Search results for: binary data

Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Rupali Bhardwaj

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Cryptography is the science of using mathematics to encrypt and decrypt data; the data are converted into some other gibberish form, and then the encrypted data are transmitted. The primary purpose of this paper is to provide two levels of security through a two-step process, rather than transmitted the message bits directly, first encrypted it using 2D cellular automata and then scrambled with Arnold Cat Map transformation; it provides an additional layer of protection and reduces the chance of the transmitted message being detected. A comparative analysis on effectiveness of scrambling technique is provided by scrambling degree measurement parameters i.e. Gray Difference Degree (GDD) and Correlation Coefficient.

Keywords: scrambling, cellular automata, Arnold cat map, game of life, gray difference degree, correlation coefficient

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Authors: Naveed Ghani, Samreen Javed

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In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.

Keywords: network worms, malware infection propagating malicious code, virus, security, VPN

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Authors: Abdallah Al-ZoubI, Mamoun Dmour

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The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

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Authors: Abdul-Rahman Al-Ali

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As the number of mobile devices is growing exponentially, it is estimated to connect about 50 million devices to the Internet by the year 2020. At the end of this decade, it is expected that an average of eight connected devices per person worldwide. The 50 billion devices are not mobile phones and data browsing gadgets only, but machine-to-machine and man-to-machine devices. With such growing numbers of devices the Internet of Things (I.o.T) concept is one of the emerging technologies as of recently. Within the smart grid technologies, smart home appliances, Intelligent Electronic Devices (IED) and Distributed Energy Resources (DER) are major I.o.T objects that can be addressable using the IPV6. These objects are called the smart grid internet of things (SG-I.o.T). The SG-I.o.T generates big data that requires high-speed computing infrastructure, widespread computer networks, big data storage, software, and platforms services. A company’s utility control and data centers cannot handle such a large number of devices, high-speed processing, and massive data storage. Building large data center’s infrastructure takes a long time, it also requires widespread communication networks and huge capital investment. To maintain and upgrade control and data centers’ infrastructure and communication networks as well as updating and renewing software licenses which collectively, requires additional cost. This can be overcome by utilizing the emerging computing paradigms such as cloud computing. This can be used as a smart grid enabler to replace the legacy of utilities data centers. The talk will highlight the role of I.o.T, cloud computing services and their development models within the smart grid technologies.

Keywords: intelligent electronic devices (IED), distributed energy resources (DER), internet, smart home appliances

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Authors: Shishen Xie, Yingda L. Xie

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Tuberculosis (TB) is a potentially serious infectious disease that remains a health concern. The Interferon Gamma Release Assay (IGRA) is a blood test to find out if an individual is tuberculous positive or negative. This study applies statistical analysis to the clinical data of interferon-gamma levels of seventy-three subjects who diagnosed pulmonary TB in an anti-tuberculous treatment. Data analysis is performed to determine if there is a significant decline in interferon-gamma levels for the subjects during a period of six months, and to infer if the anti-tuberculous treatment is effective.

Keywords: data analysis, interferon gamma release assay, statistical methods, tuberculosis infection

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Jinghui Peng, Shanyu Tang, Jia Li

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Steganalysis seeks to detect the presence of secret data embedded in cover objects, and there is an imminent demand to detect hidden messages in streaming media. This paper shows how a steganalysis algorithm based on Fast Fourier Transform (FFT) can be used to detect the existence of secret data embedded in streaming media. The proposed algorithm uses machine parameter characteristics and a network sniffer to determine whether the Internet traffic contains streaming channels. The detected streaming data is then transferred from the time domain to the frequency domain through FFT. The distributions of power spectra in the frequency domain between original VoIP streams and stego VoIP streams are compared in turn using t-test, achieving the p-value of 7.5686E-176 which is below the threshold. The results indicate that the proposed FFT-based steganalysis algorithm is effective in detecting the secret data embedded in VoIP streaming media.

Keywords: steganalysis, security, Fast Fourier Transform, streaming media

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Authors: Sanaz Kavianpour, Zuraini Ismail, Bharanidharan Shanmugam

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Social Network Sites (SNSs) can be served as an invaluable platform to transfer the information across a large number of individuals. A substantial component of communicating and managing information is to identify which individual will influence others in propagating information and also whether dissemination of information in the absence of social signals about that information will be occurred or not. Classifying the final audience of social data is difficult as controlling the social contexts which transfers among individuals are not completely possible. Hence, undesirable information diffusion to an unauthorized individual on SNSs can threaten individuals’ privacy. This paper highlights the information diffusion in SNSs and moreover it emphasizes the most significant privacy issues to individuals of SNSs. The goal of this paper is to propose a privacy-preserving model that has urgent regards with individuals’ data in order to control availability of data and improve privacy by providing access to the data for an appropriate third parties without compromising the advantages of information sharing through SNSs.

