Search results for: data gathering

Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Heba M. Jahin, Ali F. Bakr, Zeyad T. Elsayad

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As smart spaces can be defined as any working environment which integrates embedded computers, information appliances and multi-modal sensors to remain focused on the interaction between the users, their activity, and their behavior in the space; hence, smart space must be aware of their contexts and automatically adapt to their changing context-awareness, by interacting with their physical environment through natural and multimodal interfaces. Also, by serving the information used proactively. This paper suggests a dynamic framework through the architectural design process of the space based on the principles of computational methods and context-awareness principles to help in creating a field of changes and modifications. It generates possibilities, concerns about the physical, structural and user contexts. This framework is concerned with five main processes: gathering and analyzing data to generate smart design scenarios, parameters, and attributes; which will be transformed by coding into four types of models. Furthmore, connecting those models together in the interaction model which will represent the context-awareness system. Then, transforming that model into a virtual and ambient environment which represents the physical and real environments, to act as a linkage phase between the users and their activities taking place in that smart space . Finally, the feedback phase from users of that environment to be sure that the design of that smart space fulfill their needs. Therefore, the generated design process will help in designing smarts spaces that can be adapted and controlled to answer the users’ defined goals, needs, and activity.

Keywords: computational methods, context-awareness, design process, smart spaces

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Authors: Md. Kamrul Hassan, Ari Pappinen

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Wood energy is the single most important form of renewable energy in many parts of the world especially in the least developing countries in South Asia like Bangladesh. The last portion of the national population of this country depends on wood energy for their daily primary energy need. This paper deals with the estimation of wood fuel at the current level and identifies the challenges and strategies related to the development of this resource. Desk research, interactive research and field survey were conducted for gathering and analyzing of data for this study. The study revealed that wood fuel plays a significant role in total primary energy supply in Bangladesh, and the contribution of wood fuel in final energy consumption in 2013 was about 24%. Trees on homestead areas, secondary plantation on off forest lands, and forests are the main sources of supplying wood fuel in the country. Insufficient supply of wood fuel against high upward demand is the main cause of concern for sustainable consumption, which eventually leads deterioration and depletion of the resources. Inadequate afforestation programme, lack of initiatives towards the utilization of set-aside lands for wood energy plantations, and inefficient management of the existing resources have been identified as the major impediments to the development of wood energy in Bangladesh. The study argued that enhancement of public-private-partnership afforestation programmes, intensifying the waste and marginal lands with short-rotation tree species, and formulation of biomass-based rural energy strategies at the regional level are relevant to the promotion of sustainable wood energy in the country.

Keywords: Bangladesh, challenge, supply, wood energy

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

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Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

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Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Zakieh Piri, Shahla Damanabi, Peyman Rezaii Hachesoo

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Introduction: Healthcare organizations like other organizations suffer from a number of disorders such as Business Sponsor Disorder, Business Acceptance Disorder, Cultural/Political Disorder, Data Disorder, etc. As quality in healthcare care mostly depends on the quality of data, we aimed to identify data disorders and its symptoms in two teaching hospitals. Methods: Using a self-constructed questionnaire, we asked 20 questions in related to quality and usability of patient data stored in patient records. Research population consisted of 150 managers, physicians, nurses, medical record staff who were working at the time of study. We also asked their views about the symptoms and treatments for any data disorders they mentioned in the questionnaire. Using qualitative methods we analyzed the answers. Results: After classifying the answers, we found six main data disorders: incomplete data, missed data, late data, blurred data, manipulated data, illegible data. The majority of participants believed in their important roles in treatment of data disorders while others believed in health system problems. Discussion: As clinicians have important roles in producing of data, they can easily identify symptoms and disorders of patient data. Health information managers can also play important roles in early detection of data disorders by proactively monitoring and periodic check-ups of data.

Keywords: data disorders, quality, healthcare, treatment

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Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines.

