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In this era of technology, online data is increasing enormously in various forms be it images, videos, texts, sound, etc. Now with increasing data, there arises the issue of storage and that's the time when Bigdata and its tools come into the picture. To analyze the big data and process it with the available traditional methods, becomes an extremely strenuous task. And to overcome this drawback, there are various big data tools and techniques available. Some of the techniques that are used to analyze big data are the techniques of data mining such as clustering, classification, division, and prediction. The tools that are used in analyzing the big data are MongoDB, NoSQL, HPCC, Apache Storm, Spark, Apache Hadoop, also these tools are used in handling big data. In this paper, analysis of big data tools and techniques has been done with different examples and instances. To understand the tools better, their summaries along with examples are presented. [ABSTRACT FROM AUTHOR]

The Box-Cox (BC) transformation is widely used in data analysis for achieving approximate normality in the trans-formed scale. The transformation is only possible for non-negative data. This positiveness requirement implies a truncation to the distribution on the transformed scale and the distribution in the transformed scale is truncated normal. This fact has consequences for the estimation of the parameters specially if the truncated probability is high. In the seminal paper Box and Cox proposed to estimate parameters using the normal distribution which in practice means to ignore any consequences of the truncation on the estimation process. In this work we present the framework for exact likelihood estimation on the PN distribution to which we call method m1 and how to calculate the parameters estimates using consistent estimators. We also present a pseudo-Likelihood function for the same model not taking into account truncation and allowing to replace parameters μ and σ for their estimates. We call m2 to this estimation method. We conclude that for cases where the truncated probability is low both methods give good estimation results. However for larger values of the truncated probability the m2 method does not present the same efficiency. [ABSTRACT FROM AUTHOR]

Climate change is getting more attention in recent years as it is bringing more devastating natural disasters such as heat waves, drought and floods around the world. Human activities such as the burning of fossil fuels, which produce heat-trapping gases, are the main driver of climate change. The enormous amount of greenhouse gases that are released into the atmosphere accelerates the pace of global warming. On the other hand, renewable energy is usually clean and sustainable. Thus, turning down the role of fossil fuel in the energy sector should be a priority and facilitating the use of renewable energy is the best solution to mitigate climate change. To have a clear picture on the current situation, the paper will include a literature review about climate change, greenhouse gases and renewable energy. The methodology, analysis methods and results will be included in the paper. Data collection, pre-processing, data analysis and data visualisation dashboard will be involved during the process. A data analysis dashboard will be created to incorporate the background of the story and the results of the data analysis. The users will be exploring the trends of renewable energy and greenhouse gas emissions with data analysis tools to gain useful insights from the data. [ABSTRACT FROM AUTHOR]

Power plants become critical facilities that need to run every time without failure. The component in the power plant system has been equipped with sensors. These sensors will monitor the element's condition by periodically sending the related metric data to the control room or storing it for later evaluation. These sensor data can be analyzed for early anomaly detection on power plants. Traditional methods still need humans as experts to detect these anomalies; hence, human error is a factor. Using deep learning, we can take advantage of abundant data to learn the usual pattern and detect anomalies in future data. Many deep learning algorithms have been implemented in this case. However, the selection of these algorithms depends on the data's characteristics. This paper aims to provide a comprehensive, structured analysis of the data features and the algorithm used to detect anomalies in the data. Twenty-two recent power plant anomaly detection studies were compiled as a systematic review. This review will discuss power plant sensor data characteristics that need to be considered, such as raw data format, label usage, data dependence, and detection results. This review also discusses methods used to process unsupervised data, the relation of the multivariate data, and anomaly detection. This review aims to ease the selection of the appropriate anomaly detection algorithm for future research by considering the data characteristics. [ABSTRACT FROM AUTHOR]

In this paper, technical and statistical analysis of Smart Living, one of the most important aspects of the next generation, has been provided with detailed information. Smart living refers to using technology and the internet of things (IoT) to pursue a more efficient, controllable, and productive lifestyle. In this century, with the increase in the global population, smart living is a significantly important criterion to ensure equal opportunities for all. Here a complete overview and case implementation are discussed, followed by an analysis of statistical data about the demand for smart living from a Techno-Socio point of view. Lastly, a few aspects and issues of Smart Living have been discussed, and a proposal is given to improvise it further for future research and possible implementation. [ABSTRACT FROM AUTHOR]

The first, and at the same time one of the most important tasks in building a predictive system is the task of analyzing the original data set. The next task is to process the data according to the purpose and objectives of the predictive system. These two tasks are often not given enough attention and, as a consequence, the developer does not get the desired result as a result of building the system. This paper discusses methods and algorithms for data analysis and processing that can help in building a predictive system. The data set from the machine learning competition "ASHRAE - Great Energy Predictor III" is used as input data. The analysis phase focuses on constructing graphs and drawing conclusions from them. The paper shows how to use graphical information to find anomalies in the data and eliminate them. Examples of graphs that provide information useful for building machine learning models are given. The data processing phase describes the transformations performed on the data. It considers the transformations that lead to positive results as well as those that lead to negative results. [ABSTRACT FROM AUTHOR]

