Search results for: data access

Authors: Pichamon Chansuchai

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The objective of this research were to investigate electronic payment system on the internet and offer the guidelines for proper internal control of the payment system based on international standard security control (ISO/IEC 17799:2005),in a case study of payment of the internet, Thailand. The guidelines covered five important areas: (1) business requirement for access control, (2) information systems acquisition, development and maintenance, (3) information security incident management, (4) business continuity management, and (5) compliance with legal requirement. The findings from this qualitative study revealed the guidelines for proper internet control that were more reliable and allow the same line of business to implement the same system of control.

Keywords: audit, best practice, internet, payment

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Authors: Rima T. Alfaraj, Murtadha J. Al Tammar, Khaqan Khan, Khalid M. Alruwaili

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OBJECTIVE/SCOPE (25-75): Caliper logging data provides critical information about wellbore shape and deformations, such as stress-induced borehole breakouts or washouts. Multiarm mechanical caliper logs are often run using wireline, which can be time-consuming, costly, and/or challenging to run in certain formations. To minimize rig time and improve operational safety, it is valuable to develop analytical solutions that can estimate caliper logs using available Logging-While-Drilling (LWD) data without the need to run wireline caliper logs. As a first step, the objective of this paper is to perform statistical analysis using an extensive datasetto identify important physical parameters that should be considered in developing such analytical solutions. METHODS, PROCEDURES, PROCESS (75-100): Caliper logs and LWD data of eleven wells, with a total of more than 80,000 data points, were obtained and imported into a data analytics software for analysis. Several parameters were selected to test the relationship of the parameters with the measured maximum and minimum caliper logs. These parameters includegamma ray, porosity, shear, and compressional sonic velocities, bulk densities, and azimuthal density. The data of the eleven wells were first visualized and cleaned.Using the analytics software, several analyses were then preformed, including the computation of Pearson’s correlation coefficients to show the statistical relationship between the selected parameters and the caliper logs. RESULTS, OBSERVATIONS, CONCLUSIONS (100-200): The results of this statistical analysis showed that some parameters show good correlation to the caliper log data. For instance, the bulk density and azimuthal directional densities showedPearson’s correlation coefficients in the range of 0.39 and 0.57, which wererelatively high when comparedto the correlation coefficients of caliper data with other parameters. Other parameters such as porosity exhibited extremely low correlation coefficients to the caliper data. Various crossplots and visualizations of the data were also demonstrated to gain further insights from the field data. NOVEL/ADDITIVE INFORMATION (25-75): This study offers a unique and novel look into the relative importance and correlation between different LWD measurements and wireline caliper logs via an extensive dataset. The results pave the way for a more informed development of new analytical solutions for estimating the size and shape of the wellbore in real-time while drilling using LWD data.

Keywords: LWD measurements, caliper log, correlations, analysis

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Authors: Thi Chang Duyen Can

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Althoughthe COVID-19 epidemic is under control, online education technology in Vietnam will still thrive in the learner-centered trend. Most of the Vietnamese students are now ready to familiarize themselves with and access to online learning. Even in some cases, online learning, if combined with new tools, is far more effective and exciting for students than some traditional instruction. However, little research has been conducted to explore Vietnamese teachers’ difficulties in moderating learner-centered E-learning. Therefore, the study employed the mixed method (n=9) to (i) uncover the challenges faced by Vietnamese teachers in English language online classes using learner-centred approach and (ii) propose the recommendations to improve the quality of online training in universities.

Keywords: learner-centered e-learning, english language classes, teachers' challenges, online learning

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Authors: Arka Roy, Chandra Prakash Dubey

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Inverse problem generally used for recovering hidden information from outside available data. Vertical component of gravity field we will be going to use for underneath density structure calculation. Ill-posing nature is main obstacle for any inverse problem. Linear regularization using Tikhonov formulation are used for appropriate choice of SVD and GSVD components. For real time data handle, signal to noise ratios should have to be less for reliable solution. In our study, 2D and 3D synthetic model with rectangular grid are used for gravity field calculation and its corresponding inversion for density reconstruction. Fine grid also we have considered to hold any irregular structure. Keeping in mind of algebraic ambiguity factor number of observation point should be more than that of number of data point. Picard plot is represented here for choosing appropriate or main controlling Eigenvalues for a regularized solution. Another important study is depth resolution plot (DRP). DRP are generally used for studying how the inversion is influenced by regularizing or discretizing. Our further study involves real time gravity data inversion of Vredeforte Dome South Africa. We apply our method to this data. The results include density structure is in good agreement with known formation in that region, which puts an additional support of our method.

