Fundamentals Of Neural Networks Architectures Algorithms And Applications

McCabe's cyclomatic complexity metric (1976) is widely cited as a useful predictor of various software attributes such as reliability and development effort This critique demonstrates that it is based upon poor theoretical foundations and an inadequate model of software development The argument that the metric provides the developer with a useful engineering approximation is not borne out by the empirical evidence Furthermore, it would appear that for a large class of software it is no more than a proxy for, and in many cases is outperformed by, lines of code<
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A critique of cyclomatic complexity as a software metric

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of intelligent and smart environments and systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Due to the reliance of conventional Machine Learning (ML) techniques on handcrafted features in the extraction process, current research suggests that deep-learning approaches are more applicable to automated feature extraction from raw sensor data. In this work, the generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains. Four baseline LSTM networks are comparatively studied to analyze the impact of using different kinds of smartphone sensor data. In addition, a hybrid LSTM network called 4-layer CNN-LSTM is proposed to improve recognition performance. The HAR method is evaluated on a public smartphone-based dataset of UCI-HAR through various combinations of sample generation processes (OW and NOW) and validation protocols (10-fold and LOSO cross validation). Moreover, Bayesian optimization techniques are used in this study since they are advantageous for tuning the hyperparameters of each LSTM network. The experimental results indicate that the proposed 4-layer CNN-LSTM network performs well in activity recognition, enhancing the average accuracy by up to 2.24% compared to prior state-of-the-art approaches.

https://www.mdpi.com/1424-8220/21/5/1636/pdf

Sakorn Mekruksavanich

Papers

Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network