Search results for: Privacy and Data Protection Law

Authors: Amit R. Bhende, G. K. Awari

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Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling.

Keywords: bearing degradation data, remaining useful life (RUL), back propagation, prognosis

Procedia PDF Downloads 417

Authors: Tolga Aydin, M. Fatih Alaeddinoğlu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: apriori algorithm, association rules, data mining, spatio-temporal data

Procedia PDF Downloads 358

Authors: Busayo Fashoto, Abdulhakeem Shaibu, Justice Agbadu, Samuel Aiyeoribe

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Data has gone from just rows and columns to being an infrastructure itself. The traditional medium of data infrastructure has been managed by individuals in different industries and saved on personal work tools; one of such is the laptop. This hinders data sharing and Sustainable Development Goal (SDG) 9 for infrastructure sustainability across all countries and regions. However, there has been a constant demand for data across different agencies and ministries by investors and decision-makers. The rapid development and adoption of open-source technologies that promote the collection and processing of data in new ways and in ever-increasing volumes are creating new data infrastructure in sectors such as lands and health, among others. This paper examines the process of developing data infrastructure and, by extension, a data portal to provide baseline data for sustainable development and decision making in Nigeria. This paper employs the FAIR principle (Findable, Accessible, Interoperable, and Reusable) of data management using open-source technology tools to develop data portals for public use. eHealth Africa, an organization that uses technology to drive public health interventions in Nigeria, developed a data portal which is a typical data infrastructure that serves as a repository for various datasets on administrative boundaries, points of interest, settlements, social infrastructure, amenities, and others. This portal makes it possible for users to have access to datasets of interest at any point in time at no cost. A skeletal infrastructure of this data portal encompasses the use of open-source technology such as Postgres database, GeoServer, GeoNetwork, and CKan. These tools made the infrastructure sustainable, thus promoting the achievement of SDG 9 (Industries, Innovation, and Infrastructure). As of 6th August 2021, a wider cross-section of 8192 users had been created, 2262 datasets had been downloaded, and 817 maps had been created from the platform. This paper shows the use of rapid development and adoption of technologies that facilitates data collection, processing, and publishing in new ways and in ever-increasing volumes. In addition, the paper is explicit on new data infrastructure in sectors such as health, social amenities, and agriculture. Furthermore, this paper reveals the importance of cross-sectional data infrastructures for planning and decision making, which in turn can form a central data repository for sustainable development across developing countries.

Keywords: data portal, data infrastructure, open source, sustainability

Procedia PDF Downloads 79

Authors: Hyun-Woo Cho

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Unexpected operational events or abnormalities of industrial processes have a serious impact on the quality of final product of interest. In terms of statistical process control, fault detection and diagnosis of processes is one of the essential tasks needed to run the process safely. In this work, nonlinear representation of process measurement data is presented and evaluated using a simulation process. The effect of using different representation methods on the diagnosis performance is tested in terms of computational efficiency and data handling. The results have shown that the nonlinear representation technique produced more reliable diagnosis results and outperforms linear methods. The use of data filtering step improved computational speed and diagnosis performance for test data sets. The presented scheme is different from existing ones in that it attempts to extract the fault pattern in the reduced space, not in the original process variable space. Thus this scheme helps to reduce the sensitivity of empirical models to noise.

Keywords: fault diagnosis, nonlinear technique, process data, reduced spaces

Procedia PDF Downloads 232

Authors: Giulia Balestracci

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Pakistan is endowed with diversified natural capital spanning along the 1000-kilometer-long coastline, shared by the coastal provinces of Sindh and Balochistan. It includes some of the most diverse, extensive, and least disturbed reef areas in the Indian Ocean. Pakistani marine and coastal ecosystems are fundamental for the social and economic well-being of the region. They support economic activities such as fishing, shrimp farming, tourism, and shipping, which contribute to income, food security, and the livelihood of millions of people. The coastal regions of Sindh and Balochistan are rich in natural resources and diverse ecosystems, and host also rural coastal communities that have been the keepers of rich cultural legacies and pristine natural landscapes. However, significant barriers hinder tourism development, such as the daunting socio-economic challenges, including the post-COVID-19 scenario, forced migration, institutional gaps, and the ravages of climate change. Pakistan holds immense potential for the tourism sector development within the framework of a sustainable blue economy, thereby fostering greener economic growth and employment opportunities, securing financing for the protection and conservation of its coastal and marine natural assets. Based on the assessment of Pakistan’s natural and cultural coastal and maritime tourism resources, a deep study of the regulatory and institutional aspects of the tourism sector in the country accompanied by the SWOT analysis and accompanied by an in-depth interview with a member of the Pakistan National Tourism Coordination Board (NTCB). A market analysis has been developed, and Lao PDR, Thailand, and Indonesia’s ecotourism development have been analyzed under a comparative analysis length to recommend some nature-based tourism activities for the sustainable development of the coastal areas in Pakistan. Nature-based tourism represents a win-win option as it uses economic incentives for the protection and cultural uses of natural resources. This article stresses the importance of nature-based activities for blue tourism, aligning conservation with developmental goals to safeguard natural resources and cultural heritage, all while fostering economic prosperity.

