Journal articles on the topic 'Developed learn algorithims'
To see the other types of publications on this topic, follow the link: Developed learn algorithims.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Developed learn algorithims.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full textAbstract:
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.
2
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.
Full textAbstract:
<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>
3
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.
Full textAbstract:
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.
4
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.
Full textAbstract:
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
5
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.
Full textAbstract:
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.
6
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.
Full textAbstract:
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.
7
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.
Full textAbstract:
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.
8
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.
Full textAbstract:
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.
9
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.
Full textAbstract:
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.
10
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.
Full textAbstract:
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.
11
Falgenti, Kursehi. "Studi Komparatif Program Visual Dinamis untuk Pembelajaran Algoritma dan Pemograman Berorientasi Objek." Journal of Applied Computer Science and Technology 1, no. 1 (July 21, 2020): 38–43. http://dx.doi.org/10.52158/jacost.v1i1.53.
Full textAbstract:
As beginners, many first-year students have difficulty understanding object-oriented programming material. To help students learn algorithmic and object-oriented programming material researchers have developed visual programming (PV). Visual programming is a tool to facilitate learning programming. The concept of learning to use PV visualizes the work processes of algorithms and programming. This research aims to compare three dynamic PV tools for object-oriented learning programming that are the most studied. To determine the PV to be compared, a survey was conducted in an online journal database, such as IEEE explore, ACM, and several well-known online publishers. From the survey results, three dynamic PVs were chosen, most widely discussed, namely Jeliot 3, Ville and Jive. All three tools are installed and studied. Comparison results show that each dynamic PV has advantages on certain characteristics. The instructor can choose visual programming by considering the advantages of each PV.
12
Zhang, Pinggai, Ling Wang, Jiaojie Du, Zixiang Fei, Song Ye, Minrui Fei, and Panos M. Pardalos. "Differential Human Learning Optimization Algorithm." Computational Intelligence and Neuroscience 2022 (April 30, 2022): 1–19. http://dx.doi.org/10.1155/2022/5699472.
Full textAbstract:
Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.
13
Zhan, Guang, Xinmiao Zhang, Zhongchao Li, Lin Xu, Deyun Zhou, and Zhen Yang. "Multiple-UAV Reinforcement Learning Algorithm Based on Improved PPO in Ray Framework." Drones 6, no. 7 (July 4, 2022): 166. http://dx.doi.org/10.3390/drones6070166.
Full textAbstract:
Distributed multi-agent collaborative decision-making technology is the key to general artificial intelligence. This paper takes the self-developed Unity3D collaborative combat environment as the test scenario, setting a task that requires heterogeneous unmanned aerial vehicles (UAVs) to perform a distributed decision-making and complete cooperation task. Aiming at the problem of the traditional proximal policy optimization (PPO) algorithm’s poor performance in the field of complex multi-agent collaboration scenarios based on the distributed training framework Ray, the Critic network in the PPO algorithm is improved to learn a centralized value function, and the muti-agent proximal policy optimization (MAPPO) algorithm is proposed. At the same time, the inheritance training method based on course learning is adopted to improve the generalization performance of the algorithm. In the experiment, MAPPO can obtain the highest average accumulate reward compared with other algorithms and can complete the task goal with the fewest steps after convergence, which fully demonstrates that the MAPPO algorithm outperforms the state-of-the-art.
14
Brkić, Dejan, and Pavel Praks. "What Can Students Learn While Solving Colebrook’s Flow Friction Equation?" Fluids 4, no. 3 (June 27, 2019): 114. http://dx.doi.org/10.3390/fluids4030114.
Full textAbstract:
Even a relatively simple equation such as Colebrook’s offers a lot of possibilities to students to increase their computational skills. The Colebrook’s equation is implicit in the flow friction factor and, therefore, it needs to be solved iteratively or using explicit approximations, which need to be developed using different approaches. Various procedures can be used for iterative methods, such as single the fixed-point iterative method, Newton–Raphson, and other types of multi-point iterative methods, iterative methods in a combination with Padé polynomials, special functions such as Lambert W, artificial intelligence such as neural networks, etc. In addition, to develop explicit approximations or to improve their accuracy, regression analysis, genetic algorithms, and curve fitting techniques can be used too. In this learning numerical exercise, a few numerical examples will be shown along with the explanation of the estimated pedagogical impact for university students. Students can see what the difference is between the classical vs. floating-point algebra used in computers.
15
Hao, Jiangang, and Tin Kam Ho. "Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language." Journal of Educational and Behavioral Statistics 44, no. 3 (February 20, 2019): 348–61. http://dx.doi.org/10.3102/1076998619832248.
Full textAbstract:
Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.
16
Kayacan, Erkan. "Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis." Transactions of the Institute of Measurement and Control 41, no. 6 (August 13, 2018): 1750–60. http://dx.doi.org/10.1177/0142331218788125.
