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Academic literature on the topic 'Developed learn algorithims'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Developed learn algorithims"

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Gonda, Dalibor, Viliam Ďuriš, Anna Tirpáková, and Gabriela Pavlovičová. "Teaching Algorithms to Develop the Algorithmic Thinking of Informatics Students." Mathematics 10, no. 20 (October 18, 2022): 3857. http://dx.doi.org/10.3390/math10203857.

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Modernization and the ever-increasing trend of introducing modern technologies into various areas of everyday life require school graduates with programming skills. The ability to program is closely related to computational thinking, which is based on algorithmic thinking. It is well known that algorithmic thinking is the ability of students to work with algorithms understood as a systematic description of problem-solving strategies. Algorithms can be considered as a fundamental phenomenon that forms a point of contact between mathematics and informatics. As part of an algorithmic graph theory seminar, we conducted an experiment where we solved the knight’s tour problem using the backtracking method to observe the change in students’ motivation to learn algorithms at a higher cognitive level. Seventy-four students participated in the experiment. Statistical analysis of the results of the experiment confirmed that the use of the algorithm with decision-making in teaching motivated students to learn algorithms with understanding.
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Hussein, Maryam Mahmood, Ammar Hussein Mutlag, and Hussain Shareef. "Developed artificial neural network based human face recognition." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1279. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1279-1285.

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<p>Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in the recent years. Theses algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes soft computing algorithm based face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of the face recognition. The implementation of the developed BP-ANN has been achieved using MATLAB environment. The developed BP-ANN requires supervised training to learn how to anticipate results from the desired data. The BP-ANN has been developed to recognition 10 persons. Ten images have been used for each person. Therefore, 100 images have been utilized to train the developed BP-ANN. In this research 50 images have been used for testing purpose. The results show that the developed BP-ANN has produced a success ratio of 82%.</p>
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Carroll, William M., and Denise Porter. "Invented Strategies Can Develop Meaningful Mathematical Procedures." Teaching Children Mathematics 3, no. 7 (March 1997): 370–74. http://dx.doi.org/10.5951/tcm.3.7.0370.

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Over the past decade, a growitig consensus among educators favors a shift in mathematics instruction from a curriculum in which children learn and practice the standard school algorithms to one in which reasoning, problem solving, and conceptual understanding play a major role.
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Garg, Kartik. "An Approach to Develop Web-Based Application for Simulation and Visualization of Operating System Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1893–900. http://dx.doi.org/10.22214/ijraset.2021.39093.

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Abstract: Learning about algorithms and different system concepts related to programming is a difficult task and often becomes complex for students to grasp. If ever a concept is being learned using visualization techniques it becomes easy to learn and remember. There are many Algorithm visualizers built for it, though students may interpret them in a wrong way and that is why it should be appropriate and correct. Based on the previous works this paper describes an effective way to learn the various complex algorithms of the operating system in which we have considered various levels of interactivity, that includes zero interactivity, partial interactivity, and complete interactivity that will be discussed further in detail.[10] Keywords: Algorithm, Visualization, Operating System, Simulator, Programming, FCFS, SJF, etc
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Zhang, Haodi, Zhichao Zeng, Keting Lu, Kaishun Wu, and Shiqi Zhang. "Efficient Dialog Policy Learning by Reasoning with Contextual Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11667–75. http://dx.doi.org/10.1609/aaai.v36i10.21421.

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Goal-oriented dialog policy learning algorithms aim to learn a dialog policy for selecting language actions based on the current dialog state. Deep reinforcement learning methods have been used for dialog policy learning. This work is motivated by the observation that, although dialog is a domain with rich contextual knowledge, reinforcement learning methods are ill-equipped to incorporate such knowledge into the dialog policy learning process. In this paper, we develop a deep reinforcement learning framework for goal-oriented dialog policy learning that learns user preferences from user goal data, while leveraging commonsense knowledge from people. The developed framework has been evaluated using a realistic dialog simulation platform. Compared with baselines from the literature and the ablations of our approach, we see significant improvements in learning efficiency and the quality of the computed action policies.
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Olari, Viktoriya, Kostadin Cvejoski, and Øyvind Eide. "Introduction to Machine Learning with Robots and Playful Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15630–39. http://dx.doi.org/10.1609/aaai.v35i17.17841.