Keywords: blue tourism, coastal Pakistan, nature-based tourism, sustainable blue economy, sustainable development

Procedia PDF Downloads 50

Authors: Kais Rtibi, Slimen Selmi, Jamel El-Benna, Lamjed Marzouki, Hichem Sebai

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Background: Myeloperoxidase (MPO) is a hemic enzyme found in high concentrations in the primary neutrophils granules. In addition to its peroxidase activity, it has a chlorination activity, using hydrogen peroxide and chloride ions to form hypochlorous acid, a strong oxidant, capable of chlorinating molecules. Bioactive compounds contained in medicinal plants could limit the action of this enzyme to reduce the reactive oxygen species production and its chlorination activity. The purpose of this study is to evaluate the effect of the carob aqueous extract (CAE) on the release of MPO by human neutrophils in vitro and its activity following stimulation of these cells by PMA. Methods: Neutrophils were isolated by simple sedimentation using the Dextran/Ficoll method. After stimulation with phorbol 12-myristate 13-acetate (PMA), neutrophils release the MPO by degranulation. The effect of CAE on the release of MPO was analyzed by the Western blot technique, while, its activity was determined by biochemical method using the method of 3,3', 5,5'- Tetramethylbenzidine (TMB) and hydrogen peroxide. The data were expressed as mean ± SEM. Results: The carob aqueous extract causes a decrease in MPO quantity and activity in a concentration-dependent manner which leads to a reduction of the production of the ROS (reactive oxygen species) and the protection of the molecules against oxidation and chlorination mechanisms. Conclusion: Thanks to its richness in bioactive compounds, the aqueous extract of carob could limit the development of damages related to the uncontrolled activity of MPO.

Keywords: carob, MPO, myeloperoxidase, neutrophils, PMA, phorbol 12-myristate 13-acetate

Procedia PDF Downloads 148

Authors: Iris Maria Velez Osorio

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There has been in the last decade a concern about the environment, particularly about clean and enough water for human consumption but, some enterprises had some trouble to understand the limited resources in the environment. This research tries to understand how some industries are better oriented to the preservation of the environment through investment for strategic management of scarce resources and try in the best way possible, the contaminants. It was made an industry classification since four different group of theories for Corporate Social Responsibility agree with variables of: investment in environmental care, water protection, and residues treatment finding different levels of commitment with CSR.

Keywords: corporate social responsibility, environment, strategic management, water

Procedia PDF Downloads 361

Authors: S. J. Bindhu, Edwina G. Rodrigues, Jijo Balakrishnan

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A grid-connected photovoltaic (PV) micro inverter with good performance properties is proposed in this paper. The proposed inverter with a quadrupler, having more efficiency and less voltage stress across the diodes. The stress that come across the diodes that use in the inverter section is considerably low in the proposed converter, also the protection scheme that we provided can eliminate the chances of the error due to fault. The proposed converter is implemented using perturb and observe algorithm so that the fluctuation in the voltage can be reduce and can attain maximum power point. Finally, some simulation and experimental results are also presented to demonstrate the effectiveness of the proposed converter.

