Investigation of multi-site micro recordings of subthalamic nucleus neurons using machine learning MER with DBS in Parkinson`s – A simulation study

Multineural spikes were acquired with a multisite electrode placed in the hippocampus pyramidal cell layer of non-primate anesthetized snitch animals. If the impedance of each electrode-site is relatively low and the distance amongst electrode sites is appropriately miniatured, a spike generated by a neuron is parallelly recorded at multielectrode sites with different amplitudes. The covariance between the spike of the at each electrode-point and a template was computed as a damping-factor due to the volume conduction of the spike from the neuron to electrode-site. Computed damping factors were vectorized and analyzed by simple but elegant hierarchical-clustering using a multidimensional statistical-test. Since a cluster of damping vectors was shown to correspond to an antidromically identified neuron, spikes of distinct neurons are classified by suggesting to the scatterings of damping vectors. Errors in damping vector computing due to partially overlapping spikes were minimized by successively subtracting preceding spikes from raw data. Clustering errors due to complex-spike-bursts (i.e., spikes with variable-amplitudes) were prevented by detecting such bursts and using only the first spike of a burst for clustering.

Automatic Color Extraction Algorithm of Graphic Design Image Based on Artificial Intelligence

The extraction of color features plays an important role in image recognition and image retrieval. In the past, feature extraction mainly depends on manual or supervised learning, which limits the automation of the whole recognition or retrieval process. In order to solve the above problems, an automatic color extraction algorithm based on artificial intelligence is proposed. According to the characteristics of BMP image, the paper makes use of the conversion between image color space and realizes it in the visual C++6.0 environment. The experimental results show that the algorithm realizes the basic operation of image preprocessing, and realizes the automatic extraction of image color features by proper data clustering algorithm.

Clustering Deviation Index (CDI): A robust and accurate unsupervised measure for evaluating scRNA-seq data clustering

Single-cell RNA-sequencing (scRNA-seq) technology allows us to explore cellular heterogeneity in the transcriptome. Because most scRNA-seq data analyses begin with cell clustering, its accuracy considerably impacts the validity of downstream analyses. Although many clustering methods have been developed, few tools are available to evaluate the clustering "goodness-of-fit" to the scRNA-seq data. In this paper, we propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. Particularly, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.

Data Clustering using Genomic Analysis in Graph model

In the event that the data is addressed as a diagram, wherein the hubs are devices and the hyperlinks establish associations among devices then a bunch might be defined as an associated perspective; i.e., a gathering of devices that are identified with each other, yet that don’t have any association with objects outside the gathering. Bunching is an essential test in the quality examination. This ponders monster impact genetic field. Thusly in the current system, the various genomic assessments are scattered in various dispersed structures. In our proposed work, we endeavour to develop a normal data base for genomic and proteomic assessment using diagram grouping.

Maxmin Data Range Heuristic-based Initial Centroid Method of Partitional Clustering for Big Data Mining

The centroid-based clustering algorithm depends on the number of clusters, initial centroid, distance measures, and statistical approach of central tendencies. The initial centroid initialization algorithm defines convergence speed, computing efficiency, execution time, scalability, memory utilization, and performance issues for big data clustering. Nowadays various researchers have proposed the cluster initialization techniques, where some initialization techniques reduce the number of iterations with the lowest cluster quality, and some initialization techniques increase the cluster quality with high iterations. For these reasons, this study proposed the initial centroid initialization based Maxmin Data Range Heuristic (MDRH) method for K-Means (KM) clustering that reduces the execution times, iterations, and improves quality for big data clustering. The proposed MDRH method has compared against the classical KM and KM++ algorithms with four real datasets. The MDRH method has achieved better effectiveness and efficiency over RS, DB, CH, SC, IS, and CT quantitative measurements.