Search results for: spatial and temporal data

Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

Procedia PDF Downloads 318

Authors: Sara Bonetti

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The use of quantitative data to support policies and justify investments has become imperative in many fields including the field of education. However, the topic of data literacy has only marginally touched the early care and education (ECE) field. In California, within the ECE workforce, there is a group of professionals working in policy and advocacy that use quantitative data regularly and whose educational and professional experiences have been neglected by existing research. This study aimed at analyzing these experiences in accessing, using, and producing quantitative data. This study utilized semi-structured interviews to capture the differences in educational and professional backgrounds, policy contexts, and power relations. The participants were three key professionals from county-level organizations and one working at a State Department to allow for a broader perspective at systems level. The study followed Núñez’s multilevel model of intersectionality. The key in Núñez’s model is the intersection of multiple levels of analysis and influence, from the individual to the system level, and the identification of institutional power dynamics that perpetuate the marginalization of certain groups within society. In a similar manner, this study looked at the dynamic interaction of different influences at individual, organizational, and system levels that might intersect and affect ECE professionals’ experiences with quantitative data. At the individual level, an important element identified was the participants’ educational background, as it was possible to observe a relationship between that and their positionality, both with respect to working with data and also with respect to their power within an organization and at the policy table. For example, those with a background in child development were aware of how their formal education failed to train them in the skills that are necessary to work in policy and advocacy, and especially to work with quantitative data, compared to those with a background in administration and/or business. At the organizational level, the interviews showed a connection between the participants’ position within the organization and their organization’s position with respect to others and their degree of access to quantitative data. This in turn affected their sense of empowerment and agency in dealing with data, such as shaping what data is collected and available. These differences reflected on the interviewees’ perceptions and expectations for the ECE workforce. For example, one of the interviewees pointed out that many ECE professionals happen to use data out of the necessity of the moment. This lack of intentionality is a cause for, and at the same time translates into missed training opportunities. Another interviewee pointed out issues related to the professionalism of the ECE workforce by remarking the inadequacy of ECE students’ training in working with data. In conclusion, Núñez’s model helped understand the different elements that affect ECE professionals’ experiences with quantitative data. In particular, what was clear is that these professionals are not being provided with the necessary support and that we are not being intentional in creating data literacy skills for them, despite what is asked of them and their work.

Keywords: data literacy, early childhood professionals, intersectionality, quantitative data

Procedia PDF Downloads 253

Authors: Yael Shai, Dror L. Angel, Dror Zurel, Peleg Astrahan, Maxim Rubin-Blum, Eyal Rahav

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The high demand for oil and its by-products is symptomatic of the 21st century and occasionally leads to oil spills and pollution of coastal waters. Marine oil pollution may originate from a variety of sources -urban runoff, tanker cleaning, drilling activities, and oil spills. These events may release large amounts of highly toxic polycyclic aromatic hydrocarbons (PAHs) and other pollutants to coastal water, thereby threatening local marine life. Here, we investigated the effects of crude oil on the temporal dynamics of phytoplankton and heterotrophic bacteria in Israeli coastal waters. To this end, we added crude oil (500 µm thick layer, with and without additional nutrients; NO₃ and PO₄) to mesocosms (1m³ bags) containing oligotrophic surface coastal water collected near Haifa during summer and winter. Changes in phytoplankton biomass, activity and diversity were monitored daily for 5-6 days. Our results demonstrate that crude oil addition resulted in a pronounced decrease in phytoplankton biomass and production rates, while heterotrophic bacterial production increased significantly. Importantly, a few days post addition we found that the oil-degrading bacteria, Oleibacter sp. and Oleispira sp. appeared in the mesocosms and that the addition of nutrients (along with the crude oil) further increased this trend. This suggests that oil-degrading bacteria may be NO₃ and PO₄ limited in Israeli coastal waters. The results of this study should enable us to establish improved science-based environmental policy with respect to handling crude oil pollution in this region.