Keywords: DC-DC converter, MPPT, quadrupler, PV panel

Procedia PDF Downloads 828

Authors: Mansi Patel, Priti Mehta

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Background: Ionizing radiations have detrimental effects on humans, and the growing technological encroachment has increased human exposure to it enormously. So, the safety issues have emphasized researchers to develop radioprotector from natural resources having minimal toxicity. A substance having anti-inflammatory, antioxidant, and immunomodulatory activity can be a potential candidate for radioprotection. One such plant with immense potential i.e. Bamboo was selected for the present study. Purpose: The study aims to evaluate the potential of Indian bamboo leaves for protection against the clastogenic effect of gamma radiation. Methods: The protective effect of bamboo leaf extract against gamma radiation-induced genetic damage in human peripheral blood lymphocytes (HPBLs) was evaluated in vitro using Cytokinesis blocked micronuclei assay (CBMN). The blood samples were pretreated with varying concentration of extract 30 min before the radiation exposure (4Gy & 6Gy). The reduction in the frequency of micronuclei was observed for the irradiated and control groups. The effect of various concentration of bamboo leaf extract (400,600,800 mg/kg) on the development of radiation induced sickness and altered mortality in mice exposed to 8 Gy of whole-body gamma radiation was studied. The developed symptoms were clinically scored by multiple endpoints for 30 days. Results: Treatment of HPBLs with varying concentration of extract before exposure to a different dose of γ- radiation resulted in significant (P < 0.0001) decline of radiation induced micronuclei. It showed dose dependent and concentration driven activity. The maximum protection ~ 70% was achieved at nine µg/ml concentration. Extract treated whole body irradiated mice showed 50%, 83.3% and 100% survival for 400, 600, and 800mg/kg with 1.05, 0.43 and 0 clinical score respectively when compared to Irradiated mice having 6.03 clinical score and 0% survival. Conclusion: Our findings indicate bamboo leaf extract reduced the radiation induced cytogenetic damage. It has also increased the survival ratio and reduced the radiation induced sickness and mortality when exposed to a lethal dose of gamma radiation.

Keywords: bamboo leaf extract, Cytokinesis blocked micronuclei (CBMN) assay, ionizing radiation, radio protector

Procedia PDF Downloads 131

Authors: Dzhavidan Zeinalov, Bugra Sen, Firangiz Aslanova

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Most text-to-speech models cannot operate well in low-resource languages and require a great amount of high-quality training data to be considered good enough. Yet, with the improvements made in ASR systems, it is now much easier than ever to collect data for the design of custom text-to-speech models. In this work, our work on using the ASR model to collect data to build a viable text-to-speech system for one of the leading financial institutions of Azerbaijan will be outlined. NVIDIA’s implementation of the Tacotron 2 model was utilized along with the HiFiGAN vocoder. As for the training, the model was first trained with high-quality audio data collected from the Internet, then fine-tuned on the bank’s single speaker call center data. The results were then evaluated by 50 different listeners and got a mean opinion score of 4.17, displaying that our method is indeed viable. With this, we have successfully designed the first text-to-speech model in Azerbaijani and publicly shared 12 hours of audiobook data for everyone to use.

Keywords: Azerbaijani language, HiFiGAN, Tacotron 2, text-to-speech, transfer learning, whisper

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

Procedia PDF Downloads 405

Authors: Gus Calderon, Richard McCreight, Tammy Schwartz

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Forensic analysis of community wildfire destruction in California has shown that reducing or removing flammable vegetation in proximity to buildings and structures is one of the most important wildfire defenses available to homeowners. State laws specify the requirements for homeowners to create and maintain defensible space around all structures. Unfortunately, this decades-long effort had limited success due to noncompliance and minimal enforcement. As a result, vulnerable communities continue to experience escalating human and economic costs along the wildland-urban interface (WUI). Quantifying vegetative fuels at both the community and parcel scale requires detailed imaging from an aircraft with remote sensing technology to reduce uncertainty. FireWatch has been delivering high spatial resolution (5” ground sample distance) wildfire hazard maps annually to the community of Rancho Santa Fe, CA, since 2019. FireWatch uses a multispectral imaging system mounted onboard an aircraft to create georeferenced orthomosaics and spectral vegetation index maps. Using proprietary algorithms, the vegetation type, condition, and proximity to structures are determined for 1,851 properties in the community. Secondary data processing combines object-based classification of vegetative fuels, assisted by machine learning, to prioritize mitigation strategies within the community. The remote sensing data for the 10 sq. mi. community is divided into parcels and sent to all homeowners in the form of defensible space maps and reports. Follow-up aerial surveys are performed annually using repeat station imaging of fixed GPS locations to address changes in defensible space, vegetation fuel cover, and condition over time. These maps and reports have increased wildfire awareness and mitigation efforts from 40% to over 85% among homeowners in Rancho Santa Fe. To assist homeowners fighting increasing insurance premiums and non-renewals, FireWatch has partnered with Black Swan Analytics, LLC, to leverage the multispectral imagery and increase homeowners’ understanding of wildfire risk drivers. For this study, a subsample of 100 parcels was selected to gain a comprehensive understanding of wildfire risk and the elements which can be mitigated. Geospatial data from FireWatch’s defensible space maps was combined with Black Swan’s patented approach using 39 other risk characteristics into a 4score Report. The 4score Report helps property owners understand risk sources and potential mitigation opportunities by assessing four categories of risk: Fuel sources, ignition sources, susceptibility to loss, and hazards to fire protection efforts (FISH). This study has shown that susceptibility to loss is the category residents and property owners must focus their efforts. The 4score Report also provides a tool to measure the impact of homeowner actions on risk levels over time. Resiliency is the only solution to breaking the cycle of community wildfire destruction and it starts with high-quality data and education.