Keywords: depth resolution plot, gravity inversion, Picard plot, SVD, Tikhonov formulation

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Authors: Pariya Sheykhmaleki, Debajyoti Pati

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Rural areas in the United States face numerous challenges in providing quality and assessable primary healthcare services, especially during emergencies such as natural disasters or pandemics. This study showcases a cutting-edge flexible module that aims to overcome these challenges by offering adaptable healthcare facilities capable of providing comprehensive health services in remote and disaster-prone regions. According to the Health Resources and Services Administration (HRSA), approximately 62 million Americans, or 1 in 5 individuals, live in areas designated as Health Professional Shortage Areas (HPSAs) for primary care. These areas are characterized by limited access to healthcare facilities, shortage of healthcare professionals, transportation barriers, inadequate healthcare infrastructure, higher rates of chronic diseases, mental health disparities, and limited availability of specialized care, including urgent circumstances like pandemics that can exacerbate this issue. To address these challenges, the literature study began by examining primary health solutions in very remote areas, e.g., spaceships, to identify the state-of-the-art technologies and the methods used to facilitate primary care needs. The literature study on flexibility in architecture and interior design was also adapted to develop a conceptual design for rural areas. The designed flexible module provides an innovative solution. This module can be prefabricated as all parts are standardized. The flexibility of the module allows the structure to be modified based on local and geographical requirements as well as the ability to expand as required. It has been designed to stand either by itself or work in tandem with public buildings. By utilizing sustainable approaches and flexible spatial configurations, the module optimizes the utilization of limited resources while ensuring efficient and effective healthcare delivery. Furthermore, the poster highlights the key features of this flexible module, including its ability to support telemedicine and telehealth services for all five levels of urgent care conditions, i.e., from facilitating fast tracks to supporting emergency room services, in two divided zones. The module's versatility enables its deployment in rural areas located far from urban centers and disaster-stricken regions, ensuring access to critical healthcare services in times of need. This module is also capable of responding in urban areas when the need for primary health becomes vastly urgent, e.g., during a pandemic. It emphasizes the module's potential to bridge the healthcare gap between rural and urban areas and mitigate the impact of rural health challenges.

Keywords: rural health, healthcare challenges, flexible modular design, telemedicine, telehealth

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Authors: Zaka Nisa, Farooq Sher

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Adverse drug reactions (ADRs) underreporting is a great challenge to Pharmacovigilance. Health care professionals have to consider ADR reporting as their professional obligation, an effective system of ADR reporting is important to improve patient health care and safety. The present study is designed to assess the knowledge, attitude, practice and factors associated with ADR reporting by health care professionals (physicians and pharmacists) in public and private hospitals of Pakistan. A pretested questionnaire was administered to 384 physicians and pharmacists in public and private hospitals. Respondents were evaluated for their knowledge, attitude, and practice related to ADR reporting. The data was analyzed using the SPSS statistical software, the factors which encourage and discourage respondents in reporting ADRs were determined. Most of the respondents have shown a positive attitude towards ADR reporting. The response rate was 95.32%. Of the 367 questionnaires, including 333 (86.5%) physicians and 34 (8.8%) pharmacists with the mean age 28.34 (SD= 6.69), most of the respondents showed poor ADR reporting knowledge (83.1%). The majority of respondents (78.2%) showed positive attitude towards ADR reporting and only (12.3%) hospitals have good ADR reporting practice. Knowledge of respondents in public hospitals (8.6%) was less as compare to those in the private hospitals (29.7%) (P < 0.001). Attitude of respondents in private hospitals was more positive (92.4%) than those in public hospitals (68.8%) (P < 0.001). No significant difference was observed in practicing of ADR reporting in public (11.8%) and private hospitals (13.1%) (P value 0.89). Seriousness of ADR, unusualness of reaction, new drug involvement and confidence in diagnosis of ADR were the factors which encourage respondents to report ADR, however, lack of knowledge regarding where and how to report ADR, lack of access to ADR reporting form, managing patients was more important than reporting ADR, legal liability issues were the factors which discourage respondents to report ADR. The study reveals poor knowledge and practice regarding ADR reporting. However positive attitude was seen regarding ADR reporting. There is a need of educational training for health care professionals as well as genuine and continuous efforts are required by Government and health authorities to ensure the proper implementation of ADR reporting system in all of the hospitals.