Keywords: heterotrophic bacteria, nutrients, mesocosm, oil pollution, oligotrophic, phytoplankton

Procedia PDF Downloads 159

Authors: Owolabi Kolade Matthew

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In this work, we investigate both the analytical and numerical studies of the dynamical model comprising of three species system. We analyze the linear stability of stationary solutions in the one-dimensional multi-system modeling the interactions of two predators and one prey species. The stability analysis has a lot of implications for understanding the various spatiotemporal and chaotic behaviors of the species in the spatial domain. The analysis results presented have established the possibility of the three interacting species to coexist harmoniously, this feat is achieved by combining the local and global analyzes to determine the global dynamics of the system. In the presence of diffusion, a viable exponential time differencing method is applied to multi-species nonlinear time-dependent partial differential equation to address the points and queries that may naturally arise. The scheme is described in detail, and justified by a number of computational experiments.

Keywords: asymptotically stable, coexistence, exponential time differencing method, global and local stability, predator-prey model, nonlinear, reaction-diffusion system

Procedia PDF Downloads 412

Authors: Tigabu Dagne Akal

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Electronic health records (EHRs) can help to store, maintain, and make the appropriate handling of patient histories for proper treatment and decision. Merging the EHRs with big data analytics (BDA) capabilities enable healthcare stakeholders to provide effective and efficient treatments for chronic diseases. Though there are huge opportunities and efforts that exist in the deployment of EMRs and the development of BDA, there are challenges in addressing resources and organizational capabilities that are required to achieve the competitive advantage and sustainability of EHRs and BDA. The resource-based view (RBV), information system (IS), and non- IS theories should be extended to examine organizational capabilities and resources which are required for successful data analytics in the healthcare industries. The main purpose of this study is to develop a conceptual framework for the development of healthcare BDA capabilities based on past works so that researchers can extend. The research question was formulated for the search strategy as a research methodology. The study selection was made at the end. Based on the study selection, the conceptual framework for the development of BDA capabilities in the healthcare settings was formulated.

Keywords: EHR, EMR, Big data, Big data analytics, resource-based view

Procedia PDF Downloads 131

Authors: Juan Facetti, Rocky McBride, Karina Loup

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The Chaco Region of Paraguay is a key biodiverse area for the conservation of jaguars (Panthera onca), the largest feline of the Americas. It comprises five eco-regions, which holds important but decreasing populations of this species. The last decades, the expansion of soybean over the Atlantic Forest, forced the translocation of cattle-ranches towards the Chaco. Few studies of Jaguar's population densities in the American hemisphere were done until now. In the region, the specie is listed as vulnerable or threatened and more information is needed to implement any conservation policy. Among the factors that threaten the populations are land-use change, habitat fragmentation, prey depletion and illegal hunting. Two largest eco-regions were studied: the Wet Chaco and the Dry Chaco. From 2002 more than 20 jaguars were captured and fitted with GPS-collar. Data collected from 11 GPS-collars were processed, transformed numerically and finally converted into maps for analyzing. 8.092 locations were determined for four adult females (AF) and one adult male (AM) in the Wet Chaco, and one AF, one juvenile male (JM) and four AM in the Dry Chaco, during 1,867 days. GIS and kernel methodology were used to calculate daily distance of movement, home range-HR (95% isopleth), and core area (considered as 50% isopleth). In the Wet Chaco HR were 56 Km2 and 238 km2 for females and males respectively; while in the Dry Chaco HR were 685 Km2 and 844.5 km2 for females and males respectively, and 172 Km2 for a juvenile. Core areas of individual activity for each jaguar, were on average 11.5 Km2 and 33.55 km2 for AF and AM respectively in the Wet Chaco, while in the Dry Chaco were larger: 115 km2 for five AM and 225 Km2 for an AF and 32.4 Km2 for a JM. In both ecoregions, only one relevant overlap of HR of adults was reported. During the reproduction season, the HR (95% K) of one AM overlapped 49.83% with that of one AF. At the Wet Chaco, the maximum daily distance moved by an AF was 14.5 Km and 11.6 Km for the AM, while the Maximum Mean Daily Moved (MMDM) distance was 5.6 km for an AF and 3.1 km for an AM. At the Dry Chaco, the maximum daily distance for an AF was 61.7Km., 50.9Km for the AM and 6.6 Km for the JM, while the MMDM distance was 13.2 km for an AM and 8.4 km for an AF. This study confirmed that, as the invasion to jaguar habitat increased, it resulted in fragmented landscapes that influence spacing patterns of jaguars. Males used largest HR that of the smaller females and males covers largest distances that of the females. There appeared to be important spatial segregation between not only females but also males. It is likely that the larger areas used by males are partly caused by the sexual dimorphism in body size that entails differences in prey requirements. These could explain the larger distances travelled daily by males.

