Search results for: support sector machine

Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Babatunde Joel Todowede

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The challenges of nation building and sustainable development have continued to feature prominently in the general reckoning of problems of underdevelopment in the developing countries of the world. Thus, since the attainment of political independence from the British colonial administration in 1960, one of the critical thrusts of central governance in Nigeria has been the particular policy attention of the educational sector. Of course, education is perceived as the logical bridge between the two contrasting worlds of underdevelopment and development, hence, its fundamental importance. The various public policies and practices associated with the Nigerian educational sector are specifically elaborated and critically assessed in this paper. In the final analysis, it is concluded that the educational sector should be better configured and managed in ways that the wider challenges of nation-building and sustainable development are effectively tractable.

Keywords: Nigeria education, educational need, educational plans and policies, educational challenges, corrective measures, emerging economy

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Authors: Maria Granados

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Information technology (IT) facilitates the management of knowledge in organisations through the effective leverage of collective experience and knowledge of employees. This supports information processing needs, as well as enables and facilitates sense-making activities of knowledge workers. The study of IT support for knowledge management (KM) has been carried out mainly in larger organisations where resources and competitive conditions can trigger the use of KM. However, there is still a lack of understanding on how IT can support the management of knowledge under different organisational settings influenced by: constant tensions between social and economic objectives, more focus on sustainability than competiveness, limited resources, and high levels of democratic participation and intrinsic motivations among employees. All these conditions are presented in Social Enterprises (SEs), which are normally micro and small businesses that trade to tackle social problems, improve communities, people’s life chances, and the environment. Thus, their importance to society and economies is increasing. However, there is still a need for more understanding of how these organisations operate, perform, innovate and scale-up. This knowledge is crucial to design and provide accurate strategies to enhance the sector and increase its impact and coverage. To obtain a conceptual and empirical understanding of how IT can facilitate KM in the particular organisational conditions of SEs, a quantitative study was conducted with 432 owners and senior members of SEs in UK, underpinned by 21 interviews. The findings demonstrated how IT was supporting more the recovery and storage of necessary information in SEs, and less the collaborative work and communication among enterprise members. However, it was established that SEs were using cloud solutions, web 2.0 tools, Skype and centralised shared servers to manage informally their knowledge. The possible impediments for SEs to support themselves more on IT solutions can be linked mainly to economic and human constraints. These findings elucidate new perspectives that can contribute not only to SEs and SE supporters, but also to other businesses.

Keywords: social enterprises, knowledge management, information technology, collaboration, small firms

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Authors: Hongxia Wang, Yanjie Wang, Xiuqin Wang, Linlin Mo, Shuangshuang Niu

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Special Education Resource Center (SERC) is the localized product in the development of inclusive education in People’s Republic of China, which provides professional support and service for the students with special education needs(SEN) and their parents, teachers as well as inclusive schools. The study investigated 155 administrators, resource teachers and inclusive education teachers from primary and secondary schools in Beijing. The results indicate that: (1) The surveyed teachers put highest expectation of SERC on specialized guidance and teacher training , instead of research and administration function; (2) Each dimension of professional support needs gets higher scores, in which individual guidance gets highest score, followed by instruction guidance, psychological counseling, proposing suggestions, informational support and teacher training; (3) locality and training experience of surveyed teachers significantly influence their expectations and support needs of SERC.

Keywords: special education resource center (SERC) , functional expectation, professional support needs, support system

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: José Luis Gallizo, Pilar Gargallo, Ramon Saladrigues, Manuel Salvador

