Search results for: calibration data requirements

Authors: Dejana Bouillet, Vlatka Domović

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Both policymakers and researchers recognize that participating in early childhood education and care (ECEC) is useful for all children, especially for those who are exposed to the high risk of social exclusion. Social exclusion of children is understood as a multidimensional construct including economic, social, cultural, health, and other aspects of disadvantage and deprivation, which individually or combined can have an unfavorable effect on the current life and development of a child, as well as on the child’s development and on disadvantaged life chances in adult life. ECEC institutions should be able to promote educational approaches that portray developmental, cultural, language, and other diversity amongst children. However, little is known about the ways in which Croatian ECEC institutions recognize and respect the diversity of children and their families and how they respond to their educational needs. That is why this paper is dedicated to the analysis of the capacities of ECEC professionals to respond to the demands of educational needs of this very diverse group of children and their families. The results obtained in the frame of the project “Models of response to educational needs of children at risk of social exclusion in ECEC institutions,” funded by the Croatian Science Foundation, will be presented. The research methodology arises from explanations of educational processes and risks of social exclusion as a complex and heterogeneous phenomenon. The preliminary results of the qualitative data analysis of educational practices regarding capacities to identify and appropriately respond to the requirements of children at risk of social exclusion will be presented. The data have been collected by interviewing educational staff in 10 Croatian ECEC institutions (n = 10). The questions in the interviews were related to various aspects of inclusive institutional policy, culture, and practices. According to the analysis, it is possible to conclude that Croatian ECEC professionals are still faced with great challenges in the process of implementation of inclusive policies, culture, and practices. There are several baselines of this conclusion. The interviewed educational professionals are not familiar enough with the whole complexity and diversity of needs of children at risk of social exclusion, and the ECEC institutions do not have enough resources to provide all interventions that these children and their families need.

Keywords: children at risk of social exclusion, ECEC professionals, inclusive policies, culture and practices, quallitative analysis

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Authors: Daniele Calvi, Loris Franchi, Sabrina Corpino

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Thanks to the latest Mars mission, the planetary exploration has made enormous strides over the past ten years increasing the interest of the scientific community and beyond. These missions aim to fulfill many complex operations which are of paramount importance to mission success. Among these, a special mention goes to the Entry, Descent and Landing (EDL) functions which require a dedicated system to overcome all the obstacles of these critical phases. The general objective of the system is to safely bring the spacecraft from orbital conditions to rest on the planet surface, following the designed mission profile. For this reason, this work aims to develop a simulation tool integrating the re-entry trajectory algorithm in order to support the EDL design during the preliminary phase of the mission. This tool was used on a reference unmanned mission, whose objective is finding bio-evidence and bio-hazards on Martian (sub)surface in order to support the future manned mission. Regarding the concept of operations (CONOPS) of the mission, it concerns the use of Space Penetrator Systems (SPS) that will descend on Mars surface following a ballistic fall and will penetrate the ground after the impact with the surface (around 50 and 300 cm of depth). Each SPS shall contain all the instrumentation required to sample and make the required analyses. Respecting the low-cost and low-mass requirements, as result of the tool, an Entry Descent and Impact (EDI) system based on inflatable structure has been designed. Hence, a solution could be the one chosen by Finnish Meteorological Institute in the Mars Met-Net mission, using an inflatable Thermal Protection System (TPS) called Inflatable Braking Unit (IBU) and an additional inflatable decelerator. Consequently, there are three configurations during the EDI: at altitude of 125 km the IBU is inflated at speed 5.5 km/s; at altitude of 16 km the IBU is jettisoned and an Additional Inflatable Braking Unit (AIBU) is inflated; Lastly at about 13 km, the SPS is ejected from AIBU and it impacts on the Martian surface. Since all parameters are evaluated, it is possible to confirm that the chosen EDI system and strategy verify the requirements of the mission.