Keywords: adverse drugs reactions (ADR), pharmacovigilance, spontaneous ADR reporting, knowledge of ADR, attitude of health care profesionals, practice of ADR reporting

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Authors: Vishnu Pratap Singh Kirar, Madhuri Saxena

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Advancement in sequence data generation technologies is churning out voluminous omics data and posing a massive challenge to annotate the biological functional features. With so much data available, the use of machine learning methods and tools to make novel inferences has become obvious. Machine learning methods have been successfully applied to a lot of disciplines, including computational biology and bioinformatics. Researchers in computational biology are interested to develop novel machine learning frameworks to classify the huge amounts of biological data. In this proposal, it plan to employ novel machine learning approaches to aid the understanding of how apparently innocuous mutations (in intergenic DNA and at synonymous sites) cause diseases. We are also interested in discovering novel functional sites in the genome and mutations in which can affect a phenotype of interest.

Keywords: genome wide association studies (GWAS), next generation sequencing (NGS), deep learning, omics

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Authors: Daniela Prado, Lexi Frankel, Amalia Ardeljan, Lokesh Manjani, Matthew Cardeiro, Omar Rashid

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Background: Clostridium difficile (C. diff) is a gram-positive bacterium that is known to cause life-threatening diarrhea and severe inflammation of the colon. It originates as an alteration of the gut microbiome and can be transmitted through spores. Recent studies have shown a high association between the development of C. diff in cancer patients due to extensive hospitalization. However, research is lacking regarding C. diff’s association in the causation or prevention of cancer. The objective of this study was to therefore assess the correlation between Clostridium difficile infection (CDI) and the incidence of bone cancer. Methods: This retrospective analysis used data provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to evaluate the patients infected versus patients not infected with C. diff using ICD-10 and ICD-9 codes. Access to the database was granted by the Holy Cross Health, Fort Lauderdale, for the purpose of academic research. Standard statistical methods were used. Results: Between January 2010 and December 2019, the query was analyzed and resulted in 78863 patients in both the infected and control group, respectively. The two groups were matched by age range and CCI score. The incidence of bone cancer was 659 patients (0.835%) in the C. diff group compared to 1941 patients (2.461%) in the control group. The difference was statistically significant by a P-value < 2.2x10^-16 with an odds ratio (OR)= 0.33 (0.31-0.37) with a 95% confidence interval (CI). Treatment for CDI was analyzed for both C. diff infected and noninfected populations. 91 out of 16,676 (0.55%) patients with a prior C. diff infection and treated with antibiotics were compared to the control group were 275 out of 16,676 (1.65%) patients with no history of CDI and received antibiotic treatment. Results remained statistically significant by P-value <2.2x10-16 with an OR= 0.42 (0.37, 0.48). and a 95% CI. Conclusion: The study shows a statistically significant correlation between C. diff and a reduced incidence of bone cancer. Further evaluation is recommended to assess the potential of C. difficile in reducing bone cancer incidence.