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This study offers an exploratory statistical analysis of the persistence of annual profits across a sample of firms from different European Union (EU) countries. To this end, a hierarchical Bayesian dynamic model has been used which enables the annual behaviour of those profits to be broken down into a permanent structural and a transitory component, while also distinguishing between general effects affecting the industry as a whole to which each firm belongs and specific effects affecting each firm in particular. This breakdown enables the relative importance of those fundamental components to be more accurately evaluated by country and sector. Furthermore, Bayesian approach allows for testing different hypotheses about the homogeneity of the behaviour of the above components with respect to the sector and the country where the firm develops its activity. The data analysed come from a sample of 23,293 firms in EU countries selected from the AMADEUS data-base. The period analysed ran from 1999 to 2007 and 21 sectors were analysed, chosen in such a way that there was a sufficiently large number of firms in each country sector combination for the industry effects to be estimated accurately enough for meaningful comparisons to be made by sector and country. The analysis has been conducted by sector and by country from a Bayesian perspective, thus making the study more flexible and realistic since the estimates obtained do not depend on asymptotic results. In general terms, the study finds that, although the industry effects are significant, more important are the firm specific effects. That importance varies depending on the sector or the country in which the firm carries out its activity. The influence of firm effects accounts for around 81% of total variation and display a significantly lower degree of persistence, with adjustment speeds oscillating around 34%. However, this pattern is not homogeneous but depends on the sector and country analysed. Industry effects depends also on sector and country analysed have a more marginal importance, being significantly more persistent, with adjustment speeds oscillating around 7-8% with this degree of persistence being very similar for most of sectors and countries analysed.

Keywords: dynamic models, Bayesian inference, MCMC, abnormal returns, persistence of profits, return on assets

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Authors: Ryan Messina, Mehedi Hasan

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This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

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Authors: Shael Brown, Reza Farivar

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Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information.

Keywords: machine learning, persistence diagrams, R, statistical inference

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Authors: Pao-Peng Hsu, Ying-Hsiu Chen

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Historical data shows that there were high correlations among TAIEX Futures, Electronic Sector Index Futures, Finance Sector Index Futures and Taiwan Top 50 ETF. The performance under various futures is also discussed. We found that the worst portfolio is consisted of T50-ETF and T50-ETF futures and best portfolio is consisted of T50-ETF and TF. It implies that the annual return of a portfolio increases if a portfolio’s risk diversifies.

Keywords: arbitrage opportunities, ETF, futures, TAIEX

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Authors: Ayşe Coban, Orhan Coban, Haldun Soydal, Sükrü Sürücü

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While health is a source of prosperity for individuals, it is also one of the most important determinants of economic growth for a country. Health, by increasing the productivity of labor, contributes to economic growth. Therefore, countries should give the necessary emphasis to health services. The primary aim of this study is to analyze the changes occurring in health services in Turkey by examining the developments in the sector. In this scope, the second aim of the study is to reveal the place of health expenditures in the Turkish economy. As a result of the analysis in the dataset, in which the 1999-2013 periods is considered, it was determined that some increase in health expenditures took place and that the increase in the share of health expenditures in GDP was too small. Furthermore, analysis of the results points out that in financing health expenditures, the public sector is prominent compared to the private sector.

Keywords: health, health service, health expenditures, Turkey

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Authors: Maitham Al-Safwani

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Motor vibration issues were analyzed by several studies. It is generally accepted that vibration issues result from poor equipment installation. We had a water injection pump tested in the factory and exceeded the pump the vibration limit. Once the pump was brought to the site, its half-size shim plates were replaced with full-size shims plates that drastically reduced the vibration. In this study, vibration data was recorded for several similar motors run at the same and different speeds. The vibration values were recorded -for two and a half hours- and the vibration readings were analyzed to determine when the readings became consistent. This was as well supported by recording the audio noises produced by some machines seeking a relationship between changes in machine noises and machine abnormalities, such as vibration.

Keywords: vibration, noise, installation, machine

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Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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Authors: Newton Muhury, Armando A. Apan, Tek Maraseni

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This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Ofra Walter Btel Liran Hazan

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This study’s goal was to clarify whether psychological capital (PsyCap) mediated the relations between social support and subjective well-being among post-secondary students during the Covid-19 pandemic and to assess whether students diagnosed with a learning disability (LD) and/or attention deficit hyperactivity disorder (ADHD) differed from others in their reliance on social support and their level of PsyCap and subjective wellbeing. Participants were257 students, 152 diagnosed with LD/ADHD and the rest neurotypical. The study used four questionnaires: demographic and academic information; Psychological Capital Questionnaire (PCQ); Subjective Well-Being Index; social support questionnaire. The results indicated PsyCapmediated relations between social support and subjective wellbeing. Students diagnosed with LD/ADHD differed from neurotypicals in their PsyCap and subjective wellbeing levels but not in their social support. In addition, the relations between PsyCap and social support were stronger among students diagnosed with LD/ADHD. PsyCap was an important resource for all participants and was related to social support and subjective wellbeing, making it especially valuable for LD/ADHD students facing new and threatening situations, such as the Covid-19 pandemic.