Full textAbstract:
This paper addresses the Sliding Mode Learning Control (SMLC) of uncertain nonlinear systems with Lyapunov stability analysis. In the control scheme, a conventional control term is used to provide the system stability in compact space while a type-2 neuro-fuzzy controller (T2NFC) learns system behaviour so that the T2NFC completely takes over overall control of the system in a very short time period. The stability of the sliding mode learning algorithm has been proven in the literature; however, it is restrictive for systems without overall system stability. To address this shortcoming, a novel control structure with a novel sliding surface is proposed in this paper, and the stability of the overall system is proven for nth-order uncertain nonlinear systems. To investigate the capability and effectiveness of the proposed learning and control algorithms, the simulation studies have been carried out under noisy conditions. The simulation results confirm that the developed SMLC algorithm can learn the system behaviour in the absence of any mathematical model knowledge and exhibit robust control performance against external disturbances.
17
Daumé, Hal, and Daniel Marcu. "Induction of Word and Phrase Alignments for Automatic Document Summarization." Computational Linguistics 31, no. 4 (December 2005): 505–30. http://dx.doi.org/10.1162/089120105775299140.
Full textAbstract:
Current research in automatic single-document summarization is dominated by two effective, yet naïve approaches: summarization by sentence extraction and headline generation via bagof-words models. While successful in some tasks, neither of these models is able to adequately capture the large set of linguistic devices utilized by humans when they produce summaries. One possible explanation for the widespread use of these models is that good techniques have been developed to extract appropriate training data for them from existing document/abstract and document/ headline corpora. We believe that future progress in automatic summarization will be driven both by the development of more sophisticated, linguistically informed models, as well as a more effective leveraging of document/abstract corpora. In order to open the doors to simultaneously achieving both of these goals, we have developed techniques for automatically producing word-to-word and phrase-to-phrase alignments between documents and their human-written abstracts. These alignments make explicit the correspondences that exist in such document/abstract pairs and create a potentially rich data source from which complex summarization algorithms may learn. This paper describes experiments we have carried out to analyze the ability of humans to perform such alignments, and based on these analyses, we describe experiments for creating them automatically. Our model for the alignment task is based on an extension of the standard hidden Markov model and learns to create alignments in a completely unsupervised fashion. We describe our model in detail and present experimental results that show that our model is able to learn to reliably identify word- and phrase-level alignments in a corpus of (document, abstract) pairs.
18
Gautam, Suruchi, and Pradhat Mittal. "COMPREHENSIVE ANALYSIS OF PRIVACY PRESERVING DATA MINING ALGORITHMS FOR FUTURE DEVELOP TRENDS." International Research Journal of Computer Science 9, no. 10 (October 30, 2022): 367–76. http://dx.doi.org/10.26562/irjcs.2022.v0910.01.
Full textAbstract:
There is incredible volume of data that is generated at exponential rate by various organizations such as hospitals, insurance companies, banks, stock market etc. It is done by excellence of digitization of technology. Every minute, huge amount of data is generated by various digital devices. This data could be processed to help decision making. However data analytics is prone to privacy violations. There is no doubt that the data analytics is extremely helpful in decision making process, but it will cause some serious privacy concerns. So protect the individual privacy in the process of data analytics became most important and necessary task. In this paper, various threats related to privacy are examined. Techniques and models of privacy preserving are also discussed with their limitations. Nowadays the role of algorithms of PPDM is very crucial. A number of techniques have been developed in order to execute privacy preserving data mining. Some of them are cryptography, secured sum algorithms, perturbation and k-anonymity. Here main focus is on current researches related to privacy preserving data mining. The complete study will enable to understand the different challenges that are confronted in PPDM. It will also help to learn and apply the best applicable technique according to different data circumstances.
19
Lin, Guichao, Peichen Huang, Minglong Wang, Yao Xu, Rihong Zhang, and Lixue Zhu. "An Inverse Kinematics Solution for a Series-Parallel Hybrid Banana-Harvesting Robot Based on Deep Reinforcement Learning." Agronomy 12, no. 9 (September 11, 2022): 2157. http://dx.doi.org/10.3390/agronomy12092157.
Full textAbstract:
A series-parallel hybrid banana-harvesting robot was previously developed to pick bananas, with inverse kinematics intractable to an address. This paper investigates a deep reinforcement learning-based inverse kinematics solution to guide the banana-harvesting robot toward a specified target. Because deep reinforcement learning algorithms always struggle to explore huge robot workspaces, a practical technique called automatic goal generation is first developed. This draws random targets from a dynamic uniform distribution with increasing randomness to facilitate deep reinforcement learning algorithms to explore the entire robot workspace. Then, automatic goal generation is applied to a state-of-the-art deep reinforcement learning algorithm, the twin-delayed deep deterministic policy gradient, to learn an effective inverse kinematics solution. Simulation experiments show that with automatic goal generation, the twin-delayed deep deterministic policy gradient solved the inverse kinematics problem with a success rate of 96.1% and an average running time of 23.8 milliseconds; without automatic goal generation, the success rate was just 81.2%. Field experiments show that the proposed method successfully guided the robot to approach all targets. These demonstrate that automatic goal generation enables deep reinforcement learning to effectively explore the robot workspace and to learn a robust and efficient inverse kinematics policy, which can, therefore, be applied to the developed series-parallel hybrid banana-harvesting robot.