This paper examines the analysis of the different data types within the field of Bioinformatics. The aim is to study and handpick machine learning algorithms that are best suited for handling large datasets and data compendia with its specific features. The paper proposes a specific informed approach for analyzing each bioinformatics data type, combining, and comparing existing knowledge on the use of machine learning algorithms. [ABSTRACT FROM AUTHOR]

In today's world, women's safety is a significant and crucial problem. Even in these modern times, where technology has improved considerably, women's safety remains a worry. Incidences of rape, stalking, taunting, sexual assault, molestation, harassment, domestic abuse and other forms of violence are progressing daily. Many rules and regulations have been enacted to prevent these heinous acts. Despite this, the rate of crime is rapidly increasing. The main objective of the paper is to analyze the demographic profile of the women respondents in order to determine and provide women safety. The paper highlights various research papers in the context of women safety, women awareness and women safety parameters. After the survey, it is seen that although the government and society promotes gender equality but in reality it is not there. Most of the working women are prone to violence and harassment by men during travelling, workplace, educational institutions, residential areas etc. As a result many of women remain in stress because of stalking, sexual assault, verbal abuse, rape, domestic violence etc. The Government, NGO's, people should come forward to stop such kind of violence. Women should move freely and proper safety measures needs to be taken. A more sophisticated mobile based application need to be developed by making use of advanced technologies like Machine Learning, Data Science etc. Women safety is the major issue all over the world and everyone should support women in order to make them feel safe and secure. [ABSTRACT FROM AUTHOR]

Lathe machine is a general-purpose device which is utilized for machining different workpieces. In lathe machines, we can do distinctive operations. It is commonly utilized within the mechanical field. It makes the work simpler and sorted out. It is simple to introduce and simple to work on it. In today's situation, a high-quality finished product is required and its production time should also be minimal. To attain this, as per necessity, process parameters have to be changed, which in turn requires data with respect to the ideal input parameter values that will optimize the target. In this paper, review work has been carried ou t related to various workpiece materials used, techniques employed for DOE, various process parameters considered, optimization of process parameters has been summarized. This work may serve as prepared data for the ensuing analysis and would include a rare database related to the inquiry about regions utilizing lathe machines. [ABSTRACT FROM AUTHOR]

The paper discusses the growth of MOOCs documents Worldwide and in Indonesia. This research method adopts the five-stage method: determine search keyword, initial research, refinement of search results, compile preliminary data statistic and data analysis. The initial search results using the MOOCs keyword getting 5,729 documents from scopus databased spread throughout the world, while the MOOCs keyword is limited in Indonesia only getting 56 documents, the data is specified based on the type of journal article document and obtained 2000 documents spread throughout the world and 27 documents in Indonesia, then journal article data was analyzed to determine the growth of MOOCs research every year, countries with the most MOOCs research, publishers who published the most MOOCs research and articles with the highest citations, then the data was also visualized into VOSViewer to analyze the keywords used, namely the keywords that were most frequently used, newly researched keywords, keyword cluster division and who are the prolific authors who write MOOC topics. From the results of this study it can be concluded that most of the MOOCs research is in the United States, while Indonesia occupies the 24th position out of 104 countries Worldwide, this shows that research on MOOCs in Indonesia is still very small. [ABSTRACT FROM AUTHOR]

The paper solves the problem of generating informative patterns, which is the most important stage of logical data analysis. A greedy heuristic algorithm for pattern search is proposed, with the help of which a chain of patterns of different coverage and homogeneity is generated. Such a chain of patterns is an approximation of the Pareto optimal set of patterns, maximum in coverage of observations of the target class and minimum in coverage of observations of other classes. Computational experiments show that the choice of the pattern variant with the best informativeness (according to the used criterion of informativeness) avoids both the effect of retraining due to excessive selectivity of patterns and insufficient homogeneity of the pattern due to its excessive generalization. [ABSTRACT FROM AUTHOR]

Mental stress is one of the major contributors to a variety of health issues. Various measures and diagnoses have been created by neurologists and psychiatrists to determine the intensity of mental stress in its early phases. In the literature, several neuroimaging devices and methods for assessing mental stress have been presented. The key candidate chosen is the electroencephalogram (EEG) signal which contains valuable information regarding mental states and conditions. This paper presents reviews of current works on EEG signal analysis for assessing mental stress. The reviews emphasize the significant disparities in the research outcomes and claims of different results from various data analysis methodologies. Accordingly, methods of EEG signals analysis will be used to study the effect of various extracted features and classification methods that associate with mental stress. Apart from that, the utilization of Artificial Intelligence (AI) approach is also investigated to study its significance towards stress detection. [ABSTRACT FROM AUTHOR]

