Search results for: cloud data privacy and integrity

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: Uche Lucy Onyekwelu, Uche B. Ugwuanyi

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Suffice to say that socio-economic disruptions of any form is likely to affect the wellbeing of the citizenry. The upsurge of social disequilibrium caused by the incessant disruptive tendencies exhibited by youths and some others in Nigeria are not helping matters. In Nigeria the social unrest has caused different forms of draw backs in Socio Economic Development. This study has empirically evaluated the impact of insecurity and insurgency on the Economic Development of Nigeria. The paper noted that the different forms of insecurity in Nigeria are namely: Insurgency and Banditry as witnessed in Northern Nigeria; Militancy: Niger Delta area and self-determination groups pursuing various forms of agenda such as Sit –at- Home Syndrome in the South Eastern Nigeria and other secessionist movements. All these have in one way or the other hampered Economic development in Nigeria. Data for this study were collected through primary and secondary sources using questionnaire and some existing documentations. Cost of investment in different aspects of security outfits in Nigeria represents the independent variable while the differentials in the Gross Domestic Product(GDP) and Human Development Index(HDI) are the measures of the dependent variable. Descriptive statistics and Simple Linear Regression analytical tool were employed in the data analysis. The result revealed that Insurgency/Insecurity negatively affect the economic development of the different parts of Nigeria. Following the findings, a model to analyse the effect of insecurity and insurgency was developed, named INSECUREDEVNIG. It implies that the economic development of Nigeria will continue to deteriorate if insurgency and insecurity continue. The study therefore recommends that the government should do all it could to nurture its human capital, adequately fund the state security apparatus and employ individuals of high integrity to manage the various security outfits in Nigeria. The government should also as a matter of urgency train the security personnel in intelligence cum Information and Communications Technology to enable them ensure the effectiveness of implementation of security policies needed to sustain Gross Domestic Product and Human Capital Index of Nigeria.

Keywords: insecurity, insurgency, gross domestic product, human development index, Nigeria

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Authors: Hamesh Babu Nanvala, Madhuri Malhal Rao

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The overall development of the Institution is dependent on the approach and attitude of the principal. Influence of principal’s professionalism on overall development of the Institution is the aim of this paper. Professionalism means conducting oneself with responsibility, integrity, accountability and excellence. The predominant characteristic of professionalism is the temperament of oneself to work in the public interest. By summarizing the observations based on authors’ experience regarding professionalism of principals towards the development of their respective institutions and correlating these observations with the findings in the literature and opinion of other principals and staff, the authors conceived a conceptual approach with its attributes by practicing suggested approach principals that can achieve overall development of their institutions.

Keywords: achiever, development, institution, principal, professionalism, student, teacher

Procedia PDF Downloads 279

Authors: A. Bruce Janati, Muhammad Umair Khan, Naif Alghassab, Ibrahim Alzeir, Assem Mahmoud, M. Sammour

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Rationale: Lennox-Gastaut syndrome(LGS) is an electro-clinical syndrome composed of the triad of mental retardation, multiple seizure types, and the characteristic generalized slow spike-wave complexes in the EEG. In this article, we report on two patients with LGS whose brain MRI showed dysgenesis of corpus callosum(CC). We review the literature and stress the role of CC in the genesis of secondary bilateral synchrony(SBS). Method: This was a clinical study conducted at King Khalid Hospital. Results: The EEG was consistent with LGS in patient 1 and unilateral slow spike-wave complexes in patient 2. The MRI showed hypoplasia of the splenium of CC in patient 1, and global hypoplasia of CC combined with Joubert syndrome in patient 2. Conclusion: Based on the data, we proffer the following hypotheses: 1-Hypoplasia of CC interferes with functional integrity of this structure. 2-The genu of CC plays a pivotal role in the genesis of secondary bilateral synchrony. 3-Electrodecremental seizures in LGS emanate from pacemakers generated in the brain stem, in particular the mesencephalon projecting abnormal signals to the cortex via thalamic nuclei. 4-Unilateral slow spike-wave complexes in the context of mental retardation and multiple seizure types may represent a variant of LGS, justifying neuroimaging studies.

