2204 Noise Ratio Learning System for Telemeter Data of Aircraft by Neural Networks

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

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AdaLearner: An adaptive distributed mobile learning system for neural networks

2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) ◽

10.1109/iccad.2017.8203791 ◽

2017 ◽

Cited By ~ 4

Author(s):

Jiachen Mao ◽

Zhuwei Qin ◽

Zirui Xu ◽

Kent W. Nixon ◽

Xiang Chen ◽

...

Keyword(s):

Neural Networks ◽

Mobile Learning ◽

Learning System ◽

Mobile Learning System

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Literature Survey for Applications of Artificial Neural Networks

10.4018/978-1-6684-2408-7.ch031 ◽

2022 ◽

pp. 669-682

Author(s):

Pooja Deepakbhai Pancholi ◽

Sonal Jayantilal Patel

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Weather Forecasting ◽

Handwriting Recognition ◽

Complete Solution ◽

Learning System ◽

E Learning ◽

Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

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A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA

Scientific Reports ◽

10.1038/s41598-019-49942-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Konobu Kimura ◽

Yoko Tabe ◽

Tomohiko Ai ◽

Ikki Takehara ◽

Hiroshi Fukuda ◽

...

Keyword(s):

Neural Networks ◽

Blood Cell ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Diagnostic System ◽

Learning System ◽

Automated Image Analysis ◽

Image Analysis System ◽

Cell Image ◽

Extreme Gradient Boosting

Abstract Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which diagnosis is mainly based on morphological findings of PB and bone marrow. We developed an automated diagnostic support system of MDS by combining an automated blood cell image-recognition system using a deep learning system (DLS) powered by convolutional neural networks (CNNs) with a decision-making system using extreme gradient boosting (XGBoost). The DLS of blood cell image-recognition has been trained using datasets consisting of 695,030 blood cell images taken from 3,261 PB smears including hematopoietic malignancies. The DLS simultaneously classified 17 blood cell types and 97 morphological features of such cells with >93.5% sensitivity and >96.0% specificity. The automated MDS diagnostic system successfully differentiated MDS from aplastic anemia (AA) with high accuracy; 96.2% of sensitivity and 100% of specificity (AUC 0.990). This is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated diagnostic systems for various hematological disorders.

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Implementing M-Learning System for Learning Mathematics Through Computer Games and Applying Neural Networks for Content Similarity Analysis of an Integrated Social Network

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v15i13.22185 ◽

2021 ◽

Vol 15 (13) ◽

pp. 145

Author(s):

Petar Juric ◽

Marija Brkic Bakaric ◽

Maja Matetic

Keyword(s):

Neural Networks ◽

Social Network ◽

Computer Games ◽

Primary Schools ◽

Learning System ◽

Learning Mathematics ◽

Educational Computer ◽

E Learning ◽

Content Similarity ◽

Educational Computer Games

In order to make e-learning systems more readily available for use, the majority of new systems are being developed in a form suitable for mobile learning, i.e. m-learning. The paper puts focus on the parts of the implementation of an e-learning system which is not restricted to desktop platforms, but works equally well on smartphones and tablets in the form of m-learning. The implemented system uses educational computer games for learning Mathematics in primary schools and has an integrated social network, which is used for communication and publishing of the content related to the game. Besides analysing the platforms used for accessing the system (desktop/mobile), since students are given a choice, the paper also questions how to interpret messages when they contain concepts in student jargon or generally unknown to teachers, and shows that these messages can be interpreted by applying neural networks.

