Search

Tacit knowledge, which is based on first-hand experience and is more difficult to articulate, has evolved alongside natural languages as they are passed down through the years. In computing, Natural Language Processing (or NLP) refers to a set of methods for studying and modelling human languages that may be studied and represented automatically. Extracting or searching through vast bodies of unregulated text for specific information can be a complex and time-consuming process. Knowledge comes in several shapes and sizes, but can usually be differentiated into two types: structured or unstructured. Using NLP techniques, unstructured text data can be translated into a structured and well-organized database and then used for question-answering purposes. This paper is about the implementation of NLP techniques to convert unstructured text data into a structured database for Chatbot application training. [ABSTRACT FROM AUTHOR]

The use of big data helps us to observe different trends and patterns of data and can be related to people's behavior. The more data is collected, the more data trends and patterns can be identified and used for impactful future applications. The big data process involves two important parts: (i) Data Management and (ii) Data Analytics. This study focuses on data management processes such as data cleaning and structuring that were applied before manipulating the data into useful and informative results. Usually, when a request for project development is made, the data used among students is normalized. The student does not have experience applying data cleaning and structure to their project. There were 78 participants involved in this study that took the Database Programming subject. Participants were required to do a group project on managing the data and displaying informative data using the PL/SQL approach. Findings show that most participants agreed that using reading responses is a good approach as they find new knowledge through data collection (70.5%). The participants also found this task interesting, useful, and practical (68%). This study also improves literacy skills. Participants faced some challenges and gained firsthand experience in upgrading their skills in handling data management. [ABSTRACT FROM AUTHOR]

The migration of data between NoSQL databases faces challenges due to the heterogeneity of the data schema and storage paradigm between the data-bases. In addition, the flexibility of storage paradigm allows non-standardized schema between records in NoSQL database. For example, in MongoDB, documents within the same collection could have different schema. However, this flexi-schema may cause difficulties in data migration as there is without any standard data schema to follow by for the target data-base during data migration. This paper proposed a migration method ap-plied to suit the different data model or schema for different NoSQL databases. The paper focus on the migration between document-based database and column-based database. The paper also discusses on the database schema of the target database that need to store and support the data that is with different schema. [ABSTRACT FROM AUTHOR]

In this paper it is present the analysis of meteorological data onboard aircraft, because meteorological forecasts are not 100% accurate, therefore, meteorologists, through the careful scientific study carried out with the help of computer equipment they have the capacity to make the prediction of some meteorological models, of the tendencies and features with very good accuracy. Through complex system of meteorological services, government agencies and meteorological observers, as well as pilots but also other types of aviation professionals' benefit from this extensive database in the form of frequently updated weather reports and forecasts. These reports and forecasts allow pilots to be informed before taking decisions about the weather, and about the safety of the flight ahead, but also during a flight. [ABSTRACT FROM AUTHOR]

Based on heart rate signals collected during fight tasks, the present work analyzes unmanned air vehicle pilots' workload qualified with A1/A3 Open category. Preliminary results are based on a sample of physiological data from five pilots. The physiological indexes for the workload assessment are computed by the Heart Rate Variability (HRV) based on the electrocardiogram signal acquired with a specific app developed by the authors. The tasks performed and the difficulty of the associated maneuver are also described. Results are given in terms of HRV indexes to characterize the mission difficulty based on objective measurements of mental workload. Results obtained can be assumed as a source to create a database for developing a workload assessment model of the human being interacting with an unmanned system combining HRV indexes in a single index. [ABSTRACT FROM AUTHOR]

This article focuses on the identification of a potential threshold for logistic regression models for bankruptcy prediction in the forestry sector in cases where the ratio of active and bankrupt companies in the dataset is not balanced. Model estimates are performed on a random selection of 300 active forest companies in EU countries along with eight (and 17) bankruptcy companies in 2019 (and in 2018). Analyzes are performed on the basis of accounting (micro) data of companies from the Orbis database. Empirical results show that in cases where bankruptcies are in the minority, it is necessary to replace the commonly used threshold with an optimized threshold, which will emphasize the balancing of the error rate in both the group of active and the group of bankrupt companies. [ABSTRACT FROM AUTHOR]

