “There is not any lack of client activity.”

IBM mentioned the deal would support expand its application portfolio and company analytics capabilities. Predictive analytics, mixed with IBM’s latest software and consulting potential, can aid in fighting fraud or predicting the dangers or patterns of a virulent disease, it referred to.

IBM has been shifting its focus from hardware to extra ecocnomic utility and capabilities during the last decade, and Mills mentioned the analytics enterprise yields better profit margins than the regular IBM product or provider.

at present, credit Suisse uses SPSS software to research advice about its purchasers, then offers results in its revenue drive. Police use these techniques to mine data from incident reviews to foretell patterns of crook conduct.

“The ambiance nowadays is focused on experience and reply: what’s occurring and what we should still do about it,” spoke of Ambuj Goyal, IBM’s well-known supervisor of assistance administration software. The acquisition of SPSS, he said, would help it flow to “predict and act.”

IBM already sells SPSS application via a earnings partnership. An acquisition, Goyal noted, would assist IBM combine SPSS application during its choices and making it less complicated for his or her mutual customers to use.

IBM has spent $20 billion buying greater than a hundred corporations seeing that 2000, paying prices that latitude from as little as $50 million to as plenty as $5 billion.

The deal values SPSS at about 25 instances analysts’ estimated 2010 profits per share, and the $50-per-share expense tops the all-time excessive for the inventory of $forty seven.87.

“I suppose they paid lots for it nevertheless it’s now not unreasonable,” referred to ordinary & negative’s expertise analyst Tom Smith. “The predictable analytics area is a extremely scorching area, and that i would consider that corporations in that would exchange at a top rate to agencies in different areas of know-how.”

The deal, which contains a fee of $23.5 million that SPSS would have to pay should the merger fall via, is anticipated to shut later in the 2d half of 2009, the companies referred to.

separately, IBM spoke of it has acquired closely held Ounce Labs Inc, whose utility helps organizations in the reduction of the dangers and charges linked to security and compliance concerns. fiscal terms had been now not disclosed.

IBM shares fell 35 cents, or 0.3 p.c, to $117.28 on the big apple stock alternate. (further reporting by Jim Finkle in Boston; modifying through Derek Caney, Gerald E. McCormick and Richard Chang)

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IBMSPSSSTATL1P - IBM SPSS Statistics Level 1

Salary for Certification: IBM Certified Specialist SPSS Statistics Level 1 v2 | killexams.com real questions and Pass4sure dumps

IBM Improves IT Operations with Artificial Intelligence | killexams.com real questions and Pass4sure dumps

Many IT departments have implemented software solutions that go beyond simple transaction and analytical processing. These packages contain models that describe certain data behaviors, and these models consume current data to see if these patterns of data behavior exist. If so, operational systems can use this information to make decisions. A good example of this is fraud detection. IT data engineers use analytics on historical data to determine when fraud occurred, code this into a model, and deploy the model as a service. Then, any operational system can invoke the model, pass it current data and receive a model “score” that represents the probability that a transaction may be fraudulent.

The general term for these new packages is artificial intelligence (AI). They consist of a combination of search, optimization and analytics algorithms, statistical analysis techniques and template processes for ingesting data, executing these techniques and making the results available as services called models. The subset of AI that deals with model creation and implementation is sometimes called machine learning (ML).

IT departments implement ML and AI solutions in the broader context of their data and processing footprint. This is usually depicted as the following four-layer hierarchy.

Layer 1: The Data.

This layer contains the data distributed across the enterprise. It includes mainframe and distributed data such as product and sales databases, transactional data and analytical data in the data warehouse and any big data applications. It also may include customer, vendor and supplier data, perhaps at remote sites, and even extends to public data such as twitter, news feeds and survey results. Another possible source of data is server performance logs that include resource usage history.

Note that these data exist across diverse hardware platforms including on-premises and cloud-based. As such, various data elements can exist in multiple forms and formats (e.g. text, ASCII, EBCDIC, UTF-8, XML, images, audio clips, etc.). In addition, at this level will exist hardware and software that manage the data, including high-speed data loaders, data purge and archive processes, publish-and-subscribe processes for data replication, as well as those for standard backup and recovery and disaster recovery planning.

Layer 2: The Analytics Engines.