Keywords: big data, data analytics, higher education, republic of the philippines, assessment

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Authors: Jeryes Danial, Yosi Ben Asher

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Algorithms for controlling a swarm of robots is an active research field, out of which cattle herding is one of the most complex problems to solve. In this paper, we derive an independent herding algorithm that is specifically designed for a swarm of quadcopters. The algorithm works by devising flight trajectories that cause the cows to run-away in the desired direction and hence herd cows that are distributed in a given field towards a common gathering point. Unlike previously proposed swarm herding algorithms, this algorithm does not use a flocking model but rather stars each cow separately. The effectiveness of this algorithm is verified experimentally using a simulator. We use a special set of experiments attempting to demonstrate that the herding times of this algorithm correspond to field diameter small constant regardless of the number of cows in the field. This is an optimal result indicating that the algorithm groups the cows into intermediate groups and herd them as one forming ever closing bigger groups.

Keywords: swarm, independent, distributed, algorithm

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Authors: Venkata Madhusudana Rao Kapavarapu

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The Intermediate Slug Catcher (ISC) facility was built to process nominally 234 MSCFD of export gas from the booster station on a day-to-day basis and to receive liquid slugs up to 1600 m³ (10,000 BBLS) in volume when the incoming 24” gas pipelines are pigged following upsets or production of non-dew-pointed gas from gathering centers. The maximum slug sizes expected are 812 m³ (5100 BBLS) in winter and 542 m³ (3400 BBLS) in summer after operating for a month or more at 100 MMSCFD of wet gas, being 60 MMSCFD of treated gas from the booster station, combined with 40 MMSCFD of untreated gas from gathering center. The water content is approximately 60% but may be higher if the line is not pigged for an extended period, owing to the relative volatility of the condensate compared to water. In addition to its primary function as a slug catcher, the ISC facility will receive pigged liquids from the upstream and downstream segments of the 14” condensate pipeline, returned liquids from the AGRP, pigged through the 8” pipeline, and blown-down fluids from the 14” condensate pipeline prior to maintenance. These fluids will be received in the condensate flash vessel or the condensate separator, depending on the specific operation, for the separation of water and condensate and settlement of solids scraped from the pipelines. Condensate meeting the colour and 200 ppm water specifications will be dispatched to the AGRP through the 14” pipeline, while off-spec material will be returned to BS-171 via the existing 10” condensate pipeline. When they are not in operation, the existing 24” export gas pipeline and the 10” condensate pipeline will be maintained under export gas pressure, ready for operation. The gas manifold area contains the interconnecting piping and valves needed to align the slug catcher with either of the 24” export gas pipelines from the booster station and to direct the gas to the downstream segment of either of these pipelines. The manifold enables the slug catcher to be bypassed if it needs to be maintained or if through-pigging of the gas pipelines is to be performed. All gas, whether bypassing the slug catcher or returning to the gas pipelines from it, passes through black powder filters to reduce the level of particulates in the stream. These items are connected to the closed drain vessel to drain the liquid collected. Condensate from the booster station is transported to AGRP through 14” condensate pipeline. The existing 10” condensate pipeline will be used as a standby and for utility functions such as returning condensate from AGRP to the ISC or booster station or for transporting off-spec fluids from the ISC back to booster station. The manifold contains block valves that allow the two condensate export lines to be segmented at the ISC, thus facilitating bi-directional flow independently in the upstream and downstream segments, which ensures complete pipeline integrity and facility integrity. Pipeline failures will be attended to with the latest technologies by remote techno plug techniques, and repair activities will be carried out as needed. Pipeline integrity will be evaluated with ili pigging to estimate the pipeline conditions.

Keywords: integrity, oil & gas, innovation, new technology

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Robyn Therese Munnick

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This paper aims to report on a qualitative practice-based research study which explores the notion of nothingness and how it (nothingness) is the conceptual and theoretical foundation for artistic practice. Furthermore, this study explicates how the artist used their mother’s battle with cancer and the subsequent void it created as source material for the artistic expression of nothingness. The diagnosis which was followed by a physical and emotional absence of the matriarch of the artist family led to an emotional trauma that triggered a feeling of nothingness within the artist. The overarching problem in the study is thus: how this ‘nothingness’ could be expressed in visual art? Nothingness, as a product of expectation, is a notion which refers to where something used to be, should be or isn’t anymore, which attempts to grasp what is there by not being there. In attempting to express nothingness, the research aims to build on an exploration of various materials and modes utilized in order to underpin the research objectives. The primary mode of delivery for the art-making process is painting. However, through strengthening the messages and meaning of the hypothesis of nothingness within the art and research, the use of further modes and materials became pivotal. This involves the use of unconventional contrasting modes within a painting such as the cloth doily, thread, tubing, ceramics, food colour, spray paint, polyvinyl acetate paint, plaster, wooden boxes and fragments thereof. These materials and modes were vital in visualising and aestheticising the conceptual underpinnings of the research. As a result, this strengthened and emancipated the art from the traditional bounds of pure painting. Methods of data gathering took the form of artefacts, document analysis, and field notes in the form of photographic journaling. Ultimately the body of work and research validates that the idea of nothingness can be artistically explored.