Since their emergence, high dimensional data have imposed a big challenge to researchers given their complexities. To deal with this problem, using feature selection is the best solution, it plays a very important role in high dimensional data analysis. This process will first allow us to extract the relevant features, remove redundancy and noise, decrease the computation time and improve the classification performances. In the literature, there are several methods for feature selection, of which three categories can be distinguished. In this paper, we will focus on the filter methods for feature selection, this category estimates the quality of the variables individually independently of any learning algorithm based on statistical measures. Among the most popular filters, there is the Relief method created by Kira and Rendell as an instance-based learning-inspired idea, it gives weight to each feature and selects the highest-scored features. In this study, we propose a filter method for feature selection called ReliefTP. ReliefTP is a combination of the Relief concepts and the Technique for Order Preference by Similarity to the Ideal Solution(TOPSIS). Several experiments were carried out on four biomedical datasets the most famous including a comparison with the original Relief. The classification results achieved by the different classification methods confirm the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an approach how to develop a system dedicated to analyze the problems and challenges in a relatively novel area - intelligent data analysis in a manufacturing organization in terms of operational decision making in order to achieve a production efficiency. The importance of this area for decision making through manufacturing analytics is highlighted with a description of modules for such system. Basic steps in the decision-making process in the production management are outlined and structured as well as the modules which are necessary to be built a business intelligence analysis system are described. [ABSTRACT FROM AUTHOR]

In recent world data analytics is more popular term in the field of computer science. The paper focuses on the compressive survey of various data analysis tools to connect with the people by just reading their data. Some of the most popular data analysis tools have been chosen, and a comparison has been made based on some key parameters to determine the best tool in the data science industry. Scenes can uses a huge collection of data information to analyze, predict, and deliver information such as the whole determined by various countries, HEIs, government, universities, and schools. In this paper data analytics tools has taken in account with the consideration of different parameter as Cost, data handling capabilities, graphical capabilities, big data, and other considerations. Comparative study has been made using own experiences on various data analysis tools. [ABSTRACT FROM AUTHOR]

The content analysis of the main physical and chemical indicators of the fruits of apples and mountain ash is performed, the data is illustrated in the figures. A technological scheme for obtaining a filling from fruit raw materials for confectionery products is graphically presented. The recipe ratio of the main components in experimental samples is given and the changes in organoleptic parameters are illustrated in the form of a profilogram of the identified descriptors, a polynomial curve of the dependence of tasters' assessments and the ratio of water to fruit raw materials is also presented. Based on the analysis and generalization of the averaged data of the tasting analysis, a reference sample and a list of descriptors for this product category were determined. The filling can be used as a semi-finished product for confectionery products in the form of fillers or for use as a coloring element to create an original decoration. [ABSTRACT FROM AUTHOR]

Survival analysis is a collection of statistical approaches for data analysis when the outcome variable of interest is the time until an event occurs. The time to failure of an experimental unit may be censored and this can be right, left, interval, and Partly Interval Censored (PIC). PIC contain the exact observations and interval-censored. In this paper, more than 25% of exact observations from the medical simulation data set will be used based on the exponential survival model via the imputation techniques. In addition to that, Multiple Imputation (MI) will be compared to the left imputation via the simulation study. The result of this simulation study suggested that for more exact observations, the results are well performed and easily implemented especially for the MI method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze Big Data processing technologies as well as to develop practical guidance to installing and working with the Apache Hadoop big data analysis ecosystem for its perspective application in universities. Specialists in the field of big data are in great demand, and with knowledge of Hadoop they are irreplaceable, since this system is fundamental and simpler analogues are based on it. This article can be used as the practical guidance to familiarize students with Apache Hadoop technology, as well as to provide theoretical material on "Big Data" technology. In the course of the work, the key definitions were given, the basic principles of working with "Big data" were formulated, and detailed instructions for installing and operating "Apache Hadoop" were developed. [ABSTRACT FROM AUTHOR]

Hydraulic drives are widely preferred in the industry over electrical as they offer low-cost automation. In industrial automation, accuracy in motion sequences, particularly that at the intermittent position of the valve, is essential to achieving the required stroke length. Due to unequal forces on both sides, the position of a hydraulic cylinder with a double-acting single rod is difficult to predict and control. This paper investigates the effect of load, Pressure at blank & rod ends, flow rate on the positioning accuracy of hydraulic cylinder. The full factorial design of experiments using 23 orthogonal array is used for the analysis of data. The ANOVA test was performed for proving conformity of experiment. The experiment also results in finding out the set of input parameters for best stopping accuracy for a given system configuration. [ABSTRACT FROM AUTHOR]

