Search

Aim Methods Results Conclusions To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.T2D patients with (responders) or without response (non‐responders) to metformin were recruited, and their serum samples were used for metabolomic analysis to identify candidate biomarkers. Moreover, the efficacy of metformin was verified by insulin‐resistant mice, and the candidate biomarkers were verified to determine the biomarkers. Five different machine learning methods were used to construct the integrated biomarker profiling (IBP) with the biomarkers to predict the response of T2D patients to metformin.A total of 73 responders and 63 non‐responders were recruited, and 88 differential metabolites were identified in the serum samples. After being verified in mice, 19 of the 88 were considered as candidate biomarkers. Next, after metformin regulation, nine candidate biomarkers were confirmed as the biomarkers. After comparing five machine learning models, the nine biomarkers were constructed into the IBP for predicting the response of T2D patients to metformin based on the Naïve Bayes classifier, which was verified with an accuracy of 89.70%.The IBP composed of nine biomarkers can be used to predict the response of T2D patients to metformin, enabling clinicians to start a combined medication strategy as soon as possible if T2D patients do not respond to metformin. [ABSTRACT FROM AUTHOR]

Taiwan's auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetrically around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples; handling complex patterns, correlations, and unstructured data; and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities. [ABSTRACT FROM AUTHOR]

Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies. [ABSTRACT FROM AUTHOR]

Traffic accidents are still the main cause of fatalities, injuries and significant delays in highways. Understanding the accident contributing factor is imperative to increase safety in a traffic network. Recent research confirms that predictive modeling is an important tool to comprehend accident contributing factors. However, little effort has been put forward to explain complex machine learning models and their feature effects in accident prediction models. Thus, this study aims to build predictive models based on different machine learning methods and tries to explain the most contributing factors by using Shapley values which was developed based on game theory. Decision Trees, Neural Networks with Multilayer Perceptron (MLP), Support Vector Classifier, Case-Based Reasoning and Naive Bayes Classifier were used to predict the injury severity in accidents. Belt usage, alcohol consumption and speed violations were found as the most effective features and MLP gave the highest accuracy among all the applied predictive models. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a particular form of content analysis, and its application has become popular with the growth of Internet platforms where a wide range of content is generated. Today, various classifiers use for sentiment analysis, and in this article, we show an example of using a Naïve Bayesian classifier. The aim is to examine the consistency of classifying textual materials into a positive, negative or neutral tone by analysts and the Bayesian algorithm. The hypotheses are that there is an increase in the agreement between the two ways of classifying textual materials as (1) the complexity of the formulations and (2) the size of the learning datasets increases. Based on the results, both hypotheses were accepted, but only on certain groups of messages. Increasing the size of the learning datasets and increasing the complexity of the formulations helped the classification accuracy for messages in a positive tone, while the classification accuracy for messages in other tones was high and equal regardless of varying the parameters. Correlation analysis showed a high positive correlation between the outcomes the Bayesian algorithm classified and the tones the analyst determined (r = 0,816). [ABSTRACT FROM AUTHOR]

Pharmacoencephalography (pharmaco-EEG) is a prominent instrument for the pharmacological screening of new psychoactive molecules. This experimental approach has not remained a vestige of neurobiological studies, and can be used successfully to complete today's research objectives. The development and rise to universal use of machine learning techniques opens up novel prospects for the use of pharmaco-EEG data to solve the problems of classification and prognosis. We have previously shown that naïve Bayes classifier (NBC) combined with principal component analysis (PCA) can be used to differentiate between antipsychotic and sedative drug effects as well as to distinguish among the antipsychotics' effects. In the present study, we evaluated the possibility to employ this method to assess the dose-dependency of antipsychotic effects. The experiments were carried out in white outbred male rats with chronically implanted electrocorticographic electrodes. As the agents of interest, we chose two drugs with antipsychotic activity, chlorpromazine and promethazine, in three doses each (0.1, 1, 10 mg/kg and 0.5, 5 and 20 mg/kg, respectively). The training set, used as a reference to determine the pharmacological effects of the agents of interest, included the D2-dopamine receptor blocker haloperidol, M-cholinergic receptor blocker tropicamide, H1-histamine receptor blocker chloropyramine, the sedative dexmedetomidine, and the anxiolytic phenazepam. We have shown that the lowest chlorpromazine dose (0.1 mg/kg) can be characterized as antipsychotic with a marked histaminolytic effect, while the highest one (10 mg/kg) exhibits predominantly antipsychotic activity with a cataleptogenic effect. All three doses demonstrated anticholinergic activity, which increased with the dose. For promethazine, we observed a clear dose-dependent shift from antipsychotic action to cataleptogenic, alongside a notable antimuscarinic effect of all doses. None of promethazine doses showed any resemblance to chloropyramine, which probably indicates its anti-dopaminergic and antimuscarinic effects being able to mask its H1-antihistamine effect in the used dose range. In summary, our results demonstrate that NBC combined with PCA can be used to determine the dose-dependency of antipsychotic agents' effects based on their impact on electrocorticogram parameters. Further development of this method as well as expansion of psychotropic agent electropharmacogram library would allow for more precise prediction of pharmacological activity of the agents of interest. [ABSTRACT FROM AUTHOR]

Sentiment analysis involves extricating and interpreting people's views, feelings, beliefs, etc., about diverse actualities such as services, goods, and topics. People intend to investigate the users' opinions on the online platform to achieve better performance. Regardless, the high-dimensional feature set in an online review study affects the interpretation of classification. Several studies have implemented different feature selection techniques; however, getting a high accuracy with a very minimal number of features is yet to be accomplished. This paper develops an effective hybrid approach based on an enhanced genetic algorithm (GA) and analysis of variance (ANOVA) to achieve this purpose. To beat the local minima convergence problem, this paper uses a unique two-phase crossover and impressive selection approach, gaining high exploration and fast convergence of the model. The use of ANOVA drastically reduces the feature size to minimize the computational burden of the model. Experiments are performed to estimate the algorithm performance using different conventional classifiers and algorithms like GA, Particle Swarm Optimization (PSO), Recursive Feature Elimination (RFE), Random Forest, ExtraTree, AdaBoost, GradientBoost, and XGBoost. The proposed novel approach gives impressive results using the Amazon Review dataset with an accuracy of 78.60 %, F1 score of 79.38 %, and an average precision of 0.87, and the Restaurant Customer Review dataset with an accuracy of 77.70 %, F1 score of 78.24 %, and average precision of 0.89 as compared to other existing algorithms. The result shows that the proposed model outperforms other algorithms with nearly 45 and 42% fewer features for the Amazon Review and Restaurant Customer Review datasets. [ABSTRACT FROM AUTHOR]