Now a days we see more people are dying because of heart attacks and cardiac arrest. The Electrocardiogram is mostly used for identifying the heart diseases and also, we can find the cardiovascular state of the person. The deep learning is an approach to study about the abnormalities present in an ECG cycle. In this project we discuss about two methods reduction of noise and detection of abnormalities. In this approach we use Convolutional neural networks which gives the accuracy between 75 to 85% and also this method can identify the minute changes in the given signal. This process is done using MIT-BIH database. [ABSTRACT FROM AUTHOR]

A logical schema design for a non-relational database based on the theory of data dependency and the strategy of decomposing data into a single document is presented. This approach is based on the efficiency of using a Document Oriented database that implements the logical data modeling and the design principle modes of a typical NoSQL Database. Logical data modeling is done by identifying the underlying complex data and de-normalizing the data into query output. This creates a complex schema but stores it in such a way that the data type is the same as application programming data types, which is helpful in retrieving data. Schema design patterns are proposed for efficient schema design to avoid challenges as the data volume increases. Best practices in indexing strategies to handle web data are also highlighted. This data modeling helps save storage space and time during and after development, which eventually improves performance throughout the application's lifetime. [ABSTRACT FROM AUTHOR]

A Blockchain is a decentralized database that stores data that all network participants can access. The central authority is no longer in charge of maintaining this public database by running a cryptographic protocol with designated nodes. Bitcoin is presently the latest object within the Blockchain, and the Bitcoin node can confirm the transaction content and package deal it into the block. Through the underlying consensus agreement, the blockchain ensures the consistency of the information. These consensus algorithms are unique because the set of rules protection assumptions differs from the actual requirements. This paper provides information and compares various blockchain consensus algorithms, classifies advancement based entirely on blockchain consensus, and highlights the benefits. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, also called diabetes, is a class of metabolic diseases that affects hundreds of millions of mankind. It is an extra discoverer of a variety of diseases, for example: coronary failure, blindness, urinary organ diseases etc. The detection of diabetes is of great importance, concerning its severe complications. To reduce these, several investigators are functioning to forecast diabetes at a previous period utilizing many techniques. This approach presents, Classification of Diabetes Mellitus Prediction Using Hybrid Machine Learning (ML) Techniques. The goal of this examination is to implement a technique that can assume a patient's diabetic risk level with the best accuracy. The acquired dataset as the outcome of the K-means Clustering model was fed to a hybrid model with PCA and K-means clustering. Investigations are conducted on the Pima Indians Diabetes Database (PIDD) taken from the UCI ML Repository. The performance of the three algorithms (NB, SVM and Hybrid ML technique) is calculated on different aspects such as Accuracy, Precision, F-Measure and Recall. The investigations presented that Hybrid Machine Learning classifiers method implemented better than the other classifiers exclusively. [ABSTRACT FROM AUTHOR]

"Human value and artificial intelligence methods on websites" is an essential topic due to the multidisciplinary development and integration in all domains, especially computer science, engineering, social science, management, and other 20 domain areas. The study's objective is to know knowledge (keyword co-occurrences analysis), social structure (co-authorship analysis), and the evolution of the research topic. This study uses the Elsevier database as a data source from 2003-2022. The 1427 documents retrieve for further analysis. Biblioshiny and VOS viewer 1.6 version software adapt for bibliometric analysis. This research assesses knowledge structure, social structure, frequency of publications, research areas, top four remarkable journals, and authors. The findings include the theoretical contribution of four potential clusters. Also, it includes the status of inactive countries Cypress, Slovenia, Romania, and Luxembourg regarding co-authorships. Following this, the documents, sources, and the trend of publications on this research topic are steadily growing, around 31.4% on average. Fourthly computer science, social science, and engineering consider remarkable research areas. Fifthly, Sustainability and IEEE access in frequency numbers are the top journals. Further, Dwivedi Y K and Chua A YK are considered the top remarkable authors on the research topic in terms of publication numbers; English is the primary language used in this research field; top cited global documents of the research field contribute to emerging areas of artificial intelligence methods in different disciplines. This research gives opportunities for future researchers, academicians, and technologists who are interested in human value and artificial intelligence methods on websites. [ABSTRACT FROM AUTHOR]