Keywords: Chaco ecoregions, Jaguar, home range, Panthera onca, Paraguay

Procedia PDF Downloads 302

Authors: Zhongnan Ye, Xiaoyi Liu, Shu-Chien Hsu

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Rapid urbanization has led to a significant increase in construction and demolition waste (C&D waste), underscoring the need for sustainable waste management strategies in the construction industry. Aiming to enhance the sustainability of urban construction practices, this study develops an optimization model to effectively suggest the optimal recycling and reuse strategies for C&D waste, including concrete and steel. By employing Life Cycle Assessment (LCA), the model evaluates the environmental impacts of adopted construction materials throughout their lifecycle. The model optimizes the quantity of materials to recycle or reuse, the selection of specific recycling and reuse processes, and logistics decisions related to the transportation and storage of recycled materials with the objective of minimizing the overall environmental impact, quantified in terms of carbon emissions, energy consumption, and associated costs, while adhering to a range of constraints. These constraints include capacity limitations, quality standards for recycled materials, compliance with environmental regulations, budgetary limits, and temporal considerations such as project deadlines and material availability. The strategies are expected to be both cost-effective and environmentally beneficial, promoting a circular economy within the construction sector, aligning with global sustainability goals, and providing a scalable framework for managing construction waste in densely populated urban environments. The model is helpful in reducing the carbon footprint of construction projects, conserving valuable resources, and supporting the industry’s transition towards a more sustainable future.

Keywords: circular construction, construction and demolition waste, material recycling, optimization modeling

Procedia PDF Downloads 57

Authors: H. Aksas, S. Boughrara, K. Louhab

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The goal of this study was the use of life cycle assessment (LCA) methodology to assess the environmental impact of pharmaceutical product (four kinds of syrup form and tree kinds of pomade form), which are produced in one leader manufactory in Algeria town that is SAIDAL Company. The impacts generated have evaluated using SimpaPro7.1 with CML92 Method for syrup form and EPD 2007 for pomade form. All impacts evaluated have compared between them, with determination of the compound contributing to each impacts in each case. Data needed to conduct Life Cycle Inventory (LCI) came from this factory, by the collection of theoretical data near the responsible technicians and engineers of the company, the practical data are resulting from the assay of pharmaceutical liquid, obtained at the laboratories of the university. This data represent different raw material imported from European and Asian country necessarily to formulate the drug. Energy used is coming from Algerian resource for the input. Outputs are the result of effluent analysis of this factory with different form (liquid, solid and gas form). All this data (input and output) represent the ecobalance.

Keywords: pharmaceutical product, drug residues, LCA methodology, environmental impacts

Procedia PDF Downloads 246

Authors: Zhongnan Ye, Xiaoyi Liu, Shu-Chien Hsu

Abstract:

Rapid urbanization has led to a significant increase in construction and demolition waste (C&D waste), underscoring the need for sustainable waste management strategies in the construction industry. Aiming to enhance the sustainability of urban construction practices, this study develops an optimization model to effectively suggest the optimal recycling and reuse strategies for C&D waste, including concrete and steel. By employing Life Cycle Assessment (LCA), the model evaluates the environmental impacts of adopted construction materials throughout their lifecycle. The model optimizes the quantity of materials to recycle or reuse, the selection of specific recycling and reuse processes, and logistics decisions related to the transportation and storage of recycled materials with the objective of minimizing the overall environmental impact, quantified in terms of carbon emissions, energy consumption, and associated costs, while adhering to a range of constraints. These constraints include capacity limitations, quality standards for recycled materials, compliance with environmental regulations, budgetary limits, and temporal considerations such as project deadlines and material availability. The strategies are expected to be both cost-effective and environmentally beneficial, promoting a circular economy within the construction sector, aligning with global sustainability goals, and providing a scalable framework for managing construction waste in densely populated urban environments. The model is helpful in reducing the carbon footprint of construction projects, conserving valuable resources, and supporting the industry’s transition towards a more sustainable future.