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data. [ABSTRACT FROM AUTHOR]

The information retrieved from the academic texts such as articles, proceedings, thesis and project proposals are used for a wide range of purposes. In the first phase of this study; a library, that can transform the raw text into a standard form, is created by considering the key terms/features in the engineering field. Then, the key terms that can best represent the document are retrieved and a similarity detection algorithm is developed using these terms. Finally, the Naïve Bayes Classifier in machine learning is used to assign the documents to the appropriate engineering sub-fields. The project proposals submitted to TUBITAK Academic Research Funding Program Directorate (ARDEB) are analyzed as a case study. The results indicate that the proposed similarity algorithm correctly detects almost all of the revised proposals while the accuracy of the classifier is 83.3% in the first prediction and reaches up to 96.4% in the first three predictions over a sample of 1255 proposals. [ABSTRACT FROM AUTHOR]

Along with the growth of the Internet comes a competitive environment among Internet service providers. In this regard, quality of service (QoS) and customer's quality of experience (QoE) are introduced as the two main criteria of satisfaction for network users/regulators. In this paper, we evaluate these two criteria for one of the largest Internet service communication networks in Iran. By providing a predictive model, we propose a solution to improve the quality of the communication. Our model predicts the quality of experience from the quality-of-service parameters with an accuracy of roughly 90%. Next, we reverse-engineer the relationship between the quality of experience and quality of service to develop a service level agreement (SLA) contract. The relationship between quality of experience and quality of service is then compiled into a set of if-then rules. By using a decision tree classifier, we were able to set the quality-of-service parameter thresholds for the gold, silver, and bronze SLAs. [ABSTRACT FROM AUTHOR]

Aim: Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non‐specific signs. We investigate the predictive value of machine learning‐assisted analysis of non‐invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis. Methods: Single centre study, including a representative cohort of 325 infants (2866 hospitalisation days). Personalised event timelines including interventions and clinical findings were generated. Time‐domain features from heart rate, respiratory rate and oxygen saturation values were calculated and demographic factors included. Sepsis prediction was performed using Naïve Bayes algorithm in a maximum a posteriori framework up to 24 h before clinical sepsis suspicion. Results: Twenty sepsis cases were identified. Combining multiple vital signs improved algorithm performance compared to heart rate characteristics alone. This enabled a prediction of sepsis with an area under the receiver operating characteristics curve of 0.82, up to 24 h before clinical sepsis suspicion. Moreover, 10 h prior to clinical suspicion, the risk of sepsis increased 150‐fold. Conclusion: The present algorithm using non‐invasive patient data provides useful predictive value for neonatal sepsis detection. Machine learning‐assisted algorithms are promising novel methods that could help individualise patient care and reduce morbidity and mortality. [ABSTRACT FROM AUTHOR]

We examine the significance of the predictive potential of EPI cystatin C (EPI CysC) in combination with NTproBNP, sodium, and potassium in the evaluation of renal function in patients with cardiorenal syndrome using standard mathematical classification models from the domain of artificial intelligence. The criterion for the inclusion of subjects with combined impairment of heart and kidney function in the study was the presence of newly discovered or previously diagnosed clinically manifest cardiovascular disease and acute or chronic kidney disease in different stages of evolution. In this paper, five standard classifiers from the field of machine learning were used for the analysis of the obtained data: ensemble of neural networks (MLP), ensemble of k-nearest neighbors (k-NN) and naive Bayes classifier, decision tree, and a classifier based on logistic regression. The results showed that in MLP, k-NN, and naive Bayes, EPI CysC had the highest predictive potential. Thus, our approach with utility classifiers recognizes the essence of the disorder in patients with cardiorenal syndrome and facilitates the planning of further treatment. [ABSTRACT FROM AUTHOR]

Spread-F (SF) is one of the most important types of the ionospheric irregularities as it causes ionospheric scintillation which can severely affect the performance and reliability of communication, navigation, and radar systems. The ionosonde provides the most effective and economical way to study the ionosphere and SF. However, the manual identification of SF from an ionogram is boring and hard work. To automatically identify SF on the ionogram and extend the study of SF into the middle and low latitudes of East Asia, this paper presents a statistical analysis of SF in this region, based on the naïve Bayesian classifier. The results showed that the accuracy of automatic identification reached up to 97% on both the validation datasets and test datasets composed of Mohe, I-Cheon, Jeju, Wuhan, and Sanya ionograms, suggesting that it is a promising way to automatically identify SF on ionograms. Based on the classification results, the statistical analysis shows that SF has a complicated morphology in the middle and low latitudes of East Asia. Specifically, there is a peak of occurrence of SF in the summer in I-Cheon, Jeju, Sanya, and Wuhan; however, the Mohe station has the highest occurrence rate of SF in December. The different seasonal variations of SF might be due to the different geographic local conditions, such as the inland-coastal differences and formation mechanism differences at these latitudes. Moreover, SF occurs more easily in the post-midnight hours when compared with the pre-midnight period in these stations, which is consistent with the previous results. Furthermore, this paper extracts the frequency SF (FSF) index and range SF (RSF) index to characterize the features of SF. The results shows that the most intense FSF/RSF appeared in the height range of 220–300 km/1–7 MHz in these stations, although there are different magnitude extensions on different season in these regions. In particular, strong spread-F (SSF) reached its maximum at the equinox at Sanya, confirming the frequent SSF occurrence at the equinox at the equator and low latitudes. These results would be helpful for understanding the characteristics of SF in East Asia. [ABSTRACT FROM AUTHOR]