Keywords: EDL, Mars, mission, SPS, TPS

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Authors: Narottam Puri, Bishnu Panigrahi, Narayan Pendse

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Background: Clinical Outcomes are the globally agreed upon, evidence-based measurable changes in health or quality of life resulting from the patient care. Reporting of outcomes and its continuous monitoring provides an opportunity for both assessing and improving the quality of patient care. In 2012, International Consortium Of HealthCare Outcome Measurement (ICHOM) was founded which has defined global Standard Sets for measuring the outcome of various treatments. Method: Monitoring of Clinical Outcomes was identified as a pillar of Fortis’ core value of Patient Centricity. The project was started as an in-house developed Clinical Outcomes Reporting Portal by the Fortis Medical IT team. Standard sets of Outcome measurement developed by ICHOM were used. A pilot was run at Fortis Escorts Heart Institute from Aug’13 – Dec’13.Starting Jan’14, it was implemented across 11 hospitals of the group. The scope was hospital-wide and major clinical specialties: Cardiac Sciences, Orthopedics & Joint Replacement were covered. The internally developed portal had its limitations of report generation and also capturing of Patient related outcomes was restricted. A year later, the company provisioned for an ICHOM Certified Software product which could provide a platform for data capturing and reporting to ensure compliance with all ICHOM requirements. Post a year of the launch of the software; Fortis Healthcare has become the 1st Healthcare Provider in Asia to publish Clinical Outcomes data for the Coronary Artery Disease Standard Set comprising of Coronary Artery Bypass Graft and Percutaneous Coronary Interventions) in the public domain. (Jan 2016). Results: This project has helped in firmly establishing a culture of monitoring and reporting Clinical Outcomes across Fortis Hospitals. Given the diverse nature of the healthcare delivery model at Fortis Network, which comprises of hospitals of varying size and specialty-mix and practically covering the entire span of the country, standardization of data collection and reporting methodology is a huge achievement in itself. 95% case reporting was achieved with more than 90% data completion at the end of Phase 1 (March 2016). Post implementation the group now has one year of data from its own hospitals. This has helped identify the gaps and plan towards ways to bridge them and also establish internal benchmarks for continual improvement. Besides the value created for the group includes: 1. Entire Fortis community has been sensitized on the importance of Clinical Outcomes monitoring for patient centric care. Initial skepticism and cynicism has been countered by effective stakeholder engagement and automation of processes. 2. Measuring quality is the first step in improving quality. Data analysis has helped compare clinical results with best-in-class hospitals and identify improvement opportunities. 3. Clinical fraternity is extremely pleased to be part of this initiative and has taken ownership of the project. Conclusion: Fortis Healthcare is the pioneer in the monitoring of Clinical Outcomes. Implementation of ICHOM standards has helped Fortis Clinical Excellence Program in improving patient engagement and strengthening its commitment to its core value of Patient Centricity. Validation and certification of the Clinical Outcomes data by an ICHOM Certified Supplier adds confidence to its claim of being leaders in this space.

Keywords: clinical outcomes, healthcare delivery, patient centricity, ICHOM

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Karim Karmi

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To keep pace with the requirements of globalization, a lot of universities across the globe have started teaching various academic subjects in English. In Tunisia, two higher education institutions have embarked on the experience of teaching in English instead of French. The aim of the present study was threefold. First, it sought to explore the stakeholders’ attitudes toward this shift. By stakeholders, we mean students and teachers. Second, it aimed at probing the challenges that might arise in the classroom. By challenges, we mean the linguistic and pedagogical difficulties that students and teachers might face. Third, the study investigated the reasons that led teachers and students to opt for English as a medium of instruction instead of French. The participants were 335 students and 14 teachers selected from two Tunisian universities teaching in English. Data was collected by means of questionnaires, interviews, and classroom observations. The findings showed that there is a positive attitude towards English, in contrast to French. In other words, both students and teachers are enjoying the experience, and they hope that English will officially become the medium of instruction in Tunisia. Students and teachers reported a number of linguistic and pedagogical challenges, and they mainly ascribed them to the abrupt transition from French to English. The vast majority of the respondents, be they students or teachers, opted for English as a medium of instruction to maximise their chances of getting a job abroad. It is also worth noting that most teachers stated that teaching through English helps them when it comes to publishing academic articles.

