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Fingerprinting-based indoor positioning systems require a significant amount of time to set up due to the need for prior, offline signal map creation. We propose a mobile phone-based indoor positioning system that can be set up in a short amount of time in any environment with existing Wi-Fi infrastructure. We introduce interpolation into a fingerprinting-based system to reduce the number of reference points needed, leading to a reduction in signal map creation time. The proposed interpolation method is used in conjunction with a particle filter algorithm to provide an accuracy level comparable to the state-of-the-art. We created signal maps at three separate locations using a 100%, 50%, 20%, and 10% scan coverage in order to evaluate the effectiveness of our interpolation on the localization error on a lower scan percentage. We evaluated our signal maps before and after interpolation using 16 tests which included both motion and stationary tests, as well as tests taken 2 and 3 weeks after the initial data gathering. We show that our interpolation method is able to reduce the effects of a dimensional mismatch between signal map reference point vectors and a test sample vector, as well as reduce the effects of signal map aging. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The earth’s population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient.

High-Throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request. © Copyright 2023, Mary Ann Liebert, Inc., publishers 2023.

Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.

As more people rely on smartphones to store sensitive information, the need for robust security measures is all the more pressing. Because traditional one shot authentication methods like PINs and passwords are vulnerable to various attacks, we present a behavioral biometrics based smartphone authentication system using swipes. While previous research focused on a single kind of swipe, our data set features swipes using different fingers and directions collected from 36 users across three sessions. In our system, we experimented with support vector machine (SVM) and random forest (RF) classifiers. We investigated which finger, direction, and classifier provided the best individual swipe authentication results. Then, we analyzed whether fusion of different fingers and directions improved results. The best unimodal result came from a rightward swipe with right thumb using SVM, which resulted in an area under ROC curve (AUC) of 0.936 and an equal error rate (EER) of 0.135. We found that swipes using thumbs offered better performance. Fusion improves results for the most part, and our best result was the combination of a leftward swipe with right thumb and a leftward swipe with left thumb. This combination gave an AUC of 0.969 and EER of 0.081 with the SVM classifier.

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic’s short-term example.

This work explores using Probabilistic Context Free Grammars and Artificial Neural Networks as possible machine learning models for classifying introns into major and minor introns. It presents an intron classification framework that combines probabilistic context free grammars and support vector machines. It also assesses the computational prediction power of these two models in comparison to the Position Weight Matrices technique, which is currently the exclusively used model for intron classification. The comparison is done through experimental analysis, and it shows promising results for Probabilistic Context Free Grammars and Artificial Neural Networks. © 2022 IEEE.

A multi-stage biometric verification system serially activates its verifiers and improves performance-cost trade-off by allowing users to submit a subset of the available biometrics. In the heart of a verifier in multi-stage systems lies the concept of ‘reject option’ where a reject region is used to identify a bad quality test sample. If the match-score falls inside the reject region, no binary (genuine/impostor) decision is made in the current stage and the verifier in the next stage is activated. Recent studies have demonstrated a significant promise of the ‘symmetric rejection method’ in choosing a suitable reject region for multi-stage verification systems. In this paper, we delve into the symmetric rejection method to gain more insights into its error reduction capabilities. Specifically, we develop a theory which mathematically proves that the symmetric rejection method reduces the false accept rate and false reject rate. Then, we empirically validate our theory. Results show that the symmetric rejection method significantly reduces the error rates, both the false accept rate and false reject rate. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5–14 percent among adults aged 30–70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80–90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research.

This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO’s relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.

Online markets offer sellers access to buyers’ information and, thus, the potential to alter prices and products accordingly. In light of this, we undertook an empirical analysis to test for individualization on Amazon.com. We collect data from individuals recruited to shop for household items. Our results indicate evidence of individualization of search results and net prices (via coupons). We found, contrary to what was expected, that demographic, geolocation, and account information play an insignificant role in individualization of search results. Thus, we conclude that individualization is based on more dynamic information, e.g., online browsing behavior. This highlights the fact that sellers’ need for (and use of) buyer information goes beyond the simple information accessible from the buyers’ accounts to a more rigorous monitoring of buyers’ online behavior.

Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. Bone mineral density (BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, fat mass and bone mass for the control group compared to the target group. Higher levels of BMC, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.

Breastfeeding has health benefits for both infants and mothers, yet Black mothers and infants are less likely to receive these benefits. Despite research showing no difference in breastfeeding intentions by race or ethnicity, inequities in breastfeeding rates persist, suggesting that Black mothers face unique barriers to meeting their breastfeeding intentions. The aim of this study is to identify barriers and facilitators that Black women perceive as important determinants of exclusively breastfeeding their children for at least 3 months after birth. Utilizing a Barrier Analysis approach, we conducted six focus group discussions, hearing from Black mothers who exclusively breastfed for 3 months and those who did not. Transcripts were coded starting with a priori parent codes based on theory-derived determinants mapped onto the Socioecological Model; themes were analysed for differences between groups. Facilitators found to be important specifically for women who exclusively breastfed for 3 months include self-efficacy, lactation support, appropriate lactation supplies, support of mothers and partners, prior knowledge of breastfeeding, strong intention before birth and perceptions of breastfeeding as money-saving. Barriers that arose more often among those who did not exclusively breastfeed for 3 months include inaccessible lactation support and supplies, difficulties with pumping, latching issues and perceptions of breastfeeding as time-consuming. Lack of access to and knowledge of breastfeeding laws and policies, as well as negative cultural norms or stigma, were important barriers across groups. This study supports the use of the Socioecological Model to design multicomponent interventions to increase exclusive breastfeeding outcomes for Black women.

Maintaining the excellent state of the road is critical to secure driving and is an obligation of both transportation and regulatory maintenance authorities. For a safe driving environment, it is essential to inspect road surfaces for defects or degradation frequently. This process is found to be labor-intensive and necessitates primary expertise. Therefore, it is challenging to examine road cracks visually; thus, we must effectively employ computer visualization and robotics tools to support this mission. This research provides our initial idea of simulating an Autonomous Robot System (ARS) to perform pavement assessments. The ARS for crack inspection is a camera-equipped mobile robot (i.e., an Android phone) to collect images on the road. The proposed system is simulated using an mBot robot armed with an Android phone that gathers video streams to be processed on a server that has a pre-training Convolutional Neural Networks (CNN) that can recognize crack existence. The proposed CNN model attained 99.0% accuracy in the training case and 97.5% in the testing case. The results of this research are suitable for application with a commercial mobile robot as an autonomous platform for pavement inspections. © 2022 Little Lion Scientific.