Search results for: data analyses

Authors: Bouthayna Hayou, Mohamed Mouloud Haddak

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In road accidents in French overseas territories have been understudied, with relevant data often collected late and incompletely. Although these territories account for only 3% to 4% of road traffic injuries in France, their unique characteristics merit closer attention. Despite lower mobility and, consequently, lower exposure to road risks, the actual road risk in Overseas France is as high or even higher than in Metropolitan France. Significant disparities exist not only between Metropolitan France and Overseas territories but also among the overseas territories themselves. The varying population densities in these regions do not fully explain these differences, as each territory has its own distinct vulnerabilities and road safety challenges. This analysis, based on BAAC data files from 2005 to 2018 for both Metropolitan France and the overseas departments and regions, examines key variables such as gender, age, type of road user, type of obstacle hit, type of trip, road category, traffic conditions, weather, and location of accidents. Logistic regression models were built for each region to investigate the risk factors associated with fatal road accidents, focusing on the probability of being killed versus injured. Due to insufficient data, Mayotte and the Overseas Communities (French Polynesia and New Caledonia) were not included in the models. The findings reveal that road safety is worse in the overseas territories compared to Metropolitan France, particularly for vulnerable road users such as pedestrians and motorized two-wheelers. These territories present an accident profile that sits between that of Metropolitan France and middle-income countries. A pressing need exists to standardize accident data collection between Metropolitan and Overseas France to allow for more detailed comparative analyses. Further epidemiological studies could help identify the specific road safety issues unique to each territory, particularly with regard to socio-economic factors such as social cohesion, which may influence road safety outcomes. Moreover, the lack of data on new modes of travel, such as electric scooters, and the absence of socio-economic details of accident victims complicate the evaluation of emerging risk factors. Additional research, including sociological and psychosocial studies, is essential for understanding road users' behavior and perceptions of road risk, which could also provide valuable insights into accident trends in peri-urban areas in France.

Keywords: multivariate logistic regression, overseas France, road safety, road traffic accident, territorial inequalities

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Authors: Sadegh Torfi

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Realization of anticipated energy efficiency from recuperative run-around energy recovery (RER) systems requires identification of the system components influential parameters. Because simulation modeling is considered as an integral part of the design and economic evaluation of RER systems, it is essential to calibrate the developed models and validate the performance predictions by means of comparison with data from experimental measurements. Several theoretical and numerical analyses on RER systems by researchers have been done, but generally the effect of distance between hot and cold flow is ignored. The objective of this study is to develop a thermohydroulic model for a typical RER system that accounts for energy loss from the interconnecting piping and effects of interconnecting pipes length performance of run-around energy recovery systems. Numerical simulation shows that energy loss from the interconnecting piping is change linear with pipes length and if pipes are properly isolated, maximum reduction of effectiveness of RER systems is 2% in typical piping systems.

Keywords: combined heat and power, heat recovery, effectiveness, CGS

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Authors: Emmanuel O. Ezim, Idowu A. Olayinka, Michael Oladunjoye, Izuchukwu I. Obiadi