Keywords: conceptual, nothingness, modes, unconventional

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Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

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This paper discusses the continued infringement and exploitation of data by non-state actors for destructive purposes, emphasizing radical terrorist organizations. It will discuss how terrorist organizations access and use data to foster their nefarious agendas. It further examines how cybersecurity, designed as a tool to curb data exploitation, is ineffective in raising global citizens' concerns about how their data can be kept safe and used for its acquired purpose. The study interrogates several policies and data protection instruments, such as the Data Protection Act, Cyber Security Policies, Protection of Personal Information(PPI) and General Data Protection Regulations (GDPR), to understand data use and storage in democratic states. The study outcomes point to the fact that international cybersecurity and cybercrime legislation, policies, and conventions have not curbed violations of data access and use by radical terrorist groups. The study recommends ways to enhance cybersecurity and reduce cyber risks using democratic principles.

Keywords: cybersecurity, data exploitation, terrorist organizations, data democracy

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Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: Anny Retnowati, Elisabeth Sundari

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This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights.

Keywords: access, health data, medical records, personal data, protection

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: Mahdi Eskandarlou, Mehrdad Taghipour

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Background: Partial-thickness skin graft is the cornerstone for scalp defect repair. Routine donor sites include abdomen, thighs, and buttocks. Given the potential side effects following harvesting from these sites and the potential advantages of harvesting from scalp (broad surface, rapid healing, and better cosmetics results), this study is trying to compare the outcomes of graft harvesting from scalp and lower limb. Methods: This clinical trial is conducted among a sample number of 40 partial thickness graft candidates (20 case and 20 control group) with scalp defect presenting to plastic surgery clinic at Besat Hospital during the time period between 2018 and 2019. Sampling was done by simple randomization using random digit table. Data gathering was performed using a designated checklist. The donor site in case group and control group was scalp and lower limb, respectively. The resultant data were analyzed using chi-squared and t-test and SPPS version 21 (SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp). Results: Of the total 40 patients participating in this study, 28 patients (70%) were male, and 12 (30%) were female with and mean age of 63.62 ± 09.73 years. Hypertension and diabetes mellitus were the most common comorbidities among patients with basal cell carcinoma (BCC) and trauma being the most common etiology for the defects. There was a statistically meaningful relationship between two groups regarding the etiology of defect (P=0.02). The most common anatomic location of defect for case and control groups was temporal and parietal, respectively. Most of the defects were deep to galea zone. The mean diameter of defect was 24.28 ± 45.37 mm for all of the patients. The difference between diameter of defect in both groups was statistically meaningful, while no such difference between graft diameter was seen. The graft 'Take' was completely successful in both groups according to evaluations. The level of postoperative pain was lower in the case group compared to the control according to VAS scale, and the satisfaction was higher in them per Likert scale. Conclusion: Scalp can safely be used as donor site for skin graft to be used for scalp defects, which is associated with better results and lower complication rates compared to other donor sites.

Keywords: donor site, leg, partial-thickness graft, scalp

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Jean Gray C. Achapero, Mary Margareth P. Ancheta, Patricia Anjelika A. Angeles, Shannon Denzel S. Ao Tai, Carl Brandon C. Barlis, Chrislen Mae B. Benavidez