Keywords: attitudes, challenges, English as a medium of instruction, opportunities

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Rahma Souli-Jbali, Minyar Sassi Hidri, Rahma Ben Ayed

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Grid of computing nodes has emerged as a representative means of connecting distributed computers or resources scattered all over the world for the purpose of computing and distributed storage. Since fault tolerance becomes complex due to the availability of resources in decentralized grid environment, it can be used in connection with replication in data grids. The objective of our work is to present fault tolerance in data grids with data replication-driven model based on clustering. The performance of the protocol is evaluated with Omnet++ simulator. The computational results show the efficiency of our protocol in terms of recovery time and the number of process in rollbacks.

Keywords: data grids, fault tolerance, clustering, chandy-lamport

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Authors: Muhammet Dursun Kaya, Hasan Asil

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One of the most important factors of research and development is the deep insight into the evolutions of scientific development. The state-of-the-art tools and instruments can considerably assist the researchers, and many of the world organizations have become aware of the advantages of data mining for the acquisition of the knowledge required for the unstructured data. This paper was an attempt to review the articles on the information technology published in the past five years with the aid of data mining. A clustering approach was used to study these articles, and the research results revealed that three topics, namely health, innovation, and information systems, have captured the special attention of the researchers.

Keywords: information technology, data mining, scientific development, clustering

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Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

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Wireless nodes in a sensor network gather and process critical information designed to process and communicate, information flooding through such network is critical for decision making and data processing, the integrity of such data is one of the most critical factors in wireless security without compromising the processing and transmission capability of the network. This paper presents mechanism to securely transmit data over a chain of sensor nodes without compromising the throughput of the network utilizing available battery resources available at the sensor node.

Keywords: hybrid protocol, data integrity, lightweight encryption, neighbor based key sharing, sensor node data processing, Z-MAC

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Authors: M. Sai Veerendra

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Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project on developing a new data mining solution for AML Units in an international investment bank in Ireland, we survey recent data mining approaches for AML. In this paper, we present not only these approaches but also give an overview on the important factors in building data mining solutions for AML activities.

Keywords: data mining, clustering, money laundering, anti-money laundering solutions

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Authors: Carlos Cuenca, Diego Sarzosa

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Ductile fracture occurs by the mechanism of void nucleation, void growth and coalescence. Potential sites for initiation are second phase particles or non-metallic inclusions. Modelling of ductile damage at the microscopic level is very difficult and complex task for engineers. Therefore, conservative predictions of ductile failure using simple models are necessary during the design and optimization of critical structures like pressure vessels and pipelines. Nowadays, it is well known that the initiation phase is strongly influenced by the stress triaxiality and plastic deformation at the microscopic level. Thus, a simple model used to study the ductile failure under multiaxial stress condition is the Stress Modified Critical Strain (SMCS) approach. Ductile rupture has been study for a structural steel under different stress triaxiality conditions using the SMCS method. Experimental tests are carried out to characterize the relation between stress triaxiality and equivalent plastic strain by notched round bars. After calibration of the plasticity and damage properties, predictions are made for low constraint bending specimens with and without side grooves. Stress/strain fields evolution are compared between the different geometries. Advantages and disadvantages of the SMCS methodology are discussed.

Keywords: damage, SMSC, SEB, steel, failure

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Authors: Bingjie Wang, Su-Yeon Kwon, Ik-Joong Kang

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A Surface-enhanced Raman Scattering (SERS) as the principle for enhancing Raman scattering by molecules adsorbed on rough metal surfaces or by nanostructures is used to detect the concentration change of γ-Aminobutyric Acid (GABA). As for the gold-chitosan nanoshell, it is made by using chitosan nanoparticles crosslinking with sodium tripolyphosphate(TPP) for the first step to form the chitosan nanoparticles, which would be covered with the gold sequentially. The size of the fabricated product was around 100nm. Based on the method that the sulfur end of the MPA linked to gold can form the very strong S–Au bond, and the carboxyl group, the other end of the MPA, can easily absorb the GABA. GABA is the mainly inhibitory neurotransmitter in the mammalian central nervous system in the human body. It plays such significant role in reducing neuronal excitability throughout the nervous system. When the system formed, it generated SERS, which made a clear difference in the intensity of Raman scattering within the range of GABA concentration. So it is obtained from the experiment that the calibration curve according to the GABA concentration relevant with the SERS scattering. In this study, DLS, SEM, FT-IR, UV, SERS were used to analyze the products to obtain the conclusion.

