Significant Level Of Accuracy Research Articles

An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart

Control charts are tools of paramount importance in statistical process control. They are broadly applied in monitoring processes and improving quality, as they allow the detection of special causes of variation with a significant level of accuracy. Furthermore, there are several strategies able to be employed in different contexts, all of which offer their own advantages. Therefore, this study focuses on monitoring the variability in univariate processes through variance using the Binomial version of the ATTRIVAR Same Sample S2 (B-ATTRIVAR SS S2) control chart, given that it allows coupling attribute and variable inspections (ATTRIVAR means attribute + variable), i.e., taking advantage of the cost-effectiveness of the former and the wealth of information and greater performance of the latter. Its Binomial version was used for such a purpose, since inspections are made using two attributes, and the Same Sample was used due to being submitted to both the attribute and variable stages of inspection. A computational application was developed in the R language using the Shiny package so as to create an interface to facilitate its application and use in the quality control of the production processes. Its application enables users to input process parameters and generate the B-ATTRIVAR SS control chart for monitoring the process variability with variance. By comparing the data obtained from its application with a simpler code, its performance was validated, given that its results exhibited striking similarity.

Energy parameter modeling in plug-in hybrid electric vehicles using supervised machine learning approaches

In this study, supervised machine learning techniques like random forest (RF), K-nearest neighbour (KNN), multiple linear regression (MLR), and artificial neural networks (ANNs) were used to predict energy parameters in plug-in hybrid electric vehicles. The primary objective of the study is to propose a machine learning model for estimating combined energy consumption for city and highway driving. For the prediction of combined, city, and highway fuel consumption, accuracy is found to range from 3.40 to 6.13, 0.029 to 0.0625, 0.030 to 0.091, and 0.022 to 0.038 for KNN, MLR, RF, and ANN, respectively, in terms of mean square error (MSE). The accuracy of the artificial neural network model is found to be higher than that of the random forest, K-nearest neighbour, and multiple linear regression for the estimations of the selected energy parameters. For the prediction of the combined fuel consumption, the accuracy of the artificial neural network is 183.51, 42.53, and 1.85 times higher than that of the K-nearest neighbour, random forest, and multiple linear regression, respectively. For the prediction of city fuel consumption (C), the accuracy of the ANN is 113.33, 19.75, and 2.06 times greater than that of the KNN, RF, and MLR, respectively. The predicted accuracy of highway fuel consumption (H) is 34.58, 16.62, and 1.17 times higher for ANN than that of the KNN, RF, and MLR, respectively. The accuracy of the artificial neural network is 1.046 times higher in terms of MSE when three hidden layers are used than when two hidden layers are used for the prediction of combined fuel consumption. The study suggests that supervised machine learning approaches like artificial neural networks and random forests can predict or model the energy parameters of plug-in hybrid electric vehicles with a significant level of accuracy.

Bayesian Intelligent Tutoring System for Vocational High Schools

The absence of individualized tutorials during regular school hours has resulted in a suboptimal learning method at Vocational High Schools(SMK), limiting students' ability to reach their optimum competency. Several Computerized self-study systems have been created as potential solutions to these challenges. Regrettably, a notable drawback of the system lies in its failure to address students' diverse range of abilities adequately. This study presents a proposed model for an Intelligent Tutoring System (ITS) utilizing the Bayesian Network (BN) at Vocational High Schools. The model aims to assess students' proficiency levels and deliver skill-based instructional materials tailored to individual students' abilities. This type of research is called research and development (R&D), to develop and know the validity of a product. The system under development will undergo trials within the Computer and Network Engineering (TKJ) program at SMK Negeri 4 Gowa. These trials will employ a quasi-experimental approach, explicitly utilizing a one-group pretest-posttest design.The findings indicated that there were notable disparities in the learning outcomes of students following the implementation of the proposed ITS. To put it otherwise, the proposed ITS has improved students' proficiency in Vocational High Schools. The evaluation outcomes suggest that the BN model had a significant level of accuracy, reaching 84%.

