scholarly journals A SURVEY ON MACHINE LEARNING APPROACH TO MAINFRAME ANALYSIS

2017 ◽  
Vol 10 (13) ◽  
pp. 36
Author(s):  
Priyanka P ◽  
Deivanai K
Keyword(s):  
Machine Learning ◽  
Job Scheduling ◽  
Daily Basis ◽  
Automated System ◽  
Workload Management ◽  
The Core ◽  
Machine Learning Approach ◽  
End Times ◽  
System Variables ◽  
Core Part

Mainframe system processing includes a “Batch Cycle” that approximately spans in regular interval on a daily basis. The core part of the cycle completes in the middle of the regular interval with key client deliverables associated with the end times of certain jobs are tracked by service delivery. There are single and multi-client batch streams, a QA stream which includes all clients, and about huge batch jobs per day that execute. Despite a sophisticated job scheduling software and automated system workload management, operator intervention is required. The outcome of our proposed work is to bring out the high priority job first. According to our method, the jobs are re-prioritized the schedules so that prioritized jobs can get theavailable system resources. Furthermore, the characterization, analysis, and visualization of the reasons for a manual change in the schedule are to be considered. This work requires extensive data preprocessing and building machine learning models for the causal relationship between various system variables and the time of manual changes. 

Rethinking Software Protection

2021 ◽  
pp. 341-362
Author(s):  
Peter R Slowinski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Evolutionary Algorithms ◽  
Future Development ◽  
Computer Programs ◽  
Software Protection ◽  
Computing Power ◽  
The Core ◽  
Quantum Leap ◽  
Core Part

The core of artificial intelligence (AI) applications is software of one sort or another. But while available data and computing power are important for the recent quantum leap in AI, there would not be any AI without computer programs or software. Therefore, the rise in importance of AI forces us to take—once again—a closer look at software protection through intellectual property (IP) rights, but it also offers us a chance to rethink this protection, and while perhaps not undoing the mistakes of the past, at least to adapt the protection so as not to increase the dysfunctionality that we have come to see in this area of law in recent decades. To be able to establish the best possible way to protect—or not to protect—the software in AI applications, this chapter starts with a short technical description of what AI is, with readers referred to other chapters in this book for a deeper analysis. It continues by identifying those parts of AI applications that constitute software to which legal software protection regimes may be applicable, before outlining those protection regimes, namely copyright and patents. The core part of the chapter analyses potential issues regarding software protection with respect to AI using specific examples from the fields of evolutionary algorithms and of machine learning. Finally, the chapter draws some conclusions regarding the future development of IP regimes with respect to AI.

scholarly journals A Machine Learning Approach for the Diagnosis of Diabetes : A Review

2020 ◽  
pp. 509-516
Author(s):  
Pravin S. Rahate ◽  
Nikhat Raza
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Selection Process ◽  
High Sensitivity ◽  
Healthcare Services ◽  
Automated System ◽  
Retinal Images ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Patients With Diabetes

Diabetes mellitus (DM) is a chronic disease that affects 382 million patients’ worldwide (2013 data) and is predicted to increase to as many as 592 million adults by 2035. DM is one of the major causes of blindness in young adults around the world. The most serious ocular complication of DM is diabetic retinopathy (DR).Diabetic retinopathy is the most common microvascular complication in diabetes1, for the screening of which the retinal imaging is the most widely used method due to its high sensitivity in detecting retinopathy. Prompt diagnosis is important through efficient screening. The evaluation of the severity and degree of retinopathy associated with a person having diabetes is currently performed by medical experts based on the fundus or retinal images of the patient’s eyes As the number of patients with diabetes is rapidly increasing, the number of retinal images produced by the screening programmes will also increase, which in turn introduces a large labor-intensive burden on the medical experts as well as cost to the healthcare services. Manual grading of these images to determine the severity of DR is rather slow and resource demanding. This could be alleviated with an automated system either as support for medical experts’ work or as full diagnosis tool. This labor-intensive task could greatly benefit from automatic detection using machine learning technique. Early detection and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. The objective of this paper is to explore the work happening on the detection, progression and feature selection process for the prediction of DR and to establish the extent and depth of existing knowledge on RD prediction process.

scholarly journals A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 267-283
Author(s):  
Alireza Rezazadeh
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Optimal Decision ◽  
Computing Platform ◽  
Machine Learning Approach ◽  
Successful Business ◽  
Decision Boundaries ◽  
Sales Decision ◽  
Business To Business ◽  
Core Part

Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, undertaking this prediction has relied mostly on subjective human evaluations in the process of sales decision-making. In this paper, we addressed the problem of forecasting the outcome of Business to Business (B2B) sales by proposing a thorough data-driven Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to use the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.

scholarly journals PhGC: A Machine Learning Based Workflow for Phenotype-Genotype Co-analysis on Autism

10.29007/ctfl ◽  
2020 ◽  
Author(s):  
Safa Shubbar ◽  
Chen Fu ◽  
Zhi Liu ◽  
Anthony Wynshaw-Boris ◽  
Qiang Guan
Keyword(s):  
Machine Learning ◽  
Clustering Algorithms ◽  
Autism Spectrum ◽  
Diagnostic Tools ◽  
Learning Approach ◽  
The Core ◽  
Core Member ◽  
Machine Learning Approach ◽  
Genetic Mechanisms ◽  
The Relationship

Autism spectrum disorder (ASD) is a heterogeneous disorder, diagnostic tools attempt to identify homogeneous subtypes within ASD. Previous studies found many behavioral/- physiological commodities for ASD, but the clear association between commodities and underlying genetic mechanisms remains unknown. In this paper, we want to leverage ma- chine learning to figure out the relationship between genotype and phenotype in ASD. To this purpose, we propose PhGC pipeline to leverage machine learning approach to to identify behavioral phenotypes of ASD based on their corresponding genomics data. We utilize unsupervised clustering algorithms to extract the core members of each clusters and profile the core member subsets to explore the characteristics using genotype data from the same dataset. Our genome annotation results showed that most of the alleles with different frequency among clusters were represented by the core members.

scholarly journals Detection of Phishing Websites Using Ensemble Machine Learning Approach

2021 ◽  
Vol 40 ◽  
pp. 03012
Author(s):  
Dharani M. ◽  
Soumya Badkul ◽  
Kimaya Gharat ◽  
Amarsinh Vidhate ◽  
Dhanashri Bhosale
Keyword(s):  
Machine Learning ◽  
Accurate Method ◽  
Automated System ◽  
Gradient Boosting ◽  
Sensitive Data ◽  
Ensemble Machine Learning ◽  
Extreme Gradient Boosting ◽  
Machine Learning Approach ◽  
Online Trading ◽  
Trading Activities

In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

2021 ◽  
Vol 35 (7) ◽  
pp. 1007-1015
Author(s):  
Arezoo Movaghar ◽  
David Page ◽  
Krishanu Saha ◽  
Moira Rynn ◽  
Jan Greenberg
Keyword(s):  
Machine Learning ◽  
Expressed Emotion ◽  
Learning Approach ◽  
The Core ◽  
Machine Learning Approach
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