Keywords: defensible space, geospatial data, multispectral imaging, Rancho Santa Fe, susceptibility to loss, wildfire risk.

Procedia PDF Downloads 93

Authors: Olakanmi Oladayo Olufemi, Bamifewe Olusegun James, Badmus Yaya Opeyemi, Adegoke Kayode

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The wireless sensor network (WSN) has made a significant contribution to the emergence of various intelligent services or cloud-based applications. Most of the time, these data are stored on a cloud platform for efficient management and sharing among different services or users. However, the sensitivity of the data makes them prone to various confidentiality and performance-related attacks during and after harvesting. Various security schemes have been developed to ensure the integrity and confidentiality of the WSNs' data. However, their specificity towards particular attacks and the resource constraint and heterogeneity of WSNs make most of these schemes imperfect. In this paper, we propose a secure variance-aware routing and authentication scheme with two-tier verification to collect, share, and manage WSN data. The scheme is capable of classifying WSN into different subnets, detecting any attempt of wormhole and black hole attack during harvesting, and enforcing access control on the harvested data stored in the cloud. The results of the analysis showed that the proposed scheme has more security functionalities than other related schemes, solves most of the WSNs and cloud security issues, prevents wormhole and black hole attacks, identifies the attackers during data harvesting, and enforces access control on the harvested data stored in the cloud at low computational, storage, and communication overheads.

Keywords: data block, heterogeneous IoT network, data harvesting, wormhole attack, blackhole attack access control

Procedia PDF Downloads 57

Authors: A. Sathiya Susuman, Hamisi F. Hamisi

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Background: Many socio-economic and demographic data are age-sex attributed. However, a variety of irregularities and misstatement are noted with respect to age-related data and less to sex data because of its biological differences between the genders. Noting the misstatement/misreporting of age data regardless of its significance importance in demographics and epidemiological studies, this study aims at assessing the quality of 2012 Tanzania Population and Housing Census Results. Methods: Data for the analysis are downloaded from Tanzania National Bureau of Statistics. Age heaping and digit preference were measured using summary indices viz., Whipple’s index, Myers’ blended index, and Age-Sex Accuracy index. Results: The recorded Whipple’s index for both sexes was 154.43; male has the lowest index of about 152.65 while female has the highest index of about 156.07. For Myers’ blended index, the preferences were at digits ‘0’ and ‘5’ while avoidance were at digits ‘1’ and ‘3’ for both sexes. Finally, Age-sex index stood at 59.8 where sex ratio score was 5.82 and age ratio scores were 20.89 and 21.4 for males and female respectively. Conclusion: The evaluation of the 2012 PHC data using the demographic techniques has qualified the data inaccurate as the results of systematic heaping and digit preferences/avoidances. Thus, innovative methods in data collection along with measuring and minimizing errors using statistical techniques should be used to ensure accuracy of age data.

