scholarly journals Memory Utilization and Machine Learning Techniques for Compiler Optimization

2021 ◽  
Vol 37 ◽  
pp. 01021
Author(s):  
A V Shreyas Madhav ◽  
Siddarth Singaravel ◽  
A Karmel
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Compiler Optimization ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Machine Learning Techniques ◽  
Peak Performance ◽  
Learning Techniques ◽  
Overall Performance ◽  
Memory Utilization

Compiler optimization techniques allow developers to achieve peak performance with low-cost hardware and are of prime importance in the field of efficient computing strategies. The realm of compiler suites that possess and apply efficient optimization methods provide a wide array of beneficial attributes that help programs execute efficiently with low execution time and minimal memory utilization. Different compilers provide a certain degree of optimization possibilities and applying the appropriate optimization strategies to complex programs can have a significant impact on the overall performance of the system. This paper discusses methods of compiler optimization and covers significant advances in compiler optimization techniques that have been established over the years. This article aims to provide an overall survey of the cache optimization methods, multi memory allocation features and explore the scope of machine learning in compiler optimization to attain a sustainable computing experience for the developer and user.

scholarly journals Implementation of machine learning techniques to control the speed of the vehicles

2018 ◽  
Vol 7 (4.5) ◽  
pp. 654
Author(s):  
M. S. Satyanarayana ◽  
Aruna T.M ◽  
Divyaraj G.N
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
New Technique ◽  
Vehicle Speed ◽  
Learning Techniques ◽  
Traffic Awareness ◽  
Alarm Sound ◽  
The Government ◽  
A New Technique

Accidents have become major issue in Developing countries like India now a day. As per the Surveys 60% of the accidents are happening due to over speed. Though the government has taken so many initiatives like Traffic Awareness & Driving Awareness Week etc.., but still the percentage of accidents are not getting reduced. In this paper a new technique has been introduced to reduce the percentage of accidents. The new technique is implemented using the concept of Machine Learning [1]. The Machine Learning based systems can be implemented in all vehicles to avoid the accidents at low cost [1]. The main objective of this system is to calculate the speed of the vehicle at three various locations based on the place where the vehicle speed must be controlled and if the speed is greater than the designated speed in that road then the vehicle automatically detects the problem and same will be intimated to the driver to control the speed of the vehicle. If the speed is less or equal to the designated speed in that road then the vehicle will be passed without any disturbance. The system will be giving beep sound along with color indication to driver in each and every scenario. The other option implemented in this system is if the driver is driving the vehicle in the night and if he feel drowsy the system detects it immediately and alarm sound will be initiated to wake up the driver. This system though it won’t avoid 100% accidents at least it will reduce the percentage of accidents. This system is not only to avoid accidents it will also intelligently control the speed of the vehicles and creates awareness amongst the drivers.  

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

2020 ◽  
pp. 1423-1439
Author(s):  
Zhiming Wu ◽  
Tao Lin ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Interaction Design ◽  
Mental Stress ◽  
Mental Workload ◽  
Low Cost ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Psychological Evidence

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

scholarly journals Intelligent Machine Learning: Tailor-Making Macromolecules

Polymers ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 579 ◽  
Author(s):  
Yousef Mohammadi ◽  
Mohammad Saeb ◽  
Alexander Penlidis ◽  
Esmaiel Jabbari ◽  
Florian J. Stadler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kinetic Monte Carlo ◽  
Engineering Problem ◽  
Optimization Techniques ◽  
Machine Learning Techniques ◽  
Coordination Polymerization ◽  
Intelligent Modeling ◽  
Learning Techniques ◽  
Input Variables ◽  
Architectural Characteristics

Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important ‘inverse’ engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization.

Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

2020 ◽  
Vol 22 (3) ◽  
pp. 27-29 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
Chris Reberg-Horton ◽  
Anna M. Locke ◽  
Steven Mirsky ◽  
Edgar Lobaton
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Drought Stress ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Stress Detection ◽  
Vision Systems ◽  
Learning Techniques ◽  
Computer Vision Systems

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

2016 ◽  
Vol 12 (3) ◽  
pp. 18-32 ◽  
Author(s):  
Zhiming Wu ◽  
Tao Lin ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Interaction Design ◽  
Mental Workload ◽  
Low Cost ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Open Research ◽  
Learning Techniques ◽  
Psychological Evidence

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

scholarly journals Water quality monitoring: from conventional to emerging technologies

2019 ◽  
Vol 20 (1) ◽  
pp. 28-45
Author(s):  
Umair Ahmed ◽  
Rafia Mumtaz ◽  
Hirra Anwar ◽  
Sadaf Mumtaz ◽  
Ali Mustafa Qamar
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Industrial Development ◽  
Low Cost ◽  
Research Work ◽  
Water Quality Monitoring ◽  
Machine Learning Techniques ◽  
Rapid Urbanization ◽  
Analysis Process ◽  
Learning Techniques

Abstract The rapid urbanization and industrial development have resulted in water contamination and water quality deterioration at an alarming rate, deeming its quick, inexpensive and accurate detection imperative. Conventional methods to measure water quality are lengthy, expensive and inefficient, including the manual analysis process carried out in a laboratory. The research work in this paper focuses on the problem from various perspectives, including the traditional methods of determining water quality to gain insight into the problem and the analysis of state-of-the-art technologies, including Internet of Things (IoT) and machine learning techniques to address water quality. After analyzing the currently available solutions, this paper proposes an IoT-based low-cost system employing machine learning techniques to monitor water quality in real time, analyze water quality trends and detect anomalous events such as intentional contamination of water.

scholarly journals Classification of Fetal State through the application of Machine Learning techniques on Cardiotocography records: Towards Real World Application.

2021 ◽  
Author(s):  
Andrew M V Dadario ◽  
Christian Espinoza ◽  
Wellington Araujo Nogueira
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Cross Validation ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
Real World Application ◽  
Learning Techniques ◽  
Qualified Personnel

Objective Anticipating fetal risk is a major factor in reducing child and maternal mortality and suffering. In this context cardiotocography (CTG) is a low cost, well established procedure that has been around for decades, despite lacking consensus regarding its impact on outcomes. Machine learning emerged as an option for automatic classification of CTG records, as previous studies showed expert level results, but often came at the price of reduced generalization potential. With that in mind, the present study sought to improve statistical rigor of evaluation towards real world application. Materials and Methods In this study, a dataset of 2126 CTG recordings labeled as normal, suspect or pathological by the consensus of three expert obstetricians was used to create a baseline random forest model. This was followed by creating a lightgbm model tuned using gaussian process regression and post processed using cross validation ensembling. Performance was assessed using the area under the precision-recall curve (AUPRC) metric over 100 experiment executions, each using a testing set comprised of 30% of data stratified by the class label. Results The best model was a cross validation ensemble of lightgbm models that yielded 95.82% AUPRC. Conclusions The model is shown to produce consistent expert level performance at a less than negligible cost. At an estimated 0.78 USD per million predictions the model can generate value in settings with CTG qualified personnel and all the more in their absence.

scholarly journals Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets

2021 ◽  
Vol 7 ◽  
pp. e670
Author(s):  
Marcio Dorn ◽  
Bruno Iochins Grisci ◽  
Pedro Henrique Narloch ◽  
Bruno César Feltes ◽  
Eduardo Avila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sampling Methods ◽  
Complete Blood Count ◽  
Low Cost ◽  
Class Imbalance ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Comparison Of The Results ◽  
Aid Decision

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil’s case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

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