Keywords: circular construction, construction and demolition waste, life cycle assessment, material recycling

Procedia PDF Downloads 81

Authors: Rajeev Kumar Bedi, Jaswinder Singh, Sunil Kumar Gupta

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Cloud storage is a model of online data storage where data is stored in virtualized pool of servers hosted by third parties (CSPs) and located in different geographical locations. Cloud storage revolutionized the way how users access their data online anywhere, anytime and using any device as a tablet, mobile, laptop, etc. A lot of issues as vendor lock-in, frequent service outage, data loss and performance related issues exist in single cloud storage systems. So to evade these issues, the concept of multi cloud storage introduced. There are a lot of multi cloud storage systems exists in the market for mobile devices. In this article, we are providing comparison of four multi cloud storage systems for mobile devices Otixo, Unclouded, Cloud Fuze, and Clouds and evaluate their performance on the basis of CPU usage, battery consumption, time consumption and data usage parameters on three mobile phones Nexus 5, Moto G and Nexus 7 tablet and using Wi-Fi network. Finally, open research challenges and future scope are discussed.

Keywords: cloud storage, multi cloud storage, vendor lock-in, mobile devices, mobile cloud computing

Procedia PDF Downloads 407

Authors: M. Zayoud, S. Oueida, S. Ionescu, P. AbiChar

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Background: Wireless sensor network is encompassed of diversified tools of information technology, which is widely applied in a range of domains, including military surveillance, weather forecasting, and earthquake forecasting. Strengthened grounds are always developed for wireless sensor networks, which usually emerges security issues during professional application. Thus, essential technological tools are necessary to be assessed for secure aggregation of data. Moreover, such practices have to be incorporated in the healthcare practices that shall be serving in the best of the mutual interest Objective: Aggregation of encrypted data has been assessed through homomorphic stream cipher to assure its effectiveness along with providing the optimum solutions to the field of healthcare. Methods: An experimental design has been incorporated, which utilized newly developed cipher along with CPU-constrained devices. Modular additions have also been employed to evaluate the nature of aggregated data. The processes of homomorphic stream cipher have been highlighted through different sensors and modular additions. Results: Homomorphic stream cipher has been recognized as simple and secure process, which has allowed efficient aggregation of encrypted data. In addition, the application has led its way to the improvisation of the healthcare practices. Statistical values can be easily computed through the aggregation on the basis of selected cipher. Sensed data in accordance with variance, mean, and standard deviation has also been computed through the selected tool. Conclusion: It can be concluded that homomorphic stream cipher can be an ideal tool for appropriate aggregation of data. Alongside, it shall also provide the best solutions to the healthcare sector.

Keywords: aggregation, cipher, homomorphic stream, encryption

Procedia PDF Downloads 260

Authors: Omar Al Ali

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The current study was aimed to explore the relationships between emotional intelligence, cognitive ability, and leader's performance. Data were collected from 260 senior managers from UAE. The results showed that there are significant relationships between emotional intelligence and leadership performance as measured by the annual internal evaluations of each participant (r = .42, p < .01). Data from regression analysis revealed that both variables namely emotional intelligence (beta = .31, p < .01), and cognitive ability (beta = .29, p < .01), predicted leadership competencies, and together explained 26% of its variance. Data suggests that EI and cognitive ability are significantly correlated with leadership performance. In depth implications of the present findings for human resource development theory and practice are discussed.

Keywords: emotional intelligence, cognitive ability, leadership, performance

Procedia PDF Downloads 477

Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

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Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: energy production, meteorological data, irradiance decomposition, solar photovoltaic system