Predicting financial distress of public and non-public construction sub-sector companies

This study examines if there are variations among financial crisis models. It is intended to investigate whether it has the most significant level of accuracy in predicting potential corporate bankruptcies. This is a quantitative study; Secondary information from financial reports serves as the data source. The study population is public and non-public companies in the construction sector listed on the Indonesia Stock Exchange (IDX) for 2014–2020. In order to obtain a sample of eight businesses, targeted selection was used for sampling. The results of this study show that the conditions differ from those of financial distress models for public and non-public companies. For public companies, the most accurate models are Grover and Lavin’s (2001), Karas and Srbová’s (2019), Fulmer’s (1984), and Ohlson’s (1980) models proven to be 100 percent. In contrast, only Fulmer’s model is entirely applicable to non-public companies. Forecast results and best-fit models can provide positive information or warnings for external and internal parties.

AVOCADO STOCK PREDICTION IN BANTUL CITY USING MAMDANI FUZZY LOGIC

This journal discusses the development of an avocado stock prediction model in Bantul City using the Mamdani fuzzy logic approach. In this context, fuzzy logic is used to address the uncertainty and complexity associated with factors affecting avocado supplies, such as stock variability, demand and price variables. The Mamdani approach is applied to formulate fuzzy rules based on a combination of expert knowledge and historical avocado stock data. This method aims to produce avocado stock predictions that are more accurate and adaptive to market dynamics. Through a series of experiments, the results show that the Mamdani fuzzy logic model has a significant level of accuracy, outperforming traditional stock prediction methods. The results obtained show the potential of this model in improving the efficiency of avocado inventory management at the local level. This research makes an important contribution especially in the context of agribusiness, providing a foundation for a more sophisticated and adaptive prediction approach to avocado fruit stock management. The implications are widely applicable in the agribusiness sector and provide a basis for the development of similar prediction systems for other agricultural commodities.

ASPECT-BASED SENTIMENT ANALYSIS TERHADAP ULASAN APLIKASI FLIP MENGGUNAKAN PEMBOBOTAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) DENGAN METODE KLASIFIKASI K-NEAREST NEIGHBORS (K-NN)

The rapid growth of online transactions in Indonesia has increased the demand for efficient interbank transfer solutions. However, the costs associated with such transactions have become a significant obstacle. Flip, a company with a vision to become a global leader in customer satisfaction-driven services, offers a solution to this challenge. This study proposes an aspect-based sentiment analysis method using the K-Nearest Neighbors (K-NN) algorithm to analyze user sentiment on key aspects, namely speed, security, and the cost of using the Flip application. The results of this research provide valuable information that can be used as a basis to provide insights, suggestions and recommendations to businesses, so they can create better solutions and promote optimal user experience. The research results show that the K-NN model has the ability to predict user psychology well in all aspects, with a significant level of accuracy, specifically speed (73.04%), security (86, 05%) and costs (80.11%). In addition, this study also compares two model validation methods: simple data splitting method and K-Fold cross-validation. Although the simple data splitting method has a higher average accuracy, K-fold cross-validation is considered superior as it provides a more accurate and reliable estimate of the overall performance of the model. Sentiment analysis results show that Flip app users tend to give negative feedback on speed and security, while they give positive feedback on cost. Therefore, the main recommendation is that the company PT Fliptech Lentera Inspirasi Pertiwi improves the speed and security aspects to increase user satisfaction with the Flip application. Therefore, this customer-centric service will continue to prioritize user satisfaction as its primary goal.