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Inspired by explanations of machine learning concepts in children’s books, we developed an approach to introduce supervised, unsupervised, and reinforcement learning using a block-based programming language in combination with the benefits of educational robotics. Instead of using blocks as high-end APIs to access AI cloud services or to reproduce the machine learning algorithms, we use them as a means to put the student “in the algorithm’s shoes.” We adapt the training of neural networks, Q-learning, and k-means algorithms to a design and format suitable for children and equip the students with hands-on tools for playful experimentation. The children learn about direct supervision by modifying the weights in the neural networks and immediately observing the effects on the simulated robot. Following the ideas of constructionism, they experience how the algorithms and underlying machine learning concepts work in practice. We conducted and evaluated this approach with students in primary, middle, and high school. All the age groups perceived the topics to be very easy to moderately hard to grasp. Younger students experienced direct supervision as challenging, whereas they found Q-learning and k-means algorithms much more accessible. Most high-school students could cope with all the topics without particular difficulties.
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Mühlenbein, Heinz, and Robin Höns. "The Estimation of Distributions and the Minimum Relative Entropy Principle." Evolutionary Computation 13, no. 1 (March 2005): 1–27. http://dx.doi.org/10.1162/1063656053583469.

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Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. In this paper we explain the relationship of EDA to algorithms developed in statistics, artificial intelligence, and statistical physics. The major design issues are discussed within a general interdisciplinary framework. It is shown that maximum entropy approximations play a crucial role. All proposed algorithms try to minimize the Kullback-Leibler divergence KLD between the unknown distribution p(x) and a class q(x) of approximations. However, the Kullback-Leibler divergence is not symmetric. Approximations which suppose that the function to be optimized is additively decomposed (ADF) minimize KLD(q||p), the methods which learn the approximate model from data minimize KLD(p||q). This minimization is identical to maximizing the log-likelihood. In the paper three classes of algorithms are discussed. FDAuses the ADF to compute an approximate factorization of the unknown distribution. The factors are marginal distributions, whose values are computed from samples. The second class is represented by the Bethe-Kikuchi approach which has recently been rediscovered in statistical physics. Here the values of the marginals are computed from a difficult constrained minimization problem. The third class learns the factorization from the data. We analyze our learning algorithm LFDA in detail. It is shown that learning is faced with two problems: first, to detect the important dependencies between the variables, and second, to create an acyclic Bayesian network of bounded clique size.
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Rahman, S., P. Quin, T. Walsh, T. Vidal-Calleja, M. J. McPhee, E. Toohey, and A. Alempijevic. "Preliminary estimation of fat depth in the lamb short loin using a hyperspectral camera." Animal Production Science 58, no. 8 (2018): 1488. http://dx.doi.org/10.1071/an17795.

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The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass.
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Jang, Jeongmin, and Geunseok Yang. "A Bug Triage Technique Using Developer-Based Feature Selection and CNN-LSTM Algorithm." Applied Sciences 12, no. 18 (September 18, 2022): 9358. http://dx.doi.org/10.3390/app12189358.

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With an increase in the use of software, the incidence of bugs and resulting maintenance costs also increase. In open source projects, developer reassignment accounts for approximately 50%. Software maintenance costs can be reduced if appropriate developers are recommended to resolve bugs. In this study, features are extracted by applying feature selection for each developer. These features are entered into CNN-LSTM algorithm to learn the model and recommend appropriate developers. To compare the performance of the proposed model, open source projects (Google Chrome, Mozilla Core, and Mozilla Firefox) were used to compare the performance of the proposed method with a baseline for developer recommendation. In this paper, the performance showed 54% for F-measure and 52% for accuracy in open source projects. The proposed model has improved and showed about a 13% more effective performance improvement than with DeepTriage. It was discovered that the performance of the proposed model was better.
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Fiori, Simone, Lorenzo Del Rossi, Michele Gigli, and Alessio Saccuti. "First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices." Electronics 9, no. 2 (February 15, 2020): 334. http://dx.doi.org/10.3390/electronics9020334.

