scholarly journals Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent

2021 ◽  
Author(s):  
Stuart Synakowski ◽  
Qianli Feng ◽  
Aleix Martinez
Keyword(s):  
Unsupervised Algorithms

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

scholarly journals Mitigating DoS attacks in IoT using Supervised and Unsupervised Algorithms – A Survey

2021 ◽  
Vol 1055 (1) ◽  
pp. 012072
Author(s):  
S.B. Gopal ◽  
C. Poongodi ◽  
D. Nanthiya ◽  
R. Snega Priya ◽  
G. Saran ◽  
...  
Keyword(s):  
Dos Attacks ◽  
Unsupervised Algorithms

scholarly journals Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

scholarly journals Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation

2021 ◽  
Author(s):  
Pinar Demetci ◽  
Rebecca Santorella ◽  
Bjorn Sandstede ◽  
Ritambhara Singh
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Integrated Analysis ◽  
Omics Data ◽  
Cell Alignment ◽  
Cell Type ◽  
Type Representation ◽  
Cellular Processes ◽  
Self Tuning ◽  
Unsupervised Algorithms

Integrated analysis of multi-omics data allows the study of how different molecular views in the genome interact to regulate cellular processes; however, with a few exceptions, applying multiple sequencing assays on the same single cell is not possible. While recent unsupervised algorithms align single-cell multi-omic datasets, these methods have been primarily benchmarked on co-assay experiments rather than the more common single-cell experiments taken from separately sampled cell populations. Therefore, most existing methods perform subpar alignments on such datasets. Here, we improve our previous work Single Cell alignment using Optimal Transport (SCOT) by using unbalanced optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. We show that our proposed method, SCOTv2, consistently yields quality alignments on five real-world single-cell datasets with varying cell-type proportions and is computationally tractable. Additionally, we extend SCOTv2 to integrate multiple ($M\geq2$) single-cell measurements and present a self-tuning heuristic process to select hyperparameters in the absence of any orthogonal correspondence information.

scholarly journals Gromov-Wasserstein optimal transport to align single-cell multi-omics data

2020 ◽  
Author(s):  
Pinar Demetci ◽  
Rebecca Santorella ◽  
Björn Sandstede ◽  
William Stafford Noble ◽  
Ritambhara Singh
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Single Cells ◽  
Wasserstein Distance ◽  
Cell Alignment ◽  
Shared Space ◽  
Real World Datasets ◽  
Unsupervised Algorithms

AbstractData integration of single-cell measurements is critical for understanding cell development and disease, but the lack of correspondence between different types of measurements makes such efforts challenging. Several unsupervised algorithms can align heterogeneous single-cell measurements in a shared space, enabling the creation of mappings between single cells in different data domains. However, these algorithms require hyperparameter tuning for high-quality alignments, which is difficult in an unsupervised setting without correspondence information for validation. We present Single-Cell alignment using Optimal Transport (SCOT), an unsupervised learning algorithm that uses Gromov Wasserstein-based optimal transport to align single-cell multi-omics datasets. We compare the alignment performance of SCOT with state-of-the-art algorithms on four simulated and two real-world datasets. SCOT performs on par with state-of-the-art methods but is faster and requires tuning fewer hyperparameters. Furthermore, we provide an algorithm for SCOT to use Gromov Wasserstein distance to guide the parameter selection. Thus, unlike previous methods, SCOT aligns well without using any orthogonal correspondence information to pick the hyperparameters. Our source code and scripts for replicating the results are available at https://github.com/rsinghlab/SCOT.

scholarly journals DDoS Detection on Internet of Things using Unsupervised Algorithms

2021 ◽  
Vol 297 ◽  
pp. 01005
Author(s):  
Hailyie Tekleselassie
Keyword(s):  
Learning Algorithms ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Accuracy Score ◽  
Ddos Attacks ◽  
Knowledge Based ◽  
Ddos Detection ◽  
Iot Devices ◽  
Unsupervised Algorithms