Sea Ice Elevation Measurements Using 3-D Laser Scanner

This study aims to introduce a sea ice elevation dataset estimated by using a 3-D laser scanner during the ice camp of the 2022 Arctic summer field survey. The equipment used is FARO’s Laser scanner FOCUS 3D X 130 HDR. The observed sea ice floe is located in the Arctic Ocean (76°13' N, 174°35') and is a multi-year ice with several melt ponds and ice ridges. We scanned eight sections separately, considering the equipment’s maximum horizontal scan range and the ice surface’s topographic features. The raw data were co-registered based on the positions of reference spheres. The result indicated a significant level of accuracy with a target-based vertical mean error of 4.8 mm. The laser scanner data were merged into point clouds ranging from 160×210 m. As a result, sea ice elevation data were generated in 0 to 2.8 m based on the minimum vertical point in the observation range. Sea ice elevation data is an essential variable in estimating the various properties of sea ice, such as ice thickness and roughness. In addition, using climatic variables such as air temperature and energy budget observed simultaneously can help to understand the physical interaction between the sea ice surface and the atmosphere on a local scale.

Effects of Methods, Genders, and Interaction to Human Body Temperature Measurement Results Using Tailor-Made Infrared Thermometer and Application Program

The 16-temperature measurement site method is holistic but inefficient. This study compared 8 to 16 temperature measurement site methods on seven married couples. The data gathering was obtained using our infrared thermometer, while the data processing was carried out utilizing Two Way ANOVA with Repetition inserted in our developed application program. Unlike the thermometer in general, our thermometer can equally measure the ear canal and forehead temperature equally. Our made application software was used and validated. The result shows no significant difference between 16 and 8 temperature measurement site methods (F method 0.05 < 4.26). There was no significant difference between male and female temperatures (F gender 1.46 < 4.26), and there was no significant interaction between the method and gender (F interaction 1.51 < 4.26). Male and female temperatures, in general, were almost similar, around 0.57 %, but there was a tendency that they were different in detail. The front head (face), lower part arm, hand, thigh, calf, and feet temperatures for men were slightly higher than for women, while the trunk, upper part arm, and back neck temperatures were slightly higher than men's. Based on these findings, eight temperature measurement site methods can replace the 16-temperature measurement site method. A further study should be carried out to obtain the simplest temperature measurement site but with a significant level of accuracy as the 16-temperature measurement site method. Detail differences between male and female temperatures should also be further investigated.

Graph embeddings in criminal investigation: towards combining precision, generalization and transparency

Criminal investigation adopts Artificial Intelligence to enhance the volume of the facts that can be investigated and documented in trials. However, the abstract reasoning implied in legal justification and argumentation requests to adopt solutions providing high precision, low generalization error, and retrospective transparency. Three requirements that hardly coexist in today’s Artificial Intelligence solutions. In a controlled experiment, we then investigated the use of graph embeddings procedures to retrieve potential criminal actions based on patterns defined in enquiry protocols. We observed that a significant level of accuracy can be achieved but different graph reformation procedures imply different levels of precision, generalization, and transparency.

Identification of user’s credibility on twitter social networks

&lt;p&gt;Twitter is one of the most influential social media platforms, facilitates the spreading of information in the form of text, images, and videos. However, the credibility of posted content is still trailed by an interrogation mark. Introduction: In this paper, a model has been developed for finding the user’s credibility based on the tweets which they had posted on Twitter social networks. The model consists of machine learning algorithms that assist not only in categorizing the tweets into credibility classes but also helps in finding user’s credibility ratings on the social media platform. Methods and results: The dataset and associated features of 100,000 tweets were extracted and pre-processed. Furthermore, the credibility class labelling of tweets was performed using four different human annotators. The meaning cloud and natural language understanding platforms were used for calculating the polarity, sentiment, and emotions score. The K-Means algorithm was applied for finding the clusters of tweets based on features set, whereas, random forest, support vector machine, naïve Bayes, K-nearest-neighbours (KNN), J48 decision tree, and multilayer perceptron were used for classifying the tweets into credibility classes. A significant level of accuracy, precision, and recall was provided by all the classifiers for all the given credibility classes.&lt;/p&gt;

