Preprocessing Techniques for Neuroimaging Modalities: An In-Depth Analysis

Neuroimage analysis and data processing from various neuro-imaging modalities have been a multidisciplinary research field for a long time. Numerous types of research have been carried out in the area for multiple applications of neuroimaging and intelligent techniques to make faster and more accurate results. Different modalities gather information for detecting, treating, and identifying various neurological disorders. Each modality generates different kinds of data, including images and signals. Applying artificial intelligence-based techniques for analysing the inputs from the neuroimaging modalities requires preprocessing. Preprocessing techniques are used to fine-tune the data for better results and the application of intelligent methods. Various techniques and pipelines/workflows (steps for preprocessing the data from the imaging modalities) have been developed and followed by multiple researchers for the preprocessing of neuroimaging data. The preprocessing steps include the steps followed in removing noisy data from the inputs, converting the data to a different format, and adding additional information to improve the performance of the algorithm on the data. In this chapter, we compare the various neuroimaging techniques, the type of data they generate and the preprocessing techniques that various researchers frequently use to process data to apply them in artificial intelligence-based algorithms for the classification, prediction, and prognosis of various neurological disorders

Pemrosesan Awal Data Runtun Waktu Hasil Pengukuran Untuk Identifikasi Sistem Tungku Sinter Degussa

─Data preprocessing is an important and critical step and have a huge impact on the success of the analysis or the subsequent use of the data. Measurement data with free of noise can never be obtained from the sensor measurement in chemical or physical process laboratory due to the noise arising from thermodynamics and quantum effects. In addition, noise also occurs because of a transmission error, faulty memory location, and timing errors at the analog to digital conversion. In this paper carried out research and experiments to find the optimal parameters for filtering spikes noise (outliers) using a median filter on input-output time series data that obtained from the data acquisition of the sintering process. From the experimental results obtained that median filter optimal parameter is using moving windows size N=25. These parameters produce sufficiently high Signal to Noise Ratio (SNR) with the average of 3.6685 and a low Mean Square Error (MSE) with the average of 0.0352 while maintaining the shape of the original signal peaks on the data. Thus the results of data preprocessing can be used in the next step of the data USAge i.e. for system identification using intelligent technique efficiently and accurately. Keywords – Data preprocessing, time series, median filtering, sinterin

DATA ANALYSIS OF ENVIRONMENTAL CONDITIONS INFLUENCING THE WORK OF LABORATORY EQUIPMENT AND APPLICATION OF MACHINE LEARNING MODELS FOR CLASSIFICATION OF POOR CONDITIONS

Environmental conditions can have a crucial impact on the functioning of laboratory equipment. Electric components are sensitive to the influence of certain environmental factors such as temperature, humidity, vibrations, etc. Environmental factors should, therefore, be monitored to avoid their negative influence on the system and potential faults and failures they could cause. Unlike the traditional approaches which required the presence of special staff to monitor environmental factors and react if they are poor, the rise of the Internet of Things enhanced the application of intelligent solutions where human factor is not necessary. In this paper, research on data analysis, preprocessing and intelligent classification of environmental conditions has been conducted. The data was collected by sensors connected to Raspberry Pi. The applied monitoring system setup enabled long-distance monitoring of laboratory conditions through the internet and full applicability of fundamental IoT concepts. Since data preparation is an important step in the process of designing machine learning models, the collected data was analyzed and preprocessed in Python. Intelligent classification of environmental conditions was performed using machine learning models k-Nearest Neighbors and Random Forest. Grid search was used for model selection, and the performances of k-Nearest Neighbors and Random Forest machine learning models were compared. Experimental results show that these machine learning models can be successfully used for intelligent classification of environmental conditions

Towards predictive part quality and predictive maintenance in industrial machining - a data-driven approach

Programs such as Industry 4.0 and Internet of Things contain the promise of intelligent production with smart services . In fact, great advances have already been made in sensor technology and machine connectivity. Production plants continuously generate and communicate large amounts of data and have become cyber-physical systems . However, the task of gaining knowledge from these large amounts of data is still challenging. Data generated by numerical control (NC) and programmable logic controllers (NC) comes in a raw format that doesn’t allow the application of analytical methods directly. Extensive preprocessing and feature engineering has to be applied to structure this data for further analysis. An important application is the timely detection of deviations in the production process which allows immediate reactions and adjustments of production parameters or indicates the necessity of a predictive maintenance action. In our research, we aimed at the identification of special deviant behavior of a grinding machine based on NC data. One finding wast the distinguishing the warm-up program from regular production and the other to recognize imprecise identification of the grinding process window. Both tasks could be solved with extensive preprocessing of the raw data, appropriate feature extraction and feature reduction, and the subsequent application of a clustering algorithm

Training Process Reduction Based On Potential Weights Linear Analysis To Accelarate Back Propagation Network

Learning is the important property of Back Propagation Network (BPN) and finding the suitable weights and thresholds during training in order to improve training time as well as achieve high accuracy. Currently, data pre-processing such as dimension reduction input values and pre-training are the contributing factors in developing efficient techniques for reducing training time with high accuracy and initialization of the weights is the important issue which is random and creates paradox, and leads to low accuracy with high training time. One good data preprocessing technique for accelerating BPN classification is dimension reduction technique but it has problem of missing data. In this paper, we study current pre-training techniques and new preprocessing technique called Potential Weight Linear Analysis (PWLA) which combines normalization, dimension reduction input values and pre-training. In PWLA, the first data preprocessing is performed for generating normalized input values and then applying them by pre-training technique in order to obtain the potential weights. After these phases, dimension of input values matrix will be reduced by using real potential weights. For experiment results XOR problem and three datasets, which are SPECT Heart, SPECTF Heart and Liver disorders (BUPA) will be evaluated. Our results, however, will show that the new technique of PWLA will change BPN to new Supervised Multi Layer Feed Forward Neural Network (SMFFNN) model with high accuracy in one epoch without training cycle. Also PWLA will be able to have power of non linear supervised and unsupervised dimension reduction property for applying by other supervised multi layer feed forward neural network model in future work.Comment: 11 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.42