This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University of Potsdam.

Potassco: The Potsdam Answer Set Solving Collection

The estimation of remaining useful life (RUL) of a faulty component is at the centre of system prognostics and health management. It gives operators a potent tool in decision making by quantifying ...

https://ti.arc.nasa.gov/m/pub-archive/1454h/1454%20(Saha).pdf

Comparison of prognostic algorithms for estimating remaining useful life of batteries

Building energy use prediction plays an important role in building energy management and conservation as it can help us to evaluate building energy efficiency, conduct building commissioning, and detect and diagnose building system faults. Building energy prediction can be broadly classified into engineering, Artificial Intelligence (AI) based, and hybrid approaches. While engineering and hybrid approaches use thermodynamic equations to estimate energy use, the AI-based approach uses historical data to predict future energy use under constraints. Owing to the ease of use and adaptability to seek optimal solutions in a rapid manner, the AI-based approach has gained popularity in recent years. For this reason and to discuss recent developments in the AI-based approaches for building energy use prediction, this paper conducts an in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple single AI-based prediction models improves the prediction accuracy manifold. This paper elaborates the principles, applications, advantages and limitations of these AI-based prediction methods and concludes with a discussion on the future directions of the research on AI-based methods for building energy use prediction.

A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

Variable importance analysis: A comprehensive review

Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields - time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.

Support Vector Regression

In this paper a neural network based method of local rainfall prediction is proposed. This method is developed based on past observations on various atmospheric parameters such as temperature, relative humidity, vapor presser, etc. We propose a neural network model whose architecture combines several multilayer perceptron networks (MLPs) to realize better performance after capturing the seasonality effect in the atmospheric data. We also demonstrate that the use of appropriate features can further improve the performance in prediction accuracy. These observations inspired us to use a feature selection MLP, FSMLP, (instead of MLP) which can select good features on-line while learning the prediction task. The FSMLP is used as a preprocessor to select good features. The combined use of FSMLP and SOFM-MLP results in a network system that uses only very few inputs but can produce good prediction.

A connectionist model for rainfall prediction

The manual identification of different types of atmospheric microstructures recorded by SODAR SOund Detection And Ranging is a tedious task and can be performed only by an expert with broad experience. To avoid this manual task, a neural network based method of SODAR structure classification system is proposed. This method is developed based on past observations of various meteorological parameters such as temperature, relative humidity and vapour pressure, along with different features computed from the SODAR structure data, which are images representing the dynamics of the planetary boundary layer PBL. The patterns of these images indicate the structure of different thermal patterns of the atmosphere. We propose a neural network model whose architecture combines multilayer perceptron networks MLPs to realize better performance after capturing the seasonality and other related effects in the atmospheric data. We also demonstrate that the use of appropriate features can further improve performance of the prediction system. These observations inspired us to use a feature selection neural network which can select good features online while learning the prediction task. The feature selection neural network is used as a preprocessor to select good features. The combined use of feature selection neural network and MLP, i.e. FSMLP feature selection multilayer perception results in a neural network system that uses only very few inputs but can produce a good classifier. Here we develop a real-time system that classifies the SODAR patterns automatically.

A neurocomputing model for SODAR structure classification

Breast cancer is a significant global health concern, and early detection is crucial for improving survival rates. The article reviews various studies that investigate different methods for detecting breast cancer using non-invasive imaging techniques with a focus on thermal imaging, including feature extraction, image segmentation, and machine learning. It concludes that developing efficient and accurate breast cancer screening software requires an integrated approach to data collection, image processing, and machine learning algorithms. The article presents a novel technique for developing breast cancer screening software using Rotational Thermographic Imaging, dynamic temperature-based data collection, Colour-based Infrared Image Processing, and a Machine Learning algorithm to provide a complete breast imaging in sitting position to reduce the chances of missing abnormalities. Image processing and machine learning techniques are utilised to extract a comprehensive relevant feature set from the captured images and used to train a machine learning model. The system was tested on an increasing patient population in a clinical setting deployed at a hospital. The algorithm’s performance was evaluated using several metrics, including sensitivity (82.14%), specificity (98.33%), and accuracy (93.27%). The results demonstrate that the proposed algorithm achieved high accuracy and sensitivity, making it a promising tool for breast cancer screening. 

Dipak Chandra Patranabis

Papers

Support Vector Regression

A connectionist model for rainfall prediction

A neurocomputing model for SODAR structure classification

Efficient infrared image processing and machine learning algorithm for breast cancer screening