Machine Learning as a Service for Software Application Categorization

2021 ◽  
pp. 191-204
Author(s):  
Cagatay Catal ◽  
Besme Elnaccar ◽  
Ozge Colakoglu ◽  
Bedir Tekinerdogan
Keyword(s):  
Machine Learning ◽  
Software Application

scholarly journals Enabling Technology for Remote Prosthetic Alignment Tuning

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 659-664
Author(s):  
David A Boone ◽  
Sarah R Chang
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Coordinate Measuring Machine ◽  
Software Application ◽  
Prosthetic Socket ◽  
Prosthetic Alignment ◽  
Prosthesis Alignment ◽  
Alignment Tool ◽  
Measuring Machine

ABSTRACT Introduction This research has resulted in a system of sensors and software for effectively adjusting prosthetic alignment with digital numeric control. We called this suite of technologies the Prosthesis Smart Alignment Tool (ProSAT) system. Materials and Methods The ProSAT system has three components: a prosthesis-embedded sensor, an alignment tool, and an Internet-connected alignment expert system application that utilizes machine learning to analyze prosthetic alignment. All components communicate via Bluetooth. Together, they provide for numerically controlled prosthesis alignment adjustment. The ProSAT components help diagnose and guide the correction of very subtle, difficult-to-see imbalances in dynamic gait. The sensor has been cross-validated against kinetic measurement in a gait laboratory, and bench testing was performed to validate the performance of the tool while adjusting a prosthetic socket based on machine learning analyses from the software application. Results The three-dimensional alignment of the prosthetic socket was measured pre- and postadjustment from two fiducial points marked on the anterior surface of the prosthetic socket. A coordinate measuring machine was used to derive an alignment angular offset from vertical for both conditions: pre- and postalignment conditions. Of interest is the difference in the angles between conditions. The ProSAT tool is only controlling the relative change made to the alignment, not an absolute position or orientation. Target alignments were calculated by the machine learning algorithm in the ProSAT software, based on input of kinetic data samples representing the precondition and where a real prosthetic misalignment condition was known a priori. Detected misalignments were converted by the software to a corrective adjustment in the prosthesis alignment being tested. We demonstrated that a user could successfully and quickly achieve target postalignment change within an average of 0.1°. Conclusions The accuracy of a prototype ProSAT system has been validated for controlled alignment changes by a prosthetist. Refinement of the ergonomic form and technical function of the hardware and clinical usability of the mobile software application are currently being completed with benchtop experiments in advance of further human subject testing of alignment efficiency, accuracy, and user experience.

scholarly journals UniAssist- Implementation of Machine Learning Based Higher Education University Recommendation System

2020 ◽  
pp. 262-266
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Work Experience ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Relevant Information ◽  
Mobile App ◽  
Admission Process ◽  
Software Application ◽  
Tuition Fee

UniAssist project is implemented to help students who have completed their Bachelorette degree and are looking forward to study abroad to pursue their higher education such as Masters. Machine Learning would help identify appropriate Universities for such students and suggest them accordingly. UniAssist would help such individuals by recommending those Universities according to their preference of course, country and considering their grades, work experience and qualifications. There is a need for students hoping to pursue higher education outside India to get to know about proper universities. Data collected is then converted into relevant information that is currently not easily available such as courses offered by their dream universities, the avg. tuition fee and even the avg. expense of living near the chosen university on single mobile app based software platform. This is the first phase of the admission process for every student. The machine-learning algorithm used is Collaborative filtering memory-based approach using KNN calculated using cosine similarity. A mobile-based software application is implemented in order to help and guide students for their higher education.

scholarly journals A Mobile Platform for Food Donation and Delivery System using AI and Machine Learning

2021 ◽  
Author(s):  
George Zhou ◽  
Marisabel Chang ◽  
Yu Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Orange County ◽  
Qualitative Evaluation ◽  
Free Food ◽  
Food Sources ◽  
Multiple Constraints ◽  
Software Application ◽  
Food Pantries ◽  
To Receive

Within the last year through the turmoil of the Covid-19 pandemic, an increasing number of families and individuals are experiencing food insecurity due to a loss of job, illnesses, or other financial struggles [4]. Many families in the Orange County area and abroad are turning to free food sources such as community food pantries or banks. Using specified surveys to food insecure families, we discovered a need for a solution to enhance the accessibility and usability of food pantries [5]. Therefore, we created a software application that uses artificial intelligence to locate specific items for users to request, and allow volunteers to see those requests and pick up the resources from food pantries, and deliver them directly to the homes of individuals. This paper shows the process in which this idea was created and how it was applied, along with the conduction of the qualitative evaluation of the approach. The results show that the software application allowed families and individuals to receive quality groceries at a much higher frequency, regardless of multiple constraints.