Keywords: bone cancer, colitis, clostridium difficile, microbiome

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Authors: Waheed Akhtar

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In the current study, we aim to access the different month-wise reproductive biology of S. esocinus. Samples were collected from Rive Swat in the period of March 2022 to March 2023. Samples were collected using different gills nets of different sizes. Gonado Somatic Index and fecundity were studied using gravimetric to identify the breeding season and reproductive potential. The highest GSI was recorded in the month of April and November. Male to female ratio was in balance. The weight of the fish, size of the fish and ovary were parallel to the fecundity. This is the baseline study for the breeding biology of S. esocinus and further molecular study is required to identify the internal and external factors associated with the breeding biology of S. esocinus.

Keywords: snow trout, length and weight relationship, fecundity, river Swat

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Authors: Geetu Gahlawat, Manisha Singh

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In the recent years, the concept of media literacy is being extended from its traditional focus on print and audio-visual media to encompass the internet and other new media within academic and policy discourses. This article throws revolves around three significant queries which are to be dealt by the academia, general public and the policy-makers: What is media literacy? How is it changing? And what is the significance of media literacy? At the beginning of the article, the definition 'media literacy' is the ability to access, analyse, evaluate and create messages across a variety of contexts are given and then this is further being tested in connection with the internet and other information and communication technologies.Having advocated this skills-based approach to media literacy in relation to the internet, the article identifies some outstanding issues for new media literacy crucial to any policy of promoting media literacy among the population. The outcome is better understanding of media literacy and also the impact of ICT on media literacy by the public as well as media literate people.

Keywords: media literacy, ICT, internet, education

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Authors: Mohammed Al-Shalabi, Alaa Obeidat

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In any food stores, some items will be expired or destroyed because the demand on these items is infrequent, so we need a system that can help the decision maker to make an offer on such items to improve the demand on the items by putting them with some other frequent item and decrease the price to avoid losses. The system generates hundreds or thousands of patterns (offers) for each low demand item, then it uses the association rules (support, confidence) to find the interesting patterns (the best offer to achieve the lowest losses). In this paper, we propose a data mining method for determining the best offer by merging the data mining techniques with the e-commerce strategy. The task is to build a model to predict the best offer. The goal is to maximize the profits of a store and avoid the loss of products. The idea in this paper is the using of the association rules in marketing with a combination with e-commerce.

Keywords: data mining, association rules, confidence, online stores

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Authors: Yasmeen A. S. Essawy, Khaled Nassar

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With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

Keywords: building information modeling (BIM), elemental graph data model (EGDM), geometric and topological data models, graph theory

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Authors: Okike Benjami, Garba Ejd

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Today, it has been observed security of information along the superhighway is often compromised by those who are not authorized to have access to such information. In other to ensure the security of information along the superhighway, such information should be encrypted by some means to conceal the real meaning of the information. There are many encryption techniques out there in the market. However, some of these encryption techniques are often decrypted by adversaries with ease. The researcher has decided to develop an encryption technique that may be more difficult to decrypt. This may be achieved by splitting the message to be encrypted into parts and encrypting each part separately and swapping the positions before transmitting the message along the superhighway. The method is termed Okike’s Merged Irregular Transposition Cipher. Also, the research would determine the complexity level in respect to the number of splits of the message.

Keywords: transposition cipher, merged irregular cipher, encryption, complexity level

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Authors: Franco Miguel Nodado, Adrienne Marie Bugayong Janagap, Allen Claire Arances, Kirsten Anne Gerez, Frances Catherine Rosario, Charise Alvyne Samaniego, Matt Andrew Secular, Rommel Gestuveo, Marilyn Sumayo, Joseph Arbizo, Philip Ian Padilla