The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable into "time" and "event" components and using the latter as a target variable for various machine-learning classification algorithms, which are almost assumption-free, unlike the Cox model. While the time component is continuous and is used as one of the covariates, i.e., input variables for various classification algorithms such as logistic regression, naïve Bayes classifiers, decision trees, random forests, and artificial neural networks, the event component is binary and thus may be modeled using these classification algorithms. Moreover, we apply the proposed method to predict a decrease or non-decrease of IgG and IgM blood antibodies against COVID-19 (SARS-CoV-2), respectively, below a laboratory cut-off, for a given individual at a given time point. Using train-test splitting of the COVID-19 dataset ( n = 663 individuals), models for the mentioned algorithms, including the Cox proportional hazard model, are learned and built on the train subsets while tested on the test ones. To increase robustness of the model performance evaluation, models' predictive accuracies are estimated using 10-fold cross-validation on the split dataset. Even though the time-to-event variable decomposition might ignore the effect of individual data censoring, many algorithms show similar or even higher predictive accuracy compared to the traditional Cox proportional hazard model. In COVID-19 IgG decrease prediction, multivariate logistic regression (of accuracy 0.811 ), support vector machines (of accuracy 0.845 ), random forests (of accuracy 0.836 ), artificial neural networks (of accuracy 0.806 ) outperform the Cox proportional hazard model (of accuracy 0.796 ), while in COVID-19 IgM antibody decrease prediction, neither Cox regression nor other algorithms perform well (best accuracy is 0.627 for Cox regression). An accurate prediction of mainly COVID-19 IgG antibody decrease can help the healthcare system manage, with no need for extensive blood testing, to identify individuals, for instance, who could postpone boosting vaccination if new COVID-19 variant incomes or should be flagged as high risk due to low COVID-19 antibodies. [ABSTRACT FROM AUTHOR]

Background: In mammals, normal fertilization depends on the structural and functional integrity of the zona pellucida (ZP), which is an extracellular matrix surrounding oocytes. Mutations in ZP may affect oogenesis, fertilization and early embryonic development, which may cause female infertility. Methods: A PubMed literature search using the keywords 'zona pellucida', 'mutation' and 'variant' limited to humans was performed, with the last research on June 30, 2022. The mutation types, clinical phenotypes and pregnancy outcomes were summarized and analyzed. The naive Bayes classifier was used to predict clinical pregnancy outcomes for patients with ZP mutations. Results: A total of 29 publications were included in the final analysis. Sixty-nine mutations of the ZP genes were reported in 87 patients with different clinical phenotypes, including empty follicle syndrome (EFS), ZP-free oocytes (ZFO), ZP-thin oocytes (ZTO), degenerated and immature oocytes. The phenotypes of patients were influenced by the types and location of the mutations. The most common effects of ZP mutations are protein truncation and dysfunction. Three patients with ZP1 mutations, two with ZP2 mutations, and three with ZP4 mutations had successful pregnancies through Intracytoplasmic sperm injection (ICSI) from ZFO or ZTO. A prediction model of pregnancy outcome in patients with ZP mutation was constructed to assess the chance of pregnancy with the area under the curve (AUC) of 0.898. The normalized confusion matrix showed the true positive rate was 1.00 and the true negative rate was 0.38. Conclusion: Phenotypes in patients with ZP mutations might be associated with mutation sites or the degree of protein dysfunction. Successful pregnancy outcomes could be achieved in some patients with identified ZP mutations. Clinical pregnancy prediction model based on ZP mutations and clinical characteristics will be helpful to precisely evaluate pregnancy chance and provide references and guidance for the clinical treatment of relevant patients. [ABSTRACT FROM AUTHOR]

Motivation: Application of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical–chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced. Methods and results: We propose a new method for extracting CNEs from texts based on the naïve Bayes classifier combined with specially developed filters. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparati'on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method. Conclusion: The obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry. [ABSTRACT FROM AUTHOR]

Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'. [ABSTRACT FROM AUTHOR]

Research and development of novel methods to determine the effects of antipsychotic agents is an important challenge for experimental biomedicine. Although behavioural tests, the ones most commonly used for pharmacological screening, are quite efficient for the evaluation of drug effects on animal anxiety and locomotion, they hardly allow to detect antipsychotic activity. Pharmacoelectroencephalography (pharmaco-EEG), which is based on the principle of different psychoactive agents producing distinct changes in brain electrical activity, could represent a viable alternative approach to that task. The rapid evolution of machine learning techniques has opened new possibilities for using pharmaco-EEG data for the purposes of classification and prediction. This work describes an experimental approach to the assessment of specific activity and pharmacological profiling of antipsychotic agents using naïve Bayes classifier, a simple probabilistic classifier widely employed in biomedical research. The experiments were conducted in white outbred male rats with chronically implanted electrocorticographic electrodes. To serve as the training set, a library was assembled containing electrocorticograms (ECoG) following the administration of antipsychotic agents: chlorpromazine, haloperidol, droperidol, tiapride, and sulpiride. For each sample, ECoG parameters before and after drug administration were calculated, and a total of 132 amplitude and spectral signal parameters were taken into analysis. Principal component analysis was used to reduce dimensionality. Using naïve bayes classifier, we were able to detect and qualify distinct effects of antipsychotic agents on brain electrical activity parameters in rats, allowing them to be differentiated from phenazepam, a benzodiazepine tranquilizer with sedative properties. Moreover, this approach proved effective to distinguish among the antipsychotics as well as between them and other agents with similar receptor binding affinity profiles, e.g., the tricyclic antidepressant amitriptyline. Thus, the method we propose can be used to discern between antipsychotic and sedative effects of drugs as well as to compare the effects across different antipsychotic agents. [ABSTRACT FROM AUTHOR]