Procedia PDF Downloads 142

Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

Procedia PDF Downloads 231

Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Effective management of hypertension remains a critical public health challenge, particularly among racially and ethnically diverse populations. This study employs sophisticated statistical models to rigorously investigate the predictors of poor blood pressure (BP) control, with a specific focus on demographic, socioeconomic, and clinical risk factors. Leveraging a large sample of 19,253 adults drawn from the National Health and Nutrition Examination Survey (NHANES) across three distinct time periods (2013-2014, 2015-2016, and 2017-2020), we applied multivariable logistic regression and generalized estimating equations (GEE) to account for the clustered structure of the data and potential within-subject correlations. Our multivariable models identified significant associations between poor BP control and several key predictors, including race/ethnicity, age, gender, body mass index (BMI), prevalent diabetes, and chronic kidney disease (CKD). Non-Hispanic Black individuals consistently exhibited higher odds of poor BP control across all periods (OR = 1.99; 95% CI: 1.69, 2.36 for the overall sample; OR = 2.33; 95% CI: 1.79, 3.02 for 2017-2020). Younger age groups demonstrated substantially lower odds of poor BP control compared to individuals aged 75 and older (OR = 0.15; 95% CI: 0.11, 0.20 for ages 18-44). Men also had a higher likelihood of poor BP control relative to women (OR = 1.55; 95% CI: 1.31, 1.82), while BMI ≥35 kg/m² (OR = 1.76; 95% CI: 1.40, 2.20) and the presence of diabetes (OR = 2.20; 95% CI: 1.80, 2.68) were associated with increased odds of poor BP management. Further analysis using GEE models, accounting for temporal correlations and repeated measures, confirmed the robustness of these findings. Notably, individuals with chronic kidney disease displayed markedly elevated odds of poor BP control (OR = 3.72; 95% CI: 3.09, 4.48), with significant differences across the survey periods. Additionally, higher education levels and better self-reported diet quality were associated with improved BP control. College graduates exhibited a reduced likelihood of poor BP control (OR = 0.64; 95% CI: 0.46, 0.89), particularly in the 2015-2016 period (OR = 0.48; 95% CI: 0.28, 0.84). Similarly, excellent dietary habits were associated with significantly lower odds of poor BP control (OR = 0.64; 95% CI: 0.44, 0.94), underscoring the importance of lifestyle factors in hypertension management. In conclusion, our findings provide compelling evidence of the complex interplay between demographic, clinical, and socioeconomic factors in predicting poor BP control. The application of advanced statistical techniques such as GEE enhances the reliability of these results by addressing the correlated nature of repeated observations. This study highlights the need for targeted interventions that consider racial/ethnic disparities, clinical comorbidities, and lifestyle modifications in improving BP control outcomes.

Keywords: hypertension, blood pressure, NHANES, generalized estimating equations

Procedia PDF Downloads 11

Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

Procedia PDF Downloads 84

Authors: Felermino D. M. A. Ali, S. C. Ng

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E-learning is an evolutionary form of distance learning and has become better over time as new technologies emerged. Today, efforts are still being made to embrace E-learning systems with emerging technologies in order to make them better. Among these advancements, Educational Data Mining (EDM) is one that is gaining a huge and increasing popularity due to its wide application for improving the teaching-learning process in online practices. However, even though EDM promises to bring many benefits to educational industry in general and E-learning environments in particular, its principal drawback is the lack of easy to use tools. The current EDM tools usually require users to have some additional technical expertise to effectively perform EDM tasks. Thus, in response to these limitations, this study intends to design and implement an EDM application framework which aims at automating and simplify the development of EDM in E-learning environment. The application framework introduces a Service-Oriented Architecture (SOA) that hides the complexity of technical details and enables users to perform EDM in an automated fashion. The framework was designed based on abstraction, extensibility, and interoperability principles. The framework implementation was made up of three major modules. The first module provides an abstraction for data gathering, which was done by extending Moodle LMS (Learning Management System) source code. The second module provides data mining methods and techniques as services; it was done by converting Weka API into a set of Web services. The third module acts as an intermediary between the first two modules, it contains a user-friendly interface that allows dynamically locating data provider services, and running knowledge discovery tasks on data mining services. An experiment was conducted to evaluate the overhead of the proposed framework through a combination of simulation and implementation. The experiments have shown that the overhead introduced by the SOA mechanism is relatively small, therefore, it has been concluded that a service-oriented architecture can be effectively used to facilitate educational data mining in E-learning environments.

Keywords: educational data mining, e-learning, distributed data mining, moodle, service-oriented architecture, Weka

Procedia PDF Downloads 236

Authors: Zahid Ullah, Atlas Khan

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In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society.