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The present paper deals with neural algorithms to learn the singular value decomposition (SVD) of data matrices. The neural algorithms utilized in the present research endeavor were developed by Helmke and Moore (HM) and appear under the form of two continuous-time differential equations over the special orthogonal group of matrices. The purpose of the present paper is to develop and compare different numerical schemes, under the form of two alternating learning rules, to learn the singular value decomposition of large matrices on the basis of the HM learning paradigm. The numerical schemes developed here are both first-order (Euler-like) and second-order (Runge-like). Moreover, a reduced Euler scheme is presented that consists of a single learning rule for one of the factors involved in the SVD. Numerical experiments performed to estimate the optical-flow (which is a component of modern IoT technologies) in real-world video sequences illustrate the features of the novel learning schemes.
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Dissertations / Theses on the topic "Developed learn algorithims"

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(9828605), S. M. Rahman. "A feedforward neural network and its application to system indentification and control." Thesis, 1996. https://figshare.com/articles/thesis/A_feedforward_neural_network_and_its_application_to_system_indentification_and_control/20346819.

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 The aim of this thesis is to study a feedforward neural network and its application to system identification and control. 

Attention is focused firstly on feedforward neural networks and their weight adaptation algorithms. A new class of weight adaptation learning algorithms are introduced based on the sliding mode concept. The effectiveness of the new class of algorithms are studied and simulations are conducted to present their performance. 

Second part of this thesis deals with the application of the feedforward neural network with the developed learning algorithms. Two classes of problems are chosen to test the suitability of the feedforward neural network with proposed adaptation learning algorithms. The first problem is dynamic system identification and the other is dynamic system control. Results are presented in this thesis show the effectiveness of the feedforward neural network with the proposed learning algorithms in system identification and control.  

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Books on the topic "Developed learn algorithims"

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Cevelev, Aleksandr. Strategic development of railway transport logistics. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1194747.

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The monograph is devoted to the methodology of material and technical support of railway transport. According to the types of activities, the nature of the material and technical resources used, technologies, means and management systems, Russian railways belong to the category of high-tech industries that must have high quality and technical level, reliability and technological efficiency in operation. For this reason, the logistics system itself, both in structure and in the algorithm of the functions performed as a whole, needs a serious improvement in the quality of its work. The economic situation in Russia requires a revision of the principles and mechanisms of management based on the corporate model of supply chain management, focused on logistics knowledge. In the difficult economic conditions of the current decade, it is necessary to improve the quality of the supply organization of enterprises and structural divisions of railway transport, directly related to the implementation of the process approach, the advantage of which is a more detailed regulation of management actions and their mutual coordination. In order to increase the efficiency of its activities and develop the management system, Russian Railways is developing a lean production system aimed at further expanding the implementation of the principles of customer orientation, ideology and corporate culture. At the present time, the solution of many issues is impossible without a cybernetic approach to the formulation of problems of material and technical support and logistics analysis of information technologies, to the implementation of the developed algorithms and models of development strategies and concepts for improving the business processes of the production system. The management strategy, or the general plan for the implementation of activities for the management of material resources, is based on a fundamental assessment of the alignment and correlation of forces and factors operating in the economic and political field, taking into account the impact on the specific form of the management strategy. The materials will be useful to the heads and specialists of the directorates of the MTO, CDZs and can be used in the scientific research of bachelors, masters and postgraduates interested in the economics of railway transport and supply logistics.
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Lonza, Andrea. Reinforcement Learning Algorithms with Python: Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges. Packt Publishing, Limited, 2019.

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Kommadi, Bhagvan. Learn Data Structures and Algorithms with Golang: Level up Your Go Programming Skills to Develop Faster and More Efficient Code. Packt Publishing, Limited, 2019.

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Saleh, Hyatt. the Machine Learning Workshop: Get Ready to Develop Your Own High-Performance Machine Learning Algorithms with Scikit-learn, 2nd Edition. Packt Publishing, Limited, 2020.

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Okonkwo, Raphael. Full Stack Expert Advisor Programming for Meta Trader 5: Learn How to Develop the Perfect Trading Algorithm for Gold /Forex Market. Independently Published, 2022.