Keywords: anonymization algorithm, classification algorithm, information diffusion, privacy, social network sites

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Authors: Sidi Yang, Haiyi Zhang

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Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields.

Keywords: text mining, Twitter, topic model, sentiment analysis

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Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

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Authors: D. Miroud, H. Mokaddem, M. Tata, N. Foucha

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The body of PDC (polycrystalline diamond compact) drill bit can be manufactured from two different materials, steel and tungsten carbide matrix. Commonly the steel body is produced by machining, thermal spraying a bonding layer and hardfacing of Ni-based matrix reinforced with fused eutectic tungsten carbide (WC/W2C). The matrix body bit is manufactured by infiltrating tungsten carbide particles, with a Copper binary or ternary alloy. By erosion-corrosion mechanisms, the PDC drill bits matrix undergoes severe damage, occurring particularly around the PDC inserts and near injection nozzles. In this study, we investigated the possibility to repair the damaged matrix regions by hardfacing technic. Ni-based hardfacing alloy reinforced with fused eutectic tungsten carbide is deposited on infiltrated WC-W-Ni substrate by oxyacetylene welding (OAW). The microstructure at the hardfacing / matrix interface is characterized by SEM- EDS, XRD and micro hardness Hv0.1. The hardfacing conditions greatly affect the dilution phenomenon and the distribution of carbides at the interface, without formation of transition zone. During OAW welding deposition, interdiffusion of atoms occurs: Cu and Sn diffuse from infiltrated matrix substrate into hardfacing and simultaneously Cr and Si alloy elements from hardfacing diffuse towards the substrate. The dilution zone consists of a nickel-rich phase with a heterogeneous distribution of eutectic spherical (Ni-based hardfacing alloy) and irregular (matrix) WC/W2C carbides and a secondary phase rich in Cr-W-Si. Hardfacing conditions cause the dissolution of banding around both spherical and irregular carbides. The micro-hardness of interface is significantly improved by the presence of secondary phase in the inter-dendritic structure.

Keywords: dilution, dissolution, hardfacing, infiltrated matrix, PDC drill bits

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: William Thomas Worster

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Diplomatic assurances issued by states declaring that they will not mistreat individuals returned to them occupy a strange middle ground between being legal and non-legal obligations. States assert that they are non-binding, yet at other times that they are binding. However, this assertion may not be the end of the discussion. The International Court of Justice and other tribunals have concluded that similar instruments were binding, states have disagreed that certain similar instruments were binding, and the Vienna Convention on the Law of Treaties and its travaux prépératoires do not appear to contemplate non-binding instruments. This paper is a case study of diplomatic assurances but, by necessity, touches on the delicate question of whether certain texts are treaties, promises, or non-binding political statements. International law, and law in general, requires a binary approach to obligation. All communications must be binding or not, even if the fit is not precise. Through this study, we will find that some of the obligations in certain assurances can be understood as legal and some not. We will attempt to state the current methodology for determining which obligations are legal under the law of treaties and law on binding unilateral promises. The paper begins with some background of the legal environment of diplomatic assurances and their use in cases of expulsion. The paper then turns to discuss the legal nature of diplomatic assurances, proceeding to address various possibilities for legal value as treaties and as binding unilateral statements. This paper will not examine the legal value of diplomatic assurances solely under customary international law other than the way in which customary international law might further refine the treaty definition. In order to identify whether any assurances are contained in legal acts, this study identifies a pool of relevant assurances and qualitatively analyzes whether any of those are contained in treaties or binding unilateral statements. To the author’s best knowledge, this study is the first large-scale, qualitative qualitative analysis of assurances as a group of instruments that accounts for their heterogenous nature. It is also the first study to identify the indicators of whether an instrument is a treaty or promise.