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

2021 ◽  
Vol 11 (2) ◽  
pp. 38-52
Author(s):  
Abhinav Juneja ◽  
Sapna Juneja ◽  
Sehajpreet Kaur ◽  
Vivek Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Healthy Lifestyle ◽  
Age Groups ◽  
Machine Learning Techniques ◽  
Multi Criteria Decision Making ◽  
Learning Techniques ◽  
Supervised Learning Algorithms ◽  
Unsupervised Algorithms ◽  
Early Diagnosis Of Diabetes

Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

Crater Detection Using Unsupervised Algorithms and Convolutional Neural Networks

2019 ◽  
Vol 57 (8) ◽  
pp. 5373-5383 ◽  
Author(s):  
Ebrahim Emami ◽  
Touqeer Ahmad ◽  
George Bebis ◽  
Ara Nefian ◽  
Terry Fong
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crater Detection ◽  
Unsupervised Algorithms

scholarly journals cytometree: a binary tree algorithm for automatic gating in cytometry analysis

2018 ◽  
Author(s):  
Daniel Commenges ◽  
Chariff Alkhassim ◽  
Raphael Gottardo ◽  
Boris Hejblum ◽  
Rodolphe Thiébaut
Keyword(s):  
Flow Cytometry ◽  
Binary Tree ◽  
Computation Time ◽  
R Package ◽  
Information Criteria ◽  
Supplementary Information ◽  
Flow Cytometry Data ◽  
Human Immunology ◽  
Supplementary Material ◽  
Unsupervised Algorithms

AbstractMotivationFlow cytometry is a powerful technology that allows the high-throughput quantification of dozens of surface and intracellular proteins at the single-cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time-consuming nature.ResultsWe present a new unsupervised algorithm called “cytometree” to perform automated population discovery (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distribution. Node splitting is done according to a normalized difference of Akaike information criteria (AIC) between the two models. Post-processing of the tree structure and derived populations allows us to complete the annotation of the derived populations. The algorithm is shown to perform better than the state-of-the-art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP I) project. The algorithm is also applied to a T-cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open-source available algorithm while requiring the shortest computation time.AvailabilityAn R package named “cytometree” is available on the CRAN [email protected]; [email protected] informationSupplementary data are available.

scholarly journals Behavioral and nociceptor states of inflammatory pain across timescales in 2D and 3D

2021 ◽  
Author(s):  
Manon Bohic ◽  
Luke A. Pattison ◽  
Z. Anissa Jhumka ◽  
Heather Rossi ◽  
Joshua K. Thackray ◽  
...  
Keyword(s):  
Sensory Neurons ◽  
Inflammatory Pain ◽  
Physiological State ◽  
Pain Experience ◽  
Behavioral Signatures ◽  
Therapeutic Development ◽  
Unsupervised Algorithms ◽  
2D And 3D

Inflammatory pain represents a complex state involving sensitization of peripheral and central neuronal signaling. Resolving this high-dimensional interplay at the cellular and behavioral level is key to effective therapeutic development. Here, using the carrageenan model of local inflammation of the hind paw, we determine how carrageenan alters both the physiological state of sensory neurons and behaviors at rapid and continuous timescales. We identify higher excitability of sensory neurons innervating the site of inflammation by profiling their physiological state at different time points. To identify millisecond-resolved sensory-reflexive signatures evoked by inflammatory pain, we used a combination of supervised and unsupervised algorithms, and uncovered abnormal paw placement as a defining behavioral feature. For long-term detection and characterization of spontaneous behavioral signatures representative of affective-motivational pain states, we use computer vision coupled to unsupervised machine learning in an open arena. Using the non-steroidal anti-inflammatory drug meloxicam to characterize analgesic states during rapid and ongoing timescales, we identify a return to pre-injury states of some sensory-reflexive behaviors, but by and large, many spontaneous, affective-motivational pain behaviors remain unaffected. Taken together, this comprehensive exploration across cellular and behavioral dimensions reveals peripheral versus centrally mediated pain signatures that define the inflamed state, providing a framework for scaling the pain experience at unprecedented resolution.

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