The global view requires sustainable development. The principle that must be applied in the national development activities of each nation. This has implications for management that must pay attention to social, environment and economic sustainability. The use of sustainable technology is one means that must be chosen. The main goal can be realized. The problem that became the study in this paper is how do you use sustainable technology? For key words used are general forms, data forms as secondary data, regulatory approach, prescriptive nature of research and deductive data analysis techniques. The results of the study show that in the perspective of positive law, the enthusiasm to use technology is available in the PPLH Law, but unfortunately the Mining Law does not explicitly require "sustainable mining technology". Therefore, in the future there needs to be a policy reform on the use of technology to support sustainability in the Mining Law. [ABSTRACT FROM AUTHOR]

This study makes a speciality of a way to use the Statistical Analysis for Data Science approach to a real-international commercial enterprise scenario. Through this paper, the prominence of the Statistical Learning is known. Statistical Learning helps us, locating out the answer for a statistical inferential trouble through which it is easy to discover a predictive characteristic primarily based on the information we have. Statistical learning performs a distinguished position in the various fields like Computer Vision, Speech Recognition, Emotion and Gender identification of Data Science. [ABSTRACT FROM AUTHOR]

The present empirical paper deals with the concept of Financial Literacy, and the purpose is to validate a questionnaire to measure the mediation effect of financial knowledge and attitude on the effect of financial skills on financial behavior. In the present study financial literacy is measured and analyzed in terms of four elements such as financial skills, knowledge, behavior, and attitude on a 5 points scale with 33-item questionnaire. The data is collected from the primary survey conducted in KL University to measure the financial literacy in among working women. The primary survey is conducted on 217 respondents and has taken the same size as 142 by using Proportionate Quota Sampling Technique. Data analysis in this study uses Andrew Hayes mediation analysis model 4 and 6. The results show that there is no mediation effect of financial knowledge and attitude on the effect of financial skills on financial behavior but having partial mediation separately. [ABSTRACT FROM AUTHOR]

Big data is the future, when information is the key to all problems of global development. With the ability to provide endless data, Big data will be an important application in many industries and be the future of the global economy. There are many industries and fields participating in the development and use of Big data, including the architecture and construction industry. The application of big data in architectural design is in its early stages with many potentials and challenges. The aim of this paper is to synthesis and recommend the solutions to overcome the challenges of Big Data application in architectural design. Research data focuses on the object and scope of the research which is Big data with its figures: data analysis models, methods techniques, applicability and other important characteristics. The paper uses traditional reasearch methods such as: collecting information, analyzing – synthesizing and diagramming. The results of the paper are summerized into overall recommendations to enable the utilizing of Big data and strategies to cope with the challenges of Big data application in architectural design. They also show the ability to further extend its application to both project implementation process and construction's life cycle. The conclusions contribute to detecting the development trend of Big Data applications in future, with the application further meeting users' needs in dealing with complicated analytical problems. [ABSTRACT FROM AUTHOR]

Remarkable number of healthcare data produced on day to day basis. Cloud computing has seen exponential growth over a short period of time in various sectors. Cloud computing is advantageous in providing economical services to its customers, for data storage, data collection, efficient data analysis of large patient's population. A survey of three hundred twenty hospitals in Indian, of rural and urban areas has shown the interest of hospitals for adopting cloud technology. In order to deal with the brisk improvement in IT and usage of cloud based services, numerous attempts should be applied to change healthcare data from traditional paper based towards the electronic form. To regulate and control the usage of healthcare data many regional legislation and policies should be enacted. [ABSTRACT FROM AUTHOR]

The paper focuses on methods for dynamic analysis used in modern construction. The analysis of experimental data of the building, after action of seismic loading is used. Described technique enables the user to simulate behaviour of the structural system of a building based on the finite elements developed and implemented in LIRA-SAPR program. The technique allows solving dynamic problems taking into account physical nonlinearity. [ABSTRACT FROM AUTHOR]

The aim of the paper is to bring new knowledge from ongoing specific university research. As part of this project, optical data collect locally and historically significant states of the chapel in Horní Nivy were collected using aerial and ground digital photogrammetry. The obtained data are subsequently analyzed for the needs of creating a virtual model. Data are generated from individual photographs as a point cloud. With regard to the parameters of the object, the data are captured partly from the ground using a camera and partly from the air using a camera mounted on a drone. By combining the exterior and interior model and removing unwanted points (noise), a model - a digital twin building - ai develop. The created model serves as a basis for the processing of construction technical and construction historical research. [ABSTRACT FROM AUTHOR]