Keywords: age heaping, digit preference/avoidance, summary indices, Whipple’s index, Myer’s index, age-sex accuracy index

Procedia PDF Downloads 460

Authors: Norshima Humaidi, Saif Hussein Abdallah Alghazo

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Cybersecurity threat have become a serious issue recently, and one of the cause is because human error, which is usually constituted by carelessness, ignorance, and failure to practice cybersecurity behaviour adequately. Using a data from a quantitative survey, Partial Least Squares-Structural Equation Modelling (PLS-SEM) analysis was used to determine the factors that affect cybersecurity protective behaviour (CPB). This study adapts cybersecurity protective behaviour model by focusing on two constructs that can enhance CPB: manager’s information security competencies (MISI) and procedural information security countermeasure (PCM) awareness. Theory of leadership competencies were adapted to measure user’s perception towards competencies among security managers/leader in the organization. Confirmatory factor analysis (CFA) testing shows that all the measurement items of each constructs were adequate in their validity individually based on their factor loading value. Moreover, each constructs are valid based on their parameter estimates and statistical significance. The quantitative research findings show that PCM awareness strongly influences CPB compared to MISI. Meanwhile, MISI was significantlyPCM awarenss. This study believes that the research findings can contribute to human behaviour in IS studies and are particularly beneficial to policy makers in improving organizations’ strategic plans in information security, especially in this new era. Most organizations spend time and resources to provide and establish strategic plans of information security; however, if employees are not willing to comply and practice information security behaviour appropriately, then these efforts are in vain.

Keywords: cybersecurity, protection behaviour, information security, information security competencies, countermeasure awareness

Procedia PDF Downloads 82

Authors: Rebecca A. Deek

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There is evidence that erratic and extreme weather is becoming a common occurrence, and even predictions that this will become even more frequent and more severe. It also appears that the severity of earthquakes is intensifying. Some observers believe that human conduct has given reasons for such change; others attribute this to environmental and geological cycles. However, as some physicists, environmental scientists, politicians, and others continue to debate the connection between weather events, seismic activities, and climate change, other scientists, engineers, and urban planners are exploring how can our habitat become more responsive and resilient to such phenomena. There are a number of recent instances of nature’s destructive events that provide basis for the development of defensive measures.

Keywords: biomimicry, natural disasters, protection of human lives, resilient infrastructures

Procedia PDF Downloads 490

Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

Procedia PDF Downloads 413

Authors: Vikas Kumar

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Portable batteries are reliable source of mobile energy to power smart wearable electronics, medical devices, communications, and others internet of thing (IoT) devices. There is a continuous increase in demand for thinner, more flexible battery with high energy density and reliability to meet the requirement. For a flexible battery, factors that affect these properties are the stability of current collectors, electrode materials and their interfaces with the corrosive electrolytes. State-of-the-art conventional and flexible batteries utilise carbon as an electrode and current collectors which cause high internal resistance (~100 ohms) and limit the peak current to ~1mA. This makes them unsuitable for a wide range of applications. Replacing the carbon parts with metallic components would reduce the internal resistance (and hence reduce parasitic loss), but significantly increases the risk of corrosion due to galvanic interactions within the battery. To overcome these challenges, low cost electroplated nickel (Ni) on copper (Cu) was studied as a potential anode current collector for a zinc-manganese oxide primary battery with different concentration of NH4Cl/ZnCl2 electrolyte. Using electrical impedance spectroscopy (EIS), we monitored the open circuit potential (OCP) of electroplated nickel (different thicknesses) in different concentration of electrolytes to optimise the thickness of Ni coating. Our results show that electroless Ni coating suffer excessive corrosion in these electrolytes. Corrosion rates of Ni coatings for different concentrations of electrolytes have been calculated with Tafel analysis. These results suggest that for electroplated Ni, channelling and/or open porosity is a major issue, which was confirmed by morphological analysis. These channels are an easy pathway for electrolyte to penetrate thorough Ni to corrode the Ni/Cu interface completely. We further investigated the incorporation of a special printed graphene layer on Ni to provide corrosion protection in this corrosive electrolyte medium. We find that the incorporation of printed graphene layer provides the corrosion protection to the Ni and enhances the chemical bonding between the active materials and current collector and also decreases the overall internal resistance of the battery system.