The new life adaptation protocol implemented in Institut Teknologi Sumaterea (ITERA) during the COVID-19 pandemic requires visitor identity check to restrict access to several buildings and reduce the massive spread of the corona virus. The inspection is carried out by manually collecting visitor information, which can result in a queues buildup of vehicles that want to enter the ITERA environment, and also, the identity of incoming visitors is not adequately monitored. In addition, the smart camera system for visit recording is built with the ability to identify visitors' faces, which triggers automatic gate access and records visitor information. Visitor information is stored in the form of identity number, name, visitor status, time/date of visiting, and information about the location of visitors. The visitor data is stored in a database and can be accessed by ITERA on the visitor website page. The system has an ability to trigger automatic gate to give an order to open or close the gate based on the face recognition results. The system development is divided into three stages: face recognition development, website-based data visualization creation, and hardware development. At each stage, the development produces a system with face recognition capabilities, a visitor recorder website, and a series of automatic gate hardware consecutively. The face recognition system connects directly to the website via the Firebase database. The development of the system capability for face recognition is carried out using a learning model of the K-Nearest Neighbor (K-NN) algorithm method, which obtains the training results have an accuracy of 99% using a confusion matrix method. [ABSTRACT FROM AUTHOR]

4C Skills refer to four essential skills needed for education and development in the 21st century. These skills include critical thinking, communication, collaboration, and creativity. 4C skills can be developed through learning by applying learning models that can train these skills. The problem-based learning model is a practical approach to developing 4C skills. This study aims to review indexed articles explaining how the PBL model develops 4C skills in science learning. The data for this study were researched using the Scopus database and focused on problem-based learning models and 4C skills (critical thinking, communication, collaboration, and creativity) in science education. This search was an exceptional literature review using the four-step PRISMA approach to identification, screening, eligibility, and inclusion. Sixty-three relevant articles from 22 Scopus-indexed journals over the past six years (2018-2023) were analyzed using bibliometrics and Excel spreadsheets. Based on the results, the PBL model and 4C skills research were mainly carried out at the upper secondary level, and Indonesia is the country with the most publications. Physics is a widely studied field that uses a quantitative method. Based on the results of the review of articles, it is clear that the PBL model can be used to develop 4C skills, especially critical thinking. [ABSTRACT FROM AUTHOR]

In recent times, the interaction between human beings and computers has become an increasing field of research due to its wide application of computational systems. Determination of the current state of human psychological problems, one of the most relevant techniques is emotion recognition. The dynamic behavior of human is crucial for its interpretation. So, Facial emotion recognition (FER) is an important research area for detection the of different facial emotions. In this paper, we proposed an Unconstrained Minimum Average Correlation Energy (UMACE) filter as a classifier for facial emotion recognition. We are applying this proposed method in the CK+ database and it can give a better recognition rate. [ABSTRACT FROM AUTHOR]

Aim-The aim of the study is to perform innovative detection of lung cancer using the Decision Tree classifier and also comparing it with the K-Nearest Neighbor classifier. Materials and methods-The lung cancer database includes records 32 samples (patients) and is therefore compiled with 32 ∗ 6 numerical data. The G power for samples is calculated from clincalc which contains two different groups where group 1 is taken as (n1=16) and for group 2 (n2=16), alpha (0.05), power (80 %) and enrollment ratio. Results-Implementation based on the MATLAB toolbox is done on CT lung images and the classification of these images is performed. In the proposed method, D-Tree numbers of samples are selected for each iteration and 90 % phase accuracy is obtained. There is a significant difference in Accuracy rate (P=0.072). Conclusion-The results of the study suggest that the accuracy of the decision tree algorithm is significantly higher than the nearest k-neighboring algorithm. [ABSTRACT FROM AUTHOR]