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Okonkwo, Raphael. Full Stack Expert Advisor Programming for Meta Trader 4: Learn How to Develop the Perfect Trading Algorithm for Gold /forex Markets. Independently Published, 2022.

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Press, Investors. Algorithmic Trading: Step-By-Step Guide to Develop Your Own Winning Trading Strategy Using Financial Machine Learning Without Having to Learn Code. Muze Publishing, 2021.

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Book chapters on the topic "Developed learn algorithims"

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Dutta, Saikat, Zixin Huang, and Sasa Misailovic. "SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning." In Fundamental Approaches to Software Engineering, 123–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99429-7_7.

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AbstractProbabilistic programming aims to open the power of Bayesian reasoning to software developers and scientists, but identification of problems during inference and debugging are left entirely to the developers and typically require significant statistical expertise. A common class of problems when writing probabilistic programs is the lack of convergence of the probabilistic programs to their posterior distributions.We present SixthSense, a novel approach for predicting probabilistic program convergence ahead of run and its application to debugging convergence problems in probabilistic programs. SixthSense’s training algorithm learns a classifier that can predict whether a previously unseen probabilistic program will converge. It encodes the syntax of a probabilistic program as motifs – fragments of the syntactic program paths. The decisions of the classifier are interpretable and can be used to suggest the program features that contributed significantly to program convergence or non-convergence. We also present an algorithm for augmenting a set of training probabilistic programs that uses guided mutation.We evaluated SixthSense on a broad range of widely used probabilistic programs. Our results show that SixthSense features are effective in predicting convergence of programs for given inference algorithms. SixthSense obtained Accuracy of over 78% for predicting convergence, substantially above the state-of-the-art techniques for predicting program properties Code2Vec and Code2Seq. We show the ability of SixthSense to guide the debugging of convergence problems, which pinpoints the causes of non-convergence significantly better by Stan’s built-in warnings.
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Gao, Wen, Xuanming Zhang, Weixin Huang, and Shaohang Shi. "Command2Vec: Feature Learning of 3D Modeling Behavior Sequence—A Case Study on “Spiral-stair”." In Proceedings of the 2021 DigitalFUTURES, 45–54. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_5.

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AbstractIn this study, we applied machine learning to mine the event logs generated in modeling process for behavior sequence clustering. The motivation for the study is to develop cognitively intelligent 3D tools through process mining which has been a hot area in recent years. In this study, we develop a novel classification method Command2Vec to perceive, learn and classify different design behavior during 3D-modeling aided design process. The method is applied in a case study of 112 participate students on a ‘Spiral-stair’ modeling task. By extracting the event logs generated in each participate student’s modeling process into a new data structures: ‘command graph’, we classified participants’ behavior sequences from final 99 valid event logs into certain groups using our novel Command2Vec. To verify the effectiveness of our classification, we invited five experts with extensive modeling experience to grade the classification results. The final grading shows that our algorithm performs well in certain grouping of classification with significant features.
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Yin, Chengjiu, Hiroaki Ogata, and Yoneo Yano. "Participatory Simulation for Collaborative Learning Experiences." In Innovative Mobile Learning, 197–214. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-062-2.ch010.

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In traditional education, most of the communication is one-way, where the teacher teaches and the students listen. The students are therefore less motivated to learn. In this chapter, we use the scaffolding technique to design an interactive simulation framework called SPS (scaffolding participatory simulation) for collaborative learning. Based on the SPS framework, a system called PLASPS (PDA-based learning algorithm system using participatory simulation) was developed to help students learn about sorting algorithms. Using this system, the teacher can assign tasks to their students and ask them to sort a list of numbers according to a certain algorithm. Learners then collaborate together to complete the task. The system checks the result and provides feedback to the students if there is a mistake with the positions of the numbers. The learners then correct the number positions and send the result back to the system. The collaborative activities and discussions, along with information about any errors, help the students to understand the sorting algorithm.
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Gautam, Kanika, Sunil Kumar Jangir, Manish Kumar, and Jay Sharma. "Malaria Detection System Using Convolutional Neural Network Algorithm." In Machine Learning and Deep Learning in Real-Time Applications, 219–30. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch010.