Those who are found in close contact with COVID-19 patients and are also negative by polymerase chain reaction (PCR) test may act without waiting for the incubation period to elapse, can become infectious and spread the infection. A machine learning model that can evaluate the risk of infection in close contact with COVID-19 patients within the incubation period from the contact status reported from the index case was created using posterior probabilities. To confirm actual predictability, a verification test was conducted on 169 new close contacts, and the machine learning model was compared with four experienced healthcare workers for the predictability. In a verification test, 33 of the 169 contacts were infected with COVID-19 during the incubation period, and 13 of 33 were negative on initial PCR test, after that the disease developed and their PCR test became positive. The machine learning model predicted the eventual infection in 12 of 13 patients who had negative results on the initial PCR test. In the verification test, the sensitivity of the machine learning model was 0.879 and the specificity was 0.588. The mean−standard deviation of the sensitivity and the specificity of the four health care workers was 0.568 (0.230) for sensitivity and 0.689 (0.103) for specificity. If it is possible to convey that individual risk of infection, the close contact may take suppressive action during the incubation period regardless of the result of the initial PCR test, thereby preventing secondary spread of infection. [ABSTRACT FROM AUTHOR]

Due to insufficiency of parking space, people face many problems in finding a vacant slot to park their vehicles in the parking area. This problem is giving rise to many other problems such as traffic congestion, wastage of fuel, money and time. Therefore, there is a need to develop an intelligent parking management system for better utilization of the resources. The main objective of the paper is to design an efficient parking system to search for the vacant slots in the parking area. In this paper, experiments have been conducted on standard dataset "PKLot" which consists of 695,899 images of two different parking lots. These images were captured from three different camera views under different climatic conditions like rainy, sunny and cloudy. Various machine learning (ML) techniques like Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) with different parameters have been applied on the dataset. Further, an ensemble model for classification of occupied and vacant slots in the parking area has been developed which improves the performance of the system as compared with other state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Self-grooming behavior in rodents serves as a valuable behavioral index for investigating stereotyped and perseverative responses. Most current grooming analyses rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer. Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are input into a naïve Bayes classifier, trained with manual video observations. The effectiveness of this method was tested using CIN-d mice, an animal model developed through early-life depletion of striatal cholinergic interneurons (CIN-d) and featuring prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording. The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations than controls. However, this elevation was not correlated with increases in grooming force. Notably, the dopaminergic antagonist haloperidol reduced grooming force and duration. In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force. Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of neuropsychiatric disorders. • A novel method is presented to automatically detect grooming behaviors in rodents. • Frequency analyses of force plate actometric tracking enables grooming detection. • A naïve Bayes classifier trained on video observation yielded 93.7% accuracy. • This method provides information on not only grooming time, but also force. • This method validated recent findings from a mouse model of tic pathophysiology. [ABSTRACT FROM AUTHOR]

Purpose: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.Methods: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.Results: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.Conclusion: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma. [ABSTRACT FROM AUTHOR]

Unfounded child sexual abuse (CSA) allegations take investigative resources from real cases and have detrimental consequences for the people involved. The Finnish Investigative Instrument of Child Sexual Abuse (FICSA) supports investigators by estimating the probability of a CSA allegation being true based on the child's background information. In the current study, we aimed at making FICSA resistant to deception. Two gender-specific questionnaires with FICSA questions and additional "trap" questions were constructed. The trap questions were designed to seem statistically related to CSA although they were not. Combining the answers of 278 real victims and 275 16–year-old students, instructed to simulate being CSA victims, we built a Naïve Bayes classifier that successfully separated the two groups (AUC = 0.91 for boys and 0.92 for girls). By identifying false allegations early in the investigation, authorities' resources can be directed towards allegations that are more likely true, effectively helping actual CSA victims. [ABSTRACT FROM AUTHOR]

Software defect prediction constitutes an important discipline in software development life-cycle. Among the techniques employed in this domain, Naive Bayes (NB) classifier is cited by a large number of researchers for its simple structure and remarkable classification performance notwithstanding the concern of whether it is theoretically justified or not. More concisely, NB is fundamentally built on the strong assumption of conditional independence of attributes, and the major question here is the compliance of software metrics with this assumption. To address this question, we propose a novel framework "MLMNB-SDP" equipped with a statistical hypothesis testing method to detect those software metrics with a significant conditional dependency. MLMNB-SDP is designed to handle conditional dependencies via a single latent variable in a predefined structure which is responsible for preserving the connection between pairs of software metrics when the class variables are instantiated. We evaluate the effectiveness of our approach based on its capability to measure conditional dependency of software metrics and defect prediction performance. For the former one, we employ Conditional Mutual Information (CMI), and for the later one we use three settings for defect prediction; (1) Within-Project Defect-Prediction (WPDP), (2) Cross-Project Defect-Prediction (CPDP), and (3) stratified k-fold cross-validation. Our metrics dependency analysis results indicate that traditional file-level software metrics demonstrate a significant conditional mutual dependency and the application of naive Bayes classifier in this domain is not theoretically acceptable. Our results based on the three settings indicate that MLMNB-SDP improves naive Bayes classifier 5.45% to 75.86% and outperforms well-known benchmark classifiers, i.e., Random Forest and Logistic Regression, regarding a significant increase in Precision, Recall, and F1 Score, Mathew's Correlation Coefficient (MCC), and area under the ROC curve (AUC) values. [ABSTRACT FROM AUTHOR]

Software defect prediction constitutes an important discipline in software development life-cycle. Among the techniques employed in this domain, Naive Bayes (NB) classifier is cited by a large number of researchers for its simple structure and remarkable classification performance notwithstanding the concern of whether it is theoretically justified or not. More concisely, NB is fundamentally built on the strong assumption of conditional independence of attributes, and the major question here is the compliance of software metrics with this assumption. To address this question, we propose a novel framework "MLMNB-SDP" equipped with a statistical hypothesis testing method to detect those software metrics with a significant conditional dependency. MLMNB-SDP is designed to handle conditional dependencies via a single latent variable in a predefined structure which is responsible for preserving the connection between pairs of software metrics when the class variables are instantiated. We evaluate the effectiveness of our approach based on its capability to measure conditional dependency of software metrics and defect prediction performance. For the former one, we employ Conditional Mutual Information (CMI), and for the later one we use three settings for defect prediction; (1) Within-Project Defect-Prediction (WPDP), (2) Cross-Project Defect-Prediction (CPDP), and (3) stratified k-fold cross-validation. Our metrics dependency analysis results indicate that traditional file-level software metrics demonstrate a significant conditional mutual dependency and the application of naive Bayes classifier in this domain is not theoretically acceptable. Our results based on the three settings indicate that MLMNB-SDP improves naive Bayes classifier 5.45% to 75.86% and outperforms well-known benchmark classifiers, i.e., Random Forest and Logistic Regression, regarding a significant increase in Precision, Recall, and F1 Score, Mathew's Correlation Coefficient (MCC), and area under the ROC curve (AUC) values. [ABSTRACT FROM AUTHOR]