Keywords: chitosan-gold nanoshell, mercaptopropionic acid, γ-aminobutyric acid, surface-enhanced Raman scattering

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Authors: Gabriela Iordachescu, Mariana Cretu Stuparu, Mirela Praisler, Camelia Busila, Doina Voinescu, Camelia Vizireanu

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The aim of the study was to evaluate the consumers’ degree of confidence in food labeling, how they use and understand the label and respectively food labeling elements. The label is a bridge between producers, suppliers, and consumers. It has to offer enough information in terms of public health and food safety, statement of ingredients, nutritional information, warnings and advisory statements, producing date and shelf-life, instructions for storage and preparation (if required). The survey was conducted on 500 consumers group in Romania, aged 15+, males and females, from urban and rural areas and with different graduation levels. The questionnaire was distributed face to face and online. It had single or multiple choices questions and label images for the efficiency and best understanding of the question. The law 1169/2011 applied to food products from 13 of December 2016 improved and adapted the requirements for labeling in a clear manner. The questions were divided on following topics: interest and general trust in labeling, use and understanding of label elements, understanding of the ingredient list and safety information, nutrition information, advisory statements, serving sizes, best before/use by meanings, intelligent labeling, and demographic data. Three choice selection exercises were also included. In this case, the consumers had to choose between two similar products and evaluate which label element is most important in product choice. The data were analysed using MINITAB 17 and PCA analysis. Most of the respondents trust the food label, taking into account some elements especially when they buy the first time the product. They usually check the sugar content and type of sugar, saturated fat and use the mandatory label elements and nutrition information panel. Also, the consumers pay attention to advisory statements, especially if one of the items is relevant to them or the family. Intelligent labeling is a challenging option. In addition, the paper underlines that the consumer is more careful and selective with the food consumption and the label is the main helper for these.

Keywords: consumers, food safety information, labeling, labeling nutritional information

Procedia PDF Downloads 196

Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Kisik Song, Kyuwoong Kim, Sungjoo Lee

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Many global firms and corporations derive new technology and opportunity by identifying vacant technology from patent analysis. However, previous studies failed to focus on technologies that promised continuous growth in industrial fields. Most studies that derive new technology opportunities do not test practical effectiveness. Since previous studies depended on expert judgment, it became costly and time-consuming to evaluate new technologies based on patent analysis. Therefore, research suggests a quantitative and systematic approach to technology evaluation indicators by using patent data to and from customer communities. The first step involves collecting two types of data. The data is used to construct evaluation indicators and apply these indicators to the evaluation of new technologies. This type of data mining allows a new method of technology evaluation and better predictor of how new technologies are adopted.

Keywords: data mining, evaluating new technology, technology opportunity, patent analysis

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: Tokirhusen Iqbalbhai Shaikh, M. V. Shah

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With an increase in travel demand and a reduction in travel time, high-speed rail (HSR) has been introduced in India. Simplified 3-D finite element modelling is necessary to predict the stability and deformation characteristics of railway embankments and soil structure interaction behaviour under high-speed design requirements for Indian soil conditions. The objective of this study is to analyse the rigid ballast-less and flexible ballast-based high speed rail track embankments for various critical conditions subjected to them, viz. static condition, moving train condition, sudden brake application, and derailment case, using software. The input parameters for the analysis are soil type, thickness of the relevant strata, unit weight, Young’s modulus, Poisson’s ratio, undrained cohesion, friction angle, dilatancy angle, modulus of subgrade reaction, design speed, and other anticipated, relevant data. Eurocode 1, IRS-004(D), IS 1343, IRS specifications, California high-speed rail technical specifications, and the NHSRCL feasibility report will be followed in this study.

