Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732; UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876; UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented.i.ANFIS and OLVQ1 are combined to predict UPDRS.ii.OLVQ1 is used for PD data segmentation.iii.ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.
Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics. Extracted data encompassed subject count, age range, syncope protocols, ML type, hemodynamic parameters, and performance metrics. Of the 6301 studies initially identified, 10 studies, involving 1205 participants aged 5 to 82 years, met the inclusion criteria, and formed the basis for it. Selected studies must use ML algorithms in syncope detection with hemodynamic parameters recorded throughout HUTT. The overall ML algorithm performance achieved a sensitivity of 88.8% (95% CI: 79.4-96.1%), specificity of 81.5% (95% CI: 69.8-92.8%) and accuracy of 85.8% (95% CI: 78.6-92.8%). Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. Future enhancements with larger databases are anticipated. Integrating ML can curb needless admissions, refine diagnostics, and enhance the quality of life for syncope patients.
Colorectal cancer poses a significant threat to global health, necessitating the development of effective early detection techniques. However, the potential of the fungal microbiome as a putative biomarker for the detection of colorectal adenocarcinoma has not been extensively explored. We analyzed the viability of implementing the fungal mycobiome for this purpose. Biopsies were collected from cancer and polyp patients. The total genomic DNA was extracted from the biopsy samples by utilizing a comprehensive kit to ensure optimal microbial DNA recovery. To characterize the composition and diversity of the fungal mycobiome, high-throughput amplicon sequencing targeting the internal transcribed spacer 1 (ITS1) region was proposed. A comparative analysis revealed discrete fungal profiles among the diseased groups. Here, we also proposed pipelines based on a predictive model using statistical and machine learning algorithms to accurately differentiate colorectal adenocarcinoma and polyp patients from normal individuals. These findings suggest the utility of gut mycobiome as biomarkers for the detection of colorectal adenocarcinoma. Expanding our understanding of the role of the gut mycobiome in disease detection creates novel opportunities for early intervention and personalized therapeutic strategies for colorectal cancer.•Detailed method to identify the gut mycobiome in colorectal cancer patients using ITS-specific amplicon sequencing.•Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening.•Contribution to the advancement of innovative colorectal cancer diagnostic methods and targeted therapies by applying gut mycobiome knowledge.
The exposure of the air microbiome in indoor air posed a detrimental health effect to the building occupants compared to the outdoor air. Indoor air in hospitals has been identified as a reservoir for various pathogenic microbes. The conventional culture-dependent method has been widely used to access the microbial community in the air. However, it has limited capability in enumerating the complex air microbiome communities, as some of the air microbiomes are uncultivable, slow-growers, and require specific media for cultivation. Here, we utilized a culture-independent method via amplicon sequencing to target the V3 region of 16S rRNA from the pool of total genomic DNA extracted from the dust samples taken from hospital interiors. This method will help occupational health practitioners, researchers, and health authorities to efficiently and comprehensively monitor the presence of harmful air microbiome thus take appropriate action in controlling and minimizing the health risks to the hospital occupants. Key features;•Culture-independent methods offer fast, comprehensive, and unbias profiles of pathogenic and non-pathogenic bacteria from the air microbiomes.•Unlike the culture-dependent method, amplicon sequencing allows bacteria identification to the lowest taxonomy levels.
This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.
Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas. Specifically, we describe the procedure for using rechargeable lithium batteries and solar panels to power the mosquito traps, demonstrate a workflow for processing and storing the mosquitoes in a -20 °C freezer, data management tools including microclimate data, and quality assurance processes to ensure the validity and reliability of the results. A pre- and post-test was utilized to measure participant knowledge levels. Additional research is needed to validate this protocol for monitoring vector-borne diseases in hard-to-reach areas within other countries and settings.
Biochemical oxygen demand (BOD) serves as an important indicator in water quality monitoring. It provides valuable information for studying biology and conducting environmental impact assessments, making it the preferred method for environmental applications. Currently, the most common approach for BOD monitoring is the BOD5 standard detection method. However, this method has several drawbacks, like a long 5-day culture time, extended detection duration, complex operations, and low reproducibility of results. To address these issues, our study introduces a rapid BOD detection method, that focused on optimizing microbial immobilized particles and their detection capabilities. The method demonstrated better detection accuracy, stability, and reproducibility, with results available in less than 8 min. Our customization includes: •Prepared the particles using the cross-linking-embedding method by adding specific modifiers which are Polyvinyl Alcohol (PVA) and diatomite.•Improved the detection results, reducing the overall detection error by over 10%.•Confirmed our method' effectiveness in rapidly detecting BOD solution prepared in the lab, outsourced BOD standard solution and actual waste water samples with high accuracy.
The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. •The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network.