The aim of the paper is to bring new knowledge from ongoing specific university research. As part of this project, optical data collect locally and historically significant states of the chapel in Horní Nivy were collected using aerial and ground digital photogrammetry. The obtained data are subsequently analyzed for the needs of creating a virtual model. Data are generated from individual photographs as a point cloud. With regard to the parameters of the object, the data are captured partly from the ground using a camera and partly from the air using a camera mounted on a drone. By combining the exterior and interior model and removing unwanted points (noise), a model - a digital twin building - ai develop. The created model serves as a basis for the processing of construction technical and construction historical research. [ABSTRACT FROM AUTHOR]

The present paper is devoted to the analysis of experimental data on the parameters of plastic strain localization within the model of autowave plasticity using specimens of commercially pure polycrystalline aluminum. This material lacks the stage of linear strain hardening: the deformation process occurs with parabolic strain hardening by the law σ~εn at the exponent n=0.5. The analysis of displacement fields of the surface points of a specimen deformed in the temperature range 170–350 K reveals perfect matching of the parabolic strain hardening law working in a region to the plasticity localization pattern in it. It is shown that temperature affects the length of the strain localization autowave at the stage of parabolic strain hardening with the exponent n=0.5. [ABSTRACT FROM AUTHOR]

Esports domain is growing with an goal to give the player a skill set to improve his skills. The paper, gives detail analysis performed on a dataset which is built by our team. The data used in the dataset is collected from Counter-Strike: Global Offensive (CS:GO) world championship major where top teams from all over the world had participated. The main goal was to create a customized dataset, predict the winning percentage for a team based on various features. It also provides descriptive analysis for a team's performance and prescriptive analysis for any improvements required to made by the team in order to improve their winning percentage and implementing different machine algorithms on the same. Extra features such as assigning roles, ranking and rating were also performed to provide in depth results. [ABSTRACT FROM AUTHOR]

The quality of data is an important issue in data analysis process. While raw data comes from real word may have different problems in its quality; missing data, outliers. Also numeric data may be come in different scales because it is measured using different units according to features that maintains. The less data quality can give down the analysis results. This problem is more obvious in data clustering, however, data clustering is unsupervised task that based on the data. One of probabilistic solutions to overcome the problem of using different scales in measuring data, normalization technique has been used to scale the data in a unified ones. So the produced data have same domain. Different normalization techniques can be applied. This paper shed light on the performance of normalization with one of the most famous clustering algorithms that is k-means clustering algorithm. We chose this algorithm because it is very sensitive to data quality, so the results can be distinguished according to this problem. The practical work used four specific data sets. The work covers the application of data without normalization. Then the different normalization techniques would applied on these data separately. To perform the comparison, accuracy metric would be done to evaluate the performance. The results were obtained revel the important of the normalization in increasing the accuracy of data sets; but in diverse directions according to data used. However, data sets respond very well to specific normalization technique, in opposite side, there is shut down in accuracy with other. So this results must be taken more attention to work in this field in selection the suitable technique to improve the results. Not always give the same tool for applying with different data set. At last, the paper was organized in: introduction, methods and materials, practical work and results, discussion and conclusion. [ABSTRACT FROM AUTHOR]

Science learning media can support science teaching and improve student science proficiency. The purpose of this study is to analyze the trend of science learning media in Indonesia. The data was obtained through the published papers about media used in science education. The type of published papers taken comes from science education journals in the last 10 years in Indonesia with a rank of S1-S6. The data analysis carried out includes media based on 1) the topic, 2) the material, 3) the integrated learning model, and 4) the type. The results in the first analysis show that many media are developed on the topic of biology and physics. Then the results of the second analysis show that the media has been widely developed on the material of movement systems and light-optic. Furthermore, the results of the third analysis, show that the media is widely integrated with inquiry and problem-based learning models. As for the fourth analysis, the most used type of media is flash based. Many advantages of this flash-based media have been found, therefore for 10 years, this media has been widely developed by researchers. The disadvantage of this media lies on its novelty. Therefore, the researchers should develop new types of science learning media in the future. [ABSTRACT FROM AUTHOR]

Topic modeling is the task of identifying topics in a corpus of documents. This is useful for search engines, customer service automation, and any other situation where document topics are important to know. There are numerous studies conducted about this field. Given the importance of topic modeling in various areas, this survey paper, explore some articles related to topic modeling (between 2013 to 2020) based on multiple methods such as: Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non Negative Matrix Factorization (NMF), on different social media sources such as (Amazon, Reddit, and Yelp). LDA and NMF general concepts are presented, in addition to the challenges of topic modeling and methods of evaluation. This paper does not go deep into the details of each of these methods. It only describes the high-level view that related to topic modeling in text mining. [ABSTRACT FROM AUTHOR]