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Monitoring of reservoir properties prior to well placements and production is a requirement for optimisation and efficient oil and gas production. This is usually done using well log analyses and 3-D seismic, which are often prone to errors. However, 4-D (Time-lapse) seismic, incorporating numerous 3-D seismic surveys of the same field with the same acquisition parameters, which portrays the transient changes in the reservoir due to production effects over time, could be utilised because it generates better resolution. There is, however dearth of information on the applicability of this approach in the Niger Delta. This study was therefore designed to apply 4-D seismic, well-log and geologic data in monitoring of reservoirs in the EK field of the Niger Delta. It aimed at locating bypassed accumulations and ensuring effective reservoir management. The Field (EK) covers an area of about 1200km2 belonging to the early (18ma) Miocene. Data covering two 4-D vintages acquired over a fifteen-year interval were obtained from oil companies operating in the field. The data were analysed to determine the seismic structures, horizons, Well-to-Seismic Tie (WST), and wavelets. Well, logs and production history data from fifteen selected wells were also collected from the Oil companies. Formation evaluation, petrophysical analysis and inversion alongside geological data were undertaken using Petrel, Shell-nDi, Techlog and Jason Software. Well-to-seismic tie, formation evaluation and saturation monitoring using petrophysical and geological data and software were used to find bypassed hydrocarbon prospects. The seismic vintages were interpreted, and the amounts of change in the reservoir were defined by the differences in Acoustic Impedance (AI) inversions of the base and the monitor seismic. AI rock properties were estimated from all the seismic amplitudes using controlled sparse-spike inversion. The estimated rock properties were used to produce AI maps. The structural analysis showed the dominance of NW-SE trending rollover collapsed-crest anticlines in EK with hydrocarbons trapped northwards. There were good ties in wells EK 27, 39. Analysed wavelets revealed consistent amplitude and phase for the WST; hence, a good match between the inverted impedance and the good data. Evidence of large pay thickness, ranging from 2875ms (11420 TVDSS-ft) to about 2965ms, were found around EK 39 well with good yield properties. The comparison between the base of the AI and the current monitor and the generated AI maps revealed zones of untapped hydrocarbons as well as assisted in determining fluids movement. The inverted sections through EK 27, 39 (within 3101 m - 3695 m), indicated depletion in the reservoirs. The extent of the present non-uniform gas-oil contact and oil-water contact movements were from 3554 to 3575 m. The 4-D seismic approach led to better reservoir characterization, well development and the location of deeper and bypassed hydrocarbon reservoirs.

Keywords: reservoir monitoring, 4-D seismic, well placements, petrophysical analysis, Niger delta basin

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: Praveena Sridhar, Nihar Devathi, Parikshit Chakraborty

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As the software product changes versions across releases, there are changes to the API’s and features and the upgrades become necessary. Hence, it becomes imperative to get the impact of upgrading the dependent components. This tool finds out API changes across two versions and their impact on other API’s followed by execution of the automated regression suites relevant to updates and their impacted areas. This tool has 4 layer architecture, each layer with its own unique pre-assigned capability which it does and sends the required information to next layer. This are the 4 layers. 1) Comparator: Compares the two versions of API. 2) Analyzer: Analyses the API doc and gives the modified class and its dependencies along with implemented interface details. 3) Impact Filter: Find the impact of the modified class on the other API methods. 4) Auto Executer: Based on the output given by Impact Filter, Executor will run the API regression Suite. Tool reads the java doc and extracts the required information of classes, interfaces and enumerations. The extracted information is saved into a data structure which shows the class details and its dependencies along with interfaces and enumerations that are listed in the java doc.

Keywords: automation impact regression, java doc, executor, analyzer, layers

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Authors: Ann Vibert, Heather Hemming

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This paper presentation summarizes the findings and implications of research on the plight and practices of women educational leaders in public school systems and in one university. The authors, both women university administrators, are also scholars and researchers of education. The research project on which this paper presentation is based proposed to examine how women educational leaders imagined, experienced, and carried out their leadership roles in the context of a growing local and global accountability-based performativity discourse which is reshaping educational work especially for women, we argue, in both public school and post-secondary sites. The research employed critical ethnographic interviews with 20 women educational leaders in P-12 school systems and three women university level educational leaders. Data were collected on women educational leaders’ perceptions of the effects of accountability and performativity discourses on the nature of their work. Specifically, leaders were asked to speak to whether they experienced a growth in managerial work as a consequence of increased accountability demands; how they experienced their work changing as a consequence of accountability and performativity demands; how these changes impacted the central values they enacted in their work as women educational leaders changes; and how they responded to/negotiated/accommodated changes in the nature of their work developing as a consequence of accountability and performativity frameworks. Findings from the research data and analyses confirm and extend recent scholarly work on the gendered nature of performativity and accountability discourses and frameworks, and their differential effects across differing genders. The oral presentation we propose here focusses on those findings in terms of similarities for women educational leaders across different educational contexts.