In this layer exist a mixture of hardware and software that executes business analytics against the data layer. There are several common players in this space. They include:

Just as the data layer occurs across multiple hardware platforms and distributed sites, so will the analytics engines layer. The major function of this layer is to provide an optimized data access layer against the underlying data as a service for AI and operational applications.

Layer 3: The Machine Learning Platform.

IT implements machine learning software in this layer. It accesses the data through one or more of the analytics engines. It is in this layer that IBM delivers its latest offering, Watson Machine Learning for z/OS (WMLz). WMLz provides a basic machine learning workflow consisting of the following steps:

Data scientists know that one of the greatest benefits of machine learning is to use the results in operational systems; for example, having an ML model analyze financial data to determine the possibility of fraud. This means that you will achieve best performance when you deploy ML in the hardware environment where transaction processing occurs. For many large organizations this means the IBM zServer environment.

Layer 4: Machine Learning Solutions.

Now that we have the machine learning platform available as ML services, we can create combined AI/ML solutions that invoke those services. IBM has several ready-made solutions for this layer, including the following:

Let’s take a deeper dive into how Watson Machine Learning on Z (WMLz) works and what services it can provide.

Key Performance Indicators (KPIs). WMLz does not inherently know what performance factors are important to you. However, once these KPIs are defined (either by a user or by implementing one of the machine learning solutions noted above), WMLz can analyze KPI data to look for correlations. For example, when one KPI (say, I/O against a critical database) goes up, another KPI (say CPU usage) may go up as well. As another example, several KPIs may be behaviorally similar, so WMLz can cluster them as a group and perform further analysis across groups. WMLz can also determine KPI baseline behaviors based on time-of-day, time zone of transactions or seasonal activity.

Anomaly Detection. Once correlations are discovered, WMLz can look opposite effects and report them as anomalies. In our I/O example above, an anomaly would be reported if I/O against a critical database increased but CPU usage decreased.

Pattern Recognition. As with many machine learning engines, WMLz will look for patterns among KPIs and data identifiers. For example, CPU may increase when processing certain categories of transactions.

KPI prediction. An extension of basic KPI processing, WMLz can use the past behaviors of groups of KPIs to predict the future. Consider our I/O example once again. The product may detect that certain transactions become more numerous during a particular time period, and these transactions consume significantly more CPU cycles. The product may then predict future CPU spikes.

Batch workload analysis. Many IT shops have a large contingent of batch processing that is tightly scheduled and includes job and resource dependencies. Some jobs must wait for their predecessors to complete, some use significant shared resources (such as tape drives or specialty hardware) and some are so resource-intensive that then cannot be executed at the same time. WMLz can analyze the workload data, including resource usage, and provide recommendations for balancing resources or tuning elapsed times.

MLC cost pattern analysis and cost reduction. Some IBM software license charges are billed monthly, and the license amount may depend upon maximum CPU usage during peak periods. WMLz can analyze CPU usage across time, look for patterns and make predictions and recommendations for software license cost reduction.

IBM’s Watson Machine Learning for z/OS allows IT its choice of development environments to develop models including IBM SPSS Modeler. These environments assist data scientists by using notebooks, data visualization tools and wizards to speed the development process. Several quick-start application templates are also incorporated in the toolset for common business requirements such as fraud detection, load approval and IT operational analytics. The latest version of WMLz (version 2.1.0) includes support for Ubuntu Linux on Z, java APIs, simplified Python package management and several other features.

Interested readers should reference the links below for more detailed technical information.

# # #

See all articles by Lockwood Lyon

Machine Learning and Artificial Intelligencehttps://en.wikipedia.org/wiki/Machine_learning

Data and AI on IBM Zhttps://www.ibm.com/analytics/z-analytics

Using Anaconda with Spark — Anaconda 2.0 documentationhttps://docs.anaconda.com/anaconda-scale/spark/

Watson Machine Learning - Overviewhttps://www.ibm.com/cloud/machine-learning

Watson Machine Learning - Resourceshttps://www.ibm.com/cloud/machine-learning/resources

Effects of animal-assisted therapy on social behaviour in patients with acquired brain injury: a randomised controlled trial | killexams.com real questions and Pass4sure dumps