scholarly journals Avionics Graphics Hardware Performance Prediction with Machine Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Simon R. Girard ◽  
Vincent Legault ◽  
Guy Bois ◽  
Jean-François Boland
Keyword(s):  
Machine Learning ◽  
System Design ◽  
Application Programming Interface ◽  
Design Tool ◽  
Graphics Hardware ◽  
Training Phase ◽  
Desktop Computer ◽  
Software Application ◽  
Nonparametric Models ◽  
3D Scene

Within the strongly regulated avionic engineering field, conventional graphical desktop hardware and software application programming interface (API) cannot be used because they do not conform to the avionic certification standards. We observe the need for better avionic graphical hardware, but system engineers lack system design tools related to graphical hardware. The endorsement of an optimal hardware architecture by estimating the performance of a graphical software, when a stable rendering engine does not yet exist, represents a major challenge. As proven by previous hardware emulation tools, there is also a potential for development cost reduction, by enabling developers to have a first estimation of the performance of its graphical engine early in the development cycle. In this paper, we propose to replace expensive development platforms by predictive software running on a desktop computer. More precisely, we present a system design tool that helps predict the rendering performance of graphical hardware based on the OpenGL Safety Critical API. First, we create nonparametric models of the underlying hardware, with machine learning, by analyzing the instantaneous frames per second (FPS) of the rendering of a synthetic 3D scene and by drawing multiple times with various characteristics that are typically found in synthetic vision applications. The number of characteristic combinations used during this supervised training phase is a subset of all possible combinations, but performance predictions can be arbitrarily extrapolated. To validate our models, we render an industrial scene with characteristic combinations not used during the training phase and we compare the predictions to those real values. We find a median prediction error of less than 4 FPS.

scholarly journals Machine Learning: A Software Process Reengineering in Software Development Organization

2019 ◽  
Vol 9 (2) ◽  
pp. 4492-4500
Keyword(s):  
Machine Learning ◽  
Life Cycle ◽  
Software Engineering ◽  
Software Development ◽  
Software Project ◽  
Software Application ◽  
Development Organization ◽  
Organizational Mechanism ◽  
Organizational Ability ◽  
Automation Technology

BPR (Business Process Re-engineering) is an organizational mechanism that improves the organizational ability in responding to the challenges of qualitative result by change management and improvement in software engineering processes, productivity, product quality and competitive advantage. BPR inherits, explores and implements the building of process change, to incorporate enhancements to the essential considerations and protocols of (SEM) Software Engineering Management. Machine Learning (ML) can be the key aspect for BPR in software development organization. The goal of this research study is raising the conceptual vision about integration of automation technology like ML and its life cycle development within Software Development Life Cycle (SDLC) of the software product and highlights benefits and drawbacks ML techniques in SPM (Software Project Management), and how to implement ML in standard SEM practices. We have attempted the introduction of machine learning in SEM to determine specific performance and tasks reuse using empirical analysis and discussion on implementation of ML algorithms. The empirical study of software technologies includes control structure of an autonomous software application. In current era, ML imparts consistently promising accuracy in some SEM fields. The goal of this paper is an empirical and analytical study and literature review to propose desired level of quality software, through the comparative evaluation of existing processes and their respective support for Software Quality Engineering (SQE).

Data search computing based on the similarity-dıfference metric

2020 ◽  
Vol 60 (11) ◽  
pp. 46-60
Author(s):  
Vugar Hajimahmud Abdullayev ◽  
Keyword(s):  
Machine Learning ◽  
Social Computing ◽  
Data Retrieval ◽  
Critical Systems ◽  
Primitive Elements ◽  
Software Application ◽  
Data Search ◽  
Common Interests ◽  
Social Critical ◽  
High Level