Keywords: women in educational leadership, gender and educational performativity, accountability and women leaders, gender and educational leadership

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Michaels Aibangbee, Sowbhagya Micheal, Pranee Liamputtong, Elias Mpofu, Tinashe Dune

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Background: The challenges migrants and refugee youth (MRY) experience in maintaining their sexual and reproductive health and rights (SRHR) continues to be a global public health issue. Consequently, MRY is more likely to encounter adverse SRH experiences due to limited access to and knowledge of SRH services. Using a socio-ecological framework, this study examined the MRY’s SRHR micro-level experiences to macro-levels analyses of SRH-related social systems and constructions. Methods: Eighteen focus groups were conducted using participatory action research (PAR) methodology to understand the phenomena. The focus groups included MRY participants (ages 16-26) living in Greater Western Sydney and facilitated by youth project liaisons (YPL). The data was afterward synthesised and analysed using the thematic-synthesis method. Results: In total, 86 MRY (male n= 25, female n= 61) MRY (across 20 different cultural backgrounds) participated in the focus groups. The findings identified socio-ecological factors characterising MRY SRHR, highlighting facilitators such as social media and significant barriers such as lack of access to services and socio-cultural dissonance, and the under-implementation of SRHR support and services by MRY. Key themes from the data included traditional and institutional stigma, lack of SRH education, high reliance on social media for SRH information, anonymity, and privacy concerns. Conclusion: The data shows a limited extent to which MRY SRHR is considered and the intergenerational understanding and stigma affecting the rights of MRY. Therefore, these findings suggest a need for policies and practices to empower MRY’s agency through a collaborative SRHR strategy and policy design to maintain relevance in multicultural contexts.

Keywords: migrant and refugee youth, sexual health, reproductive health, sexual and reproductive health and rights, culture, agency

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Authors: Umit Duru

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The Soil and Water Assessment Tool (SWAT) was tested for prediction of water balance and sediment yield in the Ankara gauged basin, Turkey. The overall objective of this study was to evaluate the performance and applicability of the SWAT in this region of Turkey. Thirteen years of monthly stream flow, and suspended sediment, data were used for calibration and validation. This research assessed model performance based on differences between observed and predicted suspended sediment yield during calibration (1987-1996) and validation (1982-1984) periods. Statistical comparisons of suspended sediment produced values for NSE (Nash Sutcliffe efficiency), RE (relative error), and R² (coefficient of determination), of 0.81, -1.55, and 0.93, respectively, during the calibration period, and NSE, RE (%), and R² of 0.77, -2.61, and 0.87, respectively, during the validation period. Based on the analyses, SWAT satisfactorily simulated observed hydrology and sediment yields and can be used as a tool in decision making for water resources planning and management in the basin.

Keywords: calibration, GIS, sediment yield, SWAT, validation

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Authors: José Carlos Rodríguez, Mario Gómez

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This paper analyses innovation trends in Latin America countries by means of the number of patent applications filed by residents and non-residents during the period 1965 to 2012. Making use of patent data released by the World Intellectual Property Organization (WIPO), we search for the presence of multiple structural changes in patent application series in Argentina, Brazil Chile, and Mexico. These changes may suggest that firms’ innovative activity has been modified as a result of implementing a particular science, technology and innovation (STI) policy. Accordingly, the new regulations implemented in these countries during 1980s and 1990s have influenced their intellectual property regimes. The question conducting this research is thus how STI policies in these countries have affected their innovation activity? The results achieved in this research confirm the existence of multiple structural changes in the series of patent applications resulting from STI policies implemented in these countries.