The risk of incurring financial losses from fraudulent claims is an issue concerning all insurance companies. The detection of such claims is not an easy task. Moreover, a number of old-school methods have proven to be inefficient. Statistical techniques for predictive modelling have been applied to detect fraudulent claims. In this article, we compare two techniques: Artificial neural networks and the Naïve Bayes classifier. The theory underpinning both techniques is discussed and an application of these techniques to a dataset of labelled automobile insurance claims is then presented. Fraudulent claims only constitute a small percentage of the total number of claims. As a result, datasets tend to be unbalanced. This in turn causes a number of problems. To overcome such issues, techniques which deal with unbalanced datasets are also discussed. The suitability of Neural Networks and the Naïve Bayes classifier to the dataset is discussed and the results are compared and contrasted by using a number of performance measures including ROC curves, Accuracy, AUC, Precision, and Sensitivity. Both classification techniques gave comparable results with the Neural network giving slightly better results than the Naïve Bayes classifier on the training dataset. However, when applied to the test data, the Naïve Bayes classifier slightly outperformed the artificial neural network. [ABSTRACT FROM AUTHOR]

Ontology is the fundamental representation used to link the various concepts of each field. It is widely utilized in the agricultural sector for the classification and expansion of new data and understanding. Besides this, the agriculture ontology enables optimization of feature weights to exact text from the source. However, the conventional methods exploit the vector space model for the indication of context. Nevertheless, it is not an exact solution since it has some shortcomings like undefined dimensionality and insufficiency of semantic data. Hence we propose a novel Naïve Bayes Classifier using Levy Flight distributed optimization algorithm-based agricultural ontology to optimize the text mining. The dimensionality of the text tagging can be reduced with the aid of Principal Component Analysis and the dimensionally reduced data can be mapped by a feature mapping method. Further, the Naïve Bayes classifier is exploited to estimate the weight of the text features. Furthermore, the Levy Flight distributed optimization algorithm is exploited to optimize the feature weight. Hence our proposed method provides better-optimized feature weight to perform the agricultural ontology. Besides, our proposed method is compared with existing GA-DAM, RENT, GRO, and CCE methods and concluded that our work has a better performance than all other works. [ABSTRACT FROM AUTHOR]

The main factor in the success of the projects is project management. Project management consists of many processes and elements whose international standards have been defined and continue to be defined. One of these processes is stakeholder analysis. There is no software tool that meets the standards in stakeholder analysis in project management in the world and in our country. Therefore, it seems that there is a serious deficiency in this regard. The purpose of this study is to design the algorithm and software tool that enables stakeholder analysis independent from the field of application (project subject) and according to international standards. For this purpose, firstly, the requirements that enable stakeholder analysis to be carried out independently of the problem area have been researched and defined. In defining the requirements, the standards determined by the international project board are taken into consideration. After defining the requirements, actors of stakeholder analysis, the functions that each actor should have, stakeholder analysis classes and data models were developed. An important stage of stakeholder analysis is the effective collection and processing of data that will be a source for the analysis. For this purpose, a flexible application architecture is designed that enables data collection from mobile devices and various social media applications. In designing the software architecture, it was adhered to the software design principles. In the modeling of data that is the source of stakeholder analysis using artificial intelligence-based algorithms, software modules that provide classification algorithms, primarily navie bayes and decision trees, are designed. The designed stakeholder analysis tool is expected to be the source of many future studies. [ABSTRACT FROM AUTHOR]

Poultry red mite (PRM), Dermanyssus gallinae, causes egg drop production, anemia and can be a vector in transmitting diseases. The PRM control mainly focuses on usage of the conventional chemical biocides. The objective of this study was to analyze the farmers' perception regarding the impact, management and control of PRM in Macedonian layer farms. The data were collected with direct on-site visits using a unified questionnaire. In total, 29 poultry farms (28% of farms in the country), all with conventional cages, were part of this study. The data were analyzed by descriptive statistics, inferential statistics and Naïve Bayes Classifier technique. In 30% of the poultry farms the farmers had observed that the flock was infested with PRM. In total, 32 different treatments against PRM were reported from the farmers, and three of them were non-biocide treatments. The most used biocides (17% of the farms) were crude oil, Formalin, Neopitroid® and disinfectants. The highest agreement regarding biocides application among the farms (38%) was before the production starts. Most of the farmers applied biocides routinely, before the infestation is evident (75%). The median costs for PRM treatment were 175€ per flock, higher in the infested farms 493±677€ compared to non - infested 100±71€, p<0.05. None of the Macedonian farmers included in the study was using monitoring method for PRM infestation, contributing to poor data records. This study highlights the need of developing unified strategy for PRM control included in the Integrated Pest Management in poultry layer farms. [ABSTRACT FROM AUTHOR]

Automatic conversion of sign language to text or speech is indeed helpful for interaction between deaf or mute people with people who even do not have knowledge of sign language. This is the demand of current times to develop an automatic system to convert ISL signs to normal text and vice versa. This will be beneficial for both communities to express their fillings to one another in accessing publicly available facilities like ticketing, banking services, traffic signals, etc. A new feature extraction and selection technique using structural features and some of the best available classifiers are proposed to recognize ISL signs for better communication for computer‐human interface. This paper narrates a system for automatic recognition of ISL immobile numeric signs, in which a standard digital camera was only used to acquire the signs, no wearable devices are required to capture electrical signals. The system is intended to convert isolated digit signs into text, that is, each entered sign image should contain precisely one numeric sign. To recognize ISL sign images in real time environment, a sign database containing ISL digits is created which contains 5000 images, 500 images for each numeral sign (0‐9). Among two classifiers, the k‐Nearest Neighbor outperforms the Naive Bayes classifier in stipulations of classification accuracy. [ABSTRACT FROM AUTHOR]