Keywords: diplomatic assurances, deportation, extradition, expulsion, non-refoulement, torture, persecution, death penalty, human rights, memorandum of understanding, promises, secret, monitoring, compliance, enforcement

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Authors: Seonjae Lee, Sungjoo Lee

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Excavation is necessary to remain competitive in the current business environment. The company survived the rapidly changing industry conditions by adapting new business strategy and reducing technology challenges. Traditionally, the two methods are conducted excavations for new businesses. The first method is, qualitative analysis of expert opinion, which is gathered through opportunities and secondly, new technologies are discovered through quantitative data analysis of method patents. The second method increases time and cost. Patent data is restricted for use and the purpose of discovering business opportunities. This study presents the company's characteristics (sector, size, etc.), of new business opportunities in customized form by reviewing the value chain perspective and to contributing to creating new business opportunities in the proposed model. It utilizes the trademark database of the Korean Intellectual Property Office (KIPO) and proprietary company information database of the Korea Enterprise Data (KED). This data is key to discovering new business opportunities with analysis of competitors and advanced business trademarks (Module 1) and trading analysis of competitors found in the KED (Module 2).

Keywords: value chain, trademark, trading analysis, new business opportunity

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Authors: Mousouris Spiridon, Kavakli Evangelia

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This paper focuses on Digital Libraries (DLs) that contain and geovisualise cultural data, highlighting the need to define them as a separate category termed Cultural Mapping Libraries, based on their inherent connection of culture with geographic location and their design requirements in support of visual representation of cultural data on the map. An exploratory analysis of DLs that conform to the above definition brought forward the observation that existing Cultural Mapping Libraries fail to geovisualise the entirety of cultural data per point of interest thus resulting in a Cultural Snapshot phenomenon. The existence of this phenomenon was reinforced by the results of a systematic bibliographic research. In order to address the Cultural Snapshot, this paper proposes the use of the Semantic Web principles to efficiently interconnect spatial cultural data through time, per geographic location. In this way points of interest are transformed into scenery where culture evolves over time. This evolution is expressed as occurrences taking place chronologically, in an event oriented approach, a conceptualization also endorsed by the CIDOC Conceptual Reference Model (CIDOC CRM). In particular, we posit the use of CIDOC CRM as the baseline for defining the logic of Cultural Mapping Libraries as part of the Culture Domain in accordance with the Digital Library Reference Model, in order to define the rules of cultural data management by the system. Our future goal is to transform this conceptual definition in to inferencing rules that resolve the Cultural Snapshot and lead to a more complete geovisualisation of cultural data.

Keywords: digital libraries, semantic web, geovisualization, CIDOC-CRM

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Authors: Ibrahim Rabiu Darazo

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The research has been conducted on the impact of E-banking on the operational efficiency of Banks in Nigeria, A case of some selected banks (Diamond Bank Plc, GTBankPlc, and Fidelity Bank Plc) in Nigeria. The research is a quantitative research which uses both primary and secondary sources of data collection. Questionnaire were used to obtained accurate data, where 150 Questionnaire were distributed among staff and customers of the three Banks , and the data collected where analysed using chi-square, whereas the secondary data where obtained from relevant text books, journals and relevant web sites. It is clear from the findings that, the use of e-banking by the banks has improved the efficiency of these banks, in terms of providing efficient services to customers electronically, using Internet Banking, Telephone Banking ATMs, reducing time taking to serve customers, e-banking allow new customers to open an account online, customers have access to their account at all the time 24/7.E-banking provide access to customers information from the data base and cost of check and postage were eliminated using e-banking. The recommendation at the end of the research include; the Banks should try to update their electronic gadgets, e-fraud(internal & external) should also be controlled, Banks shall employ qualified man power, Biometric ATMs shall be introduce to reduce fraud using ATM Cards, as it is use in other countries like USA.

Keywords: banks, electronic banking, operational efficiency of banks, biometric ATMs

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Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

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Authors: O. O. Aiyelokun, O. A. Agbede