Keywords: LD/ADHD post-secondary students, subjective wellbeing, social support, PsyCap, covid-19

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Authors: Sajed Yaghoubnezhad, Mina Karimi, Seyede Marjan Modirkhazeni

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The goal of this research was to study the relationship between codependency, perceived social support and depression in mothers of children with intellectual disability (ID). The correlational method was used in this study. The research population is comprised of mothers of educable children with ID in the age range of 25 to 61 years. From among this, a sample of 251 individuals, in the multistage cluster sampling method, was selected from educational districts in Tehran, who responded to the Spann-Fischer Codependency Scale (SFCDS), the Social Support Questionnaire and the Beck Depression Inventory (BDI). The findings of this study indicate that among mothers of children with ID depression has a positive and significant correlation with codependency (P<0.01, r=0.4) and a negative and significant correlation with the total score of social support (P<0.01, r=-0.34). Moreover, the results of stepwise multiple regression analysis showed that codependency is allocated a higher variance than social support in explaining depression (R²=0.023).

Keywords: codependency, social support, depression, mothers of children with ID

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Pedro Paulino

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The majority of the world’s population is already living in urban areas and the urban population is expected to keep increasing in the next decades. This exponential increase in urban population carries with it obvious mobility problems. Not only a new paradigm in the transport sector is needed in order to address these problems; effective regulatory policies to ensure the quality of services, passenger rights, competition between operators and consistency of the entire mobile ecosystem are needed as well. The purpose of this paper is to present the problems the world faces in this sector and contribute to their solution. Indeed, our study concludes that only through the active supervision of the markets and the activity of monitoring the various operators will it be possible to develop a sustainable and efficient transport system which meets the needs of a changing world.

Keywords: mobility, regulation policies, sanctioning powers, sustainable transport

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Authors: Norah Alshayhan

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Dealing with public problems is one of the struggles that may face the leaders in the public sector. Collaborative leadership is one of the most important approaches in dealing with difficult situations that affect both public, private, and nonprofit organizations. Current literature does not show exactly the extent of utilizing collaborative leadership during the post-COVID-19 world in Saudi Arabia. This study is worth exploring in order to examine collaborative leadership in similar environments. This research will utilize both integrative public leadership and transformational leadership theories to guide the researcher in answering the research question. The researcher utilizes content analysis and reviews government documents, plans, and reports to gain more information about collaborative leadership in Saudi Arabia. The researcher analyzes the data in themes and sub-themes to categorize the data in answering the research question. Leader’s behavior and performance in the public sector will be the focus of this study. Findings from this research will benefit leaders in both public, private, and nonprofit sectors in their leadership during a post-disaster time. Findings from this study support collaborative leadership practices and performance in leading future post-crises/disasters.

Keywords: collaborative leadership, post-COVID-19, Saudi Arabia, performance, skills

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Authors: Dejen Alemu, Murray E. Jennex, Temtim Assefa