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COVID-19 pandemic adversely affected the delivery of health care services, but its impacts on Maternal, Newborn, Child Health and Nutrition (MNCHN) programs in rural municipalities in the Philippines remains understudied. Thus, this study explored the effects of the pandemic on MNCHN indicators in the municipalities of Miagao, Iloilo and Sibunag, Guimaras. A cross-sectional design was employed to compare the MNCHN indicators before and during the pandemic, and between Miagao and Sibunag. Key informant interviews (KII) were performed to identify the factors affecting access to MNCHN programs. During the pandemic, Miagao had a significant increase in positive outcomes of eight out of ten maternal health indicators, while Sibunag showed a significant decrease in six indicators. For child health and nutrition, Miagao obtained significant improvements in five of seven indicators, while Sibunag showed a significant increase in positive outcomes for six. KII data showed that the primary concern of mothers in Miagao is accessibility, while mothers in Sibunag raised concerns on accessibility, availability, and affordability of these MNCHN services. Miagao MHO employed various strategies such as telemedicine, activation of barangay health workers, and decentralization of health services to Barangay Health Centers, which can explain the improvements in MNCHN indicators. Sibunag also decentralized its health services, but its limited resources might have led them to prioritize child health and nutrition services. The findings suggest that the impacts of the COVID-19 pandemic on MNCHN depend on local health measures employed by the municipality, while telemedicine is a very useful tool in mitigating the negative effects of disrupted health services.

Keywords: maternal, child, COVID-19, Miagao, Sibunag, nutrition

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Authors: Tarek Dandashi

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WSNs may provide a fast and reliable solution for the early detection of environment events like forest fires. This is crucial for alerting and calling for fire brigade intervention. Sensor nodes communicate sensor data to a host station, which enables a global analysis and the generation of a reliable decision on a potential fire and its location. A WSN with TinyOS and nesC for the capturing and transmission of a variety of sensor information with controlled source, data rates, duration, and the records/displaying activity traces is presented. We propose a similarity distance (SD) between the distribution of currently sensed data and that of a reference. At any given time, a fire causes diverging opinions in the reported data, which alters the usual data distribution. Basically, SD consists of a metric on the Cumulative Distribution Function (CDF). SD is designed to be invariant versus day-to-day changes of temperature, changes due to the surrounding environment, and normal changes in weather, which preserve the data locality. Evaluation shows that SD sensitivity is quadratic versus an increase in sensor node temperature for a group of sensors of different sizes and neighborhood. Simulation of fire spreading when ignition is placed at random locations with some wind speed shows that SD takes a few minutes to reliably detect fires and locate them. We also discuss the case of false negative and false positive and their impact on the decision reliability.

Keywords: forest fire, WSN, wireless sensor network, algortihm

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Authors: Mohamed Bin Alhaj, Hexu Liu, Mohammed Sulaiman, Osama Abudayyeh

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An effective facility maintenance management (FMM) system plays a crucial role in improving the quality of services and maintaining the facility in good condition. Current FMM heavily relies on the quality of the data collection function of the FMM systems, at times resulting in inefficient FMM decision-making. The new technology-based crowdsourcing provides great potential to improve the current FMM practices, especially in terms of timeliness and quality of data. This research aims to investigate the feasibility of using new technology-driven crowdsourcing for FMM and highlight its opportunities and challenges. A survey was carried out to understand the human, data, system, geospatial, and automation characteristics of crowdsourcing for an educational campus FMM via social networks. The survey results were analyzed to reveal the challenges and recommendations for the implementation of crowdsourcing for FMM. This research contributes to the body of knowledge by synthesizing the challenges and opportunities of using crowdsourcing for facility maintenance and providing a road map for applying crowdsourcing technology in FMM. In future work, a conceptual framework will be proposed to support data-driven FMM using social networks.

Keywords: crowdsourcing, facility maintenance management, social networks

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Authors: Elyta Widyaningrum

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The LiDAR data acquisition has been recognizable as one of the fastest solution to provide the basis data for topographic base mapping in Indonesia. The challenges to accelerate the provision of large-scale topographic base maps as a development plan basis gives the opportunity to implement the automated scheme in the map production process. The one stop processing will also contribute to accelerate the map provision especially to conform with the Indonesian fundamental spatial data catalog derived from ISO 19110 and geospatial database integration. Thus, the automated LiDAR classification, DTM generation and feature extraction will be conducted in one GIS-software environment to form all layers of topographic base maps. The quality of automated topographic base map will be assessed and analyzed based on its completeness, correctness, contiguity, consistency and possible customization.