In recent years, more patients were detected with corneal abnormalities. It is critical to perform diagnosis of clinical or subclinical Keratoconus accurately. The main diagnostic tool for Keratoconus is corneal topography; leading to more Laser refractive surgeries. In this paper, we recommend a novel approach which can detect Keratoconus more efficiently. The parameters derived for feature extraction are corneal volume, corneal thickness, thinnest corneal thickness, corneal area, corneal perimeter. Hybrid feed forward network along with Naive Bayes classifier is used for classification. This proposed method detects and classifies Keratoconus and the performance metrics are also evaluated using MATLAB-based simulations and finally the performance of proposed method is compared with some existing techniques. [ABSTRACT FROM AUTHOR]

This study aims to determine how to deal with the identification from input and output data of switched linear systems (SLSs) with Box and Jenkins models. The identification difficulties of this system are that there exist unknown switched signal, unknown middle variables, and colored noise terms in the identification process. To address these issues, the proposed identification method proceeds in two stages, including the estimation of the switched signal of SLSs and the identification of the parameters of all subsystems. First, the Gaussian mixture model is established to represent the distribution of the input and output data of SLSs. Then, the posterior probability is calculated by the expectation-maximization (EM) algorithm and the naive Bayes classifier, and the switched signal is estimated according to the maximum probability criterion. Next, the auxiliary model based multi-innovation generalized extended least square (AM-MI-GELS) algorithm is used to estimate the parameters of all subsystems. Finally, the effectiveness of the proposed method is verified through the simulation example. [ABSTRACT FROM AUTHOR]

Under demand response enabled demand-side management, the home energy management (HEM) schemes schedule appliances for balancing both energy and demand within a residence. This scheme enables the user to achieve either a minimum electricity bill (EB) or maximum comfort. There is always the added burden on a HEM scheme to obtain the least possible EB with comfort. However, if a time window that contains comfortable time slots of the day for an appliance operation, is identified, and if the cost-effective schedule-pattern gets generated from these windows autonomously, then the burden can be reduced. Therefore, this paper proposes a two-level method that can assist the HEM scheme by generating a cost-effective schedule-pattern for scheduling home appliances. The first level uses a classifier to identify the comfortable time window from past ON and OFF events. The second level uses pattern generation algorithms to generate a cost-effective schedule-pattern from the identified window. The generated cost-effective schedule-pattern is applied to a HEM scheme as input to demonstrate the proposed two-level approach. The simulation results exhibit that the proposed approach helps the HEM scheme to schedule home appliances cost-effectively with a satisfactory user-comfort between 90% and 100%. [ABSTRACT FROM AUTHOR]

With progressing competitive market, different organizations were desperate to hold this churn rate as minimum value, hence to achieve this, building an effective (CCP) customer churn prediction model is essential. In order to address those issues in CCP, the study deliberated the churn prediction model using DFE-WUNB (Deep Feature Extraction with Weight Updated Tuned Naïve Bayes classifier) in a cloud-computing environment. Due to the huge non-linear features of the Telco customer churn dataset, the pre-processed features are deeply learned by the subsequent two models of ANN. In ANN, the input feature gets multiplied by the weight value, and the resultant output feature passes to the next dense layer in ANN. The deep feature extraction in ANN models facilitates precise accuracy in determining relevant churn features. However, the higher matrix dimensions of features exhibit complexity in prediction, hence this Block Jacobi SVD algorithm is applied to decrease the dimensions of features, such as to create a sparse dataset projected to get fit to the training model for efficient classification. The dimension-reduced features pass through enhanced weighing Naïve Bayes algorithm gets updated with ANN weights and tunes the parameters having greater and best weights to NB classifier, improvising the classification accuracy performance. The comparative assessment of the proposed DFE-WUNB churn prediction model delineated the efficiency of highly accurate churn prediction, outperforming other conventional churn prediction models. [ABSTRACT FROM AUTHOR]

In recent years, face detection has been widely applied to a variety of fields, such as face recognition, image focusing, and surveillance systems. This study proposes a real-time multi-faces detection system based on naive Bayesian classifier using Field Programmable Gate Array (FPGA). The system includes three main parts, feature extraction, candidate face detection, and false elimination. First, downscale the image to the image pyramid and extract local binary image features from each downscaling image. With the bit-plane slicing for Local Binary Pattern (LBP) can save the memory consumption and speed up the computation. Then, adopt the naive Bayesian classifier to identify candidate faces. Finally use skin color filter and face overlapping elimination to remove false positives. The experimental results show that the accuracy rate is up to 96.14% in face detection, which demonstrates the proposed real-time multi-faces detection system is indeed effective and efficient. [ABSTRACT FROM AUTHOR]

Collaborative filtering is specialized in suggesting appropriate products and services to the users concerning personal characteristics and past preferences without requiring any effort of users. It might be more efficient to collect preferences of users based on multiple subcriteria of products and services. For this purpose, researchers propose multicriteria recommender systems that are convenient for more accurate and useful evaluation of items. In such systems, it might be preferable to collect binary ratings instead of numerical ones due to the large number of subcriteria. However, there is a gap in the literature to satisfy a binary preferences-based multicriteria recommender system. In this study, the applicability of multicriteria recommender systems based on binary ratings is investigated. Firstly, recommendations for users on the overall criterion are produced by employing naïve Bayes classifier. In order to improve the quality of recommendations, user- and item-based similarity models are proposed enabling the formation of more successful neighborhoods. Such models are further improved by integrating a concordance measure between overall preference and subcriteria ratings, which helps to provide more personalized and meaningful similarities among users. Finally, a hybrid model is proposed employing user- and item-based models together and real data-based experimental outcomes demonstrate that the quality of estimated binary referrals is improved statistically significantly. [ABSTRACT FROM AUTHOR]