Adult (≥18 years) inpatients in stationary neurorehabilitation with an acquired brain injury from either traumatic (TBI) or nontraumatic cause (non-TBI) were invited to participate in the study. For inclusion in the study, patients had to meet the following criteria: (a) be medically stable, (b) be able to walk or to be transported to the therapy-animal facility, (c) be able to interact with an animal autonomously, (d) have no medical contraindications (e.g. phobias or allergies), and (e) exhibit no aggressive behaviour towards the animals. The head physician proposed inpatients for the study and the patients were then screened for inclusion criteria. All the experiments were performed in accordance with relevant guidelines and regulations. The human-related protocols were approved by the Human Ethics Committee for Northwest and Central Switzerland (EKNZ), and all patients or their legal guardians provided written informed consent. The animal-related protocols were approved by the Veterinary Office of the Canton Basel-Stadt, Switzerland. AAT was performed according to the IAHAIO-guidelines30. No therapy session had to be ended early, and no adverse incidents occurred. The patients were offered the possibility to continue with AAT after the end of the study. The study was registered at ClinicalTrials.gov (Identifier: NCT02599766, date 09/11/2015).

The study had a randomised controlled, within-subject design with repeated measurement and was conducted at a clinic for neurorehabilitation and paraplegiology in Switzerland (REHAB Basel). Patients were randomly assigned by the principal investigator, using random numbers generated with Microsoft Excel, to either start with an AAT session or a conventional therapy session (control). Patients and therapists were not blinded because animals were either present or not. Coders were not blinded because the animals were visible in the videos.

The study program included two experimental and two control therapy sessions per week over a six-week period, with a total of 24 therapy sessions (N experimental = 12, N control = 12) per patient. Due to illness of patients or therapists, some sessions had to be cancelled and some data were lost due to technical problems. This resulted in a total of 441 analysed therapy sessions within this study consisting of 222 AAT and 219 conventional therapy sessions. The experimental condition consisted of speech, occupational, or physiotherapy sessions including an animal (referred to as AAT). The control condition consisted of conventional speech, occupational, or physiotherapy sessions (treatment as usual).

First, therapists and patients chose a suitable animal for the AAT sessions. The animals involved in the project were horses, donkeys, sheep, goats, miniature pigs, cats, chickens, rabbits and guinea pigs. All animals were housed in the therapy-animal facility at REHAB Basel, had experience in working with brain-injured patients and were kept and handled according to the IAHAIO-standards30.

Every session lasted approximately 30 minutes. After each therapy session, the patients and therapists filled out the questionnaires. AAT- and conventional therapy sessions were conducted concurrently and pairwise with similar comparable therapeutic activities. This was planned such that the conditions alternated and the matching sessions respectively took place within two successive weeks to control for improvements over time. Matched AAT and conventional therapy sessions were conducted by the same therapist and controlled for time of day and day of the week. The AAT sessions were held at the therapy-animal facility at REHAB Basel in the presence of an AAT specialist who assisted the therapist.

Although therapy sessions were matched within one patient for activities, goals and setting, there was a great amount of variability between patients depending on the involved animal. However, in the animal-assisted therapy sessions, the procedure always followed a scheme: First, the patient and the therapist greeted the animal and then the therapist explained the therapeutic activity that had to be related with the presence of the animal. Examples for therapeutic activities were as reported in a previous paper31: Cutting vegetables and feeding it to the present guinea pigs (AAT session) versus cutting vegetables to make a salad (conventional occupational therapy/physiotherapy/speech therapy); building a course and walking through it with, for example, a minipig (AAT), versus building a course and walking through managing a ball (conventional occupational therapy/physiotherapy); cleaning the rabbit’s cage in the presence of the animal (AAT) versus cleaning furniture (conventional occupational therapy/physiotherapy/speech therapy); walking with a sheep and the therapist (AAT) versus walking with the therapist (conventional physiotherapy); reading questions about the involved, present animal and filling in the answers (AAT) versus reading questions about an animal in general and filling in the answers (conventional speech therapy). In the previous paper, we also presented the number of sessions held with the different species involved in the study31.