Keywords: mathematics, bridging theory and applications, data-driven world, mathematical models

Procedia PDF Downloads 75

Authors: Harris Niavis, Dimitra Politaki

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The manufacturing industry was collecting vast amounts of data for monitoring product quality thanks to the advances in the ICT sector and dedicated IoT infrastructure is deployed to track and trace the production line. However, industries have not yet managed to unleash the full potential of these data due to defective data collection methods and untrusted data storage and sharing. Blockchain is gaining increasing ground as a key technology enabler for Industry 4.0 and the smart manufacturing domain, as it enables the secure storage and exchange of data between stakeholders. On the other hand, AI techniques are more and more used to detect anomalies in batch and time-series data that enable the identification of unusual behaviors. The proposed scheme is based on smart contracts to enable automation and transparency in the data exchange, coupled with anomaly detection algorithms to enable reliable data ingestion in the system. Before sensor measurements are fed to the blockchain component and the smart contracts, the anomaly detection mechanism uniquely combines artificial intelligence models to effectively detect unusual values such as outliers and extreme deviations in data coming from them. Specifically, Autoregressive integrated moving average, Long short-term memory (LSTM) and Dense-based autoencoders, as well as Generative adversarial networks (GAN) models, are used to detect both point and collective anomalies. Towards the goal of preserving the privacy of industries' information, the smart contracts employ techniques to ensure that only anonymized pointers to the actual data are stored on the ledger while sensitive information remains off-chain. In the same spirit, blockchain technology guarantees the security of the data storage through strong cryptography as well as the integrity of the data through the decentralization of the network and the execution of the smart contracts by the majority of the blockchain network actors. The blockchain component of the Data Traceability Software is based on the Hyperledger Fabric framework, which lays the ground for the deployment of smart contracts and APIs to expose the functionality to the end-users. The results of this work demonstrate that such a system can increase the quality of the end-products and the trustworthiness of the monitoring process in the smart manufacturing domain. The proposed AI-enabled data traceability software can be employed by industries to accurately trace and verify records about quality through the entire production chain and take advantage of the multitude of monitoring records in their databases.

Keywords: blockchain, data quality, industry4.0, product quality

Procedia PDF Downloads 189

Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

Procedia PDF Downloads 175

Authors: R. Gautam, P. Sastha Kanagasabai, G. N. Rathna

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The proposed healthcare system enables quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and monitor their vitals. The growth of Brain-Computer-Interface (BCI) has led to rapid development in 'assistive systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands over the internet to the desired mobile phone user. Other Vital Information like the heartbeat, blood pressure, ECG and body temperature are monitored and uploaded to the server. Data analytics enables physicians to scan databases for a specific illness. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service.

Keywords: brain computer interface, Intel Edison, Emotiv EPOC, IoT analytics, electroencephalogram

Procedia PDF Downloads 186

Authors: Ren Junn Hwang, Chung-Chien Lu, Jain-Shing Wu

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Cloud outsource storage is one of important services in cloud computing. Cloud users upload data to cloud servers to reduce the cost of managing data and maintaining hardware and software. To ensure data confidentiality, users can encrypt their files before uploading them to a cloud system. However, retrieving the target file from the encrypted files exactly is difficult for cloud server. This study proposes a protocol for performing multikeyword searches for encrypted cloud data by applying k-nearest neighbor technology. The protocol ranks the relevance scores of encrypted files and keywords, and prevents cloud servers from learning search keywords submitted by a cloud user. To reduce the costs of file transfer communication, the cloud server returns encrypted files in order of relevance. Moreover, when a cloud user inputs an incorrect keyword and the number of wrong alphabet does not exceed a given threshold; the user still can retrieve the target files from cloud server. In addition, the proposed scheme satisfies security requirements for outsourced data storage.

Keywords: fault-tolerance search, multi-keywords search, outsource storage, ranked search, searchable encryption

Procedia PDF Downloads 383

Authors: Fatemah A. Alqallaf, Debasis Kundu

Abstract:

The aim of this paper is to introduce a bivariate inverse generalized exponential distribution which has a singular component. The proposed bivariate distribution can be used when the marginals have heavy-tailed distributions, and they have non-monotone hazard functions. Due to the presence of the singular component, it can be used quite effectively when there are ties in the data. Since it has four parameters, it is a very flexible bivariate distribution, and it can be used quite effectively for analyzing various bivariate data sets. Several dependency properties and dependency measures have been obtained. The maximum likelihood estimators cannot be obtained in closed form, and it involves solving a four-dimensional optimization problem. To avoid that, we have proposed to use an EM algorithm, and it involves solving only one non-linear equation at each `E'-step. Hence, the implementation of the proposed EM algorithm is very straight forward in practice. Extensive simulation experiments and the analysis of one data set have been performed. We have observed that the proposed bivariate inverse generalized exponential distribution can be used for modeling dependent competing risks data. One data set has been analyzed to show the effectiveness of the proposed model.

