A large number of time series forecasting models including traditional statistical models, machine learning and more recently deep have been proposed in the literature. However, choosing right model along with good parameter values that performs well on a given data is still challenging. Automatically providing set to users for dataset saves both effort from using trial-and-error approaches wide variety available optimization. We present AutoAI Time Series Forecasting (AutoAI-TS) provides...

Social networks consist of various communities that host members sharing common characteristics. Often some one community are also other communities. Such shared membership different leads to overlapping Detecting such is a challenging and computationally intensive problem. In this paper, we investigate the usability high performance computing in area social detection. We present highly scalable variants detection algorithm called Speaker-listener Label Propagation Algorithm (SLPA). show...

Performance monitoring of cloud-native applications that consist several micro-services involves the analysis time series data collected from infrastructure, platform, and application layers cloud software stack. The runtime dependencies amongst component microservices is an essential step towards performing resource management, detecting anomalous behavior applications, meeting customer Service Level Agreements (SLAs). Finding such challenging due to non-linear nature interactions, aberrant...

The increasing interest of researchers in service oriented architecture (SOA) for wireless sensor networks (WSNs) is opening new unexplored venues the field WSNs. In systems, services are configured and composed various other thus perform complex tasks. such composite services, geospatial locations their coverage vital importance as they signify relevance to area user. this paper, we present a service-oriented system WSNs that capable performing configuration under relevancy constraints. We...

Apache Spark enables fast computations and greatly accelerates analytics applications by efficiently utilizing the main memory caching data for later use. At its core uses structures called RDDs (Resilient Distributed Datasets) to give a unified view distributed data. However, represented in remain unencrypted which can result leakage of confidential produced or processed applications. persists (unencrypted) disk storage under various circumstances including but not limited caching, RDD...

Change detection in system behavior and its root cause is essential for many large-scale systems such as, manufacturing plants, order to keep running uninterrupted avoid costly machine breakdown via predictive maintenance. In this paper, we present a novel graph based technique that uses time variant interdependencies lagged dependencies among different components of detect changes the behavior. We further find causes these detected by pointing out component historical values are responsible...

Discovering temporal lagged and inter-dependencies in multivariate time series data is an important task. However, many real-world applications with big data, such as commercial cloud management or predictive maintenance manufacturing, dependencies can be time-variant non-linear, which makes it more challenging to extract through traditional methods like Granger causality statistical models. In this work, we present a novel deep learning model that uses multiple layers of adapted gated...

Sensor applications are typically composed of a number functional components that run distributedly on the nodes sensor network, communicating and interacting with one another. Service composition is emerging as viable approach towards automatic synthesis such applications. However, for service to be practical, it has comply policies define security management constraints use these interconnections amongst them. Prior research efforts have primarily focused efficient evaluation during...

Discovering temporal lagged and inter-dependencies in multivariate time series data is an important task. However, many real-world applications, such as commercial cloud management, manufacturing predictive maintenance, portfolios performance analysis, dependencies can be non-linear time-variant, which makes it more challenging to extract through traditional methods Granger causality or clustering. In this work, we present a novel deep learning model that uses multiple layers of customized...

A large number of time series forecasting models including traditional statistical models, machine learning and more recently deep have been proposed in the literature. However, choosing right model along with good parameter values that performs well on a given data is still challenging. Automatically providing set to users for dataset saves both effort from using trial-and-error approaches wide variety available optimization. We present AutoAI Time Series Forecasting (AutoAI-TS) provides...

This paper examines the possible uses of different market mechanisms for resource allocation at levels Wireless Sensor Network (WSN) architecture. The goal is to maximize Value Information (VoI) WSN users. We discuss three lowest level focuses on individual nodes and their basic functions sensing routing. give an example showing how use auctions can help efficiently perform these functions. middle services that are abstractions applications running sensors. Complex composed automatically...

Time series value forecasting using machine learning models utilizing time features has recently got good attention of analytics community. This paper proposes an automated feature mechanisms to filter out most useful from hundreds available for prediction problems. The further a novel mechanism dynamically that are suitable the given input data. With such we create pipeline consisting data and increases performance model. Our proposed first, groups well known analysis, generates assigns...

Background: Nowadays, the medical environment has become more competitive in terms of fulfilling various desires client and to deliver improved service. Repetitive continued stress may lead anger. Anger is defined as a natural emotion that triggers self-protection mechanism oneself stressful condition. Extreme or if duration intensity anger are uncontrollable then it could have negative impact on physical health which can further cause difficulties such inadequate life, interpersonal issues...

We present a dynamic price based routing protocol in which packets from different applications dynamically choose their paths by evaluating the to be paid for taking each path and ability pay. propose mechanism prices reflect congestion on routers thus waiting time packet pass through router. These increase as usage of usually preferred shorter routes increases. The packet's pay router is defined product application's priority delay experienced at As result, low intelligently avoid with high...

Service-oriented Architectures (SOA) for Wireless Sensor Networks (WSNs) are an active research topic. Yet, autonomous configuration of services real life constraints (spatio-temporal, input/output interoperability, policies, security etc.) is still a challenging problem. In this demonstration we describe the results our into automated and intelligent composition complex tasks. We present service-oriented system capable performing service under spatial relevancy constraints. It can configure...

Background: Cardiovascular diseases, including myocardial infarction (MI), represent a significant global health burden, particularly in developing countries like Pakistan. Health-promoting behaviors (HBs) play crucial role the management and prevention of MI, potentially reducing morbidity mortality rates among affected individuals. Objective: This study aimed to identify factors influencing health-promoting patients diagnosed with evaluate level engagement such behaviors. Methods: A...

Time series forecasting presents a significant challenge, particularly when its accuracy relies on external data sources rather than solely historical values. This issue is prevalent in the financial sector, where future behavior of time often intricately linked to information derived from various textual reports and multitude economic indicators. In practice, key challenge lies constructing reliable model capable harnessing diverse extracting valuable insights predict target accurately....

Objective: To investigate the oral health behavior and practice among nursing students of Hyderabad. Methods: This cross-sectional study was conducted 390 from three different institutes Hyderabad November to December 2018. Nursing with age 16 years above no restriction gender were selected through non-probability purposive sampling technique. Data collected questionnaire designed on basis Hiroshima University Dental Behavioral Inventory (HU-DBI). Results: The mean participants 26.57±4.67...

Grid based systems require a database access mechanism that can provide seamless homogeneous to the requested data through virtual system, i.e. system which take care of tracking is stored in geographically distributed heterogeneous databases. This should an integrated view different repositories by using mechanism, hide heterogeneity backend databases from client applications. paper focuses on accessing disparate relational web service interface, and exploits features Data Warehouse Marts....

Service Oriented Sensor Networks consist of various assets and host variety services, some which are composed other services. Policies widely used for regulating access to services specially when these owned by different parties in a coalition environment. In this paper, we present novel mechanism policy implementation provide or restrict resources using policies. We "Restriction Set Theoretic Expressions (RSTE)"to represent policies the form sets at system level, therefore RSTE is...