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Numerical modelling of groundwater flow can be susceptible to calibration errors due to lack of adequate ground-based hydro-metrological stations in river basins. Groundwater resources management in Southwest Nigeria is currently challenged by overexploitation, lack of planning and monitoring, urbanization and climate change; hence to adopt models as decision support tools for sustainable management of groundwater; they must be adequately calibrated. Since river basins in Southwest Nigeria are characterized by missing data, and lack of adequate ground-based hydro-meteorological stations; the need for adopting satellite-based data for constructing distributed models is crucial. This study seeks to evaluate the suitability of satellite-based data as substitute for ground-based, for computing boundary conditions; by determining if ground and satellite based meteorological data fit well in Ogun and Oshun River basins. The Climate Forecast System Reanalysis (CFSR) global meteorological dataset was firstly obtained in daily form and converted to monthly form for the period of 432 months (January 1979 to June, 2014). Afterwards, ground-based meteorological data for Ikeja (1981-2010), Abeokuta (1983-2010), and Oshogbo (1981-2010) were compared with CFSR data using Goodness of Fit (GOF) statistics. The study revealed that based on mean absolute error (MEA), coefficient of correlation, (r) and coefficient of determination (R²); all meteorological variables except wind speed fit well. It was further revealed that maximum and minimum temperature, relative humidity and rainfall had high range of index of agreement (d) and ratio of standard deviation (rSD), implying that CFSR dataset could be used to compute boundary conditions such as groundwater recharge and potential evapotranspiration. The study concluded that satellite-based data such as the CFSR should be used as input when constructing groundwater flow models in river basins in Southwest Nigeria, where majority of the river basins are partially gaged and characterized with long missing hydro-metrological data.

Keywords: boundary condition, goodness of fit, groundwater, satellite-based data

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Authors: Ksenija Milošević, Davor Lončarević, Tihana Mudrinić, Jasmina Dostanić

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Heterogeneous photocatalytic processes have gained growing interest as an efficient method to generate hydrogen by using clean energy sources and degrading various organic pollutants. The main obstacles that restrict efficient photoactivity are narrow light-response range and high rates of charge carrier recombination. The formation of heterojunction by combining a semiconductor with low VB and a semiconductor with high CB and a suitable band gap was found to be an efficient method to prepare more sensible materials with improved charge separation, appropriate oxidation and reduction ability, and enhanced visible-light harvesting. In our research, various binary heterojunction systems based on the wide-band gap (TiO₂) and narrow bandgap (g-C₃N₄, CuO, and Co₂O₃) photocatalyst were studied. The morphology, optical, and electrochemical properties of the photocatalysts were analyzed by X-ray diffraction (XRD), scanning electron microscopy (FE-SEM), N₂ physisorption, diffuse reflectance measurements (DRS), and Mott-Schottky analysis. The photocatalytic performance of the synthesized catalysts was tested in single and simultaneous systems. The synthesized photocatalysts displayed good adsorption capacity and enhanced visible-light photocatalytic performance. The mutual interactions of pollutants on their adsorption and degradation efficiency were investigated. The interfacial connection between photocatalyst constituents and the mechanism of the transport pathway of photogenerated charge species was discussed. A radical scavenger study revealed the interaction mechanisms of the photocatalyst constituents in single and multiple pollutant systems under solar and visible light irradiation, indicating the type of heterojunction system (Z scheme or type II).

Keywords: bandgap alignment, heterojunction, photocatalysis, reaction mechanism

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Shivam Dwivedi, Sumit Prakash Gupta, Durga Toshniwal

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It is a challenging task in today’s date to keep defence forces in the highest state of combat readiness with budgetary constraints. A huge amount of time and money is squandered in the unnecessary and expensive traditional maintenance activities. To overcome this limitation Defence Intelligent Mission Readiness and Maintenance Scheduling System has been proposed, which ameliorates the maintenance system by diagnosing the condition and predicting the maintenance requirements. Based on new data mining algorithms, this system intelligently optimises mission readiness for imminent operations and maintenance scheduling in repair echelons. With modified data mining algorithms such as Weighted Feature Ranking Genetic Algorithm and SVM-Random Forest Linear ensemble, it improves the reliability, availability and safety, alongside reducing maintenance cost and Equipment Out of Action (EOA) time. The results clearly conclude that the introduced algorithms have an edge over the conventional data mining algorithms. The system utilizing the intelligent condition-based maintenance approach improves the operational and maintenance decision strategy of the defence force.

Keywords: condition based maintenance, data mining, defence maintenance, ensemble, genetic algorithms, maintenance scheduling, mission capability

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Authors: Tyler Gill, Sophia Daniels

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The paper examines how accessing the spatial-temporal constrains of data will help inform policymakers and law enforcement officials. The authors utilize Chicago crime data from 2006-2016 to demonstrate how the Emerging Hot Spot Tool is an ideal hot spot clustering approach to analyze crime data. Traditional approaches include density maps or creating a spatial weights matrix to include the spatial-temporal constrains. This new approach utilizes a space-time implementation of the Getis-Ord Gi* statistic to visualize the data more quickly to make better decisions. The research will help complement socio-cultural research to find key patterns to help frame future policies and evaluate the implementation of prior strategies. Through this analysis, homicide trends and patterns are found more effectively and recommendations for use by non-traditional users of GIS are offered for real life implementation.