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Health literacy and awareness of Polycystic ovarian syndrome (PCOS) is a global issue that is under-addressed in the Philippines. Conducting a thorough review of the country's ability to recognize and comprehend the severity of the syndrome should be undertaken, as early treatment is essential to avoid further disorder complications. This research aims to assess the health literacy and awareness of the female residents of Barangay Dagatan, Sabang, and Marauoy Lipa, Batangas on PCOS. It followed a cross-sectional study, and data gathering was done through a pre-assessment using the Single Item Literacy Screener (SILS) and an online population-based survey questionnaire about PCOS awareness. The participants, as based on the objectives and purposive sampling method, were females aged 18-45 years old. Data were analyzed statistically using STATA 13.1 software. The study showed that 339 (76%) out of 444 respondents passed the SILS meaning the residents have proficient health literacy. Among the 339 respondents, 87% (287) had previous knowledge about PCOS. The respondents showed minimal awareness of PCOS symptoms which could be attributed to its broad spectrum of information. Respondents were shown to be most knowledgeable about PCOS physiology, treatment, beliefs, and its remedies. The respondents’ age had no significant association with their health literacy (p=0.31) and PCOS awareness (p=0.60). A significant association was noted, however, in their educational attainment linked with their health literacy (p=<0.0001) and PCOS awareness (p=0.001). It is suggested that reproductive health education even in the lower year levels must be optimized and Local Government Unit (LGU)/Non-Government Organization (NGO)-held seminars should be conducted for knowledge reinforcement. Reliable health information should be more accessible to the public and clinicians must emphasize the importance of the majority of early screening as part of routine physical examination for women of reproductive age to increase health literacy and awareness about PCOS and actively engage in the management of the disease.

Keywords: age, awareness, educational attainment, health literacy, polycystic ovarian syndrome

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

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With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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Authors: Sombol Mokhles

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This paper tries to show the prospects of utilising (big)data for enabling just the transition of diverse cities. Our key purpose is to offer a framework of applications and implications of utlising (big) data in comparing sustainability transitions across different cities. Relying on the cosmopolitan comparison, this paper explains the potential application of (big) data but also its limitations. The paper calls for adopting a data-driven and just perspective in including different cities around the world. Having a just and inclusive approach at the front and centre ensures a just transition with synergistic effects that leave nobody behind.

Keywords: big data, just sustainable transition, cosmopolitan city comparison, cities

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Authors: J. E. M. Macwan, D. A. Pastagia, Reeta Meena

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Government of Gujarat (India) wishes to create smart villages to improve the quality of life. The significance of these efforts will result into higher rural productivity. The main aim of this research is to identify, design and propose implementable planning solutions suited for an Indian village set up. The methodology adopted is to create a database by conducting onsite study as well as gathering public opinion to help researchers to satisfy rural needs. The outcome of this research exercise is a planning design preparation and channelizing funds in right direction for a result oriented better village shape. The proposals are accepted after slight modifications by the stakeholders. Planning solutions were designed through public participatory approach. To control rural Urban migration, villagers were offered better quality of life.

Keywords: smart village, digitization, development plan, gram panchayats

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Authors: Modesta E. Ezema, Christopher C. Ezema, Christian C. Ugwu, Udoka F. Eze, Florence M. Babalola

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Some employees knowingly or unknowingly contribute to loss of data and also expose data to threat in the process of getting their jobs done. Many organizations today are faced with the challenges of how to secure their data as cyber criminals constantly devise new ways of attacking the organization’s secret data. However, this paper enlists the latest strategies that must be put in place in order to protect these important data from being attacked in a collaborative work place. It also introduces us to Advanced Persistent Threats (APTs) and how it works. The empirical study was conducted to collect data from the employee in data centers on how data could be protected from malicious codes and cyber criminals and their responses are highly considered to help checkmate the activities of malicious code and cyber criminals in our work places.

Keywords: data, employee, malware, work place

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Authors: Jia Yi Yap, Angela S. H. Lee

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With the use of big data technologies, organization can get result that they are interested in. Big data technologies simply load all the data that is useful for the organizations and provide organizations a better way of analysing data. The purpose of this research is to get employees’ opinion from films in Malaysia to explore the use of big data technologies in their organization in order to provide how it may affect the perception of the employees on job performance. Therefore, in order to identify will accepting big data technologies in the organization affect the perception of the employee, questionnaire will be distributed to different employee from different Small and medium-sized enterprises (SME) organization listed in Malaysia. The conceptual model proposed will test with other variables in order to see the relationship between variables.