This paper presents some of the correctional facilities systems of education. It attempts to identify the e-learning methods which are used in prisons for various educational purposes. Specifically, this research is focused on the opportunities and the limitations which the e-learning type of education has when used in prisons. The research method is survey, conducted at two Bulgarian prisons – one for juvenile criminals and one male's prison. The paper focuses on three main aspects – 1) access to technologies; 2) digital skills and 3) attitudes towards e-learning. Findings from data analysis reveal that prisoners are willing to enhance their learning experience through the use of ICT. The technologies utilised for educational purposes in these prisons are strictly limited due to a law which prohibits the use of Internet for inmates. Conclusions impose the need for advanced training of prisoners in the field of e-learning and ICT and providing adequate educational software based on intranet. [ABSTRACT FROM AUTHOR]

The supporting monitoring device inability to capture events during the transportation process resulted in difficulty dealing with abnormal conditions affecting meat spoilage in beef cold chain. Fraud occurring during delivery, such as arbitrarily modifying refrigerator setup and choosing improper routes, should be prevented to avoid customer's losses. This paper has proposed a real-time monitoring system by utilizing the internet of things (IoT) technology for capturing container conditions during the transportation process. The result shows prototypes testing capable to monitor temperature, humidity, and track vehicle positions in real-time. By utilizing data analysis features in the cloud, the proposed IoT system is able to notify if temperature and humidity fluctuation lead to shelf life degradation that exceeds the specified limit. This result is potential as an aid in a meat distributor in handling cold chain monitoring. [ABSTRACT FROM AUTHOR]

One of the most promising futuristic NoSQL database types is Graph Database or Knowledge Graph. These databases use topological data models to store big data and substantially support data relationship analysis. While developing a global Knowledge Graph for any system, there always exists a smaller subgraph called a local graph. Many local graphs combine to develop a global holistic graph. The general approach for development of these local knowledge graph is proposed in this paper. Along with the model, their possible curation approaches and generic targetable refinement areas of these databases are discussed in the work. In the end, few of the realistic application areas are being identified and conferred for future research in this domain. [ABSTRACT FROM AUTHOR]

The social media is one of the most used web application where a significant amount of data has generated. The social media has also been used in various personal and professional utilities. In this paper we are conducting a survey on social media based research work conducted in recent year. Additionally we have categorized this research work into four different area of research work. In addition, the classifier used, dataset used, and their consequences are also reported. Based on the recent work we have also provided an introduction of a Machine Learning (ML) model for future implementation point of view. Finally the conclusion has been made and the future research plan is discussed. [ABSTRACT FROM AUTHOR]

Data analysis is a dynamically developing field, currently. One of the actual tasks of data analysis is the task of classification. The problem of dividing a specific group of objects into a predetermined number of groups united in various ways is also important. On the other hand, computational performance increases and the volume of observed data increases, therefore, assigning them to certain subgroups becomes more complicated. In this paper, the binary classification problem is solved and a new parametric activation function for the machine learning model under consideration is analyzed. An important difference between the proposed classifier, for example, from standard classifiers based on logistic regression, is the connection with infinitely differentiable splines, the so-called atomic functions. At the same time, it is of interest to study the dependence of the classifier quality on the value of the variable parameter of the activation function. By changing the parameter from the activation function, you can make dependencies on the quality of the presented classifier. The comparison of quality indicators with various parameters of the activation function is considered. As data for model training, cross-validation and testing, an open multimodal data set MELD was used, consisting of a parallel set of videos and their textual interpretation. It is worth noting that MELD is a pre– labeled data corpus. The data were divided into two categories of sentiment analysis: positive and negative. A comparison of the work of a classifier based on parametric AString and logistic regression is given. [ABSTRACT FROM AUTHOR]

This paper will discuss the automation of Terra-MODIS 8-day data processing to support the availability of Analysis Ready Data (ARD). Terra-MODIS (MOD09A1) data covering the territory of Indonesia used in this processing will change the projection and tile size. The data conversion, mosaic, and tilling automation software used in this processing were created using GDAL and Strawberry Perl. The automation of the data conversion process takes 2 minutes, while mosaic and tilling take 1 minute. The result of this processing is data with a geographic projection (latitude, longitude) WGS 84, 5x5 degrees tile size, and 11Kb file size. [ABSTRACT FROM AUTHOR]

Design of experiments (DOE) extensively used in many fields namely agricultural, petroleum, aerospace, automotive, aircraft industries to seek the optimal solutions. DOE can solve single response and multi response problems through few tests. Taguchi method suggests orthogonal array (OA) and provides the range of estimates. The present paper mainly focused on the process capability index along with the significant effect of the various process parameters on output response. Here we identify the relation between variables and their contributions with the help of Analysis is of variance (ANOVA). The optimum set of parameters obtained by the Taguchi, Response surface methodology and additive law and analysis carried out using R Programming can be compared and noted that the results obtained through additive law are nearer to the test data. [ABSTRACT FROM AUTHOR]