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Malaria is a disease caused when a female Anopheles mosquito bites. There are over 200 million cases recorded per year with more than 400,000 deaths. Current methods of diagnosis are effective; however, they work on technologies that do not produce higher accuracy results. Henceforth, to improve the prediction rate of the disease, modern technologies need to be performed for obtain accurate results. Deep learning algorithms are developed to detect, learn, and determine the containing parasites from the red blood smears. This chapter shows the implementation of a deep learning algorithm to identify the malaria parasites with higher accuracy.
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Bhargavi, K. "Deep Learning Architectures and Tools." In Deep Learning Applications and Intelligent Decision Making in Engineering, 55–75. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2108-3.ch002.

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Deep learning is one of the popular machine learning strategies that learns in a supervised or unsupervised manner by forming a cascade of multiple layers of non-linear processing units. It is inspired by the way of information processing and communication pattern of the typical biological nervous system. The deep learning algorithms learn through multiple levels of abstractions and hierarchy of concepts; as a result, it is found to be more efficient than the conventional non-deep machine learning algorithms. This chapter explains the basics of deep learning by highlighting the necessity of deep learning over non-deep learning. It also covers discussion on several recently developed deep learning architectures and popular tools available in market for deep learning, which includes Tensorflow, PyTorch, Keras, Caffe, Deeplearning4j, Pylearn2, Theano, CuDDN, CUDA-Convnet, and Matlab.
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Boraud, Thomas. "The Machine-Learning Approach of Reinforcement Learning." In How the Brain Makes Decisions, 105–9. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198824367.003.0016.

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This chapter assesses alternative approaches of reinforcement learning that are developed by machine learning. The initial goal of this branch of artificial intelligence, which appeared in the middle of the twentieth century, was to develop and implement algorithms that allow a machine to learn. Originally, they were computers or more or less autonomous robotic automata. As artificial intelligence has developed and cross-fertilized with neuroscience, it has begun to be used to model the learning and decision-making processes for biological agents, broadening the meaning of the word ‘machine’. Theoreticians of this discipline define several categories of learning, but this chapter only deals with those which are related to reinforcement learning. To understand how these algorithms work, it is necessary first of all to explain the Markov chain and the Markov decision-making process. The chapter then goes on to examine model-free reinforcement learning algorithms, the actor-critic model, and finally model-based reinforcement learning algorithms.
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Vacaretu, Ariana-Stanca. "Developing High-School Students´ Competences through Math Research Workshops – the M&L Project." In Theory and Practice: An Interface or A Great Divide?, 587–92. WTM-Verlag Münster, 2019. http://dx.doi.org/10.37626/ga9783959871129.0.110.

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Mathematics is or it should be about problem solving and math thinking. However, what mathematics students learn in schools is more about procedures for solving different types of math exercises and problems. In many cases, students learn by heart algorithms and words (math concepts) and use them for solving different math tasks. School math is very far from what mathematicians do and, in many cases, doesn’t motivate students for learning math. This paper presents the way we organized the assessment of the students’ skills developed through math research workshops and some of the assessment results. Even though we didn’t assess all the competences the students develop through the math research workshop, the findings show that the students certainly develop their problem-solving skills.
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Anitha Elavarasi S. and Jayanthi J. "Programming Language Support for Implementing Machine Learning Algorithms." In Handbook of Research on Applications and Implementations of Machine Learning Techniques, 402–21. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9902-9.ch021.

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Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.
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Pradheep Kumar K. and Srinivasan N. "Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT Applications." In Edge Computing and Computational Intelligence Paradigms for the IoT, 153–69. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8555-8.ch009.