Keywords: soil structure interaction, high speed rail, numerical modelling, PLAXIS3D

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Authors: Thiraporn Chumphuming, Nuttida Boonmathi, Supattra Thanomsiang, Tawatchai Noree, Suthee Boonchaloem, Rinyaphat Kecharananta

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This research aims to study about the formats of low-cost airlines’ services in domestic route by surveying customers’ requirements and satisfactions in choosing low-cost airlines to travel domestically. Chiang Mai International Airport and other regions in Chiang Mai are the bases where the information is quantitatively collected. Passengers and questionnaires of 400 are the data base in which the researchers collected information from. Statistic units used are Percentage, Weighted Average, and Standard Deviation. The result of the study reveals that the group of 400 representative samples chooses Air Asia the most from overall six low-cost airlines that provide domestic services. Most of the representative samples book plane tickets for their traveling and they book tickets during the promotion time that provides cheap-priced tickets. Averagely, the price for a seat in one flight is around 501-1,000 Thai baht. The result of the satisfaction’s survey analyzed by the Marketing Mix Factors (7Ps) of low-cost airlines, which is divided into 4 parts including services before ticket reservations, services before boarding/purchasing tickets (ground), In-flight services, and Services after boarding they are satisfied with the baggage claim point informing, also gives the information that the passengers are highly satisfied with every process or the services.

Keywords: low-cost airline, service, satisfaction, customers' behavior

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

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Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, this project proposes AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project presents the best-in-school techniques used to preserve the data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptographic techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures and identifies potential correction/mitigation measures.

Keywords: data privacy, artificial intelligence (AI), healthcare AI, data sharing, healthcare organizations (HCOs)

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Authors: Despoina Chochtoula, Aristidis Ilias, Yannis Stamatiou

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Modbus is a protocol that enables the communication among devices which are connected to the same network. This protocol is, often, deployed in connecting sensor and monitoring units to central supervisory servers in Supervisory Control and Data Acquisition, or SCADA, systems. These systems monitor critical infrastructures, such as factories, power generation stations, nuclear power reactors etc. in order to detect malfunctions and ignite alerts and corrective actions. However, due to their criticality, SCADA systems are vulnerable to attacks that range from simple eavesdropping on operation parameters, exchanged messages, and valuable infrastructure information to malicious modification of vital infrastructure data towards infliction of damage. Thus, the SCADA research community has been active over strengthening SCADA systems with suitable data protection mechanisms based, to a large extend, on cryptographic methods for data encryption, device authentication, and message integrity protection. However, due to the limited computation power of many SCADA sensor and embedded devices, the usual public key cryptographic methods are not appropriate due to their high computational requirements. As an alternative, Elliptic Curve Cryptography has been proposed, which requires smaller key sizes and, thus, less demanding cryptographic operations. Until now, however, no such implementation has been proposed in the SCADA literature, to the best of our knowledge. In order to fill this gap, our methodology was focused on integrating Modbus, a frequently used SCADA communication protocol, with Elliptic Curve based cryptography and develop a server/client application to demonstrate the proof of concept. For the implementation we deployed two C language libraries, which were suitably modify in order to be successfully integrated: libmodbus (https://github.com/stephane/libmodbus) and ecc-lib https://www.ceid.upatras.gr/webpages/faculty/zaro/software/ecc-lib/). The first library provides a C implementation of the Modbus/TCP protocol while the second one offers the functionality to develop cryptographic protocols based on Elliptic Curve Cryptography. These two libraries were combined, after suitable modifications and enhancements, in order to give a modified version of the Modbus/TCP protocol focusing on the security of the data exchanged among the devices and the supervisory servers. The mechanisms we implemented include key generation, key exchange/sharing, message authentication, data integrity check, and encryption/decryption of data. The key generation and key exchange protocols were implemented with the use of Elliptic Curve Cryptography primitives. The keys established by each device are saved in their local memory and are retained during the whole communication session and are used in encrypting and decrypting exchanged messages as well as certifying entities and the integrity of the messages. Finally, the modified library was compiled for the Android environment in order to run the server application as an Android app. The client program runs on a regular computer. The communication between these two entities is an example of the successful establishment of an Elliptic Curve Cryptography based, secure Modbus wireless communication session between a portable device acting as a supervisor station and a monitoring computer. Our first performance measurements are, also, very promising and demonstrate the feasibility of embedding Elliptic Curve Cryptography into SCADA systems, filling in a gap in the relevant scientific literature.

