Automatic MR Spinal Cord Segmentation by Hybrid Residual Attention-Aware Convolutional Neural Networks and Learning Rate Optimization on Real World Data

Recent world events in go games between human and artificial intelligence called AlphaGo showed the big advancement in machine learning technologies. While AlphaGo was trained using real world data, AlphaGo Zero was trained using massive random data, and the fact that AlphaGo Zero won AlphaGo completely revealed that diversity and size in training data is important for better performance for the machine learning algorithms, especially in deep learning algorithms of neural networks. On the other hand, artificial neural networks and decision trees are widely accepted machine learning algorithms because of their robustness in errors and comprehensibility respectively. In this paper in order to prove that diversity and size in data are important factors for better performance of machine learning algorithms empirically, the two representative algorithms are used for experiment. A real world data set called breast tissue was chosen, because the data set consists of real numbers that is very good property for artificial random data generation. The result of the experiment proved the fact that the diversity and size of data are very important factors for better performance.

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Models of Artificial Multi-Polynomial Higher Order Neural Networks

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3563-9.ch002 ◽

2021 ◽

pp. 97-136

Keyword(s):

Neural Network ◽

Neural Networks ◽

Real World ◽

Network Models ◽

Higher Order ◽

Neural Network Models ◽

Real World Data ◽

World Data ◽

Higher Order Neural Networks ◽

Improved Accuracy

This chapter introduces multi-polynomial higher order neural network models (MPHONN) with higher accuracy. Using Sun workstation, C++, and Motif, a MPHONN simulator has been built. Real-world data cannot always be modeled simply and simulated with high accuracy by a single polynomial function. Thus, ordinary higher order neural networks could fail to simulate complicated real-world data. But MPHONN model can simulate multi-polynomial functions and can produce results with improved accuracy through experiments. By using MPHONN for financial modeling and simulation, experimental results show that MPHONN can always have 0.5051% to 0.8661% more accuracy than ordinary higher order neural network models.

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Accountability of neural networks trained with “real world” data

4th International Conference on Artificial Neural Networks ◽

10.1049/cp:19950557 ◽

1995 ◽

Cited By ~ 5

Author(s):

I.S. Helliwell

Keyword(s):

Neural Networks ◽

Real World ◽

Real World Data ◽

World Data

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Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

Journal of Advanced Transportation ◽

10.1155/2017/6057830 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Thitaree Tanprasert ◽

Chalermpol Saiprasert ◽

Suttipong Thajchayapong

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Real World ◽

Inertial Sensors ◽

Driving Behavior ◽

Real World Data ◽

World Data ◽

Input Signals ◽

Unsupervised Anomaly Detection ◽

Driver Identification

This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

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Learning Rate Optimization in Convolutional Neural Networks for Medical Images Classification

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115357 ◽

2021 ◽

pp. 115357

Author(s):

Yuanyuan Li ◽

Qianqian Zhang ◽

Sang Won Yoon

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Medical Images ◽

Learning Rate ◽

Rate Optimization

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From Reflex to Reflection: Two Tricks AI Could Learn from Us

Philosophies ◽

10.3390/philosophies4020027 ◽

2019 ◽

Vol 4 (2) ◽

pp. 27

Author(s):

Jean-Louis Dessalles

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Cognitive Processes ◽

Real World ◽

Machine Learning Techniques ◽

Human Intervention ◽

Real World Data ◽

World Data ◽

Learning Techniques

Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection.

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