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In this chapter, an automated planning algorithm has been proposed for IoT-based applications. A plan is a sequence of activities that leads to a goal or sub-goals. The sequence of sub-goals leads to a particular goal. The plans can be formulated using forward chaining where actions lead to goals or by backward chaining where goals lead to actions. Another method of planning is called partial order planning where all actions and sub-goals are not illustrated in the plan and left incomplete. When many IoT devices are interconnected, based on the tasks and activities involved resource allocation has to be optimized. An optimal plan is one where the total plan length is minimum, and all actions consume similar quantum of resources to achieve a goal. The scheduling cost incurred by way of resource allocation would be minimum. Compared to the existing algorithms L2-Plan (Learn to Plan) and API, the algorithm developed in this work improves optimality of resources by 14% and 36%, respectively.
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Bar, Nirjhar, and Sudip Kumar Das. "Applicability of ANN in Adsorptive Removal of Cd(II) from Aqueous Solution." In Handbook of Research on Natural Computing for Optimization Problems, 523–60. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0058-2.ch022.

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Cadmium is frequently used and is extremely toxic in relatively low dosages and is one of the principal heavy metals that is responsible for causing kidney damage, high blood pressure, renal disorder, destruction of red blood Cells and bone fracture. Permissible limit to discharge in the inland surface water is 2.0 mg/l, discharge in public sewers is 1.0 mg/l and drinking water is 0.01 mg/l. Adsorption is the only user-friendly technique for the removal of heavy metal. We have developed an ANN model for prediction of percentage removal of Cd(II). A multilayer perceptron with a single hidden layer has been learnt separately by three different algorithms: Backpropagation, Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for analysis purpose. Optimization for each one of the four standard transfer functions (in a single hidden layer) has been carried out in all three cases. The ANN model with Backpropagation algorithm, with the second transfer function and 25 processing elements gives the best predictability of the outlet concentration.
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Conference papers on the topic "Developed learn algorithims"

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Hao, Shuji, Peilin Zhao, Yong Liu, Steven C. H. Hoi, and Chunyan Miao. "Online Multitask Relative Similarity Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/253.

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Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose an active learning algorithm to save the labeling cost. The proposed algorithms not only enjoy theoretical guarantee, but also show high efficacy and efficiency in extensive experiments on real-world datasets.
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Zhao, Enmin, Shihong Deng, Yifan Zang, Yongxin Kang, Kai Li, and Junliang Xing. "Potential Driven Reinforcement Learning for Hard Exploration Tasks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/290.

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Experience replay plays a crucial role in Reinforcement Learning (RL), enabling the agent to remember and reuse experience from the past. Most previous methods sample experience transitions using simple heuristics like uniformly sampling or prioritizing those good ones. Since humans can learn from both good and bad experiences, more sophisticated experience replay algorithms need to be developed. Inspired by the potential energy in physics, this work introduces the artificial potential field into experience replay and develops Potentialized Experience Replay (PotER) as a new and effective sampling algorithm for RL in hard exploration tasks with sparse rewards. PotER defines a potential energy function for each state in experience replay and helps the agent to learn from both good and bad experiences using intrinsic state supervision. PotER can be combined with different RL algorithms as well as the self-imitation learning algorithm. Experimental analyses and comparisons on multiple challenging hard exploration environments have verified its effectiveness and efficiency.
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Ji, Weiqi, Julian Zanders, Ji-Woong Park, and Sili Deng. "Data-Driven Approaches to Learn HyChem Models." In ASME 2021 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icef2021-67925.

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Abstract The HyChem (Hybrid Chemistry) approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. Determining the pyrolysis submodel requires extensive experimentation on speciation measurements. Recent work has been directed to learn HyChem from an existing HyChem model for a similar fuel, which requires less data. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes. Specifically, we develop a HyChem model using the experimental datasets of ignition delay times covering a wide range of temperatures and equivalence ratios. The chemical kinetic model is treated as a neural network model, and we then employ stochastic gradient descent (SGD), a technique that was developed for deep learning, for the training. We demonstrate the approach in learning the HyChem model for F-24, which is a Jet-A derived fuel, and compare the results with previous work employing genetic algorithms. The results show that the SGD approach can achieve comparable model performance with genetic algorithms but the computational cost is reduced by 1000 times. In addition, with regularization in SGD, the SGD approach changes the kinetic parameters from their original values much less than genetic algorithm and is thus more likely to retrain mechanistic meanings. Finally, our approach is built upon open-source packages and can be applied to the development and optimization of chemical kinetic models for internal combustion engine simulations.
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Torabi, Faraz, Garrett Warnell, and Peter Stone. "Imitation Learning from Video by Leveraging Proprioception." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/497.