Don’t Forget to Include that Camera in the Threat Model: Vulnerability of ATM Systems due to Surveillance Cameras

Video Surveillance Systems (VSS) that are used to provide physical protection to assets and personnel of organizations open up new information channels, but they are often not considered an integral part of the organization's information system. Therefore, more often than not, VSS is not considered when designing and evaluating organizations' information security. Hence, a VSS may weaken the information security of an organization while strengthening physical security. We present such a threat that the VSS used in ATM kiosks of Sri Lankan banks can severely weaken the ATM PIN security due to the ad hoc placement of cameras. While we have observed that in some installations, the video camera directly captures the PIN-pad, we show that forearm movements' visibility is sufficient to infer PINs with a significant level of accuracy. We used a mock-up of an ATM kiosk for our analysis, and we show that a human observer can guess a PIN with 22.5% accuracy within 3 attempts without the PIN Pad's visuals. A computer can infer the PIN using the same footages with an accuracy of 50% using a straightforward algorithm. Critical processes in the banks, such as authentication, are built around the assumption of the confidentiality of the PIN thus invest heavily in the PIN generation process. This well-protected PIN is exposed to the VSS when entering the PIN, thus violating a crucial assumption. However, this violation has hitherto gone unnoticed by the banks' security audits because VSS is not considered an inalienable component of the information system.

Innovative in situ investigations using synchrotron‐based micro tomography and molecular dynamics simulation for fouling assessment in ceramic membranes for dairy and food industry

AbstractProteins in dairy streams result in organic fouling and function loss for ceramic membrane's porous structure. Synchrotron‐based X‐ray microtomography (SR‐µCT) is a new method with a high signal to noise ratio and accordingly significant level of accuracy. The goal of this study was to perform an in situ assessment of ceramic membrane fouling in the dairy stream filtration process, using SR‐µCT. This study attempted to assess porosity variation and membrane fouling through different layers from the top, middle, and bottom layers of the ceramic microfiltration membrane before and after skimming milk filtration. Molecular dynamics simulation (MDS) was used for depth understanding of milk protein interactions with the ceramic membrane. Fouling was found to be more intense on the top of the ceramic membrane even though the top layer was slightly more porous, which indicates that top layers were more prone to fouling. A regression model was derived to correlate the porosity loss due to membrane fouling at different membrane thicknesses. The MDS results showed more affinity of the milk proteins to the ceramic membrane compared to water molecules. The MDS studies showed how the presence of the milk's protein macromolecules could change the hydrophilicity trend on the membrane's surface.

Modelling of minor hysteresis loop of shape memory alloy wire actuator and its application in self-sensing

Shape memory alloy (SMA) wire actuators offer large force and displacement capabilities out of the stress and temperature-dependent martensitic phase transformation. Among the various modelling approaches in the literature, the one proposed by Boyd and Lagoudas (1996), is mostly used in analysing SMA based components. In practice, partial actuation is often required and is enacted by aborting and subsequently resuming the phase transformation; exhibiting minor hysteresis response. The existing models are found to be inaccurate in simulating such partial transformation cases. In this study, a novel yield parameter, which depends on the martensite volume fraction, has been proposed; enabling the model of Boyd and Lagoudas to simulate minor hysteresis loop accurately. The corresponding model parameters are derived, satisfying certain practical aspects of the SMA response. Using the modified model, pseudoelastic responses are simulated for different partial loading cases and are compared against the same obtained from the existing models, illustrating its efficacy. Following the proposed model, the discrete form of the system dynamics of a SMA wire actuator is formulated. Using this, an extended Kalman filter (EKF) has been developed, to estimate the output of the system from the change in electrical resistance of the SMA wire during actuation. This approach eliminates the requirement of external sensors for feedback control. Using the developed EKF, the response of an SMA wire actuated system is estimated from the experimentally measured electrical resistance data of the SMA wire and are compared with the corresponding measured responses. The results reveal that a significant level of accuracy can be achieved with the proposed approach.