Keywords: EEG, Lennox-Gastaut syndrome, corpus callosum , MRI

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Authors: M. Akhlaque Ahmed, Sidra A. Shaikh, Maliha A. Siddiqui

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Global and diffuse solar radiation on horizontal surface of Lower Sindh, namely Karachi, Hyderabad, Nawabshah were carried out using sunshine hour data of the area to assess the feasibility of solar energy utilization for power generation in Sindh province. The results obtained show a large variation in the direct and diffuse component of solar radiation in summer and winter months in Lower Sindh (50% direct and 50% diffuse for Karachi and Hyderabad). In Nawabshah area, the contribution of diffuse solar radiation is low during the monsoon months, July and August. The K_T value of Nawabshah indicates a clear sky throughout almost the entire year. The percentage of diffuse radiation does not exceed more than 20%. In Nawabshah, the appearance of cloud is rare even during the monsoon months. The estimated values indicate that Nawabshah has high solar potential, whereas Karachi and Hyderabad have low solar potential. During the monsoon months the Lower part of Sindh can utilize the hybrid system with wind power. Near Karachi and Hyderabad, the wind speed ranges between 6.2 m/sec to 6.9 m/sec. A wind corridor exists near Karachi, Hyderabad, Gharo, Keti Bander and Shah Bander. The short fall of solar can be compensated by wind because in the monsoon months of July and August, wind speeds are higher in the Lower region of Sindh.

Keywords: hybrid power system, lower Sindh, power generation, solar and wind energy potential

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Authors: Celina Nowak

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The development of information and communication technologies (ICT) and a consequent growth of cyberspace have become a reality of modern societies. The newest addition to this complex structure has been Internet of Things which is due to the appearance of smart devices. IoT creates a new dimension of the network, as the communication is no longer the domain of just humans, but has also become possible between devices themselves. The possibility of communication between devices, devoid of human intervention and real-time supervision, generated new societal and legal challenges. Some of them may and certainly will eventually be connected to criminal law. Legislators both on national and international level have been struggling to cope with this technologically evolving environment in order to address new threats created by the ICT. There are legal instruments on cybercrime, however imperfect and not of universal scope, sometimes referring to specific types of prohibited behaviors undertaken by criminals, such as money laundering, sex offences. However, the criminal law seems largely not prepared to the challenges which may arise because of the development of IoT. This is largely due to the fact that criminal law, both on national and international level, is still based on the concept of perpetration of an offence by a human being. This is a traditional approach, historically and factually justified. Over time, some legal systems have developed or accepted the possibility of commission of an offence by a corporation, a legal person. This is in fact a legal fiction, as a legal person cannot commit an offence as such, it needs humans to actually behave in a certain way on its behalf. Yet, the legislators have come to understand that corporations have their own interests and may benefit from crime – and therefore need to be penalized. This realization however has not been welcome by all states and still give rise to doubts of ontological and theoretical nature in many legal systems. For this reason, in many legislations the liability of legal persons for commission of an offence has not been recognized as criminal responsibility. With the technological progress and the growing use of IoT the discussions referring to criminal responsibility of corporations seem rather inadequate. The world is now facing new challenges and new threats related to the ‘smart’ things. They will have to be eventually addressed by legislators if they want to, as they should, to keep up with the pace of technological and societal evolution. This will however require a reevaluation and possibly restructuring of the most fundamental notions of modern criminal law, such as perpetration, guilt, participation in crime. It remains unclear at this point what norms and legal concepts will be and may be established. The main goal of the research is to point out to the challenges ahead of the national and international legislators in the said context and to attempt to formulate some indications as to the directions of changes, having in mind serious threats related to privacy and security related to the use of IoT.

Keywords: criminal law, internet of things, privacy, security threats

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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: Vahid Bairami Rad

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The intelligentization of agricultural fields can control the temperature, humidity, and variables affecting the growth of agricultural products online and on a mobile phone or computer. Smarting agricultural fields and gardens is one of the best and best ways to optimize agricultural equipment and has a 100 percent direct effect on the growth of plants and agricultural products and farms. Smart farms are the topic that we are going to discuss today, the Internet of Things and artificial intelligence. Agriculture is becoming smarter every day. From large industrial operations to individuals growing organic produce locally, technology is at the forefront of reducing costs, improving results and ensuring optimal delivery to market. A key element to having a smart agriculture is the use of useful data. Modern farmers have more tools to collect intelligent data than in previous years. Data related to soil chemistry also allows people to make informed decisions about fertilizing farmland. Moisture meter sensors and accurate irrigation controllers have made the irrigation processes to be optimized and at the same time reduce the cost of water consumption. Drones can apply pesticides precisely on the desired point. Automated harvesting machines navigate crop fields based on position and capacity sensors. The list goes on. Almost any process related to agriculture can use sensors that collect data to optimize existing processes and make informed decisions. The Internet of Things (IoT) is at the center of this great transformation. Internet of Things hardware has grown and developed rapidly to provide low-cost sensors for people's needs. These sensors are embedded in IoT devices with a battery and can be evaluated over the years and have access to a low-power and cost-effective mobile network. IoT device management platforms have also evolved rapidly and can now be used securely and manage existing devices at scale. IoT cloud services also provide a set of application enablement services that can be easily used by developers and allow them to build application business logic. Focus on yourself. These development processes have created powerful and new applications in the field of Internet of Things, and these programs can be used in various industries such as agriculture and building smart farms. But the question is, what makes today's farms truly smart farms? Let us put this question in another way. When will the technologies associated with smart farms reach the point where the range of intelligence they provide can exceed the intelligence of experienced and professional farmers?