Keywords: corrosion, electrical impedance spectroscopy, flexible battery, graphene, metal current collector

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Authors: Tuna Şahsuvaroğlu

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The concept of disability is a concept that has been the subject of many studies in national and international literature with its social, sociological, political, anthropological, economic and social dimensions as well as with individual and social consequences. A disabled person is defined as a person who has difficulties in adapting to social life and meeting daily needs due to loss of physical, mental, spiritual, sensory and social abilities to various degrees, either from birth or for any reason later, and they are in need of protection, care, rehabilitation, counseling and support services. The industrial revolution and the rapid industrialization it brought with it led to an increase in the rate of disabilities resulting from work accidents, in addition to congenital disabilities. This increase has resulted in disabled people included in the employment policies of nations as a disadvantaged group. Although the participation of disabled individuals in the workforce is of great importance in terms of both increasing their quality of life and their integration with society and although disabled individuals are willing to participate in the workforce, they encounter with many problems. One of these problems is the negative attitudes and prejudices that develop in society towards the employment of disabled individuals. One of the most powerful ways to turn these negative attitudes and prejudices into positive ones is education. Education is a way of guiding societies and transferring existing social characteristics to future generations. This can be maintained thanks to teachers, who are one of the most dynamic parts of society and act as the locomotive of education driven by the need to give direction and transfer and basically to help and teach. For this reason, there is a strong relationship between the teaching profession and the attitudes formed in society towards the employment of disabled individuals, as they can influence each other. Therefore, the purpose of this study is to examine teacher candidates' attitudes towards the employment of disabled individuals in terms of various variables. The participants of the study consist of 665 teacher candidates studying at various departments at Marmara University Faculty of Education in the 2022-2023 academic year. The descriptive survey model of the general survey model was used in this study as it intends to determine the attitudes of teacher candidates towards the employment of disabled individuals in terms of different variables. The Attitude Scale Towards Employment of Disabled People was used to collect data. The data were analyzed according to the variables of age, gender, marital status, the department, and whether there is a disabled relative in the family, and the findings were discussed in the context of further research.

Keywords: teacher candidates, disabled, attitudes towards the employment of disabled people, attitude scale towards the employment of disabled people

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Carlson Murray, Ali Lazrak

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Robust experimental evidence of systematic violations of expected utility (EU) establishes that individuals facing risk overweight utility from low probability gains and losses when making choices. These findings motivated development of models of preferences with probability weighting functions, such as rank dependent utility (RDU). We solve for the optimal investing strategy of an RDU investor in a dynamic binomial setting from which we derive implications for investing behavior. We show that relative to EU investors with constant relative risk aversion, commonly measured probability weighting functions produce optimal RDU terminal wealth with significant downside protection and upside exposure. We additionally find that in contrast to EU investors, RDU investors optimally choose a portfolio that contains fair bets that provide payo↵s that can be interpreted as lottery outcomes or exposure to idiosyncratic returns. In a calibrated version of the model, we calculate that RDU investors would be willing to pay 5% of their initial wealth for the freedom to trade away from an optimal EU wealth allocation. The dynamic trading strategy that supports the optimal wealth allocation implies portfolio weights that are independent of initial wealth but requires higher risky share after good stock return histories. Optimal trading also implies the possibility of non-participation when historical returns are poor. Our model fills a gap in the literature by providing new quantitative and qualitative predictions that can be tested experimentally or using data on household wealth and portfolio choice.

Keywords: behavioral finance, probability weighting, portfolio choice

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

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Authors: Janusz Bobulski, Kamila Pasternak

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Despite many years of effort and research, the problem of waste management is still current. So far, no fully effective waste management system has been developed. Many programs and projects improve statistics on the percentage of waste recycled every year. In these efforts, it is worth using modern Computer Vision techniques supported by artificial intelligence. In the article, we present a method of identifying plastic waste based on the asymmetry analysis of the histogram of the image containing the waste. The method is simple but effective (94%), which allows it to be implemented on devices with low computing power, in particular on microcomputers. Such de-vices will be used both at home and in waste sorting plants.

Keywords: waste management, environmental protection, image processing, computer vision

Procedia PDF Downloads 101

Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

Abstract:

In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

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Authors: Tanapat Chongkamunkong

Abstract:

The objective of this paper is to present a practical use of Geographic Information System (GIS) to the public health from spatial correlation between multiple factors and dengue fever outbreak. Meteorological factors, demographic factors and environmental factors are compiled using GIS techniques along with the Global Satellite Mapping Remote Sensing (RS) data. We use monthly dengue fever cases, population density, precipitation, Digital Elevation Model (DEM) data. The scope cover study area under climate change of the El Niño–Southern Oscillation (ENSO) indicated by sea surface temperature (SST) and study area in 12 provinces of Thailand as remote sensing (RS) data from January 2007 to December 2014.