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Classically, imitation learning algorithms have been developed for idealized situations, e.g., the demonstrations are often required to be collected in the exact same environment and usually include the demonstrator's actions. Recently, however, the research community has begun to address some of these shortcomings by offering algorithmic solutions that enable imitation learning from observation (IfO), e.g., learning to perform a task from visual demonstrations that may be in a different environment and do not include actions. Motivated by the fact that agents often also have access to their own internal states (i.e., proprioception), we propose and study an IfO algorithm that leverages this information in the policy learning process. The proposed architecture learns policies over proprioceptive state representations and compares the resulting trajectories visually to the demonstration data. We experimentally test the proposed technique on several MuJoCo domains and show that it outperforms other imitation from observation algorithms by a large margin.
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Rodriguez-Soto, Manel, Maite Lopez-Sanchez, and Juan A. Rodriguez Aguilar. "Multi-Objective Reinforcement Learning for Designing Ethical Environments." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/76.

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AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. A common approach, founded on the exploitation of Reinforcement Learning techniques, is to design environments that incentivise agents to behave ethically. However, to the best of our knowledge, current approaches do not theoretically guarantee that an agent will learn to behave ethically. Here, we make headway along this direction by proposing a novel way of designing environments wherein it is formally guaranteed that an agent learns to behave ethically while pursuing its individual objectives. Our theoretical results develop within the formal framework of Multi-Objective Reinforcement Learning to ease the handling of an agent's individual and ethical objectives. As a further contribution, we leverage on our theoretical results to introduce an algorithm that automates the design of ethical environments.
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Zhang, Miao, Huiqi Li, and Steven Su. "High Dimensional Bayesian Optimization via Supervised Dimension Reduction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/596.

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Bayesian optimization (BO) has been broadly applied to computational expensive problems, but it is still challenging to extend BO to high dimensions. Existing works are usually under strict assumption of an additive or a linear embedding structure for objective functions. This paper directly introduces a supervised dimension reduction method, Sliced Inverse Regression (SIR), to high dimensional Bayesian optimization, which could effectively learn the intrinsic sub-structure of objective function during the optimization. Furthermore, a kernel trick is developed to reduce computational complexity and learn nonlinear subset of the unknowing function when applying SIR to extremely high dimensional BO. We present several computational benefits and derive theoretical regret bounds of our algorithm. Extensive experiments on synthetic examples and two real applications demonstrate the superiority of our algorithms for high dimensional Bayesian optimization.
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Thatte, Azam, Ganesh Vurimi, Prabhav Borate, and Teymour Javaherchi. "An Artificial Intelligence Based Method for Performance Prediction and Inverse Design of Hydraulic Turbochargers." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16012.

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Abstract A neural network based method is developed that can learn the underlying physics of hydraulic turbocharger (a radial pump coupled with a radial turbine) from a set of sparse experimental data and can predict the performance of a new turbocharger design for any given set of previously unseen operating conditions and geometric parameters. The novelty of the algorithm is that it learns the underlying physical mechanisms from a very sparse data spanning a broad range of flow rates and geometrical size brackets and uses these deeper common features recognized through a “mass-learning process” to predict the full performance curves for any given single geometry. The deep learning algorithm is able to accurately predict the key performance parameters like total efficiency of the turbocharger, its operating speed, pressure rise provided by the radial pump of the turbocharger and the shaft power produced by the radial turbine of the turbocharger for any given input combination of pump and turbine flow rates, differential pressure across the turbine and a limited set of geometrical parameters of pump and turbine impellers and volutes. Lastly, a novel method for fast inverse design of turbomachinery using a physics trained neural network and a constrained optimization algorithms is developed. The algorithm uses Nelder-Mead and Interior Point methods to find the global minimum of turbocharger design objective function in multi-dimensional space. The newly developed method is found to be very efficient in optimizing turbomachinery design problems with both equality and inequality constraints. The inverse design algorithm is able to successfully recommend an optimal combination of geometrical parameters like pump blade exit angle, pump impeller diameter, blade width, eye diameter, turbine nozzle diameter and rotational speed for a given target efficiency and head rise requirements. The preliminary results from this study indicate that it has a great potential to minimize the need for expensive 3D CFD based methods for the design of turbomachinery.
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Garner, Stuart. "Learning Resources and Tools to Aid Novices Learn Programming." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2613.