Keywords: crime mapping, emerging hot spot analysis, Getis-Ord Gi*, spatial-temporal analysis

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Authors: Promothes Saha

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Recently, transportation engineering industry members at the study university showed concern that students lacked the skills needed to solve real-world engineering problems using spreadsheet data analysis. In response to the concerns shown by industry members, this study investigated how to engage students in a better way by incorporating spreadsheet analysis during class - also, help them learn the course topics. Helping students link theoretical knowledge to real-world problems can be a challenge. In this effort, in-class activities and worksheets were redesigned to integrate with Excel to solve example problems using built-in tools including cell referencing, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc. The effectiveness of this technique was investigated using students’ evaluations of the course, enrollment data, and students’ comments. Based on the data of those criteria, it is evident that the spreadsheet activities may increase student learning.

Keywords: civil, engineering, active learning, transportation

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Authors: Sabina Akter, Osiris Valdez Banda, Pentti Kujala, Jani Romanoff

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This paper examines the relationship between on-board environmental factors and customer overall satisfaction in the context of the cruise on-board experience. The on-board environmental factors considered are ambient, layout/design, social, product/service and on-board enjoyment factors. The study presents a data-driven framework and model for the on-board cruise experience. The data are collected from 893 respondents in an application of a self-administered online questionnaire of their cruise experience. This study reveals the cruise passengers’ on-board experience through the customer decision journey based on the publicly available data. Pearson correlation and regression analysis have been applied, and the results show a positive and a significant relationship between the environmental factors and on-board experience. These data help understand the cruise passengers’ on-board experience, which will be used for the ultimate decision-making process in cruise ship design.

Keywords: cruise behavior, customer activities, on-board environmental factors, on-board experience, user or customer satisfaction

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Authors: Cinzia Carrodano, Dimitri Konstantas

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Risk is part of our lives. In today’s society risk is connected to our safety and safety has become a major priority in our life. Each person lives his/her life based on the evaluation of the risk he/she is ready to accept and sustain, and the level of safety he/she wishes to reach, based on highly personal criteria. The assessment of risk a person takes in a complex environment and the impact of actions of other people’actions and events on our perception of risk are alements to be considered. The concept of Holistic Risk Assessment (HRA) aims in developing a methodology and a model that will allow us to take into account elements outside the direct influence of the individual, and provide a personalized risk assessment. The concept is based on the fact that in the near future, we will be able to gather and process extremely large amounts of data about an individual and his/her environment in real time. The interaction and correlation of these data is the key element of the holistic risk assessment. In this paper, we present the HRA concept and describe the most important elements and considerations.

Keywords: continuous data, dynamic risk, holistic risk assessment, risk concept

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Authors: Abdullah Al Farwan, Ya Zhang

Abstract:

In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Efthymios Chondrogiannis, Vassiliki Andronikou, Efstathios Karanastasis, Theodora Varvarigou

Abstract:

A large amount of data is typically stored in relational databases (DB). The latter can efficiently handle user queries which intend to elicit the appropriate information from data sources. However, direct access and use of this data requires the end users to have an adequate technical background, while they should also cope with the internal data structure and values presented. Consequently the information retrieval is a quite difficult process even for IT or DB experts, taking into account the limited contributions of relational databases from the conceptual point of view. Ontologies enable users to formally describe a domain of knowledge in terms of concepts and relations among them and hence they can be used for unambiguously specifying the information captured by the relational database. However, accessing information residing in a database using ontologies is feasible, provided that the users are keen on using semantic web technologies. For enabling users form different disciplines to retrieve the appropriate data, the design of a Graphical User Interface is necessary. In this work, we will present an interactive, ontology-based, semantically enable web tool that can be used for information retrieval purposes. The tool is totally based on the ontological representation of underlying database schema while it provides a user friendly environment through which the users can graphically form and execute their queries.

Keywords: ontologies, relational databases, SPARQL, web interface

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Authors: Salma Krafessi

Abstract:

The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

Procedia PDF Downloads 47