20
CRÉPUT, JEAN-CHARLES, and ARMAND CARON. "A NEURAL NETWORK MODEL TO INDEX MEDICAL IMAGES." Journal of Biological Systems 07, no. 01 (March 1999): 45–51. http://dx.doi.org/10.1142/s021833909900005x.
Full textAbstract:
With the development of computer capabilities, memories and network abilities, we need more efficient and robust algorithms to manage databases and to store and retrieve the relevant information for the user. The aim of this work is to automate the construction of a neural network Information Retrieval System (IRS) adapted to a medical image database. The user builds queries and the system must retrieve the relevant documents or images. Queries are groups of keywords or items associated with relevant images. In our approach, the set of queries and the binary relevance judgments on the documents constitute complex learning data associations. There are two phases in the automatic construction of the IRS. The indexing phase builds the learning data base and then a specific learning algorithm builds the neural network. For the system to be able to immediately learn these complex data, we have developed a new specific algorithm. It allows a perfect learning of a binary logical table in a stepwise fashion without forgetting the previously learnt logical combinations. Furthermore, this algorithm works very quickly and leads to a parallel implementation for large databases.
21
Ardiansah, Jevri Tri, Aji Prasetya Wibawa, Triyanna Widyaningtyas, and Okazaki Yasuhisa. "SQL Logic Error Detection by Using Start End Mid Algorithm." Knowledge Engineering and Data Science 1, no. 1 (December 31, 2017): 33. http://dx.doi.org/10.17977/um018v1i12018p33-38.
Full textAbstract:
Data base is an important part of a system and it stores data to be manipulated. A language called SQL (Structured Query Language) is used for manipulating those data to make needed information. There are two types of error which make SQL more difficult in practical implementation. They are syntax error and logic error. The difference between them is that syntax error can be detected by compiler so it is easy to learn by its warning. But compiler does not show error warning if logical error was occurred. It makes logic error is more difficult to understand than syntax error. To help data base's user to learn SQL in practical implementation, web based SQL compiler that be able to detect syntax and logic error is developed by using Start End Mid algorithm.
22
Gut, Kamil, Maria Skublewska-Paszkowska, Edyta Łukasik, and Jakub Smołka. "COMPARISON OF PROGRAMMING LANGUAGES ON THE IOS PLATFORM IN TERMS OF PERFORMANCE." Informatics Control Measurement in Economy and Environment Protection 7, no. 3 (September 30, 2017): 33–36. http://dx.doi.org/10.5604/01.3001.0010.5211.
Full textAbstract:
In 2014, Apple unveiled a completely new programming language for the iOS and OS X platforms. Swift was presented as a modern programming language, such as: safe, easy to learn and easy to use. This article presents the performance comparison between the Swift and Objective-C languages. For the purpose of the research, two applications were developed, one in each language, implementing sorting algorithms and data structures such as arrays, dictionaries and sets.
23
Dellaca’, Raffaele L., Chiara Veneroni, and Ramon Farre’. "Trends in mechanical ventilation: are we ventilating our patients in the best possible way?" Breathe 13, no. 2 (June 2017): 84–98. http://dx.doi.org/10.1183/20734735.007817.
Full textAbstract:
This review addresses how the combination of physiology, medicine and engineering principles contributed to the development and advancement of mechanical ventilation, emphasising the most urgent needs for improvement and the most promising directions of future development.Several aspects of mechanical ventilation are introduced, highlighting on one side the importance of interdisciplinary research for further development and, on the other, the importance of training physicians sufficiently on the technological aspects of modern devices to exploit properly the great complexity and potentials of this treatment.Educational aimsTo learn how mechanical ventilation developed in recent decades and to provide a better understanding of the actual technology and practice.To learn how and why interdisciplinary research and competences are necessary for providing the best ventilation treatment to patients.To understand which are the most relevant technical limitations in modern mechanical ventilators that can affect their performance in delivery of the treatment.To better understand and classify ventilation modes.To learn the classification, benefits, drawbacks and future perspectives of automatic ventilation tailoring algorithms.
24
Chen, Boxiao, Xiuli Chao, and Yining Wang. "Technical Note—Data-Based Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes." Operations Research 68, no. 5 (September 2020): 1445–56. http://dx.doi.org/10.1287/opre.2020.1993.