In a world where every day we produce 2.5 quintillion bytes of data, sentiment analysis has been a key for making sense of that data. However, to process huge text data in real-time requires building a data processing pipeline in order to minimize the latency to process data streams. In this paper, we explain and evaluate our proposed real-time customer' sentiment analysis pipeline on the Moroccan banking sector through data from the web and social network using open-source big data tools such as data ingestion using Apache Kafka, In-memory data processing using Apache Spark, Apache HBase for storing tweets and the satisfaction indicator, and ElasticSearch and Kibana for visualization then NodeJS for building a web application. The performance evaluation of Naïve Bayesian model show that for French Tweets the accuracy has reached 76.19% while for English Tweets the result was unsatisfactory and the resulting accuracy is 56%. To remedy this problem, we used the Stanford core NLP which, for English Tweets, reaches a precision of 80.7%. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) is expected one of promising network forms for intelligent transportation system which supports road safety applications, in-vehicle entertainment and arriving automatic driving. Establishing and maintaining stable connections in VANETs are challenging on account of the high mobility of vehicles, dynamic vehicle topology, and time-varying vehicle density. Clustering can provide scalability and reliability for VANETs by grouping vehicles with hierarchical structures. However, keeping cluster stable became a hard nut to crack due to high vehicle speed and unpredictable driving pattern. Recent rapid development of artificial intelligence (AI) provided an innovative solution for this situation. In this paper, a Naive Bayes Classifier based driving habit prediction scheme for stable clustering is proposed, briefly named NBP. According to driving speed and overtaking decisions, vehicles are classified into two alignments with different driving habit. Specifically, Naive Bayes classifier perform driving habit prediction through several relative independent factors, such as relative velocity, vehicle type, number of lanes traveled. The cluster head candidates will be chosen from alignment with mild driving pattern which will benefit for stable clusters. Combined with clustering design, the proposed method has been proven effective for stable clustering in VANET based on the real data of highways in California. [ABSTRACT FROM AUTHOR]

In view of the image blurring caused by fast target movement, it is difficult for the DSST algorithm to distinguish between the target and the background information. The filter is cyclically shifted during the training phase to collect dense samples，which easily results in boundary effect and leads to the tracking drift problem. Therefore, this paper proposes an improved DSST real-time target tracking algorithm (TLD-DSST) that incorporates the TLD framework. The algorithm improves the position filter of the DSST algorithm, adds the weight coefficient matrix through the spatial regularization me- thod to reduce the response of the non-target area, and performs rough positioning of the target under fast motion. At the same time, a naive Bayesian classifier is introduced to improve the TLD detector, in order to improve the detectors ability to distinguish between the target and the background information. Moreover，the optimal similarity matching is performed on the position of the DSST target response and the target area obtained by the TLD detector, so as to get the precise positioning result. The TLD detector positive and negative sample online update mechanism is used to continuously optimize the robustness of the algorithm. Experimental results show that the TDDSST algorithm has high accuracy and success rate for target tracking in complex scenarios such as fast motion. [ABSTRACT FROM AUTHOR]

Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co‐occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K‐nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET‐B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features. [ABSTRACT FROM AUTHOR]

In the context of digital social media, where users have multiple ways to obtain information, it is important to have tools to detect the authorship within a corpus supposedly created by a single author. With the tremendous amount of information coming from social networks there is a lot of research concerning author profiling, but there is a lack of research about the authorship identification. In order to detect the author of a group of tweets, a Naïve Bayes classifier is proposed which is an automatic algorithm based on Bayes' theorem. The main objective is to determine if a particular tweet was made by a specific user or not, based on its content. The data used correspond to a simple data set, obtained with the Twitter API, composed of four political accounts accompanied by their username and tweet identifier as it is mixed with multiple user tweets. To describe the performance of the classification model and interpret the obtained results, a confusion matrix is used as it contains values like accuracy, sensitivity, specificity, Kappa measure, the positive predictive and negative predictive value. These results show that the prediction model, after several cases of use, have acceptable values against the observed probabilities. [ABSTRACT FROM AUTHOR]

It is said that approximately 30-50% of ICT systems development have failed, therefore increasing the demand for solutions that will raise project success rates. The research aims to improve project success rates by developing a project outcome prediction system which encourages efficient participation of senior managers of projects and crossdepartment support members. The prediction system consists of two features — "prediction at the establishment of requirements which is an early stage of a project" and "prediction for ICT vendors which hold an important position in Japanese ICT systems development." The prediction system was developed and evaluated in a specific ICT vendor in Japan. The data of 172 projects in the ICT vendor were assessed using a feature assessment sheet with 17 evaluation items, and a project outcome prediction model was developed by utilizing the results of the assessment and Naive Bayes classifier technique. As a result of applying 10-fold cross-validation test, the model showed 84.0% predictive accuracy. In addition, the system was evaluated utilizing ten data sets gathered from actual operations at the ICT vendor. As a consequence, 80% of the project outcomes were predicted correctly. It is thought that the prediction system will contribute to identifying projects that they should participate in preferentially. [ABSTRACT FROM AUTHOR]

• A new nested ensemble model is proposed for improving breast cancer diagnosis. • Both voting and stacking techniques are used to build nested ensemble (NE) model. • Two-layer nested ensemble models are tested on Wisconsin diagnostic (WDBC) dataset. • Experimental results show that the two-layer nested ensemble system outperforms the single classifiers and previous works. Nowadays, breast cancer is reported as one of most common cancers amongst women. Early detection of this cancer is an essential to aid in informing subsequent treatments. This study investigates automated breast cancer prediction using machine learning and data mining techniques. We proposed the nested ensemble approach which used the Stacking and Vote (Voting) as the classifiers combination techniques in our ensemble methods for detecting the benign breast tumors from malignant cancers. Each nested ensemble classifier contains "Classifiers" and "MetaClassifiers". MetaClassifiers can have more than two different classification algorithms. In this research, we developed the two-layer nested ensemble classifiers. In our two-layer nested ensemble classifiers the MetaClassifiers have two or three different classification algorithms. We conducted the experiments on Wisconsin Diagnostic Breast Cancer (WDBC) dataset and K -fold Cross Validation technique are used for the model evaluation. We compared the proposed two-layer nested ensemble classifiers with single classifiers (i.e., BayesNet and Naïve Bayes) in terms of the classification accuracy, precision, recall, F 1 measure, ROC and computational times of training single and nested ensemble classifiers. We also compared our best model with previous works reported in the literatures in terms of accuracy. The results demonstrate that the proposed two-layer nested ensemble models outperformance the single classifiers and most of the previous works. Both SV-BayesNet-3-MetaClassifier and SV-Naïve Bayes-3-MetaClassifier achieved accuracy 98.07% (K = 10). However, SV-Naïve Bayes-3-MetaClassifier is more efficiency as it needs less time to build the model. [ABSTRACT FROM AUTHOR]