Keywords: elliptic curve cryptography, ICT security, modbus protocol, SCADA, TCP/IP protocol

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Authors: Dereje Regasa

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The global food system is at a crossroads, which requires prompt action to minimize the effects of the crises. Agroecology is gaining prominence due to its contributions to sustainable food systems. To support efforts in mitigating the crises, the Food and Agriculture Organization (FAO) established alternative approaches for sustainable agri-food systems. Agroecological elements and principles were developed to guide and support measures that countries need to achieve the Sustainable Development Goals (SDGs). The SDGs require the systemic integration of practices for a smart intensification or adaptation of traditional or industrial agriculture. As one of the countries working towards SDGs, the agricultural practices in Ethiopia need to be guided by these agroecological elements and principles. Aiming at the identification of challenging aspects of a sustainable agri-food system and the characterization of an enabling environment for agroecology, as well as exploring to what extent the existing policies and strategies support the agroecological transition process, five policy and strategy documents were reviewed. These documents are the Rural Development Policy and Strategy, the Environment Policy, the Biodiversity Policy, and the Soil Strategy of the Ministry of Agriculture (MoA). Using the Agroecology Criteria Tool (ACT), the contents were reviewed, focusing on agroecological requirements and the inclusion of sustainable practices. ACT is designed to support a self-assessment of elements supporting agroecology. For each element, binary values were assigned based on the inclusion of the minimum requirements index and then validated through discussion with the document owners. The results showed that the documents were well below the requirements for an agroecological transition of the agri-food system. The Rural Development Policy and Strategy only suffice to 83% in Human and Social Value. It does not support the transition concerning the other elements. The Biodiversity Policy and Soil Strategy suffice regarding the inclusion of Co-creation and Sharing of knowledge (100%), while the remaining elements were not considered sufficiently. In contrast, the Environment Policy supports the transition with three elements accounting for 100%. These are Resilience, Recycling, and Human and Social Care. However, when the four documents were combined, elements such as Synergies, Diversity, Efficiency, Human and Social value, Responsible governance, and Co-creation and Sharing of knowledge were identified as fully supportive (100%). This showed that the policies and strategies complemented one another to a certain extent. However, the evaluation results call for improvements concerning elements like Culture and food traditions, Circular and solidarity economy, Resilience, Recycling, and Regulation and balance since the majority of the elements were not sufficiently observed. Consequently, guidance for the smart intensification of local practices is needed, as well as traditional knowledge enriched with advanced technologies. Ethiopian agricultural and environmental policies and strategies should provide sufficient support and guidance for the intensification of sustainable practices and should provide a framework for an agroecological transition towards a sustainable agri-food system.

Keywords: agroecology, diversity, recycling, sustainable food system, transition

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Authors: Sahil Imtiyaz

Abstract:

One of the biggest challenges has emerged from the ever-expanding, dynamic, and instantaneously changing space-Big Data; and to find a data point and inherit wisdom to this space is a hard task. In this paper, we reduce the space of big data in Hamiltonian formalism that is in concordance with Ising Model. For this formulation, we simulate the system using dye laser in FORTRAN and analyse the dynamics of the data point in energy well of rhodium atom. After mapping the photon intensity and pulse width with energy and potential we concluded that as we increase the energy there is also increase in probability of tunnelling up to some point and then it starts decreasing and then shows a randomizing behaviour. It is due to decoherence with the environment and hence there is a loss of ‘quantumness’. This interprets the efficiency parameter and the extent of quantum evolution. The results are strongly encouraging in favour of the use of ‘Topological Property’ as a source of information instead of the qubit.

