Feature Analysis for Classification of Physical Actions using surface EMG Data

Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this scenario, a successful methodology is to use the EMG signal based information to control the support robotics. For this mechanism to function properly, the EMG signal from the muscles has to be sensed and then the biological motor intention has to be decoded and finally the resulting information has to be communicated to the controller of the robot. An accurate detection of the motor intention requires a pattern recognition based categorical identification. Hence in this paper, we propose an improved classification framework by identification of the relevant features that drive the pattern recognition algorithm. Major contributions include a set of modified spectral moment based features and another relevant inter-channel correlation feature that contribute to an improved classification performance. Next, we conducted a sensitivity analysis of the classification algorithm to different EMG channels. Finally, the classifier performance is compared to that of the other state-of the art algorithm

Review of Food Access in the Shade of Critical Times

Food security is a condition where people at all times have physical, economic, and social access to sufficient, safe, and nutritious food. Even though the world produces enough food for all humans, we have people with low access to food due to several socioeconomic variables, and this lack of food access affects their lives and health in return. On regular days, physical access to food outlets is limited by several variables and that creates categories in food access areas. In extreme weather events, the accessibility becomes more limited due to the impact on mobility, and interruption to electricity or transportation systems. These weather events increase due to climate change, and the future is highly unpredictable. The cumulative effect of climate change and pandemics increases the risk of complicated circumstances for food security. COVID-19 pandemic is considered the largest epidemic, and the onset of these large-scale future epidemics is predicted to be inevitable. This chapter will describe the extent of food access categories in Guilford county of North Carolina pre-COVID-19, and the influence of climate change and the COVID-19 pandemic on the food-access distribution

The Capability Maturity Model as an Industrial Process Improvement Model

Abstract This study examined the potential of adapting the software Capability Maturity Model as a process improvement paradigm within the context of industrial process improvement. Traditional methods of process improvement incorporate some facets of Total Quality Management (TQM), business process improvement (BPI), business process reengineering (BPR), business process management (BPM), benchmarking, regulation, legislation, Six Sigma, and standards. Hypothesis testing showed two statistically significant outcomes regarding the first and the fifth maturity levels reflecting ad hoc processes and optimized processes, respectively

Assessing Domestic vs. International Student Perceptions and Attitudes of Plagiarism

The authors examined students’ perceptions of plagiarism from a higher education teaching institution within the U.S. southeast. This study employed a five-point Likert-scale to examine differences of perceptions between domestic versus international students. Statistically significant outcomes were observed regarding the notions that plagiarism is a necessary evil and that plagiarism is illegal. Respectively, the analyses of the means showed that respondents tended toward disagreement concerning the former notion and neutrality regarding the latter notion

A machine learning based spatio-temporal data mining approach for coastal remote sensing data

Continuous monitoring of coastal ecosystems aids in better understanding of their dynamics and inherent harmful effects. As many of these ecosystems prevail over space and time, there is a need for mining this spatio-temporal information for building accurate monitoring and forecast systems. Harmful Algal Blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been devising a state-of-the-art monitoring and forecasting systems for these HAB events. The efficacy of a monitoring and forecasting system relies on the performance of HAB detection. A Machine Learning based Spatio-Temporal data mining approach for the detection of HAB (STML-HAB) events in the region of Gulf of Mexico is proposed in this work. The spatio-temporal cubical neighborhood around the training sample is considered to retrieve relevant spectral information pertaining to both HAB and Non-HAB classes. A unique relevant feature subset combination is derived through evolutionary computation technique towards better classification of HAB from Non-HAB. Kernel based feature transformation and classification is used in developing the model. STML-HAB model gave significant performance improvements over the current optical detection based techniques by highly reducing the false alarm rate with an accuracy of 0.9642 on SeaWiFS data. The developed model is used for prediction on new datasets for further spatio-temporal analyses such as the seasonal variations of HAB, and sequential occurrence of algal blooms. New variability visualizations are introduced to illustrate the dynamic behavior and seasonal variations of HABs from large spatiotemporal datasets. The results outperformed the ensemble of the currently available empirical methods for HAB detection. The ensemble method is implemented by a new approach for combining the empirical models using a probabilistic neural network model. The model is also compared with the results obtained using various feature extraction techniques, spatial neighborhoods and classifiers

Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues

COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R2-score = 0.44% for COVID-19 cases and R2 = 60% for COVID-19 deaths. The regression model with an R2-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors

Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of R2 = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters

Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning

There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively

Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems

The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to attain both the high detection rate and low false alarm rate. To address this issue, in this paper, we propose an IDS using different machine learning (ML) and deep learning (DL) models. This paper presents a comparative analysis of different ML models and DL models on Coburg intrusion detection datasets (CIDDSs). First, we compare different ML- and DL-based models on the CIDDS dataset. Second, we propose an ensemble model that combines the best ML and DL models to achieve high-performance metrics. Finally, we benchmarked our best models with the CIC-IDS2017 dataset and compared them with state-of-the-art models. While the popular IDS datasets like KDD99 and NSL-KDD fail to represent the recent attacks and suffer from network biases, CIDDS, used in this research, encompasses labeled flow-based data in a simulated office environment with both updated attacks and normal usage. Furthermore, both accuracy and interpretability must be considered while implementing AI models. Both ML and DL models achieved an accuracy of 99% on the CIDDS dataset with a high detection rate, low false alarm rate, and relatively low training costs. Feature importance was also studied using the Classification and regression tree (CART) model. Our models performed well in 10-fold cross-validation and independent testing. CART and convolutional neural network (CNN) with embedding achieved slightly better performance on the CIC-IDS2017 dataset compared to previous models. Together, these results suggest that both ML and DL methods are robust and complementary techniques as an effective network intrusion detection system

Assessing the Recovery Aftermaths of Selected Disasters in the Gulf of Mexico

This research examined quantitatively in-port grain loading levels during the periods preceding and succeeding selected human-made and natural disasters among U.S. Gulf Coast ports. The array of selected disasters consisted of the 2010 British Petroleum oil spill, the 2011 Mississippi River flood, Hurricane Katrina, Hurricane Gustav, and Hurricane Isaac. The outcomes of the analyses showed that the examined in-port Gulf Coast grain loading activities have not fully recovered and achieved the level of normalcy that existed before the examined cataclysms