Keywords: econometric methods, innovation activity, Latin America countries, patents, science, technology and innovation policy

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Authors: Mingbao Chen, Mingli Zhao

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The coastal zone is the intersection of land and sea system, and also is the connecting zone of the two economic systems of land and sea. In the world, all countries attach great importance to the coastal zone management and the coastal zone economy. In China, the government has developed a number of related coastal management policies and institutional, such as marine functional zoning, main function zoning, integrated coastal zone management, to ensure the sustainable utilization of the coastal zone and promote the development of coastal economic. However, in practice, the effect is not satisfactory. This paper analyses the coastal areas of coastal zone management on coastal economic growth contribution based on coastal areas economic development data with the 2007-2015 in China, which uses the method of the evaluation index system of coastal zone management institutional efficiency. The results show that the coastal zone management institutional objectives are not clear, and the institutional has high repeatability. At the same time, over management of coastal zone leads to low economic efficiency because the government management boundary is blurred.

Keywords: institutional overlap, over management, coastal zone management, coastal zone economy

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Authors: Melek Akay

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Turkey's agricultural commodity prices are prone to fluctuation but have gradually over time. A considerable amount of literature examines the changes in these prices by dealing with other commodities such as energy. Links between agricultural and energy markets have therefore been extensively investigated. Since red meat prices are becoming increasingly volatile in Turkey, this paper analyses the price volatility of veal, lamb and the relationship between red meat and crude oil, exchange rates by applying the generalize all period unconstraint volatility model, which generalises the GARCH (p, q) model for analysing weekly data covering a period of May 2006 to February 2017. Empirical results show that veal and lamb prices present volatility during the last decade, but particularly between 2009 and 2012. Moreover, oil prices have a significant effect on veal and lamb prices as well as their previous periods. Consequently, our research can lead policy makers to evaluate policy implementation in the appropriate way and reduce the impacts of oil prices by supporting producers.

Keywords: red meat price, volatility, crude oil, exchange rates, GARCH models, Turkey

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Authors: Arda Ozturk, Turgay Ozgur, Mursit Aksoy, Bahar O. Ozgur, Ozan Yilmaz

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The purpose of this study was to investigate the effect of 8 weeks of basic basketball and volleyball exercises to Bodily-Kinesthetic Intelligence (BKI) levels in 245 (92 girls and 154 boys) adolescents aged between 12 and 14 years. Data collected via Bodily-Kinesthetic Intelligence scale as a subdimension of Multiple Intelligences Inventory. BKI levels were not different between basketball and volleyball groups. Statistical analyses were made based on gender, age groups (12, 13, 14 years) and exercise type. Independent samples t-test revealed that there was no significant difference between boy’s and girl’s BKI levels. One way ANOVA test revealed that there was significant difference between age group’s (12, 13, 14) BKI levels in post-test. However, Paired samples t-test revealed no significant differences between pre-post test results of adolescent’s BKI levels. In conclusion, despite the relatively long-term (8 weeks) physical activity. BKI levels have not shown significant differences.

Keywords: bodily-kinesthetic intelligence, adolescent, basketball, volleyball

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Authors: Inas Hussein

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Communication in the modern world is increasingly becoming multimodal due to globalization and the digital space we live in which have remarkably affected how people communicate. Accordingly, Multimodal Discourse Analysis (MDA) is an emerging paradigm in discourse studies with the underlying assumption that other semiotic resources such as images, colours, scientific symbolism, gestures, actions, music and sound, etc. combine with language in order to  communicate meaning. One of the effective multimodal media that combines both verbal and non-verbal elements to create meaning is political cartoons. Furthermore, since political and social issues are mirrored in political cartoons, these are regarded as potential objects of discourse analysis since they not only reflect the thoughts of the public but they also have the power to influence them. The aim of this paper is to analyze some selected cartoons on the recognition of Jerusalem as Israel's capital by the American President, Donald Trump, adopting a multimodal approach. More specifically, the present research examines how the various semiotic tools and resources utilized by the cartoonists function in projecting the intended meaning. Ten political cartoons, among a surge of editorial cartoons highlighted by the Anti-Defamation League (ADL) - an international Jewish non-governmental organization based in the United States - as publications in different Arabic-language newspapers in Egypt, Saudi Arabia, UAE, Oman, Iran and UK, were purposively selected for semiotic analysis. These editorial cartoons, all published during 6^th–18^th December 2017, invariably suggest one theme: Jewish and Israeli domination of the United States. The data were analyzed using the framework of Visual Social Semiotics. In accordance with this methodological framework, the selected visual compositions were analyzed in terms of three aspects of meaning: representational, interactive and compositional. In analyzing the selected cartoons, an interpretative approach is being adopted. This approach prioritizes depth to breadth and enables insightful analyses of the chosen cartoons. The findings of the study reveal that semiotic resources are key elements of political cartoons due to the inherent political communication they convey. It is proved that adequate interpretation of the three aspects of meaning is a prerequisite for understanding the intended meaning of political cartoons. It is recommended that further research should be conducted to provide more insightful analyses of political cartoons from a multimodal perspective.