Keywords: big data, optimization, quantum evolution, hamiltonian, dye laser, fermionic computations

Procedia PDF Downloads 182

Authors: Muhammad Muhammad Suleiman

Abstract:

Cloud Computing is a versatile concept that refers to a service that allows users to outsource their data without having to worry about local storage issues. However, the most pressing issues to be addressed are maintaining a secure and reliable data repository rather than relying on untrustworthy service providers. In this study, we look at how stenography approaches and collaboration with Digital Watermarking can greatly improve the system's effectiveness and data security when used for Cloud Computing. The main requirement of such frameworks, where data is transferred or exchanged between servers and users, is safe data management in cloud environments. Steganography is the cloud is among the most effective methods for safe communication. Steganography is a method of writing coded messages in such a way that only the sender and recipient can safely interpret and display the information hidden in the communication channel. This study presents a new text steganography method for hiding a loaded hidden English text file in a cover English text file to ensure data protection in cloud computing. Data protection, data hiding capability, and time were all improved using the proposed technique.

Keywords: cloud computing, steganography, information hiding, cloud storage, security

Procedia PDF Downloads 172

Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: time series, fluctuation in statistical characteristics, optimal learning, change-point algorithm

Procedia PDF Downloads 411

Authors: Adil Balla Elkrail

Abstract:

Visual MODFLOW computer codes were selected to simulate head distribution, calculate the groundwater budgets of the area, and evaluate the effect of external stresses on the groundwater head and to demonstrate how the groundwater model can be used as a comparative technique in order to optimize utilization of the groundwater resource. A conceptual model of the study area, aquifer parameters, boundary, and initial conditions were used to simulate the flow model. The trial-and-error technique was used to calibrate the model. The most important criteria used to check the calibrated model were Root Mean Square error (RMS), Mean Absolute error (AM), Normalized Root Mean Square error (NRMS) and mass balance. The maps of the simulated heads elaborated acceptable model calibration compared to observed heads map. A time length of eight years and the observed heads of the year 2004 were used for model prediction. The predictive simulation showed that the continuation of pumping will cause relatively high changes in head distribution and components of groundwater budget whereas, the low deficit computed (7122 m3/d) between inflows and outflows cannot create a significant drawdown of the potentiometric level. Hence, the area under consideration may represent a high permeability and productive zone and strongly recommended for further groundwater development.

Keywords: aquifers, model simulation, groundwater, calibrations, trail-and- error, prediction

Procedia PDF Downloads 222

Authors: İbrahi̇m Kara, Seher Arslankaya

Abstract:

Frequently preferred in the field of engineering in particular, data mining has now begun to be used in the field of health as well since the data in the health sector have reached great dimensions. With data mining, it is aimed to reveal models from the great amounts of raw data in agreement with the purpose and to search for the rules and relationships which will enable one to make predictions about the future from the large amount of data set. It helps the decision-maker to find the relationships among the data which form at the stage of decision-making. In this study, it is aimed to determine the risk of heart attack at the first stage, to control it, and to make its resource planning with the method of data mining. Through the early and correct diagnosis of heart attacks, it is aimed to reveal the factors which affect the diseases, to protect health and choose the right treatment methods, to reduce the costs in health expenditures, and to shorten the durations of patients’ stay at hospitals. In this way, the diagnosis and treatment costs of a heart attack will be scrutinized, which will be useful to determine the risk of the disease at the first stage, to control it, and to make its resource planning.

Keywords: data mining, decision support systems, heart attack, health sector

Procedia PDF Downloads 343

Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

Abstract:

Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

Procedia PDF Downloads 105

Authors: John Gaber

Abstract:

Planners regularly use citizen input as empirical data to help them better understand community issues they know very little about. This type of community data is based on the lived experiences of local residents and is known as "emic" data. What is becoming more common practice for planners is their use of data from local experts and stakeholders (known as "etic" data or the outsider perspective) to help them fill in the gaps in their analysis of applied planning research projects. Utilizing international Health Impact Assessment (HIA) data, I look at who planners invite to their citizen input investigations. Research presented in this paper shows that planners access a wide range of emic and etic community perspectives in their search for the “community’s view.” The paper concludes with how planners can chart out a new empirical path in their execution of emic/etic citizen participation strategies in their applied planning research projects.

Keywords: citizen participation, emic data, etic data, Health Impact Assessment (HIA)

Procedia PDF Downloads 473