Prakriti (Human Body constitution) clearly defines harmony with human nature and cause for moving out of balance and experience disease. Basically, there are 3 energies called VATT, PITT, and KAPH which decides body function on physical and emotional levels. These 3 energies known as Tridosha. Few people may predominant in one or mixture of 2 or more. For the long time, Ayurveda Dosha have been utilized. However, these diagnostic methods actually lingers behind in quantitative unawaring quality estimations. A cautious and proper examination prompts a powerful treatment. This work review academic journal and conference papers which adopted Machine Learning (ML) techniques in Ayurveda based disease classification and diagnosis using public medical datasets published in recent years. The aftereffects of this review showed that the use of ML procedures in illness order has encountered an intense rise in recent years. The finding of this paper additionally uncovered that there was negligible spotlight on creating strategies utilizing steady form of ML procedures. We trust that this examination will give valuable data about different ML strategies, their application in illness conclusion, and especially help specialists for creating medical decision support networks with experiences into the best in class of improvement techniques. [ABSTRACT FROM AUTHOR]

The paper deals with the description of statistical evaluation of fatigue of vibration-loaded structures. Fatigue tests are performed in laboratories on so-called vibrating tables, the results are then analysed using the construction of fatigue curves. The analysis includes several statistical methods. The paper describes some of these methods, clarifies the evaluation process and shows the results of the analysis on real data. The analysis of fatigue curves is performed using a constantly evolving tool for fatigue analysis, which arises as a result of unification and improvement of the fatigue curves evaluation level in the Regional Institute of Technology of the University of West Bohemia. [ABSTRACT FROM AUTHOR]

The supporting monitoring device inability to capture events during the transportation process resulted in difficulty dealing with abnormal conditions affecting meat spoilage in beef cold chain. Fraud occurring during delivery, such as arbitrarily modifying refrigerator setup and choosing improper routes, should be prevented to avoid customer's losses. This paper has proposed a real-time monitoring system by utilizing the internet of things (IoT) technology for capturing container conditions during the transportation process. The result shows prototypes testing capable to monitor temperature, humidity, and track vehicle positions in real-time. By utilizing data analysis features in the cloud, the proposed IoT system is able to notify if temperature and humidity fluctuation lead to shelf life degradation that exceeds the specified limit. This result is potential as an aid in a meat distributor in handling cold chain monitoring. [ABSTRACT FROM AUTHOR]

The purpose of this study is to create a concept to implement environmental education for elementary students in the surrounding mining areas in Indonesia. The method includes data gathering, subject analysis, and data analysis. Data was gathered by looking through all published papers and articles that discussed studies on mining activities in Indonesia and the implementation of environmental education in Indonesia. The aim of the subject analysis was to identify multiple subjects that could be combined with environmental education topics. Data analysis was carried out by analyzing information gathered about mining activities in Indonesia, the implementation of environmental education in Indonesia, and subject analysis in order to create a concept to implement environmental education for elementary students in the mining areas in Indonesia. Environmental education is related and can be applied to the learning process in the Chapter "Clean air for health" and the Chapter "Our friendly environment" for the Science subject of grade 5 by the infusion approach of explaining, discussion, demonstration, question and answer, and experiments. [ABSTRACT FROM AUTHOR]

The geometric process is one of the renewal processes in the observation systems or components in the life time data analysis. This paper aims to introduce a geometric process model in the life test analysis under normal test conditions by determining the estimation of model parameters. The assumptions of data distribution used here are exponential and Weibull distribution, and the parameter estimation method used is maximum likelihood estimation (MLE). Confidence Interval (CI) estimation for each distribution uses CI percentile bootstrap. Estimation algorithms are made for the two distribution models that estimate parameter for both distribution models and geometric process ratio Simulations were carried out to estimate the CI selected from the two models based on the root mean standard error (RMSE) value. [ABSTRACT FROM AUTHOR]

This paper presents a security analysis of locally stored end-user data, as well as the specifics of working with them in the Whatsapp application for Android operating systems. The application test version was 2.21.15.18 build number at the time of this writing. According to a specific scenario, test user data was generated in the application, from which sources of information with critical data were selected. During the study, information stores (databases, text files, media attachments, and others) that could potentially contain sensitive information were identified in the internal data of the application. For each discovered storage, an analysis was made of the implemented protection measures against unauthorized access. For the application as a whole and for private data, a qualitative assessment was made of the implementation of protection mechanisms on a qualitative basis (the mechanism is implemented/absent), inherent for any mobile applications, as well as specific specifically for applications of the class of messengers. As a result, deficiencies were discovered related to the inaccessibility of certain protective mechanisms on specific versions of the Android operating system, which could lead to the compromise of user data. [ABSTRACT FROM AUTHOR]