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It is well known that learning introductory software development is a difficult task for many students. This paper discusses some of the resources and tools that are available, or have been experimented with, that might be of interest to instructional designers of programming. The resources and tools are discussed in the context of the four phases of the software lifecycle, these being: analyse the problem; design and develop a solution / algorithm; implement the algorithm; and test and revise the algorithm. The tools that are discussed include microworlds, videoclips, flowchart interpreters, and program animators.
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Zhao, Hong, Pengfei Zhu, Ping Wang, and Qinghua Hu. "Hierarchical Feature Selection with Recursive Regularization." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/487.

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In the big data era, the sizes of datasets have increased dramatically in terms of the number of samples, features, and classes. In particular, there exists usually a hierarchical structure among the classes. This kind of task is called hierarchical classification. Various algorithms have been developed to select informative features for flat classification. However, these algorithms ignore the semantic hyponymy in the directory of hierarchical classes, and select a uniform subset of the features for all classes. In this paper, we propose a new technique for hierarchical feature selection based on recursive regularization. This algorithm takes the hierarchical information of the class structure into account. As opposed to flat feature selection, we select different feature subsets for each node in a hierarchical tree structure using the parent-children relationships and the sibling relationships for hierarchical regularization. By imposing $\ell_{2,1}$-norm regularization to different parts of the hierarchical classes, we can learn a sparse matrix for the feature ranking of each node. Extensive experiments on public datasets demonstrate the effectiveness of the proposed algorithm.
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Zhao, Zishuo, Xi Chen, Xuefeng Zhang, and Yuan Zhou. "Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/652.

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A major challenge for ridesharing platforms is to guarantee profit and fairness simultaneously, especially in the presence of misaligned incentives of drivers and riders. We focus on the dispatching-pricing problem to maximize the total revenue while keeping both drivers and riders satisfied. We study the computational complexity of the problem, provide a novel two-phased pricing solution with revenue and fairness guarantees, extend it to stochastic settings and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively collects data to learn the demand distribution during the scheduling process. We also conduct extensive experiments to demonstrate the effectiveness of our algorithms.
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Reports on the topic "Developed learn algorithims"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Lagutin, Andrey, and Tatyana Sidorina. SYSTEM OF FORMATION OF PROFESSIONAL AND PERSONAL SELF-GOVERNMENT AMONG CADETS OF MILITARY INSTITUTES. Science and Innovation Center Publishing House, December 2020. http://dx.doi.org/10.12731/self-government.

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When carrying out professional activities, officers of the VNG of the Russian Federation are often in difficult, stressful, emotionally stressful situations associated with the use of weapons as a particularly dangerous means of destruction. The right to use a weapon by an officer makes him responsible for its use. And therefore requires the officer to make a balanced optimal decision, which is associated with the risk and transience of events, and in which no mistake can be made, since the price of it can be someone's life. It is at such a moment that it is important that the officer has stable skills in making a decision on the use of weapons, and this requires skills not only in managing subordinates or the situation,but in managing himself. The complication of the military-professional activity, manifested in the need to develop the ability to quickly and accurately make command decisions, exacerbating the problem of social responsibility of an officer who has the management of unit that leads to an understanding of his singular personal and professional responsibility, as the ability to govern themselves makes it possible to achieve a positive result of the Department for the DBA. This characterizes the need for a commander to have the ability to manage himself, as a "system" that manages others. Forming skills of self-control, patience, compassion, having mastered algorithms of making managerial decisions, the cycle of implementing managerial functions, etc., a person comes to the belief: "before effectively managing others, it is necessary to learn how to manage yourself." The required level of personal and professional maturity can be formed in a person as a result of purposeful self-management, which determines the special role of professional and personal self-management in the training of future officers.
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