Full textAbstract:
Pricing and inventory replenishment are important operations decisions for firms such as retailers. To make these decisions effectively, a firm needs to know the demand distribution and its dependency on selling price, which is usually estimated using sales data at various testing price levels. Although more testing prices can lead to a better estimation of the demand–price relationship, frequent price changes are costly and come with adverse effect such as customers’ negative perception. In this article, data-driven algorithms are developed that learn the demand structure with constraints on the number of price changes. These algorithms are shown to converge to the optimal clairvoyant solution, and the convergence rates are the best possible in terms of profit loss.
25
Qawasmeh, Ahmad, Salah Taamneh, Ashraf H. Aljammal, Nabhan Hamadneh, Mustafa Banikhalaf, and Mohammad Kharabsheh. "Parallelism exploration in sequential algorithms via animation tool." Multiagent and Grid Systems 17, no. 2 (August 23, 2021): 145–58. http://dx.doi.org/10.3233/mgs-210347.
Full textAbstract:
Different high performance techniques, such as profiling, tracing, and instrumentation, have been used to tune and enhance the performance of parallel applications. However, these techniques do not show how to explore the potential of parallelism in a given application. Animating and visualizing the execution process of a sequential algorithm provide a thorough understanding of its usage and functionality. In this work, an interactive web-based educational animation tool was developed to assist users in analyzing sequential algorithms to detect parallel regions regardless of the used parallel programming model. The tool simplifies algorithms’ learning, and helps students to analyze programs efficiently. Our statistical t-test study on a sample of students showed a significant improvement in their perception of the mechanism and parallelism of applications and an increase in their willingness to learn algorithms and parallel programming.
26
ZAGORODNA, Nataliya, Mariia STADNYK, Borys LYPA, Mykola GAVRYLOV, and Ruslan KOZAK. "Network Attack Detection Using Machine Learning Methods." Challenges to national defence in contemporary geopolitical situation 2022, no. 1 (November 3, 2022): 55–61. http://dx.doi.org/10.47459/cndcgs.2022.7.
Full textAbstract:
This paper presents the result of the study of network intrusion detection using machine learning algorithms. The creation and training of such algorithms is seriously limited by the small number of actual datasets available for public access. The CSE-CIC-IDS2018 data set, used in research, includes 7 subsets of different attack scenarios. Each subset is labeled using a few subtypes of a given attack or normal behavior. That is why the problem of network attack detection has been considered a multiclassification problem. Some of the most popular classifiers will be tested on the chosen data set. Classification algorithms are developed using a standard Python programming environment and the specialized machine learning library Scikit-learn. In the paper, a comparative analysis of the results was performed based on the the application of Random Forest, XGBoost, LR, and MLP classifiers.
27
Pantaleão, Eliana, Laurence Rodrigues Amaral, and Gláucia Braga e Silva. "Uma abordagem baseada no ambiente Robocode para ensino de programação no Ensino Médio." Revista Brasileira de Informática na Educação 25, no. 03 (October 31, 2017): 95. http://dx.doi.org/10.5753/rbie.2017.25.03.95.
Full textAbstract:
This paper describes a methodology for teaching algorithms and programming languages teaching in high school with the aid of the Robocode plattform. The experience was developed since 2012 with the help of undergraduate students that had already concluded courses on computer programming and acted as tutors and co-advisors of the younger students. Robocode environment was used as a support tool, using a playful learning strategy, providing an early contact of the high-school students with Java programming language. The obtained results show the interest of high school students to learn computer programming. Furthermore, the Robocode plataform proved to be a playful tool to support the teaching of Java. Finally, with the realization of the Robocode Tournaments, it was possible to observe how the competitiveness influenced the motivation of students to learn and overcome challenges.
28
Sohaib, Muhammad, and Jong-Myon Kim. "Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network." Shock and Vibration 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/2919637.
Full textAbstract:
Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing), but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE-) based deep neural networks (DNNs), a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.
29
Skornyakova, E. A., V. M. Vasyukov, and V. S. Sulaberidze. "Algorithmisation methods for scheduling in high-performance assembly manufacturing." Journal of «Almaz – Antey» Air and Space Defence Corporation, no. 4 (December 30, 2018): 15–22. http://dx.doi.org/10.38013/2542-0542-2018-4-15-22.
Full textAbstract:
The authors analysed the most popular production planning and scheduling software systems. Their main disadvantage is that they lack lean manufacturing tools. There exists a demand for client-oriented systems based on lean principles. The paper describes a unique algorithm for computing the optimum takt time, which forms the basis of the scheduling system developed by the authors. We present methods of algorithmising the scheduling process and results of generating schedules using several algorithms
30
Yau, Matthew, Venessa Timmerman, Merrick Zwarenstein, Pat Mayers, Ruth Vania Cornick, Eric Bateman, and Lara Fairall. "e-PC101: an electronic clinical decision support tool developed in South Africa for primary care in low-income and middle-income countries." BMJ Global Health 3, Suppl 5 (February 2019): e001093. http://dx.doi.org/10.1136/bmjgh-2018-001093.
