scholarly journals Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning

2017 ◽  
Vol 58 ◽  
pp. 829-858 ◽  
Author(s):  
Michael Abseher ◽  
Nysret Musliu ◽  
Stefan Woltran
Keyword(s):  
Machine Learning ◽  
Dynamic Programming ◽  
Tree Decomposition ◽  
Machine Learning Techniques ◽  
Problem Instance ◽  
Actual Problem ◽  
Learning Techniques ◽  
Tree Decompositions ◽  
Selection Of ◽  
General Method

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems - that are in general NP-hard - efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; and (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques that provide automated selection based on features of the decomposition rather than on the actual problem instance. Thus, one main contribution of this work is to propose novel features for tree decompositions. Moreover, we report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

scholarly journals Grain Yield Estimation in Cultivated Land Using Machine Learning Techniques

2021 ◽  
Vol 36 (2) ◽  
pp. 70-75
Author(s):  
Dr.K. Venkata Nagendra ◽  
Dr.B. Prasad ◽  
K.T.P.S. Kumar ◽  
K.S. Raghuram ◽  
Dr.K. Somasundaram
Keyword(s):  
Machine Learning ◽  
Market Price ◽  
Specific Area ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Learning Techniques ◽  
Selection Of

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points.

scholarly journals Efficiently Coupling the I-DLV Grounder with ASP Solvers

2018 ◽  
Vol 20 (2) ◽  
pp. 205-224 ◽  
Author(s):  
FRANCESCO CALIMERI ◽  
CARMINE DODARO ◽  
DAVIDE FUSCÀ ◽  
SIMONA PERRI ◽  
JESSICA ZANGARI
Keyword(s):  
Machine Learning ◽  
Answer Set Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection Of ◽  
Answer Set

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$, a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).

Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices

2020 ◽  
pp. 21-47
Author(s):  
José María Jorquera Valero ◽  
Pedro Miguel Sánchez Sánchez ◽  
Alberto Huertas Celdran ◽  
Gregorio Martínez Pérez
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Machine Learning Techniques ◽  
Security Level ◽  
Design And Implementation ◽  
Continuous Authentication ◽  
Learning Techniques ◽  
Authentication System ◽  
And Performance ◽  
Selection Of

Continuous authentication systems allow users not to possess or remember something to authenticate themselves. These systems perform a permanent authentication that improves the security level of traditional mechanisms, which just authenticate from time to time. Despite the benefits of continuous authentication, the selection of dimensions and characteristics modelling of user's behaviour, and the creation and management of precise models based on Machine learning, are two important open challenges. This chapter proposes a continuous and adaptive authentication system that uses Machine Learning techniques based on the detection of anomalies. Applications usage and the location of the mobile device are considered to detect abnormal behaviours of users when interacting with the device. The proposed system provides adaptability to behavioural changes through the insertion and elimination of patterns. Finally, a proof of concept and several experiments justify the decisions made during the design and implementation of this work, as well as demonstrates its suitability and performance.

scholarly journals Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

2018 ◽  
Vol 8 (12) ◽  
pp. 2570 ◽  
Author(s):  
Yves Rybarczyk ◽  
Rasa Zalakeviciute
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Deterministic Models ◽  
Learning Techniques ◽  
Vector Machines ◽  
Estimation Problems ◽  
Selection Of

Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

scholarly journals Determination of women’s voting behavior; A machine learning approach in the Turkish political arena

2020 ◽  
Author(s):  
Nizamettin Bayyurt ◽  
Havva Çaha
Keyword(s):  
Machine Learning ◽  
Voting Behavior ◽  
Political Life ◽  
Machine Learning Techniques ◽  
People's Party ◽  
Learning Techniques ◽  
Selection Decisions ◽  
Machine Learning Approach ◽  
Ideological Attitudes ◽  
Selection Of

Justice and Development Party (AKP) has been the ruling and biggest party in Turkey (AKP) since it has been established in 2002 and Republican People’s Party (CHP) has been the main opposition party (CHP) since then. These two parties receive about 75% of all the votes and half of the voters are females.  To our knowledge, there is no such a study focusing on women’s party preferences in Turkey. Additionally, this is one of the very few studies in Turkey concerning voters’ party preferences. Therefore, this study aims to fill this gap in the literature. In this study, the important attributes of women in party selection decisions are analyzed. Center-periphery and social mobility theories are the two main theories explaining Turkish political life. The analyzed ideological, cultural, religious, social, economic and demographic characteristics of women supporters are selected according to these theories. Machine-learning techniques are employed as predictive tools. Results show that ideological attitudes like being leftist-rightist and religious values like headscarf, fasting in Ramadan, and praying are the most important effective attributes on party selection of women. However, socioeconomic, cultural, educational and demographic atributes are not effective on party selection of women in Turkey.

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