Keywords: dengue fever, sea surface temperature, Geographic Information System (GIS), remote sensing

Procedia PDF Downloads 180

Authors: Pham Van Nha, Le Cam Binh

Abstract:

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

Procedia PDF Downloads 194

Authors: Pronob Kumar Mozumder M. Nazrul Islam, M. Abdur Rob Mollah

Abstract:

This study was conducted in Cox’s Bazar-Teknaf Peninsula and Sonadia Island Ecologically Critical Areas during the period of October, 2011 to June, 2013. Six species of marine turtle are found in the Indian Ocean. Among them, olive ridley (Lepidochelys olivacea) listed as endangered in the IUCN Red List of Threatened Species. Marine turtle populations in the Indian Ocean have been depleted through long-term exploitation of eggs and adults, incidental capture (fisheries bycatch) and many other sources of mortality. The specific objective of the study was to conserve the sea turtles specially the olive ridley (Lepidochelys olivacea) with a view to contribute towards protection of the turtle species from extinction and to facilitate hatching of eggs through providing protection to turtle eggs or nest through ex-situ conservation efforts. In order to achieve the desired outputs and success, a total of five turtle hatcheries were established at Pechardwip, Khurermukh, Hazompara, Bodormokam, and Sonadia Eastpara sites. In total, 31,853 eggs were collected from 260 nests and were transferred to five hatcheries. The number of eggs/nest varied from 38 to 190 with an average clutch size of 122 eggs/ nest. Hatching of eggs took place during January to June with a peak in April. Sea turtle eggs were incubated by metabolic heat and the heat of the sun. The incubation period of turtle eggs in Cox’s Bazar-Teknaf Peninsula and Sonadia Island ECAs extended from 54 to 75 days depending on the month with an average of 66 days. During study period the temperature in the ECAs varied between 10.5-34.5°C. A total of 27,937 hatchlings of turtle were produced from the five hatcheries and all the hatchlings produced were released into the sea. Hatching rates varied from 74-98 % depending on the location and months with an average of 88 %. Sea turtles spend the majority of their lives in the sea, only emerging on beaches to nest. Despite the intense conservation efforts on the beaches, some populations have still declined to the edge of extinction. So proper conservation and awareness measure should be taken for prevention of turtle extinction.

Keywords: conservation of sea turtle, Bangladesh, ecologically critical area, ECA, Lepidochelys olivacea

Procedia PDF Downloads 491

Authors: Pan Hongxia, Wang Zhenhua

Abstract:

In this paper, the EMD decomposition in the process of endpoint effect adopted data based on AR model to predict the continuation and window function method of combining the two effective inhibition. Proven by simulation of the simulation signal obtained the ideal effect, then, apply this method to the gearbox test data is also achieved good effect in the process, for the analysis of the subsequent data processing to improve the calculation accuracy. In the end, under various working conditions for the gearbox fault diagnosis laid a good foundation.

Keywords: gearbox, fault diagnosis, ar model, end effect

Procedia PDF Downloads 353

Authors: Handie Pramana Putra, Ani Dijah Rahajoe

Abstract:

The proliferation of smart devices and advancements in mobile communication technologies have permeated various facets of life with the widespread influence of e-commerce. Detecting abnormal transactions holds paramount significance in this realm due to the potential for substantial financial losses. Moreover, the fusion of data flow and control flow assumes a critical role in the exploration of process modeling and data analysis, contributing significantly to the accuracy and security of business processes. This paper introduces an alternative approach to identify abnormal transactions through a model that integrates both data and control flows. Referred to as the Extended Data Petri net (DPNE), our model encapsulates the entire process, encompassing user login to the e-commerce platform and concluding with the payment stage, including the mobile transaction process. We scrutinize the model's structure, formulate an algorithm for detecting anomalies in pertinent data, and elucidate the rationale and efficacy of the comprehensive system model. A case study validates the responsive performance of each system component, demonstrating the system's adeptness in evaluating every activity within mobile transactions. Ultimately, the results of anomaly detection are derived through a thorough and comprehensive analysis.

Keywords: database, data analysis, DPNE, extended data flow, e-commerce

Procedia PDF Downloads 38