Full textAbstract:
Health technology is increasingly recognised as a feasible method of addressing health needs in low and middle-income countries (LMICs). Primary Care 101, now known as PACK (Practical Approach to Care Kit), is a printed, algorithmic, checklist-based, comprehensive clinical decision support tool. It assists clinicians with delivering evidence-based medicine for common primary care presentations and conditions. These assessment and treatment guides have been adopted widely in primary care clinics across South Africa. This paper focuses on the process of designing, developing, and implementing a digital version of the clinical decision support tool for use on a tablet computer. Lessons learnt throughout its development and pilot implementation could apply to the creation of electronic health interventions and the digitisation of clinical tools in LMICs.
31
Khryaschev, Vladimir, and Leonid Ivanovsky. "Urban areas analysis using satellite image segmentation and deep neural network." E3S Web of Conferences 135 (2019): 01064. http://dx.doi.org/10.1051/e3sconf/201913501064.
Full textAbstract:
The goal of our research was to develop methods based on convolutional neural networks for automatically extracting the locations of buildings from high-resolution aerial images. To analyze the quality of developed deep learning algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with real masks. These masks were generated automatically from json files and sliced on smaller parts together with respective aerial photos before the training of developed convolutional neural networks. This approach allows us to cope with the problem of segmentation for high-resolution satellite images. All in all we show how deep neural networks implemented and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1 can be used to efficiently learn and detect needed objects. The problem of building detection on satellite images can be put into practice for urban planning, building control of some municipal objects, search of the best locations for future outlets etc.
32
Zhang, Ji, and Zijian Ran. "Face and Gender Detection Based on BP Neural Network Algorithm." Frontiers in Computing and Intelligent Systems 1, no. 1 (July 22, 2022): 1–3. http://dx.doi.org/10.54097/fcis.v1i1.985.
Full textAbstract:
Face recognition technology has a wide range of applications in real life, and many applications can be developed on the basis of face recognition, such as gender recognition, age recognition, face comparison, and beauty image decoration. In this paper, based on the computer vision library OpenCV in the python language, the face is recognized, and the mature BP neural network algorithm is used to learn the photos in the database, and finally the gender detection of the face is successfully realized.
33
Liu, Ying. "Direction Based Project Developing for Building Web-Services." Applied Mechanics and Materials 268-270 (December 2012): 1684–87. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1684.
Full textAbstract:
Software direction have been with us in many forms within Project developing community such as logic, experiences, domain expertise, laws, project developing principles, rules, design innovative thinking, conception, experimental results, programming rules, experience, observations, skills, algorithms have played major role in software development. This paper presents a new rule known as Direction Based Project developing where the aim is to learn from well known experience and filing newly developed and successful experience as a logic based when building software systems across the life cycle.
34
Fuentes-Beals, Camilo, Alejandro Valdés-Jiménez, and Gonzalo Riadi. "Hidden Markov Modeling with HMMTeacher." PLOS Computational Biology 18, no. 2 (February 10, 2022): e1009703. http://dx.doi.org/10.1371/journal.pcbi.1009703.
Full textAbstract:
Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements include variables, hidden and observed parameters, the vector of initial probabilities, and the transition and emission probability matrices. Then, we suggest a set of ordered steps, for modeling the variables and illustrate them with a simple exercise of modeling and predicting transmembrane segments in a protein sequence. Finally, we show how to interpret the results of the algorithms for this particular problem. To guide the process of information input and explicit solution of the basic HMM algorithms that answer the HMM questions posed, we developed an educational webserver called HMMTeacher. Additional solved HMM modeling exercises can be found in the user’s manual and answers to frequently asked questions. HMMTeacher is available at https://hmmteacher.mobilomics.org, mirrored at https://hmmteacher1.mobilomics.org. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.
35
Mavrevski, Radoslav, Metodi Traykov, and Iavn Trenchev. "Interactive Approach to Learning of Sorting Algorithms." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 08 (May 14, 2019): 120. http://dx.doi.org/10.3991/ijoe.v15i08.10530.