Models, methods and algorithms for cyber-social computing and machine learning implies the use of the metric of similarity – difference of unitary coded information for processing big data in order to generate adequate actuator signals for controlling cyber-social critical systems. A set-theoretic method of data search is being developed based on the similarity – difference of the frequency parameters of primitive elements, which makes it possible to determine the similarity of objects, the strategy of transforming one object into another, and also to identify the level of common interests, conflicts. Computational architectures of cyber-social computing and metric search for key data are being created. The definitions of the fundamental concepts in the field of computing are given on the basis of metric relations between interacting processes and phenomena. A software application is proposed for calculating the similarity-differences of objects based on the formation of vectors of frequencies of two sets of primitive data. A high level of correlation of the application results with the well-known system for determining plagiarism is shown. Key words: computing, cybersocial computing, decision making, unitary data codes, similarity – difference, data retrieval, plagiarism

scholarly journals Intrusion Detection System using Machine Learning

2020 ◽  
pp. 61-71
Author(s):  
Jayesh Zala ◽  
Aditya Panchal ◽  
Advait Thakkar ◽  
Bhagirath Prajapati ◽  
Priyanka Puvar
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Technique ◽  
Software Application ◽  
Malicious Activity ◽  
The Past ◽  
Learning Technique

Intrusion Detection System (IDS) is a tool, or software application, that monitors network or system activity and detects malicious activity occurring. The protected evolution of the network must incorporate new threats and related approaches to avoid these threats. The key role of the IDS is to secure resources against the attacks. Several approaches, methods and algorithms of the intrusion detection help to detect a plethora of attacks. The main objective of this paper is to provide a complete system to detect intruding attacks using the Machine Learning technique which identifies the unknown attacks using the past information gained from the known attacks. The paper explains preprocessing techniques, model comparisons for training as well as testing, and evaluation technique.

scholarly journals Machine learning for predicting the outcomes and risks of cardiovascular diseases in patients with hypertension: results of ESSE-RF in the Primorsky Krai

2020 ◽  
Vol 25 (3) ◽  
pp. 3751
Author(s):  
V. A. Nevzorova ◽  
N. G. Plekhova ◽  
L. G. Priseko ◽  
I. N. Chernenko ◽  
D. Yu. Bogdanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Exception Handling ◽  
Object Oriented Programming ◽  
Biochemical Profile ◽  
Anthropometric Parameters ◽  
Cvd Risk ◽  
Software Application ◽  
Reactive Protein ◽  
High Level

Aim. To assess the prospects of using artificial intelligence technologies in predicting the outcomes and risks of cardiovascular diseases (CVD) in patients with hypertension (HTN).Material and methods. A software application was created for data mining from respondent profiles in a semi-automatic mode; libraries with data preprocessing were analyzed. We analyzed the main and additional parameters (35) of CVD risk factors in 2131 people as a part of ESSE-RF study (2014-2019). To create a forecasting model, a high-level language Python 2.7 was used using object-oriented programming and exception handling with multithreading support. Using randomization, learning (n=488) and test (n=245) samples were formed, which included data from patients with an established diagnosis of HTN.Results. The prevalence of HTN among subjects was 34,39%. There were following significant factors for predicting CVD: anthropometric parameters, smoking, biochemical profile (total cholesterol, ApoA, ApoB, glucose, D-dimer, C-reactive protein). As a result of a 5-year follow-up, CVD was found in 235 people (32,06%) with HTN and 187 people (13,38%) without HTN; mortality rates were 1,27% in subjects with HTN and 1,12% — without HTN. The absolute mortality risk among participants with HTN (0,037) was significantly higher (p<0,05) than in patients without HTN (0,017). To create a neural network (NN), the basic Sequential model from the Keras library was used. During machine learning, 26 variables important for the CVD development were used as input and 9 neurons — as output, which corresponded to the number of established cardiovascular events. The created NN had a predictive value of up to 97,9%, which exceeded the SCORE value (34,9%).Conclusion. The data obtained indicate the importance of risk factor phenotyping using anthropometric markers and biochemical profile for determining their significance in the top 20 predictors of CVD. The Python-based machine learning provides CVD prediction according to standard risk assessments.

scholarly journals Component Detection and Evaluation Framework (CDEF): A Semantic Annotation Tool

2020 ◽  
Vol 26 (S2) ◽  
pp. 1470-1474
Author(s):  
Nathan Jessurun ◽  
Olivia Paradis ◽  
Alexandra Roberts ◽  
Navid Asadizanjani
Keyword(s):  
Machine Learning ◽  
Semantic Annotation ◽  
Semantic Segmentation ◽  
Evaluation Framework ◽  
Annotation Tool ◽  
Software Application ◽  
Image Labeling ◽  
Complex Shapes ◽  
Machine Learning Applications

AbstractImage labeling is the process of manually assigning a class to subregions within an image for machine learning applications. When these subregions are complex shapes, this process is known as semantic segmentation. We propose a new software application, the Component Detection and Evaluation Framework (CDEF), for creating such semantic labels. The benefits of CDEF over existing tools are highlighted, and further improvements are proposed.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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