In this study, we take a different approach to feature extraction by focusing on the diagonal rather than the horizontal in order to statistically and structurally utilise handwritten characters. Substances and Techniques: This paper employs the Computer Vision Laboratory's (CVL) publicly available database for its dataset (NTHU). Using a G intensity of 0.8 and alpha and beta quality values of 0.05., 0.2, and a 95% confidence interval, we determined that a sample size of 280 (group 1=140, group 2=140) handwritten characters would provide adequate statistical power. Novel sample number (N=10) diagonal feature extraction and sample number (N=10) horizontal feature extraction are used to classify statistical and structural aspects of handwritten characters, resulting in greater precision. The outcomes show that the accuracy of the diagonal feature extraction classifier is 95.883, while that of the horizontal feature extraction classifier is 92.736. This study has a significance level of p=0.0213. In conclusion, statistical and structural features of handwritten characters make diagonal feature extraction more accurate than horizontal feature extraction. [ABSTRACT FROM AUTHOR]

The K-Nearest Neighbor approach outperforms the Fuzzy Algorithm in terms of accuracy during quick data retrieval for an innovative car parking system. The Components and Techniques: K-Nearest Neighbor Algorithm enables 20-sample database vehicle parking system, which improves innovative vehicle parking system's data retrieval accuracy and speed. The implementation was accomplished by developing a Python web application with the aid of the anaconda navigator. As a result, a car parking database is constructed using the K-Nearest Neighbor Algorithm, which improves efficiency by 87 percent in terms of secure accessibility compared to the FuzzyApproach, which improves efficiency by only 75 percent. The length of time it takes to log in and access the database has been analysed using SPSS (Statistical Package for the Social Sciences) with the independent variables of time and size and a significance level of p 0.05. K-Nearest Neighbor Algorithm, which is more significant than a Fuzzy Approach in terms of Accurate efficiency, has been employed in a protected system to improve the car parking database. When data is a dependent variable and sample size is an independent variable, SPSS analysis may show how reliable the data is. [ABSTRACT FROM AUTHOR]

The goal of the research is detection of disease in fresh fruits. The sensitivity and accuracy was calculated and compared with the k-nearest neighbor (KNN) classifiers. Materials and Methods: The research's data set was obtained from the Kaggle open access dataset. The research includes two groups, in which CNN is group 1 and KNN is group 2. Kaggle database is used for collecting different fruit images. The sample size was calculated using G power (80%) and alpha (0.05) and it is found to be 20 samples for each group. Result : CNN has achieved 98 % of accuracy and 1.0477 standard deviation while KNN method has achieved 90 % of accuracy and 0.65071 standard deviation. The Novel CNN has significant accuracy of p<0.05 (2 tailed). Conclusion: It is concluded that the detection of fruit disease using Convolution Neural Network appears to be significantly better compared to k-Nearest Neighbour Classifiers. [ABSTRACT FROM AUTHOR]

The primary purpose of this research is to use the Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier to make arrhythmia predictions from the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed research employed SVM and NB machine learning algorithms to predict arrhythmia using the MIT-BIH dataset of 65 normal and 65 abnormal ECG signals downloaded from IEEE dataport in.xlsx format. We used the data mining programme WEKA 3.8.5 to determine how to categorise people with and without arrhythmia. IBM SPSS version 21 was used for the statistical analysis. When comparing SVM and NB classifiers, a statistically significant difference (p0.010) was found. Using WEKA's 10-fold cross-validation for training and testing, the SVM classifier outperformed the NB classifier with an 88.50% accuracy rate in classification (80.39 percent). [ABSTRACT FROM AUTHOR]