Full textAbstract:
<p class="abstract"><span lang="EN-US">Today we live in a society of high technologies, advanced information and com-munication systems in every field, including education. So, in modern education, teachers make full use of the possibilities of modern Information and Communi-cation Technologies (ICT). In this case, the attitude of the teachers towards the use of computers, to achieve the educational goals, is very important. To have the technologies sustained and significant effect, students in secondary and higher schools need to understand how to use them. The goal of this article is to help of students in secondary and higher schools to acquire enough practical program-ming skills and to learn the sorting algorithms, i.e. the article considers basic sort-ing algorithms. We developed and describe here software with name “Visual sorting” that shows visual, the execution of the basic sorting algorithms: Bubble sort; Selection sort; Insertion sort; Merge sort. Also, our software provides inter-active tracking of the performance (step by step) of different sorting algorithms.</span></p>
36
Thakker, Dhavalkumar, Fan Yang-Turner, and Dimoklis Despotakis. "User Interaction with Linked Data." International Journal of Distributed Systems and Technologies 7, no. 1 (January 2016): 79–91. http://dx.doi.org/10.4018/ijdst.2016010105.
Full textAbstract:
It is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed.
37
Osman, Hossam, and Moustafa M. Fahmy. "Probabilistic Winner-Take-All Learning Algorithm for Radial-Basis-Function Neural Classifiers." Neural Computation 6, no. 5 (September 1994): 927–43. http://dx.doi.org/10.1162/neco.1994.6.5.927.
Full textAbstract:
This paper proposes a new adaptive competitive learning algorithm called “the probabilistic winner-take-all.” The algorithm is based on a learning scheme developed by Agrawala within the statistical pattern recognition literature (Agrawala 1970). Its name stems from the fact that for a given input pattern once each competitor computes the probability of being the one that generated this pattern, the computed probabilities are utilized to probabilistically choose a winner. Then, only this winner is permitted to learn. The learning rule of the algorithm is derived for three different cases. Its properties are discussed and compared to those of two other competitive learning algorithms, namely the standard winner-take-all and the maximum-likelihood soft competition. Experimental comparison is also given. When all three algorithms are used to train the hidden layer of radial-basis-function classifiers, experiments indicate that classifiers trained with the probabilistic winner-take-all outperform those trained with the other two algorithms.
38
Maw, Aye Aye, Maxim Tyan, Tuan Anh Nguyen, and Jae-Woo Lee. "iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV." Applied Sciences 11, no. 9 (April 27, 2021): 3948. http://dx.doi.org/10.3390/app11093948.
Full textAbstract:
Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.
39
Susilo, Bambang, and Riri Fitri Sari. "Intrusion Detection in IoT Networks Using Deep Learning Algorithm." Information 11, no. 5 (May 21, 2020): 279. http://dx.doi.org/10.3390/info11050279.
Full textAbstract:
The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.
40
Smith, Anthony W., and David Zipser. "LEARNING SEQUENTIAL STRUCTURE WITH THE REAL-TIME RECURRENT LEARNING ALGORITHM." International Journal of Neural Systems 01, no. 02 (January 1989): 125–31. http://dx.doi.org/10.1142/s0129065789000037.
Full textAbstract:
Recurrent connections in neural networks potentially allow information about events occurring in the past to be preserved and used in current computations. How effectively this potential is realized depends on the power of the learning algorithm used. As an example of a task requiring recurrency, Servan-Schreiber, Cleeremans, and McClelland1 have applied a simple recurrent learning algorithm to the task of recognizing finite-state grammars of increasing difficulty. These nets showed considerable power and were able to learn fairly complex grammars by emulating the state machines that produced them. However, there was a limit to the difficulty of the grammars that could be learned. We have applied a more powerful recurrent learning procedure, called real-time recurrent learning2,6 (RTRL), to some of the same problems studied by Servan-Schreiber, Cleeremans, and McClelland. The RTRL algorithm solved more difficult forms of the task than the simple recurrent networks. The internal representations developed by RTRL networks revealed that they learn a rich set of internal states that represent more about the past than is required by the underlying grammar. The dynamics of the networks are determined by the state structure and are not chaotic.
41
Najeb Mohammed, Bafreen. "A Review: Genetics Algorithms in Bioinformatics Tools." ICONTECH INTERNATIONAL JOURNAL 5, no. 1 (March 28, 2021): 16–25. http://dx.doi.org/10.46291/icontechvol5iss1pp16-25.
Full textAbstract:
Bioinformaticians study biological questions by analyzing molecular data with various programs and tools. Today, bioinformatics is used in large number of fields such as microbial genome applications, biotechnology, waste cleanup, Gene therapy, fingerprint and eye detection. The field of bioinformatics, is one of the most prominent areas that our need is increasing, and the demand for it is increasing day by day. Where dealing with this vital and biological information using advanced computer technologies to generate useful information and new discoveries. For this reason, vital bioinformatics is one of the domains that combines both interested and programming at the same time. It provides you with resources for self-learning, the most important information in the field of vital information, and asked questions of those wishing to learn this field. The term bioinformatics was first used in 1968 by Margret Dayhoff, which is a pioneer in this field, but its definition appeared for the first time in 1978. This science arose and developed in conjunction with the emergence and development of computers. It is also referred to as "computational biology."