The social ecological system (SES) framework is used to investigate the success of discard management, by gathering knowledge from worldwide examples of discard management applications. The literature search confirmed that most efforts aiming to reduce discards were implemented under top-down management schemes. For about half of the case studies where management tools were applied, a reduction of discards was achieved. To determine the attributes that influence the discard management success, a probabilistic classifier model (Naive Bayes Classifier) was used. This revealed that commonly used interventions to reduce discards can be ineffective unless advice is given in alternative forms that have societal acceptance. The role of social cohesion and stakeholder enforcement mechanisms is underlined and increases the odds of meeting successfully the recommendations for achieving sustainable fisheries. A successful combination of attributes that can lead to the minimization of discards requires robust social capital along with top-down measures. Overall, the classification algorithm proves the usefulness of the SES framework to describe the fishery response to managerial efforts that aim to reduce discarding. Establishment of links between fishing communities and scientists are the key for enhancing stakeholder engagement, increasing compliance and minimizing discards. [ABSTRACT FROM AUTHOR]

The advancement of e-commerce along with the quick development of product review discussion in the most recent decade, an enormous measure of sentiment data or reviews are produced which made it practically difficult for a customer to take an educated buy choice. A good number of customers share their conclusions about the product during these days. These reviews have an important role in customers' purchase-decision process. For a popular product there may be hundreds or thousands of reviews. This is difficult for a potential customer to go to each of them to make an educated choice on whether to buy the product or not. Also, this is difficult for manufacturer of the product to follow-up and to manage client opinions. In this scenario, the aspects-based sentimental analysis helps in analyzing the reviews and categorizing them into appropriate aspects. Aspect or feature refers to attributes or qualities of a product. The proposed work begins with collecting reviews from online shopping websites, identifying aspects and classifying opinion orientation of aspects with different sentiment analysis techniques using Hadoop framework. This paper proposes a new pattern-based method for aspect extraction and sentiment analysis which gives an accuracy in the range of 72 ~ 75%. The proposed work is implemented on Hadoop MapReduce framework and the results show that Hadoop Multi-Node cluster set up performs aspect level sentiment analysis in a shorter time compared to traditional techniques. [ABSTRACT FROM AUTHOR]

Nowadays, the use of Open Data has become more common and prominent, but there are a lot of questions regarding its quality. Most of the revised researches deal with the quality of Open Data portals, rather than estimation of the open datasets quality. Therefore, the main idea of this research is lowering to the level of the dataset itself in order to assess how much such data is downloaded by end users of Open Data portals on the basis of general dataset characteristics. A model for predicting the number of downloads of open datasets based on their general characteristics was constructed using the Na've Bayes Classifier. Based on the obtained results, it is discussed if the certain dataset character is good predictor of open dataset downloading and to what extent. [ABSTRACT FROM AUTHOR]

This paper addresses the spectrum sensing problem in an orthogonal frequency-division multiplexing (OFDM) system based on machine learning. To adapt to signal-to-noise ratio (SNR) variations, we first formulate the sensing problem into a novel SNR-related multi-class classification problem. Then, we train a naive Bayes classifier (NBC), and propose a class-reduction assisted prediction method to reduce spectrum sensing time. We derive the performance bounds by translating the Bayes error rate into spectrum sensing error rate. Compared with the conventional methods, the proposed method is shown by simulation to achieve higher spectrum sensing accuracy, in particular at critical areas of low SNRs. It offers a potential solution to the hidden node problem. [ABSTRACT FROM AUTHOR]

• Hotspot detection and categorization for solar PV modules. • Thermographic and image analysis. • Detection achieved by training the machine learning algorithm. • Naive Bayes (nBayes) classifier as the machine learning algorithm. • Experimental tests demonstrate the high recognition rate of the method. Monitoring and maintenance of photovoltaic (PV) modules are critical for a reliable and efficient operation. Hotspots in PV modules due to various defects and operational conditions may challenge the reliability, and in turn, the entire PV system. From the monitoring standpoint, hotspots should be detected and categorized for subsequent maintenance. In this paper, hotspots are detected, evaluated, and categorized uniquely by using a machine learning technique on thermal images of PV modules. To achieve so, the texture and histogram of gradient (HOG) features of thermal images of PV modules are used for classification. The categorized hotspots are detected by training the machine learning algorithm, i.e., a Naive Bayes (n Bayes) classifier. Experimental results are performed on a 42.24-kWp PV system, which demonstrates that a mean recognition rate of around 94.1% is achieved for the set of 375 samples. [ABSTRACT FROM AUTHOR]

To detect the region and bleeding frame in the wireless capsule endoscopy video, an automatic computer-aided technique is highly demanded to reduce the burden of physicians. The wireless capsule endoscopy (WCE), is an imaging technology which is recently established and doesn't require any wired device. This device detects abnormalities in GI tract, i.e. (colon, esophagus, small intestine, and stomach). A WCE video consists of 57,000 images. It is very hard to examine by clinicians. To determine bleeding photos out of fifty-seven thousand WCE images makes the task very hard and expensive. The main goal is to develop an automatic obscure bleeding detection method by using superpixel segmentation and naive Bayes classifier. Naive Bayes and superpixel segmentation are used for this problem. [ABSTRACT FROM AUTHOR]