The novel adaboost method for accurate eye recognition in face photographs is the focus of this research project, which aims to implement said approach. The purpose of this investigation is to evaluate how well the boosting algorithm performs in comparison to the newly developed Adaboost method. Images of human faces were extracted from the ORL Face Database and analysed for characteristics that may be used for eye recognition. In this study, we employ a total of forty people: twenty from each of two groups. In the simulations, the pretest power was established at 0.8. In order to evaluate how effectively the innovative Adaboost algorithm works, metrics such as accuracy and sensitivity are produced and analysed. The Adaboost Algorithm has an accuracy of 98.73 percent, while the Boosting Algorithm has an accuracy of 86.41 percent, and the Sensitivity of the Adaboost Algorithm is 96.15 percent, while the Sensitivity of the Boosting Algorithm is only 83.63 percent. These results come from simulations run with SPSS. In the case of this model, we discover that the level of significance is 0.000. (2-tailed, p0.05). In this study, it is shown that the unique Adaboost method is superior to the boosting algorithm in terms of accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

The primary goal of this research is to use J48 and K-Nearest Neighbor (KNN) classifiers to predict arrhythmia using the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed study used the J48 and KNN machine learning algorithms to predict arrhythmia using data from the MIT-BIH dataset consisting of healthy (n=65) and arrhythmia (n=65) ECG signals collected from IEEE dataport in.XLSX format. WEKA 3.8.5, a data mining tool, was used to distinguish between those with arrhythmia and those without. IBM SPSS version 21 was used for the statistical analysis. There was no discernible difference (p=0.025) between the J48 and KNN classifiers. Using WEKA's 10-fold cross validation to train, test, and verify the classifiers, we find that the J48 classifier is more accurate at classifying data (89.80 percent) than the KNN classifier (87.64 percent). [ABSTRACT FROM AUTHOR]

This research endeavours to evaluate the efficacy of the support vector machine (SVM) method against that of the K-Nearest neighbour Network (KNN) approach in removing thick fog from images. We gathered the Realistic Single Image Dehazing (RESIDE) dataset from the kaggle.com database. For each group's sample size of 22, the suggested SVM method is compared against the current KNN techniques. There are 44 samples altogether. Our suggested process include measures to actually accomplish the elimination of noise in photos. Metrics of performance, such precision and sensitivity, are computed. The suggested support vector machine (svm) has an accuracy and sensitivity of 94.9 and 38.5%, respectively, whereas k-nearest neighbour had 93.7 and 37.5%. Using the G power instrument and setting the minimum power to 0.8, we can determine how many samples are needed for this study. When comparing the SVM and KNN algorithms, the significance level reached was (p 0.05). Support vector machines provide far superior performance over the standard k-nearest neighbour, as shown both by experimental findings and Independent sample T-test statistical analysis using SPSS software. [ABSTRACT FROM AUTHOR]

Aim: The main aim of this study is to assist the visually impaired to dole out money from the innovative ratified ATM compared with smart ATM. Materials and methods: The Raspberry Pi which is a microcontroller acts to convert analog input signals to the digital output signal. A Biometric system with IVRS at an ATM promotes the transaction to succeed. The significance between the two groups was calculated using SPSS. The parameters were maintained as threshold 0.05, enrollment ratio as 1, confidence level 95%, and G-power 80%. Results: Ratified ATM was found to perform better than smart ATM in aiding the visually impaired. There exists a statistically significant difference (p=0.878, p>0.05) between ratified and smart ATM groups. The execution of this method has been found promising on the account of its sensitivity to the recognition of the customer's fingerprint as contained in the database. Conclusion: This method not only helps the visually impaired but also illiterates, dyslexia, and people with visual disabilities to withdraw money safely. [ABSTRACT FROM AUTHOR]

In this investigation, we compare the performance of the state-of-the-art ANN algorithm for vehicle detection with the more conventional SVM algorithm. Twenty representatives were selected at random from the database of the website. The threshold was kept at 0.05, the confidence level at 95%, the enrollment ratio at 1, and the G power at 80%. Accuracy metrics are calculated using the SPSS software. Results from an independent t test showed that the ANN algorithm outperformed the SVM algorithm, with a mean accuracy of 95%. The difference between the two groups was not statistically significant (p=0.11, p>0.05). In this research, it appears that the ANN algorithms perform better than the SVM algorithms when it comes to detecting vehicles in the datasets used. [ABSTRACT FROM AUTHOR]