42
Srinivas, Taarun, Aditya Krishna Karigiri Madhusudhan, Joshuva Arockia Dhanraj, Rajasekaran Chandra Sekaran, Neda Mostafaeipour, Negar Mostafaeipour, and Ali Mostafaeipour. "Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer." Mathematical Problems in Engineering 2022 (May 10, 2022): 1–16. http://dx.doi.org/10.1155/2022/9619102.
Full textAbstract:
Nowadays, for many industries, innovation revolves around two technological improvements, Artificial Intelligence (AI) and machine learning (ML). ML, a subset of AI, is the science of designing and applying algorithms that can learn and work on any activity from past experiences. Of all the innovations in the field of ML models, the most significant ones have turned out to be in medicine and healthcare, since it has assisted doctors in the treatment of different types of diseases. Among them, early detection of breast cancer using ML algorithms has piqued the interest of researchers in this area. Hence, in this work, 20 ML classifiers are discussed and implemented in Wisconsin’s Breast Cancer dataset to classify breast cancer as malignant or benign. Out of 20, 9 algorithms are coded using Python in Colab notebooks and the remaining are executed using the Waikato Environment for Knowledge Analysis (WEKA) software. Among all, the stochastic gradient descent algorithm was found to yield the highest accuracy of 98%. The algorithms that gave the best results have been considered in the development of a novel ensemble model and the same was implemented in both WEKA and Python. The performance of the ensemble model in both platforms is compared based on metrics like accuracy, precision, recall, and sensitivity and investigated in detail. From this experimental comparative study, it was found that the ensemble model developed using Python has yielded an accuracy of 98.5% and that developed in the WEKA has yielded 97% accuracy.
43
Zaccheus, J. E., O. P. Fidelis, and E. O. Nwoye. "Application of neural network algorithm for schizophrenia diagnosis." Nigerian Journal of Technology 40, no. 5 (May 13, 2022): 954–65. http://dx.doi.org/10.4314/njt.v40i5.21.
Full textAbstract:
Schizophrenia is a prolonged mental condition that affects functional impairment in work, interpersonal relationships, and self-care. This research was aimed at developing a neural network model to diagnose schizophrenia using text data acquired from patients’ records. The model was developed from datasets obtained from Neuropsychiatric Hospital in Yaba and the Lagos University Teaching Hospital, both in Lagos, Nigeria, using Python programming language and is provided with significant features from data sets to learn patterns within the training data and perform classification on the test data. The results show that the model produced a test accuracy of 85%, specificity of 95% and a precision of 93%. These results indicate that the model can be used for effective computer-aided diagnosis of schizophrenia.
44
Li, Weisheng, Dongwen Cao, Yidong Peng, and Chao Yang. "MSNet: A Multi-Stream Fusion Network for Remote Sensing Spatiotemporal Fusion Based on Transformer and Convolution." Remote Sensing 13, no. 18 (September 17, 2021): 3724. http://dx.doi.org/10.3390/rs13183724.
Full textAbstract:
Remote sensing products with high temporal and spatial resolution can be hardly obtained under the constrains of existing technology and cost. Therefore, the spatiotemporal fusion of remote sensing images has attracted considerable attention. Spatiotemporal fusion algorithms based on deep learning have gradually developed, but they also face some problems. For example, the amount of data affects the model’s ability to learn, and the robustness of the model is not high. The features extracted through the convolution operation alone are insufficient, and the complex fusion method also introduces noise. To solve these problems, we propose a multi-stream fusion network for remote sensing spatiotemporal fusion based on Transformer and convolution, called MSNet. We introduce the structure of the Transformer, which aims to learn the global temporal correlation of the image. At the same time, we also use a convolutional neural network to establish the relationship between input and output and to extract features. Finally, we adopt the fusion method of average weighting to avoid using complicated methods to introduce noise. To test the robustness of MSNet, we conducted experiments on three datasets and compared them with four representative spatiotemporal fusion algorithms to prove the superiority of MSNet (Spectral Angle Mapper (SAM) < 0.193 on the CIA dataset, erreur relative global adimensionnelle de synthese (ERGAS) < 1.687 on the LGC dataset, and root mean square error (RMSE) < 0.001 on the AHB dataset).
45
Afify, AA. "A novel algorithm for fuzzy rule induction in data mining." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 5 (August 1, 2013): 877–95. http://dx.doi.org/10.1177/0954406213492273.
Full textAbstract:
Rule induction as a method of constructing classifiers is of particular interest to data mining because it generates models in the form of If-Then rules which are more expressive and easier for humans to comprehend and check. Several induction algorithms have been developed to learn classification rules. However, most of these algorithms are based on ‘crisp’ data and produce ‘crisp’ models. This paper presents FuzzySRI, a novel algorithm based on the techniques of fuzzy sets and fuzzy logic for inducing fuzzy classification rules. The algorithm possesses the clear knowledge representation capability of rule induction methods and the ability of fuzzy techniques to handle vague information. Experimental results show that FuzzySRI can outperform other fuzzy and non-fuzzy learning systems in terms of predictive accuracy, comprehensibility, and computational efficiency. It is also shown that FuzzySRI can be successfully applied to an industrial application concerning the automatic identification of machine faults.