In recent years, there has been a rise in research on sensorium in various academic disciplines. Olfaction is recognized as a sense that is most closely linked to cognition, memory and emotion. Due to this unique feature, studies on various aspects of human olfaction are steadily gaining prominence in the humanities and social sciences. In order to understand how the olfactory modality is marked, several taxonomies and semantic spaces of olfactory terms have been developed. However, the focus has been on the general olfaction lexicon and there is a lack of systematic and comprehensive lexicons for fragrant smells. This article addresses this gap. It adopts a multilingual perspective and describes the process of developing a fragrance lexicon in two languages, Russian and English. A fragrance lexicon refers to a list of words that people might use to describe a perfume. The steps in the lexicon development included •sourcing the lexical items in the two languages•translating and cleaning the word lists•revising and refining the lexiconThe fragrance lexicon presented in this article can be used to aid linguistic analyses of naturally occurring communications about perfumes, such as computational analyses of consumer-generated perfume reviews.
This research attempts to explore the total of 21 potential internal and external shocks to the European market during the Covid-19 Crisis. Using the time series of 1 Jan 2020 to 26 June 2020, I employ a machine learning technique, i.e. Least Absolute Shrinkage and Selection Operator (LASSO) to examine the research question for its benefits over the traditional regression methods. This further allows me to cater to the issue of limited data during the crisis and at the same time, allows both variable selection and regularization in the analysis. Additionally, LASSO is not susceptible to and sensitive to outliers and multi-collinearity. The European market is mostly affected by indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index. There is a significant difference in the predictors before and after the pandemic announcement by WHO. Before the Pandemic period announcement by WHO, Europe was hit by the gold market, EUR/USD exchange rate, Dow Jones index, Switzerland, Spain, France, Italy, Germany, and Turkey and after the announcement by WHO, only France and Germany were selected by the lasso approach. It is found that Germany and France are the most predictors in the European market.•A LASSO approach is used to predict the European stock market index during COVID-19•European market is mostly affected by the indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index.•There is a significant difference in the predictors before and after the pandemic announcement by WHO.
This article encompasses the method related to image segmentation of the Field Emission Scanning Electron Microscope (FESEM) images of Acacia Mangium Wood derived Activated Carbons under different conditions. Image segmentation using Hue-Saturation-Value (HSV) thresholding method was adapted to identify the different pattern composition in the grayscale images by varying the intensity Value (V) and keeping Hue (H) and Saturation (S) to zero, and each pattern was considered as one type of element that constituted the Activated Carbon. The algorithm was developed to compute the percentage of each pattern using non-zero pixels, and on the basis of different patterns, different elements having certain percentage of composition were recorded. Later, these results were compared with the Energy Dispersive X-ray Spectroscopy (EDS) to cross check the difference in percentage of each element present at the surface of the Activated Carbon. Part of this result is published in the article [1], "Comparison of surface properties of wood biomass Activated Carbons and their application against rhodamine B and methylene blue dye" Surfaces and Interfaces vol. 11 (2018) pp1-13.•The methods involved will be useful for characterization of Activated Carbon materials.•Image segmentation using HSV thresholding will inspire other researchers to apply similar concept on other materials.•Different patterns obtained for FESEM images using HSV thresholding was able to determine the presence of multiple elements present in the prepared Activated Carbon samples.
Hybrid methodologies have become popular in many fields of research as they allow researchers to explore various methods, understand their strengths and weaknesses and combine them into new frameworks. Thus, the combination of different methods into a hybrid methodology allows to overcome the shortcomings of each singular method. This paper presents the methodology for two hybrid methods that can be used for time series forecasting. The first combines singular spectrum analysis with linear recurrent formula (SSA-LRF) and neural networks (NN), while the second combines the SSA-LRF and weighted fuzzy time series (WFTS). Some of the highlights of these proposed methodologies are:•The two hybrid methods proposed here are applicable to load data series and other time series data.•The two hybrid methods handle the deterministic and the nonlinear stochastic pattern in the data.•The two hybrid methods show a significant improvement to the single methods used separately and to other hybrid methods.
In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states-such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users' requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %.•The response capability of EV is non-zero during state transformations•Users' travel demand assessment•Seamless integration of vehicle-to-grid technology into the power grid.
Firefighters encounter numerous complex and ever-changing hazards when carrying out emergency response activities, necessitating the development of effective risk profiling methods to enhance both their safety and operational efficiency. This study outlines a comprehensive approach to constructing risk profiles tailored specifically for firefighters, integrating various methodologies to create a robust and adaptable framework. The methods used incorporating historical incident data, environmental variables, and individual firefighter characteristics to identify and assess potential risks. Additionally, the risk profiling framework include Psychosocial risk factors are also considered, allowing for a holistic understanding of the human element in firefighting risk assessment. By developing risk profiles to the specific needs and characteristics of firefighters, this method aims to significantly improve their safety, ability to make decisions, and overall operational efficiency in the demanding and ever-changing setting of emergency response situations. This article discussed methods•To identify safety cultures using questionnaires•To analyse risk from incident reports using content analysis•To verify and validate risk using thematic analysis from Focus Group Discussion.