Keywords: Block and Basu bivariate distributions, competing risks, EM algorithm, Marshall-Olkin bivariate exponential distribution, maximum likelihood estimators

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Authors: T. Aboumahboub, R. Brecha, H. B. Shrestha, U. F. Hutfilter, A. Geiges, W. Hare, M. Schaeffer, L. Welder, M. Gidden

Abstract:

The proposed stringent mitigation targets require an immediate start for a drastic transformation of the whole energy system. The current Australian energy system is mainly centralized and fossil fuel-based in most states with coal and gas-fired plants dominating the total produced electricity over the recent past. On the other hand, the country is characterized by a huge, untapped renewable potential, where wind and solar energy could play a key role in the decarbonization of the Australia’s future energy system. However, integrating high shares of such variable renewable energy sources (VRES) challenges the power system considerably due to their temporal fluctuations and geographical dispersion. This raises the concerns about flexibility gap in the system to ensure the security of supply with increasing shares of such intermittent sources. One main flexibility dimension to facilitate system integration of high shares of VRES is to increase the cross-sectoral integration through coupling of electricity to other energy sectors alongside the decarbonization of the power sector and reinforcement of the transmission grid. This paper applies a multi-sectoral energy system optimization model for Australia. We investigate the cost-optimal configuration of a renewable-based Australian energy system and its transformation pathway in line with the ambitious range of proposed climate change mitigation targets. We particularly analyse the implications of linking the electricity and transport sectors in a prospective, highly renewable Australian energy system.

Keywords: decarbonization, energy system modelling, renewable energy, sector coupling

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Authors: Singara Singh Kasana, Pankaj Garg

Abstract:

In this paper, a blind data hiding technique based on interpolation of sub sampled versions of a cover image is proposed. Sub sampled image is taken as a reference image and an interpolated image is generated from this reference image. Then difference between original cover image and interpolated image is used to embed secret data. Comparisons with the existing interpolation based techniques show that proposed technique provides higher embedding capacity and better visual quality marked images. Moreover, the performance of the proposed technique is more stable for different images.

Keywords: interpolation, image subsampling, PSNR, SIM

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Authors: Sajid Hussain

Abstract:

The complex domain approach for image segmentation based on active contour has been designed, which deforms step by step to partition an image into numerous expedient regions. A novel region-based trigonometric complex pressure force function is proposed, which propagates around the region of interest using image forces. The signed trigonometric force function controls the propagation of the active contour and the active contour stops on the exact edges of the object accurately. The proposed model makes the level set function binary and uses Gaussian smoothing kernel to adjust and escape the re-initialization procedure. The working principle of the proposed model is as follows: The real image data is transformed into complex data by iota (i) times of image data and the average iota (i) times of horizontal and vertical components of the gradient of image data is inserted in the proposed model to catch complex gradient of the image data. A simple finite difference mathematical technique has been used to implement the proposed model. The efficiency and robustness of the proposed model have been verified and compared with other state-of-the-art models.

Keywords: image segmentation, active contour, level set, Mumford and Shah model

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Authors: Mehran Asadi, Afrand Agah

Abstract:

In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Ziwei Chen, Yujiang Gao

Abstract:

The rapid urbanization has caused the massive physical space in Chinese cities to be in a state of duplication and dislocation through the rapid development, forming many daily spaces that cannot be standardized, typed, and identified, such as illegal construction. This phenomenon is known as urban spatial redundancy and is often excluded from mainstream architectural discussions because of its 'remaining' and 'excessive' derogatory label. In recent years, some practice architects have begun to pay attention to this phenomenon and tried to tap the value behind it. In this context, the author takes the redundancy phenomenon of resident space as the research object and explores the inspiration to the urban architectural renewal and the innovative residential area model, based on the urban survey of redundant living space in Hongkou District of Shanghai. On this basis, it shows that the changes accumulated in the long-term use of the building can be re-applied to the goals before the design, which is an important link and significance of the existence of an architecture.

Keywords: rapid urbanization, living space redundancy, architectural renewal, residential area model

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

Abstract:

Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

Procedia PDF Downloads 315