46
Hu, Gang, Jiao Wang, Min Li, Abdelazim G. Hussien, and Muhammad Abbas. "EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications." Mathematics 11, no. 4 (February 7, 2023): 851. http://dx.doi.org/10.3390/math11040851.
Full textAbstract:
The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS algorithm still has significant development space for solving complex optimization problems with high dimensions and multiple local optima. Therefore, in this study, an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) By adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed. (ii) By adding a local escape operator, the algorithm can skip the trap of local optimization, and thereby, can enhance the exploitation ability of the JS algorithm. (iii) By applying an opposition-based learning and quasi-opposition learning strategy, the population distribution is increased, strengthened, and more diversified, and better individuals are selected from the present and the new opposition solution to participate in the next iteration, which can enhance the solution’s quality, meanwhile, convergence speed is faster and the algorithm’s precision is increased. In addition, the performance of the developed EJS algorithm was compared with those of the incomplete improved algorithms, and some previously outstanding and advanced methods were evaluated on the CEC2019 test set as well as six examples of real engineering cases. The results demonstrate that the EJS algorithm can skip the trap of local optimization, can enhance the solution’s quality, and can increase the calculation speed. In addition, the practical engineering applications of the EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and therefore, suggests future possible applications for solving such optimization problems.
47
Munoz, J., Arif Ozgelen, and Elizabeth Sklar. "Learning from Demonstration in Spatial Exploration." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1878–79. http://dx.doi.org/10.1609/aaai.v25i1.8000.
Full textAbstract:
We present the initial stage of our research on Learning from Demonstration algorithms. We have implemented an algorithm based on Confident Execution, one of the components of the Confidence-Based Autonomy algorithm developed by Chernova and Veloso. Our preliminary experiments were conducted first in simulation and then using a Sony AIBO ERS-7 robot. So far, our robot has been able to learn crude navigation strategies, despite limited trials. We are currently working on improving our implementation by including additional features that describe more broadly the state of the agent. Our long term goal is to incorporate Learning from Demonstration techniques in our HRTeam (human/multi-robot) framework.
48
Mukhopadhyay, Somnath, Asim Roy, Lark Sang Kim, and Sandeep Govil. "A Polynomial Time Algorithm for Generating Neural Networks for Pattern Classification: Its Stability Properties and Some Test Results." Neural Computation 5, no. 2 (March 1993): 317–30. http://dx.doi.org/10.1162/neco.1993.5.2.317.
Full textAbstract:
Polynomial time training and network design are two major issues for the neural network community. A new algorithm has been developed that can learn in polynomial time and also design an appropriate network. The algorithm is for classification problems and uses linear programing models to design and train the network. This paper summarizes the new algorithm, proves its stability properties, and provides some computational results to demonstrate its potential.
49
Casini, Marco, and Andrea Garulli. "MARS: An Educational Environment for Multiagent Robot Simulations." Modelling and Simulation in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5914706.
Full textAbstract:
Undergraduate robotics students often find it difficult to design and validate control algorithms for teams of mobile robots. This is mainly due to two reasons. First, very rarely, educational laboratories are equipped with large teams of robots, which are usually expensive, bulky, and difficult to manage and maintain. Second, robotics simulators often require students to spend much time to learn their use and functionalities. For this purpose, a simulator of multiagent mobile robots namedMARShas been developed within the Matlab environment, with the aim of helping students to simulate a wide variety of control algorithms in an easy way and without spending time for understanding a new language. Through this facility, the user is able to simulate multirobot teams performing different tasks, from cooperative to competitive ones, by using both centralized and distributed controllers. Virtual sensors are provided to simulate real devices. A graphical user interface allows students to monitor the robots behaviour through an online animation.
50
Brumen, Boštjan, Aleš Černezel, and Leon Bošnjak. "Overview of Machine Learning Process Modelling." Entropy 23, no. 9 (August 28, 2021): 1123. http://dx.doi.org/10.3390/e23091123.
Full textAbstract:
Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130 datasets from publicly available repositories. Three different functions (power, logarithmic, and exponential) were fit to the measured outputs. Using rigorous statistical methods and two measures for the goodness-of-fit, the power law model proved to be the most appropriate model for describing the learning curve produced by the algorithms in terms of goodness-of-fit and prediction capabilities. The presented study, first of its kind in scale and rigour, provides results (and methods) that can be used to assess the performance of novel or existing artificial learners and forecast their ‘capacity to learn’ based on the amount of available or desired data.