Keywords: Multimodal Discourse Analysis (MDA), multimodal text, political cartoons, visual modality

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Authors: Muhammad Umar Kibiya

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The study aims to examine whether regulatory changes have an impact on the financial reporting quality of Microfinance Banks in Nigeria. The research employed a panel data analysis technique, using data from 2018 to 2022. The sample includes 72 microfinance banks, using regression analyses to examine the relationship between variables. The findings indicate that Board Chairmanship has a positive and significant effect on financial reporting quality. It also reveals that share ownership has a negative and significant impact on financial reporting quality. The results suggest that regulatory changes have a positive and significant influence on financial reporting quality. Thus, findings have important implications for microfinance banks in Nigeria. It suggests that having a strong and competent board chairperson can enhance financial reporting quality, leading to more transparent and reliable information for stakeholders. Furthermore, the study highlights the importance of regulatory changes in improving financial reporting practices in the microfinance banking sector. The study contributes to the extant literature by providing empirical evidence on the relationship between board chairmanship, share ownership, financial reporting quality, and regulatory changes in microfinance banks. It further supports the concept that governance mechanisms and regulatory reforms play a crucial role in ensuring transparency and accountability within the microfinance banking sector. It recommends that microfinance banks should appoint experienced and qualified individuals as board chairpersons to enhance financial reporting quality. Furthermore, policymakers and regulatory authorities should continue to implement and enforce regulations that promote transparent financial reporting practices in microfinance banks.

Keywords: board chairman, share ownership, financial reporting quality, microfinance, regulatory changes

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Authors: Bernard Moeketsi Hlalele

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Drought is a complicated natural phenomenon that creates significant economic, social, and environmental problems. An analysis of paleoclimatic data indicates that severe and extended droughts are inevitable part of natural climatic circle. This study characterised drought in Lejweleputswa using both Standardised Precipitation Index (SPI) and neural networks (NN) to quantify and predict respectively. Monthly 37-year long time series precipitation data were obtained from online NASA database. Prior to the final analysis, this dataset was checked for outliers using SPSS. Outliers were removed and replaced by Expectation Maximum algorithm from SPSS. This was followed by both homogeneity and stationarity tests to ensure non-spurious results. A non-parametric Mann Kendall's test was used to detect monotonic trends present in the dataset. Two temporal scales SPI-3 and SPI-12 corresponding to agricultural and hydrological drought events showed statistically decreasing trends with p-value = 0.0006 and 4.9 x 10⁻⁷, respectively. The study area has been plagued with severe drought events on SPI-3, while on SPI-12, it showed approximately a 20-year circle. The concluded the analyses with a seasonal analysis that showed no significant trend patterns, and as such NN was used to predict possible SPI-3 for the last season of 2018/2019 and four seasons for 2020. The predicted drought intensities ranged from mild to extreme drought events to come. It is therefore recommended that farmers, agri-business owners, and other relevant stakeholders' resort to drought resistant crops as means of adaption.