Keywords: automation, GIS environment, LiDAR processing, map quality

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Authors: Taehan Bae

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In this paper, a class of length-biased Weibull mixtures is presented to model loss severity data. The proposed model generalizes the Erlang mixtures with the common scale parameter, and it shares many important modelling features, such as flexibility to fit various data distribution shapes and weak-denseness in the class of positive continuous distributions, with the Erlang mixtures. We show that the asymptotic tail estimate of the length-biased Weibull mixture is Weibull-type, which makes the model effective to fit loss severity data with heavy-tailed observations. A method of statistical estimation is discussed with applications on real catastrophic loss data sets.

Keywords: Erlang mixture, length-biased distribution, transformed gamma distribution, asymptotic tail estimate, EM algorithm, expectation-maximization algorithm

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Authors: Charlene Petersen, Herman Grobler, Karel Botha

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Restorative intervention with adolescents from a family violence context is a much needed resource given the limited access to mental health services in South Africa. In this research article the qualitative component which formed part of a mixed methods design of an overall research study, is discussed. A qualitative case study design was used. This article explores a psycho-education strategy, using a visual creative medium as a method for reconstructing identity in the context of family violence. The aim of this psycho-education strategy was to move away from interventions based on the biomedical model, but focused more on meaning-making about violent traumatic events adolescents have experienced, and to develop more positive, adaptive views about themselves in the present, and experience hope about the future. The research question that was asked was how the meaning, that adolescents from a specific community in South Africa give to family violence, contribute to defining their identity? Twelve participants were purposively selected for the study and included both male and female adolescents with ages ranging from 15 to 18 years from three secondary schools. The strategy was applied over five sessions with the intention to bring about awareness of different selves, identifying and naming the selves, and becoming aware of the configuration of identity that could lead to a redefined identity. The data were thematically and visually analyzed. Through the process of tailoring which forms the basis for reconstruction process, participants could identify the different selves, become aware of how they configure in the field. Through the process of tailoring the different self-parts, the sense of self became more self-cohesive and allowed the individual to become aware of the role of certain. Through identifying and naming the future self and resilient self the participants were able to accomplish some order and meaning in their lives. It provided them with a sense of predictability and an optimistic and hopefulness towards the future. The research findings indicated that this strategy can be used as a method for reconstructing identity in the context of family violence.

Keywords: identity, family violence, self-configuration, reconstructing identity, psycho-education strategy

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Authors: Harsh Vikram Singh, Ankur Rai, Anand Mohan

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In this paper, we propose secure and robust data hiding algorithm based on DCT by Arnold transform and chaotic sequence. The watermark image is scrambled by Arnold cat map to increases its security and then the chaotic map is used for watermark signal spread in middle band of DCT coefficients of the cover image The chaotic map can be used as pseudo-random generator for digital data hiding, to increase security and robustness .Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using bit error rate (BER), normalized correlation (NC), and peak signal to noise ratio (PSNR) value for different watermark and cover images such as Lena, Girl, Tank images and gain factor .We use a binary logo image and text image as watermark. The experimental results demonstrate that the proposed algorithm achieves higher security and robustness against JPEG compression as well as other attacks such as addition of noise, low pass filtering and cropping attacks compared to other existing algorithm using DCT coefficients. Moreover, to recover watermarks in proposed algorithm, there is no need to original cover image.

Keywords: data hiding, watermarking, DCT, chaotic sequence, arnold transforms

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Authors: Jonathan A. Akinwale, Ibukun J. Agotola