The aim of this research is to use the Decision Tree classifier to identify lung cancer in scanned pictures and compare its performance to that of the Support Vector Machine classifier. Methods and materials The lung cancer database contains information on 60 samples (patients). Clincalc's sample G power has two groups, with alpha (0.05), power (80%), and enrollment ratio (n1 = 30) for group 1 and (n2 = 30) for group 2. Decision Tree outperforms standard SVM classifiers in terms of accuracy, with a 95.13 percent success rate. According to the results of this study, the decision tree method is more accurate than the Support Vector Machine classifier. [ABSTRACT FROM AUTHOR]

It is known that phrases are considered a common linguistic unit consisting of several words and a syntactic whole that cannot be translated literally. Idioms have linguistic and cultural significance and were formed as a result of symbolic similes and comparisons due to various situations in the life of each nation during its history. The linguo-cultural approach to the text in translation allows identifying and overcoming the obstacles caused by the culturally defined idio-ethnic and cultural differences in artistic discourse. Therefore, to achieve efficiency in automatic translation, it is important to create a parallel database of phraseological units (PhU). This article analyzes the factors involved in identifying idioms as lexical units and the creation of an idioms database. [ABSTRACT FROM AUTHOR]

This article discusses the process of searching for information in the bibliographic database. Here developed an algorithm for obtaining results according to certain characteristics of the information related to the sought record. It is also mentioned about the functional blocks of preparation, creation of pair records, comparison of fields with pair Records, decision-making, which constitute the algorithm. [ABSTRACT FROM AUTHOR]

In this article, an algorithm and program were developed for image restoration and recognition using neural networks. Several methods of image recognition using neural networks were proposed to solve these problems. The use of artificial intelligence methods for image restoration and recognition, as well as the scientific novelty of research results, is related to the use of artificial intelligence methods and the advancement of modern practical software tools created for them, which are based on the characteristics of digital images. To recognize an image as an object, the human face was taken as an object, meaning the task was to separate the facial area from the image and determine whether or not the person's information is present in the database, and to develop a program that can recognize the person. [ABSTRACT FROM AUTHOR]

This article is devoted to the development of intelligent models of groundwater modification based on modern GIS technology as well as mathematical models. The article is based on the geographic information system or GIS (GIS), which collects, stores, processes, analyzes and reflects spatial information, allowing you to obtain the geographic area and the information and knowledge associated with them. on the study of non-technological spatial information. It is based on the creation of a database for storing, analyzing and collecting data and data using a variety of specialized GIS applications. Geography and digital cartography show the use of specialized GIS programs. Arcin's ArcInfo and its additional modules describe the use of applications such as ArcMap, ArcCatolog, and ArcToolbox, the execution of functional operations by ArcMap and many other modules, large-scale image processing, and geographic linkage methods. [ABSTRACT FROM AUTHOR]

The examined pollution sources in the current paper include combustion and industrial processes; agriculture; household heating; waste and waste water treatment; road, rail and aviation transport. The investigated group of contaminants released from each of the listed pollution sources for the period 2011-2020 in Bulgaria are the following: sulphur and nitrogen oxides, non-methane volatile organic compounds, methane, carbon and dinitrogen oxide, carbon dioxide and ammonia. A hierarchical organization of the objects from the mentioned sets is built. This studied information from time series stored in xlsx files is structured in a relational database. In this connection, the values of contaminants from a certain pollution source in the relevant file are presented in matrix form. The variation of the number of columns in the formed rectangular matrix depends on the number of the examined years. The data are regrouped to obtain the transposed matrix. The components from each column of this matrix are entered into a selected field of a certain table from the database. The presented approach integrates the processes of searching the indicated data, restructuring and grouping separate datasets, and entering them in the built database. Certain criteria are defined in order to form and extract groups of elements from one or more tables of the database, as well as to establish the relevant dependencies for the studied objects. Analysis of variance and Tukey's range test are used for statistical assessment of the data. The approach can also be applied to examine datasets on air pollutant emissions for other countries. [ABSTRACT FROM AUTHOR]