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Agriculture is the mainstay of the Ethiopian economy; however, the sector is dominated by smallholder farmers resulting in land fragmentation and suffering from low productivity. Due to these issues, much effort has been put into the transformation of the sector to bring about more sustainable rural economic development. Technological advancements have been applied for the betterment of farmers resulting in the design of tools that are potentially capable of supporting the agricultural sector; however, their use and relevance are still alien to the local rural communities. The notion of the creating, capturing and sharing of knowledge has also been repetitively raised by many international donor agencies to transform the sector, yet the most current approaches to knowledge dissemination focus on knowledge that originates from the western view of scientific rationality while overlooking the role of indigenous knowledge (IK). Therefore, in agricultural knowledge management system (KMS) development, the integration of IKS with scientific knowledge is a critical success factor. The present study aims to contribute in the discourse on how to best integrate scientific and IK in agricultural KMS development. The conceptual framework of the research is anchored in concepts drawn from the theory of situated learning in communities of practice (CoPs): knowledge brokering. Using the KMS development practices of Ethiopian agricultural transformation agency as a case area, this research employed an interpretive analysis using primary and secondary qualitative data acquired through in-depth semi-structured interviews and participatory observations. As a result, concepts are identified for understanding the integration of the two major knowledge systems (i.e., indigenous and scientific knowledge) and participation of relevant stakeholders in particular the local farmers in agricultural KMS development through the roles of extension agent as a knowledge broker including crossing boundaries, in-between position, translation and interpretation, negotiation, and networking. The research shall have a theoretical contribution in addressing the incorporation of a variety of knowledge systems in agriculture and practically to provide insight for policy makers in agriculture regarding the importance of IK integration in agricultural KMS development and support marginalized small-scale farmers.

Keywords: communities of practice, indigenous knowledge, knowledge management system development, knowledge brokering

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Authors: İbrahi̇m Kara, Seher Arslankaya

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

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

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Authors: Mais Khader, Xingjie Wei

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This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management.

Keywords: company survival, entrepreneurship, females, machine learning, SMEs

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Authors: Ramazan Bakir, Gizem Kayar

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It is known that Alzheimer and Dementia are the two most common types of Neurodegenerative diseases and their visibility is getting accelerated for the last couple of years. As the population sees older ages all over the world, researchers expect to see the rate of this acceleration much higher. However, unfortunately, there is no known pharmacological cure for both, although some help to reduce the rate of cognitive decline speed. This is why we encounter with non-pharmacological treatment and tracking methods more for the last five years. Many researchers, including well-known associations and hospitals, lean towards using non-pharmacological methods to support cognitive function and improve the patient’s life quality. As the dementia symptoms related to mind, learning, memory, speaking, problem-solving, social abilities and daily activities gradually worsen over the years, many researchers know that cognitive support should start from the very beginning of the symptoms in order to slow down the decline. At this point, life of a patient and caregiver can be improved with some daily activities and applications. These activities include but not limited to basic word puzzles, daily cleaning activities, taking notes. Later, these activities and their results should be observed carefully and it is only possible during patient/caregiver and M.D. in-person meetings in hospitals. These meetings can be quite time-consuming, exhausting and financially ineffective for hospitals, medical doctors, caregivers and especially for patients. On the other hand, digital support systems are showing positive results for all stakeholders of healthcare systems. This can be observed in countries that started Telemedicine systems. The biggest potential of our system is setting the inter-user communication up in the best possible way. In our project, we propose Machine Learning based digitalization of validated traditional cognitive tests (e.g. MOCA, Afazi, left-right hemisphere), their analyses for high-quality follow-up and communication systems for all stakeholders. R. Bakir and G. Kayar are with Gefeasoft, Inc, R&D – Software Development and Health Technologies company. Emails: ramazan, gizem @ gefeasoft.com This platform has a high potential not only for patient tracking but also for making all stakeholders feel safe through all stages. As the registered hospitals assign corresponding medical doctors to the system, these MDs are able to register their own patients and assign special tasks for each patient. With our integrated machine learning support, MDs are able to track the failure and success rates of each patient and also see general averages among similarly progressed patients. In addition, our platform also supports multi-player technology which helps patients play with their caregivers so that they feel much safer at any point they are uncomfortable. By also gamifying the daily household activities, the patients will be able to repeat their social tasks and we will provide non-pharmacological reminiscence therapy (RT – life review therapy). All collected data will be mined by our data scientists and analyzed meaningfully. In addition, we will also add gamification modules for caregivers based on Naomi Feil’s Validation Therapy. Both are behaving positively to the patient and keeping yourself mentally healthy is important for caregivers. We aim to provide a therapy system based on gamification for them, too. When this project accomplishes all the above-written tasks, patients will have the chance to do many tasks at home remotely and MDs will be able to follow them up very effectively. We propose a complete platform and the whole project is both time and cost-effective for supporting all stakeholders.