The main stage of logical analysis of data is the generation of patterns that have the maximum coverage of sample observations and also fulfill the constraints on the homogeneity of the coverage. To reduce the effect of overfitting, these constraints should be weakened and the heterogeneity of the pattern should be allowed. However, it is a challenge to determine in advance what kind of heterogeneity is acceptable for each data set. In this paper, we consider the problem of pattern generation as a multicriteria optimization problem where the criteria are the coverage of objects of the target class and coverage of objects that are not included in the target class. To solve this problem, a modified evolutionary algorithm based on the NSGA-II algorithm has been developed. The new algorithm takes into account the representation of the solution (coding patterns) in logical analysis of data and the informativeness of patterns. [ABSTRACT FROM AUTHOR]

Blockchain Technology has as of late got a great deal of interest from specialists and professionals. This is generally attributable to its particular qualities, which incorporate decentralization, security, trustworthiness, and information honesty. Regardless of this rising interest, little is had some familiarity with the current degree of information and practice with regards to the utilization of blockchain innovation in instruction. This paper presents an exhaustive outline of examination on blockchain-based instructive applications. Its concentrations on three primary themes: (1) educational applications manufactured using blockchain technology, (2) the benefits that blockchain technology might offer to education, and (3) the obstacles of implementing blockchain in education. For each subject, a full analysis of the data was performed, as well as an in-depth discussion based on the findings. Other educational fields that might benefit from blockchain technology are also discussed in this report. The present article focused on blockchain technology's potential educational uses and looked at how it may be used to address certain educational concerns. Before looking into some of the current educational blockchain applications, this article first examined the capabilities and benefits of blockchain technology. The merits and downsides of applying blockchain technology in education were discussed, as well as several unique uses of blockchain technology. [ABSTRACT FROM AUTHOR]

This paper analyzes students' errors at X MIPA 4 SMAN 1 Ampek Angkek, and it causes in solving vector based on Castolan. The type of research is descriptive with a qualitative approach—data analysis through reduction, display, and conclusion. The result showed that 1) around 66,66% of students made a conceptual error in vector operation, 33,33% in measuring the angle between two vectors, also 55,55% in orthogonal vector. 2) around 44,44% of students made a procedural error in measuring the angle between two vectors. 3) around 11,10% of students made a technical error identifying three collinear points. The cause is students' carelessness and does not recheck the answers. [ABSTRACT FROM AUTHOR]

The paper is devoted to a series of studies aimed at processing the data on job searches in Vladivostok, published on the open specialized Internet site hh.ru, followed by the development of tips for improving 'Multimedia Technologies' training course. An open-source analytics platform Knime.com was used to review the data. The article deals with the findings of the analysis, which show a certain level of salaries for occupations that require professional skills in 'Multimedia Technologies' course content. The survey of the 1st -4th-year students of 09.03.01 IT and Computer Science major was carried out at Maritime State University named after Admiral G.I. Nevelskoy to test a hypothesis about the necessity of making adjustments in the subject syllabus. 74 people participated in the survey. Finally, practical hints for improving Multimedia Technology training course content are given to perfect the course to meet the needs of both students and potential employers. [ABSTRACT FROM AUTHOR]

The substantial benefit to analyze graduate employability is known to describe Higher Education Institutions (HEIs) capability in producing career-ready graduates. The outcomes signify the success of the diploma or degree programs in meeting the market and stakeholder's demands. It is essential to identify how curriculum design and student activities can enhance the prospective ability to secure jobs. Therefore, Universiti Teknologi MARA (UiTM) gathers data about employment and employability to understand the current trends. However, developing a fast and accurate programming language for analyzing enormous data is a challenging task. This paper presents the development of an Automate Offline Employability (AOE) data analysis using Microsoft Excel; it reduces the time consumption in analyzing many data and increases the accuracy of data analysis. The system utilizes the combination of various types of Excel formulas and functions to execute multitasking algorithms simultaneously; it processes two data (initial and current information of graduates) obtained from online surveys. Graduates must answer the first survey during the last semester of their study, and the second survey during the sixth month after their graduation. The system is capable of sorting the data according to different study fields, matching the initial and current data, analyzing them based on employability attributes, and generating multiple interpretable graphs. In addition, it can extract the Uniform Resource Locator (URL) of email and WhatsApp of graduates who did not respond to the second online survey. Besides, it has a desktop layout to facilitate the user to retrieve any information by a single click. All functions provided by the system has successfully executed and verified. UiTM and HEIs can take benefit from the AOE system for assessing the correlation between the curriculum design, student activities and the employability prospects. [ABSTRACT FROM AUTHOR]