The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2 ) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3 %. When comparing the DNN model with three alternative machine learning models- support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)- the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost.•High-accuracy DNN models require hyperparameter optimization and neural network architecture selection.•The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3 %.•DNN can serve as an alternate predictive model to empirical formulas or classical numerical models.
The study of holomorphic functions has been recently extended through the application of diverse techniques, among which quantum calculus stands out due to its wide-ranging applications across various scientific disciplines. In this context, we introduce a novel q-differential operator defined via the generalized binomial series, which leads to the derivation of new classes of quantum-convex (q-convex) functions. Several specific instances within these classes were explored in detail. Consequently, the boundary values of the Hankel determinants associated with these functions were analyzed. All graphical representations and computational analyses were performed using Mathematica 12.0.•These classes are defined by utilizing a new q-differential operator.•The coefficient values | a i | ( i = 2 , 3 , 4 ) are investigated.•Toeplitz determinants, such as the second T 2 ( 2 ) and the third T 3 ( 1 ) order inequalities, are calculated.
The use of technology in healthcare is one of the most critical application areas today. With the development of medical applications, people's quality of life has improved. However, it is impractical and unnecessary for medium-risk people to receive specialized daily hospital monitoring. Due to their health status, they will be exposed to a high risk of severe health damage or even life-threatening conditions without monitoring. Therefore, remote, real-time, low-cost, wearable, and effective monitoring is ideal for this problem. Many researchers mentioned that their studies could use electrocardiogram (ECG) detection to discover emergencies. However, how to respond to discovered emergencies in household life is still a research gap in this field.•This paper proposes a real-time monitoring of ECG signals and sending them to the cloud for Sudden Cardiac Death (SCD) prediction.•Unlike previous studies, the proposed system has an additional emergency response mechanism to alert nearby community healthcare workers when SCD is predicted to occur.
Forecasting COVID-19 cases is challenging, and inaccurate forecast values will lead to poor decision-making by the authorities. Conversely, accurate forecasts aid Malaysian government authorities and agencies (National Security Council, Ministry of Health, Ministry of Finance, Ministry of Education, and Ministry of International Trade and Industry) and financial institutions in formulating action plans, regulations, and legal acts to control COVID-19 spread in the country. Therefore, this study proposes Repeated Time-Series Cross-Validation, a new data-splitting strategy to identify the best forecasting model that is capable of producing the lowest error measures value and a high percentage of forecast accuracy for COVID-19 prediction in Malaysia. Some of the highlights of the proposed method are:•A total of 21 models, five data partitioning sets, and four error measures to improve the forecast accuracy of daily COVID-19 cases in Malaysia.•The best model selected produces the lowest error measure value for the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE).•The average 8-day forecast accuracy is 90.2 %. The lowest and highest forecast accuracy was 83.7 % and 98.7 %.
Color-blind is a generic disability whereby the affected individuals are not given the opportunity to benefit from the various functions provided by color that would impact humans physically and psychologically. Although this disability is not fatal, it brought plenty of turbulence in the affected individuals' daily activities. This paper aims to develop a system for recognizing and detecting colors of clothes in images, improve accuracy by using advanced algorithms to handle lighting variations, and provide color matching recommendations to assist color-blind individuals in making informed choices when purchasing shirts. The proposed methodology for color recognition involves:•retrieving the RGB values of a given point from the input image and converting them into HSV values.•creating web application integrated with a machine learning model to classify and predict the corresponding color based on the HSV values.•predicting the color name with suggestions of matching colors will be displayed on the interface.
Environmental DNA (eDNA) metabarcoding is a valuable tool for assessing aquatic biodiversity, but the high cost and complexity of DNA extraction pose challenges for widespread adoption, especially in developing countries. This study presents a cost-effective eDNA extraction method using a guanidine hydrochloride (GuHCl) buffer, proteinase-K digestion, and isopropanol precipitation to improve the detection of fish communities. Comparison with the Qiagen DNeasy Blood & Tissue Kit using MiFish universal primers showed that the GuHCl protocol detected more fish species in freshwater samples, with comparable performance in relative read abundance metrics. However, the GuHCl method exhibited higher PCR inhibition in brackish samples, likely due to salinity and natural inhibitors. The results suggest that the GuHCl-based method is a viable alternative, offering enhanced sensitivity for low-abundance species in freshwater samples and cost savings. This protocol facilitates large-scale eDNA metabarcoding for ecological studies and conservation management efforts.•The GuHCl protocol identified a greater diversity of fish species in freshwater samples than the Qiagen kit, but detected fewer species in brackish water samples.•Both extraction methods demonstrated robust positive correlations in metrics of relative read abundance.