Keywords: drought, risk, neural networks, agri-businesses, project, Lejweleputswa

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Authors: H. R. Vosoughifar, Seyedeh Zeinab. Hosseininejad, Nahid Shabazi, Seyed Mohialdin Hosseininejad

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In recent years, structure designers advocate further application of energy absorption devices for lateral loads damping. The Un-bonded Braced Frame (UBF) system is one of the efficient damping systems, which is made of a smart combination of steel and concrete or mortar. In this system, steel bears the earthquake-induced axial force as compressive or tension forces without loss of strength. Concrete or mortar around the steel core acts as a constraint for brace and prevents brace buckling during seismic axial load. In this study, the optimal bracing system in the high-rise structures has been evaluated considering the seismic stress distribution in the connections. An actual 18-story structure was modeled using the proper Finite Element (FE) software where braced with UBF, Eccentrically Braced Frames (EBF) and Concentrically Braced Frame (CBF) systems. Nonlinear static pushover and time-history analyses are then performed so that the acquired results demonstrate that the UBF system reduces drift values in the high-rise buildings. Further statistical analyses show that there is a significant difference between the drift values of UBF system compared with those resulted from the EBF and CBF systems. Hence, the seismic stress distribution in the connections of the proposed structure which braced with UBF system was investigated.

Keywords: optimal bracing system, high-rise structure, finite element analysis (FEA), seismic stress

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Authors: Ibrahim Nadeem, Mohammed Rahman, Yasser Ad-Dab’Bagh, Mahmood Akhtar

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Depression is one of the most prevalent mental illnesses and is often associated with various other medical disorders. In this review, we aim to evaluate existing systematic reviews that investigate the use of probiotics as a treatment for depressive symptoms. Five online databases were searched for relevant studies up to December 2017. Systematic reviews that included randomized controlled trials assessing the efficacy of probiotics in the treatment of depressive symptoms were included. Seven systematic reviews met the inclusion criteria. Three of these reviews conducted meta-analyses, out of which, two found probiotics to significantly improve depressive symptoms in the sample population. Two meta-analyses conducted subgroup analysis based on health status, and both found probiotics to significantly decrease depressive symptoms in patients with major depressive disorder, but only one review found it to significantly decrease in healthy patients. Another subgroup analysis was conducted based on age, and found probiotics to produce significant effects on subjects under the age of 60, but close to no effect on patients over the age of 65. Out of the four reviews that conducted qualitative analysis, three reviews concluded that probiotics have the potential to be used as a treatment. Due to the differences in clinical trials, a definitive effect of probiotics on depressive symptoms cannot be concluded. Nonetheless, probiotics seem to produce a significant therapeutic effect for subjects with pre-existing depressive symptoms. Further studies are warranted for definitive conclusions.

Keywords: depression, gut-brain axis, gut microbiota, probiotic, psychobiotic

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Velimir Monty Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Sagir Abubakar

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Organization must move along with the employees, because organization should adapt itself to the changing environment. The paper examines the effect of training and development on employee performance. Training and development has an important role in improve the performance, skills and attitude of employee in an organization. Training and development will also help an employee to do his present job or to prepare him for a higher position with increased responsibilities. The paper analyses the employee performance towards training and development conducted in some selected banks within Bauchi metropolis. Review of related literature was done on, training, training objectives, methods and development and its method. A census survey was carried out using staff of GTB and Skye Banks Bauchi branch where a total of 40 questionnaires were administered personally by the researcher and there were 100% responses. Correlation analysis was adopted for the analysis of data collected. The study concludes that 95% of respondents agreed that training and development are vital for both employee and organizations performance. They also suggest that training and development should be made compulsory for all categories of employee in an organization. Training and Development programmes are necessary in any organization for improving the quality of work of the employee.

Keywords: training, development, employee, performance, banks

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Authors: Christopher Muhawe, Masooda Bashir

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With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.