Keywords: big data technologies, employee, job performance, questionnaire

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Authors: Noriyuki Suyama

Abstract:

This paper looked into consumer behavioral changes by analyzing the data collected by ASMARKS Co., one of a research companies in Japan. The purpose of the paper is to understand the two differences of before vs. after COVID-19 pandemic and Japanese living in Japan. Subsequently, examining the analysis results helped obtain useful insights into new business models for business parties in Japan as a microlevel perspective. The paper also tried to explore future conditions of globalization by taking into consideration nation’s political and economic changes as a macro-level perspective. The COVID-19 has been continuing its spread across the world with more than 60 million confirmed cases in 190 countries. This pandemic with restricted scopes of behavior mandates have disrupted the consumer habits of their lifestyles. Consumers have tendency to learn new ways when they have trouble in taking routine action. For example, the government forces people to refrain from going out, they try to telecommute at home. If the situation come back to normal, people still change their lifestyles to fit in the best. Some of data show typical effects of COVID-19; forceful exposure to digitalized work-life styles; more flexible time at home; importance of trustful and useful information gathering between what's good and bad;etc. in comparison with before vs. after COVID-19 pandemic. In addition, Japanese have less changed their lifestyles than Expatriates living in Japan. For example, while 94% of the expatriates have decreased their outgo because of self-quarantine, only 55% of the Japanese have done. There are more differences in both comparisons in the analysis results. The economic downtrend resulting from COVID-19 is supposed to be at least as devastating if not more so than that of the financial crisis. With unemployment levels in the US taking two weeks to reach what took 6 months in the 2008 crisis, there is no doubt of a global recession some predict could reach 10% or above of GDP. As a result, globalization in the global supply chain of goods and services will end up with negative impact. A lot of governmental financial and economic policies are supposed to focus on their own profits and interests, exclusing other countries interests as is the case with the Recovery Act just after the global financial crisis from 2007 to 2008. Both micro- and macro-levels analysis successfully reveal important connotations and managerial implications of business in Japan for Japanese consumers as well as after COVID-19 global business.

Keywords: COVID-19, lifestyle in Japan, expatriates, consumer behavior

Procedia PDF Downloads 116

Authors: Hsuan Yu Lai, Hsuan Hsuan Chang

Abstract:

The consumer decision process steps through problems by weighing evidence, examining alternatives, and choosing a decision path. Currently, the COVID 19 made the tourism industry encounter a huge challenge and suffer the biggest amount of economic loss. It would be very important to reexamine the decision-making process model, especially after the pandemic, and consider the differences among different generations. The tourism industry has been significantly impacted by the global outbreak of COVID-19, but as the pandemic subsides, the sector is recovering. This study addresses the scarcity of research on travel decision-making patterns among generations in Taiwan. Specifically targeting individuals who frequently traveled abroad before the pandemic, the study explores differences in decision-making at different stages post-outbreak. So this study investigates differences in travel decision-making among individuals from different generations during/after the COVID-19 pandemic and examines the moderating effects of social media usage and individuals' perception of health risks. The study hypotheses are “there are significant differences in the decision-making process including travel motivation, information searching preferences, and criteria for decision-making” and that social-media usage and health-risk perception would moderate the results of the previous study hypothesis. The X, Y, and Z generations are defined and categorized based on a literature review. The survey collected data including their social-economic background, travel behaviors, motivations, considerations for destinations, travel information searching preferences, and decision-making criteria before/after the pandemic based on the reviews of previous studies. Data from 656 online questionnaires were collected between January to May 2023 and from Taiwanese travel consumers who used to travel at least one time abroad before Covid-19. SPSS is used to analyze the data with One-Way ANOVA and Two-Way ANOVA. The analysis includes demand perception, information gathering, alternative comparison, purchase behavior, and post-travel experience sharing. Social media influence and perception of health risks are examined as moderating factors. The findings show that before the pandemic, the Y Generation preferred natural environments, while the X Generation favored historical and cultural sites compared to the Z Generation. However, after the outbreak, the Z Generation displayed a significant preference for entertainment activities. This study contributes to understanding changes in travel decision-making patterns following COVID-19 and the influence of social media and health risks. The findings have practical implications for the tourism industry.

Keywords: consumer decision-making, generation study, health risk perception, post-pandemic era, social media

Procedia PDF Downloads 36