Model for Estimating Travel Time on Dynamic Highway Networks in Akure, Ondo State Nigeria

Travel time variability or distribution is very important to travel time reliability studies in transportation systems. This study aimed at developing a multivariate regression model for estimating travel times for dynamic highway networks in Akure Metropolis. The independent variables for the model are Traffic volume, density, speed of vehicles, and traffic flow while the dependent response variable is the Travel time. The estimated travel time was compared with the observed travel time from the real field data and the estimation using the regression model reveals a significant level of accuracy. Also, it was discovered that traffic volume, speed, density, and flow were highly correlated with travel time. The result analyzed using descriptive statistics in the SPSS software environment reveals an R2 value of 0.998, thereby indicating that the independent variables accounted for 99% of travel time in the study area. The Hypothesis tested at 95% confidence level using ANOVA unveils that there is no significant difference between the observed and estimated travel time model. The Mean Absolute Percentage Error (MAPE) of 0.049 shows that the model performed very well and was very efficient for analyzing the probabilistic relation between travel time and the independent variables. The study recommends the use of the developed travel time model for estimating travel time within the study area.

Model for Estimating Travel Time on Dynamic Highway Networks in Akure, Ondo State Nigeria

Travel time variability or distribution is very important to travel time reliability studies in transportation systems. This study aimed at developing a multivariate regression model for estimating travel times for dynamic highway networks in Akure Metropolis. The independent variables for the model are Traffic volume, density, speed of vehicles, and traffic flow while the dependent response variable is the Travel time. The estimated travel time was compared with the observed travel time from the real field data and the estimation using the regression model reveals a significant level of accuracy. Also, it was discovered that traffic volume, speed, density, and flow were highly correlated with travel time. The result analyzed using descriptive statistics in the SPSS software environment reveals an R2 value of 0.998, thereby indicating that the independent variables accounted for 99% of travel time in the study area. The Hypothesis tested at 95% confidence level using ANOVA unveils that there is no significant difference between the observed and estimated travel time model. The Mean Absolute Percentage Error (MAPE) of 0.049 shows that the model performed very well and was very efficient for analyzing the probabilistic relation between travel time and the independent variables. The study recommends the use of the developed travel time model for estimating travel time within the study area.

Ontology evolution for personalised and adaptive activity recognition

Ontology-based knowledge driven Activity Recognition (AR) models play a vital role in realm of Internet of Things (IoTs). However, these models suffer the shortcomings of static nature, inability of self-evolution and lack of adaptivity. Also, AR models cannot be made comprehensive enough to cater all the activities and smart home inhabitants may not be restricted to only those activities contained in AR model. So, AR models may not rightly recognize or infer new activities. In this paper, a framework has been proposed for dynamically capturing the new knowledge from activity patterns to evolve behavioural changes in AR model (i.e. ontology based model). This ontology based framework adapts by learning the specialized and extended activities from existing user-performed activity patterns. Moreover, it can identify new activity patterns previously unknown in AR model, adapt the new properties in existing activity models and enrich ontology model by capturing change representation to enrich ontology model. The proposed framework has been evaluated comprehensively over the metrics of accuracy, statistical heuristics and Kappa Coefficient. A well-known dataset named DAMSH has been used for having an empirical insight to the effectiveness of proposed framework that shows a significant level of accuracy for AR models This paper is a postprint of a paper submitted to and accepted for publication in IET Wireless Sensor Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

The Effect of Information Quantity on Distinctive Accuracy and Normativity of Personality Trait Judgments