Keywords: data breach and misuse, economic harms, privacy harms, psychological harms

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Authors: Brenda Ravenscroft, Ulemu Luhanga, Bev King

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A comprehensive evaluation of online learning needs to include a blend of educational design, technology use, and online instructional practices that integrate technology appropriately for developing and delivering quality online courses. Research shows that classroom-based evaluation tools do not adequately capture the dynamic relationships between content, pedagogy, and technology in online courses. Furthermore, studies suggest that using classroom evaluations for online courses yields lower than normal scores for instructors, and may affect faculty negatively in terms of administrative decisions. In 2014, the Faculty of Arts and Science at Queen’s University responded to this evidence by seeking an alternative to the university-mandated evaluation tool, which is designed for classroom learning. The Faculty is deeply engaged in e-learning, offering large variety of online courses and programs in the sciences, social sciences, humanities and arts. This paper describes the process by which a new student survey instrument for online courses was developed and piloted, the methods used to analyze the data, and the ways in which the instrument was subsequently adapted based on the results. It concludes with a critical reflection on the challenges of evaluating e-learning. The Student Evaluation of Online Teaching Effectiveness (SEOTE), developed by Arthur W. Bangert in 2004 to assess constructivist-compatible online teaching practices, provided the starting point. Modifications were made in order to allow the instrument to serve the two functions required by the university: student survey results provide the instructor with feedback to enhance their teaching, and also provide the institution with evidence of teaching quality in personnel processes. Changes were therefore made to the SEOTE to distinguish more clearly between evaluation of the instructor’s teaching and evaluation of the course design, since, in the online environment, the instructor is not necessarily the course designer. After the first pilot phase, involving 35 courses, the results were analyzed using Stobart's validity framework as a guide. This process included statistical analyses of the data to test for reliability and validity, student and instructor focus groups to ascertain the tool’s usefulness in terms of the feedback it provided, and an assessment of the utility of the results by the Faculty’s e-learning unit responsible for supporting online course design. A set of recommendations led to further modifications to the survey instrument prior to a second pilot phase involving 19 courses. Following the second pilot, statistical analyses were repeated, and more focus groups were used, this time involving deans and other decision makers to determine the usefulness of the survey results in personnel processes. As a result of this inclusive process and robust analysis, the modified SEOTE instrument is currently being considered for adoption as the standard evaluation tool for all online courses at the university. Audience members at this presentation will be stimulated to consider factors that differentiate effective evaluation of online courses from classroom-based teaching. They will gain insight into strategies for introducing a new evaluation tool in a unionized institutional environment, and methodologies for evaluating the tool itself.

Keywords: evaluation, online courses, student survey, teaching effectiveness

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Chuping Lo

Abstract:

This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

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Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: R Palson Kennedy, P.Kiran Sai

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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

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Authors: Mohammad Mukarram, M. Masroor A. Khan, Daniel Kurjak, Marek Fabrika

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Lemongrass (Cymbopogon flexuosus (Steud.) Wats) is an aromatic grass with great industrial potential. It is cultivated for its essential oil (EO), which has great economic value due to its numerous medicinal, cosmetic, and culinary applications. The present study had the goal to evaluate whether the combined application of silicon nanoparticles (SiNPs) 150 mg L⁻¹ and irradiated chitosan (ICH) 120 mg L⁻¹ can upgrade lemongrass crop and render enhanced growth and productivity. The analyses of growth and photosynthetic parameters, leaf-nitrogen, and reactive oxygen species metabolism, as well as the content of total essential oil, indicated that combined foliar sprays of SiNPs and ICH can significantly (p≤0.05) trigger a general activation of lemongrass metabolism. Overall, the data indicate that concomitant SiNPs and ICH application elicit lemongrass physiology and defence system, and opens new possibilities for their biotechnological application on other related plant species with agronomic potential.

Keywords: photosynthesis, Cymbopogon, antioxidant metabolism, essential oil, ROS, nanoparticles, polysaccharides

Procedia PDF Downloads 81