Keywords: food security, IoT automation, wireless communication, hybrid lifestyle, arduino Uno

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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: 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: Pui-Lai To, E-Ping Sung

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The purpose of this study is to investigate hedonic online shopping motivations. A qualitative analysis was conducted to explore the factors influencing online hedonic shopping motivations. The results of the study indicate that traditional hedonic values, consisting of social, role, self-gratification, learning trends, pleasure of bargaining, stimulation, diversion, status, and adventure, and dimensions of flow theory, consisting of control, curiosity, enjoyment, and telepresence, exist in the online shopping environment. Two hedonic motivations unique to Internet shopping, privacy and online shopping achievement, were found. It appears that the most important hedonic value to online shoppers is having the choice to interact or not interact with others while shopping on the Internet. This study serves as a basis for the future growth of Internet marketing.

Keywords: internet shopping, shopping motivation, hedonic motivation

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Authors: Li-Ting Kung, Hui-Zhu Chen, Hsin-Tzu Lee, Wan-Yin Hsu

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Patients with spinal cord injury combined with diabetic nephropathy may under a lot of painful conditions due to complications related to the illness itself or treatments, such as recurrent pressure ulcers, autonomic and peripheral neuropathy, as well as dialysis, for long term. This case report illustrated the nursing experience of transferring a spine cord injure patient who received hemodialysis due to adverse lifestyle-induced diabetic nephropathy to the hospice ward. Nursing care was provided in this patient from July 25th to August 30th, 2015. The tool of 'Gordon’s 11-item functional health assessment' and clinical observation, interviews as well as physical examination were used as data collections. Based on results of health assessment as above, the patient’s health problems were identified as the following: impaired skin integrity, chronic pain, and hopeless. Besides to relieve the symptom of pain due to disease or the treatment of hemodialysis and provide wound care, the first author also played a role to assist the patient to achieve his goal of receiving the hospice palliative care. Finally, with much effort of nurses to communicate with medical teams between the surgical and hospice wards, the patient was transferred to the hospice ward to have fulfilled his last wish of having a good death. We hope this nursing experience can be applied to other similar cases in the future.

Keywords: diabetic nephropathy, hospice care, palliative care, spinal cord injury

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Authors: Noorizal Huang, Christian Girsang, Mohamad Razi Mansoor

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Shallow gas seepage was first spotted at a central processing platform offshore Malaysia in 2010, acknowledged as Platform T in this paper. Frequent monitoring of the gas seepage was performed through remotely operated vehicle (ROV) baseline survey and a comprehensive geophysical survey was conducted to understand the characteristics of the gas seepage and to ensure that the integrity of the foundation at Platform T was not compromised. The origin of the gas back then was unknown. A soil investigation campaign was performed in 2016 to study the origin of the gas seepage. Two boreholes were drilled; a composite borehole to 150m below seabed for the purpose of soil sampling and in-situ testing and a pilot hole to 155m below the seabed, which was later converted to a fit-for-purpose relief well as an alternate migration path for the gas. During the soil investigation campaign, dissipation tests were performed at several layers which were potentially the source or migration path for the gas. Five (5) soil samples were segregated for headspace test, to identify the gas type which subsequently can be used to identify the origin of the gas. Dissipation tests performed at four depth intervals indicates pore water pressure less than 20 % of the effective vertical stress and appear to continue decreasing if the test had not been stopped. It was concluded that a low to a negligible amount of excess pore pressure exist in clayey silt layers. Results from headspace test show presence of methane corresponding to the clayey silt layers as reported in the boring logs. The gas most likely comes from biogenic sources, feeding on organic matter in situ over a large depth range. It is unlikely that there are large pockets of gas in the soil due to its homogeneous clayey nature and the lack of excess pore pressure in other permeable clayey silt layers encountered. Instead, it is more likely that when pore water at certain depth encounters a more permeable path, such as a borehole, it rises up through this path due to the temperature gradient in the soil. As the water rises the pressure decreases, which could cause gases dissolved in the water to come out of solution and form bubbles. As a result, the gas will have no impact on the integrity of the foundation at Platform T. The fit-for-purpose relief well design as well as adopting headspace testing can be used to address the shallow gas issue at Platform T in a cost effective and efficient manners.