Keywords: alzheimer’s, dementia, cognitive functionality, cognitive tests, serious games, machine learning, artificial intelligence, digitalization, non-pharmacological, data analysis, telemedicine, e-health, health-tech, gamification

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Titilola Adebowale

Abstract:

Some factors are responsible for the social adjustment among the adolescents. The study investigated resiliency, peer and parental support as factors that could determine social adjustment among adolescents in Ilorin, Kwara state. The study adopted descriptive survey research design. A sample size of 300 SS1 & SS2 students from ten secondary schools, six public and four private schools were randomly selected within Ilorin Metropolis. Self-structured questionnaire that was validated and the reliability ensured was used to collect data from the respondents. Four hypotheses were postulated and tested at 0.05 level of significance. Data collected was analysed using Pearson Product Moment Correlation (PPMC) and Regression Analysis. The findings revealed that there was a positive relationship between resiliency and social adjustment: r (298) = .402, p<0.01, r2 = .162; that there was a positive relationship between peer support and social adjustment: r (298) = .570, p<0.01, r2 = .325; that there was a positive relationship between parental support and social adjustment: r (298) = .451, p<0.01, r2 = .203; also reveals significant joint contribution of the independent variables (resilience, peer support, parental support) to the prediction of social adjustment: F (3,296) = 55.587, P<0.01. Various recommendations were given which includes the roles of government, agencies, individuals, parents, teachers, religious and marriage institutions.

Keywords: resiliency, peer support, parental support, adolescents, social adjustment

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Authors: Harold Jideofor

Abstract:

The application of Project Management (PM) tools and techniques in the public sector is gradually becoming an important issue in developing economies, especially in a country like Nigeria where projects of different size and structures are undertaken. The paper examined the application of the project management practice in the public sector in Nigeria. The PM lifecycles, tools, and techniques were presented. The study was carried out in Lagos because of its metropolitan nature and rapidly growing economy. Twenty-three copies of questionnaire were administered to 23 public institutions in Lagos to generate primary data. The descriptive analysis techniques using percentages and table presentations coupled with the coefficient of correlation were used for data analysis. The study revealed that application of PM tools and techniques is an essential management approach that tends to achieve specified objectives within specific time and budget limits through the optimum use of resources. Furthermore, the study noted that there is a lack of in-depth knowledge of PM tools and techniques in public sector institutions sampled, also a high cost of the application was also observed by the respondents. The study recommended among others that PM tools and techniques should be applied gradually especially in old government institutions where resistance to change is perceived to be high.

Keywords: project management, public sector, practice, Nigeria

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Authors: Andrei-Marius Sandu

Abstract:

This study aims to analyze the bovine meat and milk sector for the period 2007-2013. For the period analyzed, it is known that Romania has benefited from EU funding through the National Rural Development Programme 2007-2013. In this programme, there were measures that addressed exclusively the animal husbandry sector in Romania. This paper presents data on bovine production of meat, milk and livestock in Romania, but also data on the price and impact the European Funds implementation had on them.

Keywords: European funds, measures, national rural development programme, price

Procedia PDF Downloads 410

Authors: Geoffrey J. Blanford, Christoph Weissbart

Abstract:

The prospects for the European power sector indicate that it has to almost fully decarbonize in order to reach the economy-wide target of CO₂-emission reduction. We apply the EU-REGEN model to explain the penetration of RES from an economic perspective, their spatial distribution, and the complementary role of conventional generation technologies. Furthermore, we identify economic consequences of national energy and climate targets. Our study shows that onshore wind power will be the most crucial generation technology for the future European power sector. Its geographic distribution is driven by resource quality. Gas power will be the major conventional generation technology for backing-up wind power. Moreover, a complete phase out of coal power proves to be not economically optimal. The paper demonstrates that existing national targets have a negative impact, especially on the German region with higher prices and lower revenues. The remaining regions profit are hardly affected. We encourage an EU-wide coordination on the expansion of wind power with harmonized policies. Yet, this requires profitable market structures for both, RES and conventional generation technologies.

Keywords: European, policy evaluation, power sector investment, technology choices

Procedia PDF Downloads 274