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There is a need to rethink how technology is being disseminated to end users in order to ensure wide adoption and utilisation. Aflasafe bio-control was developed to combat aflatoxin in maize to ensure food safety for the end users. This study was designed to assess how the pull mechanism is enhancing the utilisation of this proven technology among maize farmers in Oyo State, Nigeria. The study determines the awareness of farmers on Aflasafe, sources of purchase of Aflasafe, incentives towards the usage of Aflasafe, constraints to farmers’ utilisation and factors influencing farmers’ utilisation of Aflasafe bio-control measures. Respondents were selected using a multi-stage sampling procedure. Data were collected from respondents through interview schedule and analyzed using descriptive statistics (means, frequencies, and percentages) and inferential statistics (Pearson Product Moment Correlation and regression analysis). The result showed that 89% of the farmers indicated implementers as the outlet for the purchase of Aflasafe. Also, premium payment and provision of technical assistance were the highly ranked incentives to the utilisation of Aflasafe among the farmers. The study also revealed that the major constraints face by respondents were low access to credit facility, inadequate sources of purchase, and lack of storage facilities. A little above half (54%) of the farmers were found to have fully utilized Aflasafe in maize production. Pearson Product Moment Correlation (PPMC) analysis revealed that there was a significant correlation between incentives and utilisation of Aflasafe (r-value=0.274; p ≤ 0.01). The result of the regression analysis indicated maize production experience (β=0.572), output (β=0.531), years of formal education (β=0.404) and household size (β=0.391) as the leading factors influencing farmers utilisation of Aflasafe bio-control in maize production. The study, therefore, recommends that governments and non-governmental organisations should be interested in making Aflasafe available to the maize farmers either through loan provision or price subsidy.

Keywords: Aflasafe bio-control, maize production, production incentives, pull mechanism, utilisation

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Authors: Evisa Mitrou, Nicholas Tsitsianis, Supriya Shinde

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In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affects the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients.

Keywords: BDA-clients, BDA-vendors, big data analytics, financial performance

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Authors: Saeid Gharechelou, Ryutaro Tateishi

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Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimated or suggested combined use of optical and SAR data for improved accuracy, however finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specializd in developing SAR based technique with the target of rapid and accurate geospatial reporting. Should considers that limited time available in post-disaster situation offering quick computation exclusively based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, co-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though a higher accuracy obtained from the optical data then integration by optical-SAR data. Limitation of cloud-free images when urgently needed for earthquak evevent are and is not assured, thus further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channelling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.

Keywords: Sentinel-1A data, Landsat-8, earthquake damage, InSAR, rapid damage monitoring, 2015-Nepal earthquake

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Authors: AmirHossein Mohajerzadeh, Mohammad Alishahi, Saeed Aslishahi, Mohsen Zabihi

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In this paper, we consider sensor nodes with the capability of measuring the bearings (relative angle to the target). We use geometric methods to select a set of observer nodes which are responsible for collecting data from the target. Considering the characteristics of target tracking applications, it is clear that significant numbers of sensor nodes are usually inactive. Therefore, in order to minimize the total network energy consumption, a set of sensor nodes, called sentinel, is periodically selected for monitoring, controlling the environment and transmitting data through the network. The other nodes are inactive. Furthermore, the proposed algorithm provides a joint scheduling and routing algorithm to transmit data between network nodes and the fusion center (FC) in which not only provides an efficient way to estimate the target position but also provides an efficient target tracking. Performance evaluation confirms the superiority of the proposed algorithm.

Keywords: coverage, routing, scheduling, target tracking, wireless sensor networks

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 170

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 160

Authors: K. W. Nam, B. J. Kim, S. J. Lee

Abstract:

Along with the development of network infrastructures, such as high-speed Internet and mobile environment, the explosion of multimedia data is expanding the range of multimedia services beyond voice and data services. Amid this flow, research is actively being done on the creation, management, and transmission of metadata on digital content to provide different services to users. This paper proposes a system for the insertion, storage, and retrieval of metadata about digital content. The metadata server with Binary XML was implemented for efficient storage space and retrieval speeds, and the transport data size required for metadata retrieval was simplified. With the proposed system, the metadata could be inserted into the moving objects in the video, and the unnecessary overlap could be minimized by improving the storage structure of the metadata. The proposed system can assemble metadata into one relevant topic, even if it is expressed in different media or in different forms. It is expected that the proposed system will handle complex network types of data.

Keywords: video, multimedia, metadata, editing tool, XML

Procedia PDF Downloads 173

Authors: G. Singer, M. Golan

Abstract:

Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

Procedia PDF Downloads 102

Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

Abstract:

Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

Procedia PDF Downloads 590