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Logic Tensor Networks for Semantic Image Interpretation

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/221 ◽

2017 ◽

Cited By ~ 12

Author(s):

Ivan Donadello ◽

Luciano Serafini ◽

Artur d'Avila Garcez

Keyword(s):

Neural Networks ◽

Image Interpretation ◽

Background Knowledge ◽

Learning System ◽

Training Data ◽

Reasoning Under Uncertainty ◽

Tensor Networks ◽

Logical Constraints ◽

Bounding Boxes ◽

Efficient Learning

Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions from images. It is widely agreed that the combined use of visual data and background knowledge is of great importance for SII. Recently, Statistical Relational Learning (SRL) approaches have been developed for reasoning under uncertainty and learning in the presence of data and rich knowledge. Logic Tensor Networks (LTNs) are a SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data. In this paper, we develop and apply LTNs to two of the main tasks of SII, namely, the classification of an image's bounding boxes and the detection of the relevant part-of relations between objects. To the best of our knowledge, this is the first successful application of SRL to such SII tasks. The proposed approach is evaluated on a standard image processing benchmark. Experiments show that background knowledge in the form of logical constraints can improve the performance of purely data-driven approaches, including the state-of-the-art Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show that the use of logical background knowledge adds robustness to the learning system when errors are present in the labels of the training data.

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Modelling an Adaptive Learning System Using Artificial Intelligence

Webology ◽

10.14704/web/v19i1/web19001 ◽

2021 ◽

Vol 19 (1) ◽

pp. 01-18

Author(s):

Hayder Rahm Dakheel AL-Fayyadh ◽

Salam Abdulabbas Ganim Ali ◽

Dr. Basim Abood

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Adaptive Learning ◽

Back Propagation ◽

Learning System ◽

Spiking Neural Networks ◽

Domain Specific Language ◽

Domain Specific ◽

Adaptive Learning System ◽

Artificial Neural

The goal of this paper is to use artificial intelligence to build and evaluate an adaptive learning system where we adopt the basic approaches of spiking neural networks as well as artificial neural networks. Spiking neural networks receive increasing attention due to their advantages over traditional artificial neural networks. They have proven to be energy efficient, biological plausible, and up to 105 times faster if they are simulated on analogue traditional learning systems. Artificial neural network libraries use computational graphs as a pervasive representation, however, spiking models remain heterogeneous and difficult to train. Using the artificial intelligence deductive method, the paper posits two hypotheses that examines whether 1) there exists a common representation for both neural networks paradigms for tutorial mentoring, and whether 2) spiking and non-spiking models can learn a simple recognition task for learning activities for adaptive learning. The first hypothesis is confirmed by specifying and implementing a domain-specific language that generates semantically similar spiking and non-spiking neural networks for tutorial mentoring. Through three classification experiments, the second hypothesis is shown to hold for non-spiking models, but cannot be proven for the spiking models. The paper contributes three findings: 1) a domain-specific language for modelling neural network topologies in adaptive tutorial mentoring for students, 2) a preliminary model for generalizable learning through back-propagation in spiking neural networks for learning activities for students also represented in results section, and 3) a method for transferring optimised non-spiking parameters to spiking neural networks has also been developed for adaptive learning system. The latter contribution is promising because the vast machine learning literature can spill-over to the emerging field of spiking neural networks and adaptive learning computing. Future work includes improving the back-propagation model, exploring time-dependent models for learning, and adding support for adaptive learning systems.

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Deep (Un)Learning: Using Neural Networks to Model Retention and Forgetting in an Adaptive Learning System

Lecture Notes in Computer Science - Artificial Intelligence in Education ◽

10.1007/978-3-030-23204-7_22 ◽

2019 ◽

pp. 258-269 ◽

Cited By ~ 1

Author(s):

Jeffrey Matayoshi ◽

Hasan Uzun ◽

Eric Cosyn

Keyword(s):

Neural Networks ◽

Adaptive Learning ◽

Learning System ◽

Adaptive Learning System

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A Blind Spectrum Sensing Method Based on Deep Learning

Sensors ◽

10.3390/s19102270 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2270 ◽

Cited By ~ 6

Author(s):

Kai Yang ◽

Zhitao Huang ◽

Xiang Wang ◽

Xueqiong Li

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Spectrum Sensing ◽

Short Term Memory ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Noise Ratio

Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.

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