Information quantity is an important moderator of personality judgment accuracy. Some evidence suggests that the amount of available information is positively related to accuracy. The current study utilized the social accuracy model to investigate the effects of differences in thin slices of information quantity on the distinctive accuracy and normativity of personality trait judgments. It was hypothesized that distinctive accuracy and normativity would increase as information quantity increased. Participants were 431 individuals who participated in an online study that varied the length of stimulus target observations (30 seconds, 1 minute, 3 minutes, and 5 minutes), after which judges rated targets using other–report measures of the Big Five personality traits. For all traits combined, significant levels of accuracy were found for all observation lengths, but distinctive accuracy and normativity did not increase as video length increased. Findings varied for individual traits. For distinctive accuracy, there was a linear increase with information quantity for Extraversion and a non–linear relationship for Conscientiousness, while there was a linear decrease for Openness. For normativity, there was a linear increase with information quantity for Agreeableness and a non–linear relationship for Conscientiousness. There are important differences in how observation length affects distinctive accuracy and normativity for different personality traits. © 2019 European Association of Personality Psychology

LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning.

Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting solutions require extensive and error-free surveys of environments to build radio-map databases, which play a key role in position estimation. Fingerprinting also requires random updates of the database, when there are significant changes in the environment or a decrease in the accuracy. The calibration of the fingerprinting database is a time-consuming and laborious effort that prevents the extensive adoption of this technique. In this paper, we present a systematic LOCALIzation approach, “LOCALI”, for indoor positioning, which does not require a calibration database and extensive updates. The LOCALI exploits the floor plan/wall map of the environment to estimate the target position by generating radio maps by integrating path-losses over certain trajectories in complex indoor environments, where triangulation using time information or the received signal strength level is highly erroneous due to the fading effects caused by multi-path propagation or absorption by environmental elements or varying antenna alignment. Experimental results demonstrate that by using the map information and environmental parameters, a significant level of accuracy in indoor positioning can be achieved. Moreover, this process requires considerably lesser effort compared to the calibration-based techniques.

A collaboration based context prediction in smart office

Context prediction plays a vital role in an assistive ubiquitous environment. The environmental configuration in a ubiquitous environment is heavily dependent on the context of the events occurring in the environment. Current state of the art approaches utilize the user's history information for predicting the context of the events. When the user's history does not provide apposite contextual information for the observed activity/event at time t, the history based state of the art context prediction tech- niques fails to predict the appropriate future context. To overcome the gap of missing context information in the user's context history, we propose a Profile based Collaborative Context Prediction (PCCP) approach. PCCP is a predictive association rules based system which utilizes the history of similar users and collaborate among users of the ubiquitous environment. PCCP gener- ates rules at high level of abstraction, human readable and understandable that helps in avoiding the underline details. To evaluate the PCCP, a smart office is considered as an experimental environment. Experiments are carried out on indigenous multi user smart office data set. Our experiments showed significant level of accuracy in both environments. Due to the understandability of output and higher accuracy of PCCP, it can be extended to assist the user in a smart environment.

Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure

Obtaining an accurate estimate of the reference evapotranspiration (ETo) can be difficult, especially when there is insufficient data to utilize the Penman–Monteith method. Artificial intelligence–based methods may provide reliable prediction models for several applications in engineering. However, time-series prediction based on artificial neural network (ANN) learning algorithms is fundamentally problematic. For example, the ANN model can experience over-fitting during training and, in consequence, lose its generalization. In this research, several over-fitting procedures have been augmented with the classical ANN model, are proposed. This model was applied to the prediction of the daily ETo at Rasht city, located in the north part of Iran, by using the minimum and maximum daily temperature of the region collected from 1975–1988. In addition, three different scenarios have been developed in order to achieve better prediction accuracy. The results showed that the proposed ENN model successfully predicted the daily ETo with a significant level of accuracy using only the maximum and minimum temperatures. The model also outperformed the classical ANN method. In addition, the proposed ENN compared with Hargreaves and Samani (Appl Eng Agric 1:96–99, 1985) (HGS) model and showed the ENN provides more accurate prediction for ETo. Furthermore, the proposed model could provide relatively good level of accuracy when examined for multi-lead predictions, which could not be afford by HGS model.