All therapy sessions (N = 429) were videotaped with a handheld camera (Sony HDR-CX240). The videos were analysed with a behavioural coding system software (Observer XT 12, Noldus). Analyses were done continuously, defining each second of the video with the different variables as present or not for state behaviour variables. The percentage of the duration of each state variable in relation to the observed time period of a therapy session was calculated. Count variables were coded only if they occurred, and the total occurrence within a therapy session was calculated. All videos were coded according to a strict ethogram defined by detailed descriptions of the behaviours with inclusion and exclusion examples. The coding scheme was developed for the purpose of this study, based on previously published behaviour coding systems for studies on AAT in patients with dementia or autism spectrum disorder15,32. We modified our system only slightly according to the purpose of the present study population and the study aims to ensure comparability. Our coding scheme includes the dimensions “social behaviour” (Supplementary Table S1), “emotion” (Supplementary Table S2), “attention”, and animal presence (Supplementary Table S3). The results for the dimension “attention” were previously published31. Inter-rater reliability was measured by Cohen’s kappa. Before coding the actual data, each rater had to achieve an inter-rater reliability of k > 0.80. During the actual coding process, two follow-up assessments of agreement were conducted. No renewed training was necessary. Inter-rater reliability ranged between 0.81 and 0.95, which indicated excellent agreement among coders.

The primary outcome was patient total social behaviour, measured as the observed relative duration of verbal and non-verbal social communication and interaction of the patients via behaviour analysis (Supplementary Table 1). Verbal communication was defined as state behaviour and coded as active, reactive or undefined. Active verbal communication was initiated by the patient and was addressed to either the therapist or the animal, while reactive verbal communication was defined as direct answer to a question or as verbal reaction to a cue from the therapist. Non-verbal social communication and interaction was defined as state behaviour and included gaze (eye contact), body movement towards an interaction partner, and active physical contact. All variables could be coded in parallel and were defined as either towards animal or towards therapist. The patient’s displayed emotions were defined as state variable and comprised the mutually exclusive variables positive emotion, negative emotion, and neutral state. All behavioural categories or subcategories represent the percentage of the total duration of the respective behaviour in one therapy session.

The subcategories of measured social behaviour as well as mood, treatment motivation and satisfaction were defined as secondary outcomes. The multidimensional mood questionnaire (MDBF)33 was used to gather information about the patient’s mood during therapy sessions. Patients filled out the MDBF at the end of each session. We analysed the bipolar mood dimension (good-bad) ranging from 4 (not at all good mood) to 20 (very good mood). The patient’s treatment motivation was assessed by self-report and by the therapist using a visual analogue scale (VAS) where a cross could be made on a line ranging from 0 mm (unmotivated) to 160 mm (motivated). Satisfaction during the therapy sessions was assessed by the patients themselves and by the therapists using a VAS ranging from 0 mm (unsatisfied) to 160 mm (satisfied).

We estimated mean and standard deviation of the primary outcome on the basis of published literature regarding percentage of speaking time (M = 65%, SD = 20%-points)34 and defined an intervention effect between 5% and 10% as practically relevant. The simulation revealed a total of 19 participants (observed at 24 time points) to detect an average effect of 7.5% with a power of 80% at a significance level of 95%. We increased the final sample size to 22 to account for possible dropouts.

We used linear mixed models (LMM) to examine the effects of AAT sessions on the duration of displayed behaviours in patients with acquired brain injury, as compared to conventional therapy sessions. These account for the hierarchical structure of the data, i.e. 24 repeated measurements per patient. The model included the variable “condition” (AAT versus conventional therapy sessions) as fixed factor and a random intercept for “subject”. As effect size we used the coefficient (b) estimating the difference in percentages. Coefficients together with the 95% confidence intervals, p-values and F statistics were summarized in Table 1.

For all behaviours, the denominator “therapy on-going” was used. This ensured that the reference time (100%) only counted when the therapy was in process. The cumulative variables for “total social behaviour” and “non-verbal communication” were adjusted for possible parallel behaviour and behaviour that could only occur in the presence of an animal so that they could maximally add up to 100% during a therapy session. The intraclass correlation coefficient (ICC) was used to quantify between-patient effects. In a second step, we investigated time effects to account for possible improvement of the outcomes over time. For that, we additionally included time (time point 1–24) as fixed factor in the model. If time had a significant effect, we looked at time effects for both conditions separately and included both “AAT over time” and “control over time” as fixed effects in the model. Analysis of the questionnaires were also analysed via LMM with the same specifications as for the first model. We did not adjust for multiple comparisons regarding secondary outcomes since we had an exploratory aim with these.

All variables were visually checked for normality (histogram and Q-Q-plot). Model diagnostics of LMM included visual checks for normality and homogeneity of residuals. All data were approximately normally distributed. No data were excluded. Statistical analyses were performed with SPSS, Version 23 (IBM SPSS® Statistics) and the significance level was set at p ≤ 0.05.

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