Keywords: dissipation test, headspace test, excess pore pressure, relief well, shallow gas

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Authors: Abdelhalem Shahen

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The world is now experiencing a digital revolution and greatly accelerated technological developments, in addition to the transition from the economy in its traditional form to the digital economy, which has resulted in the emergence of new tools that are appropriate to those developments, and from this, this paper attempts to explore the economic characteristics of the bitcoin currency that circulated recently. Due to the many advantages that distinguish it from money in its traditional forms, which have a range of economic effects. The study found that Bitcoin is among the technological innovations, which contain a set of characteristics that are worth studying, those that make it the focus of attention, such as the digital currency, the peer-to-peer property, Lower and Faster Transaction Costs, transparency, decentralized control, privacy, and Double-Spending, as well as security and Cryptographic, and finally mining.

Keywords: Digital Economics, Digital Currencies, Bitcoin, Features of Bitcoin

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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: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Raja Arslan Ahmad Khan

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Audience feedback and data play an increasingly crucial role, particularly in the digital age. The advent of digital media and the digitalization of news have given rise to novel forms of audience feedback, markedly different from traditional channels. The engagement of online audiences challenges the conventional role of journalists, introducing a dynamic where audiences can wield both direct and indirect influence. This struggle between the audience and journalists is evident in their contributions and interactions. Media professionals are grappling with challenges such as derogatory remarks, hate speech, online harassment, audience hostility, and attacks from online audiences. The influence of online audiences extends to shaping journalists' daily routines and work practices. Consequently, this study seeks to analyze the impact of online audience feedback on journalists at a routine level within the Malaysian context. Employing a Hierarchy of Influence model as a theoretical framework, the study will utilize a quantitative approach with a snowball survey method. The study's findings aim to enhance our understanding of how online audiences influence journalists and their work practices, encompassing aspects like journalists' autonomy and integrity, editorial decision-making, performance and accountability, daily routines, work practices, as well as the psychological and emotional costs they bear. It's important to note that the study has limitations due to the use of the snowball survey method and its focus within the specific context of Malaysia, making it relatively small in scale.

Keywords: online audiences, feedback, influence, journalists, Malaysia

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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: M. Cortés, E. Vera, M. Avella

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Corrosion is an imminent process in nature, which affects all types of materials. In sewerage systems, the corrosion process caused by microorganisms, also known as biogenic sulfuric acid attack, has been studied. This affects the structural integrity of the concrete drainage pipes and the sewage treatment plants. This article is a review of research which focuses on the study of how to reduce the production of hydrogen sulfide, how to improve the resistance of concrete through the use of additives and the implementation of antimicrobial techniques to reduce bacterial growth.

Keywords: bactericides, biogenic sulfuric acid, corrosion, concrete, hydrogen sulphide, nano materials, zeolites

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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: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: M. Akhlaque Ahmed, Sidra A. Shaikh, Maliha A. Siddiqui, Adeel Tahir

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Global and diffuse solar radiation on horizontal surface of southern sindh namely Karachi, Hyderabad, Nawabshah were carried out using sunshine hour data of the area to asses the feasibility of solar Energy utilization at Sindh province for power generation. From the observation, result is derived which shows a drastic variation in the diffuse and direct component of solar radiation for summer and winter for Southern Sindh that is both contributes 50% for Karachi and Hyderabad. In Nawabshah area, the contribution of diffuse solar radiation is low in monsoon months, July and August. The Kᴛ value of Nawabshah indicates a clear sky almost throughout the year. The percentage of diffuse radiation does not exceed more than 20%. In Nawabshah, the appearance of cloud is rare even in monsoon months. The estimated values indicate that Nawabshah has high solar potential whereas Karachi and Hyderabad has low solar potential. During the monsoon months, the southern part of Sind can utilize the hybrid system with wind power. Near Karachi and Hyderabad, the wind speed ranges between 6.2 to 6.9 m/sec. There exist a wind corridor near Karachi, Hyderabad, Gharo, Keti Bander and Shah Bander. The short fall of solar can be compensated by wind because in monsoon months July and August the wind speed are higher in the southern region of Sindh.

