FLA-SLA aware cloud collation formation using fuzzy preference relationship multi-decision approach for federated cloud

Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPI’s (Key Performance Indicator’s) of the individual cloud providers. This paper proposes an FLA-SLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability

Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT

In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%)

Foundations to frontiers of big data analytics

In recent times, big data analytics has become a major trend in catering data queries that has been growing dramatically. The present paper gives a brief description of latest happenings of Big Data analytics. A case study using Spark is given as an example. The paper gives the importance of cloud computing in Big data paradigm

A Potential Novel Mechanism for Vagus Nerve Stimulator-Related Central Sleep Apnea

The treatment of epilepsy with vagus nerve stimulation can inadvertently cause obstructive and central sleep apnea (CSA). The mechanism for CSA seen in patients with a vagus nerve stimulator (VNS) is not fully known. We describe the case of a 13-year-old girl in whom VNS activation induced tachypnea and post-hyperventilation central apnea. Following adjustment of VNS settings, the post-hyperventilation CSA resolved. Polysomnography may assist with management when patients with epilepsy develop sleep disruption after VNS placement

Psychological, physical, and sleep comorbidities and functional impairment in irritable bowel syndrome: Results from a national survey of U.S. adults.

Background/aimsPatients with irritable bowel syndrome (IBS) in referral practice commonly report mental disorders and functional impairment. Our aim was to determine the prevalence of mental, physical and sleep-related comorbidities in a nationally representative sample of IBS patients and their impact on functional impairment.MethodsIBS was defined by modified Rome Criteria based on responses to the chronic conditions section of the National Comorbidity Survey-Replication. Associations between IBS and mental, physical and sleep disorders and 30-day functional impairment were examined using logistic regression models.ResultsOf 5,650 eligible responders, 186 met criteria for IBS {weighted prevalence 2.5% (SE = 0.3)}. Age >60 years was associated with decreased odds (OR = 0.3; 95% CI:.1-.6); low family income (OR = 2.4; 95% CI:1.2-4.9) and unemployed status (OR = 2.3; 95% CI:1.2-4.2) were associated with increased odds of IBS. IBS was significantly associated with anxiety, behavior, mood disorders (ORs 1.8-2.4), but not eating or substance use disorders. Among physical conditions, IBS was associated with increased odds of headache, chronic pain, diabetes mellitus and both insomnia and hypersomnolence related symptoms (ORs 1.9-4.0). While the association between IBS and patients' role impairment persisted after adjusting for mental disorders (OR = 2.4, 95% CI 1.5-3.7), associations with impairment in self-care, cognition, and social interaction in unadjusted models (ORs 2.5-4.2) were no longer significant after adjustment for mental disorders.ConclusionIBS is associated with socioeconomic disadvantage, comorbidity with mood, anxiety and sleep disorders, and role impairment. Other aspects of functional impairment appear to be moderated by presence of comorbid mental disorders

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making