Keywords: hybrid power system, power generation, solar and wind energy potential, southern Sindh

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Authors: Sarada Nivedita Bhuyan

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Earthquake is the sudden, rapid movement of the earth’s crust and is the natural means of releasing stress. Tectonic plates play a major role for earthquakes as tectonic plates are the crust of the planet. The boundary lines of tectonic plates are usually known as fault lines. To understand an earthquake before its occurrence, different types of earthquake precursors are studied by different researchers. Surface temperature, strange cloud cover, earth’s electric field, geomagnetic phenomena, ground water level, active faults, ionospheric anomalies, tectonic movements are taken as parameters for earthquake study by different researchers. In this paper we tried to gather complete and helpful information of earthquake precursors which have been studied until now.

Keywords: earthquake precursors, earthquake, tectonic plates, fault

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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

Procedia PDF Downloads 241

Authors: Champa Gunawardana, Anishka Hettiarachchi

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Conducive Architecture for learning environments is an area of interest for many scholars around the world. Loss of sense of hearing leads to the assumption that deaf students are visual learners. Comprehending favorable non-hearing attributes of architecture can lead to effective, rich and friendly learning environments for hearing impaired. The objective of the current qualitative investigation is to explore the nature and parameters of a sense of place of deaf children to support optimal learning. The investigation was conducted with hearing-impaired children (age: between 8-19, Gender: 15 male and 15 female) of Yashodhara deaf and blind school at Balangoda, Sri Lanka. A sensory ethnography study was adopted to identify the nature of perception and the parameters of most preferred and least preferred spaces of the learning environment. The common perceptions behind most preferred places in the learning environment were found as being calm and quiet, sense of freedom, volumes characterized by openness and spaciousness, sense of safety, wide spaces, privacy and belongingness, less crowded, undisturbed, availability of natural light and ventilation, sense of comfort and the view of green colour in the surroundings. On the other hand, the least preferred spaces were found to be perceived as dark, gloomy, warm, crowded, lack of freedom, smells (bad), unsafe and having glare. Perception of space by deaf considering the hierarchy of sensory modalities involved was identified as; light - color perception (34 %), sight - visual perception (32%), touch - haptic perception (26%), smell - olfactory perception (7%) and sound – auditory perception (1%) respectively. Sense of freedom (32%) and sense of comfort (23%) were the predominant psychological parameters leading to an optimal sense of place perceived by hearing impaired. Privacy (16%), rhythm (14%), belonging (9%) and safety (6%) were found as secondary factors. Open and wide flowing spaces without visual barriers, transparent doors and windows or open port holes to ease their communication, comfortable volumes, naturally ventilated spaces, natural lighting or diffused artificial lighting conditions without glare, sloping walkways, wider stairways, walkways and corridors with ample distance for signing were identified as positive characteristics of the learning environment investigated.

Keywords: deaf, visual learning environment, perception, sensory ethnography

Procedia PDF Downloads 221

Authors: Anny Retnowati, Elisabeth Sundari

Abstract:

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

Procedia PDF Downloads 75

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

Procedia PDF Downloads 346

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

Procedia PDF Downloads 350

Authors: Mona Misfer Alshardan, Djamal Ziani

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Enterprise resource planning (ERP) systems are the organizations tickets to the global market. With the implementation of ERP, organizations can manage and coordinate all functions, processes, resources and data from different departments by a single software. However, many organizations consider the cost of traditional ERP to be expensive and look for alternative affordable solutions within their budget. One of these alternative solutions is providing ERP over a software as a service (SaaS) model. This alternative could be considered as a cost effective solution compared to the traditional ERP system. A key feature of any SaaS system is the multi-tenancy architecture where multiple customers (tenants) share the system software. However, different organizations have different requirements. Thus, the SaaS developers accommodate each tenant’s unique requirements by allowing tenant-level customization or configuration. While customization requires source code changes and in most cases a programming experience, the configuration process allows users to change many features within a predefined scope in an easy and controlled manner. The literature provides many techniques to accomplish the configuration process in different SaaS systems. However, the nature and complexity of SaaS ERP needs more attention to the details regarding the configuration process which is merely described in previous researches. Thus, this research is built on strong knowledge regarding the configuration in SaaS to define specifically the configuration borders in SaaS ERP and to design a configuration service with the consideration of the different configuration aspects. The proposed architecture will ensure the easiness of the configuration process by using wizard technology. Also, the privacy and performance are guaranteed by adopting the databases isolation technique.

Keywords: configuration, software as a service, multi-tenancy, ERP

Procedia PDF Downloads 384