Search results for: Clinical decision support system

Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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Authors: Shachi Pathak

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We are living in knowledge based economy where firms can gain competitive advantage with the help of managing knowledge within the organization. The purpose the study is to develop a conceptual model to explain the relationship between factors affecting knowledge sharing, called as knowledge enablers, in an organization, knowledge sharing activities and organizational performance, using system dynamics approach. This research is important since it will provide better understandings on what are the key knowledge enablers to support knowledge sharing activities, and how knowledge sharing activities will affect the capability of an organization to enhance the performance of the organization.

Keywords: knowledge management, knowledge sharing, organizational performance, system dynamics

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Authors: O. Adjoul, A. Feugier, K. Benfriha, A. Aoussat

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In general, issues related to design and maintenance are considered in an independent manner. However, the decisions made in these two sets influence each other. The design for maintenance is considered an opportunity to optimize the life cycle cost of a product, particularly in the nuclear or aeronautical field, where maintenance expenses represent more than 60% of life cycle costs. The design of large-scale systems starts with product architecture, a choice of components in terms of cost, reliability, weight and other attributes, corresponding to the specifications. On the other hand, the design must take into account maintenance by improving, in particular, real-time monitoring of equipment through the integration of new technologies such as connected sensors and intelligent actuators. We noticed that different approaches used in the Design For Maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and maintainability of a multi-component system. This article proposes a method of DFM that assists designers to propose dynamic maintenance for multi-component industrial systems. The term "dynamic" refers to the ability to integrate available monitoring data to adapt the maintenance decision in real time. The goal is to maximize the availability of the system at a given life cycle cost. This paper presents an approach for simultaneous optimization of the design and maintenance of multi-component systems. Here the design is characterized by four decision variables for each component (reliability level, maintainability level, redundancy level, and level of monitoring data). The maintenance is characterized by two decision variables (the dates of the maintenance stops and the maintenance operations to be performed on the system during these stops). The DFM model helps the designers choose technical solutions for the large-scale industrial products. Large-scale refers to the complex multi-component industrial systems and long life-cycle, such as trains, aircraft, etc. The method is based on a two-level hybrid algorithm for simultaneous optimization of design and maintenance, using genetic algorithms. The first level is to select a design solution for a given system that considers the life cycle cost and the reliability. The second level consists of determining a dynamic and optimal maintenance plan to be deployed for a design solution. This level is based on the Maintenance Free Operating Period (MFOP) concept, which takes into account the decision criteria such as, total reliability, maintenance cost and maintenance time. Depending on the life cycle duration, the desired availability, and the desired business model (sales or rental), this tool provides visibility of overall costs and optimal product architecture.

Keywords: availability, design for maintenance (DFM), dynamic maintenance, life cycle cost (LCC), maintenance free operating period (MFOP), simultaneous optimization

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Authors: Jeremy Goldsmith

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The Gambia, the smallest country in mainland Africa and one of the poorest countries on Earth, is currently experiencing historically unprecedented levels of out-migration to Europe. As a result, Gambians are currently among the top four nationalities emigrating to Europe. The central question that this thesis will address is: what factors impact the migration-related decision-making capabilities of Gambians? Based on interviews with NGOs, as well as those who have migrated and returned, are planning to migrate, and their friends and families, a pattern will emerge. This pattern will be woven into first person narratives which will explore the politico-economic, environmental, and socio-cultural factors that inform individual decision-making with regards to migration.

Keywords: migration, The Gambia, Africa, politico-economic, sociocultural, environmental

Procedia PDF Downloads 323

Authors: Mariam Shah

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Strict liability is frequently used in magistrate’s courts for TV license and driving offences.There is existing research suggesting that the strict liability approach to criminal offences can result in ‘absurd’ judicial outcomes, or potentially ‘injustice’.This paper will discuss the potential merits of strict liability as a method for dealing with shoplifting offences.Currently, there is disparity in sentencing outcomes in the UK, particularly in relation to shoplifting offences.This paper will question whether ‘injustice’ is actually in the differentiation of defendants based upon their ‘perceived’ circumstances, which could be resulting in arbitrary judicial decision making.

Keywords: arbitrary, decision making, judicial decision making, shoplifting, stereotypes, strict liability

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Authors: Farzana Esmailkassam, Usakorn Kunanuvat, Zahraa Mohammed Ali

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Objective: The key barrier in Clozapine treatment of treatment-resistant schizophrenia (TRS) includes frequent bloods draws to monitor neutropenia, the main drug side effect. WBC and ANC monitoring must occur throughout treatment. Accurate WBC and ANC counts are necessary for clinical decisions to halt, modify or continue clozapine treatment. The CSAN Pronto point-of-care (POC) analyzer generates white blood cells (WBC) and absolute neutrophils (ANC) through image analysis of capillary blood. POC monitoring offers significant advantages over central laboratory testing. This study evaluated the performance of the CSAN Pronto against the Beckman DxH900 Hematology laboratory analyzer. Methods: Forty venous samples (EDTA whole blood) with varying concentrations of WBC and ANC as established on the DxH900 analyzer were tested in duplicates on three CSAN Pronto analyzers. Additionally, both venous and capillary samples were concomitantly collected from 20 volunteers and assessed on the CSAN Pronto and the DxH900 analyzer. The analytical performance including precision using liquid quality controls (QCs) as well as patient samples near the medical decision points, and linearity using a mix of high and low patient samples to create five concentrations was also evaluated. Results: In the precision study for QCs and whole blood, WBC and ANC showed CV inside the limits established according to manufacturer and laboratory acceptability standards. WBC and ANC were found to be linear across the measurement range with a correlation of 0.99. WBC and ANC from all analyzers correlated well in venous samples on the DxH900 across the tested sample ranges with a correlation of > 0.95. Mean bias in ANC obtained on the CSAN pronto versus the DxH900 was 0.07× 109 cells/L (95% L.O.A -0.25 to 0.49) for concentrations <4.0 × 109 cells/L, which includes decision-making cut-offs for continuing clozapine treatment. Mean bias in WBC obtained on the CSAN pronto versus the DxH900 was 0.34× 109 cells/L (95% L.O.A -0.13 to 0.72) for concentrations <5.0 × 109 cells/L. The mean bias was higher (-11% for ANC, 5% for WBC) at higher concentrations. The correlations between capillary and venous samples showed more variability with mean bias of 0.20 × 109 cells/L for the ANC. Conclusions: The CSAN pronto showed acceptable performance in WBC and ANC measurements from venous and capillary samples and was approved for clinical use. This testing will facilitate treatment decisions and improve clozapine uptake and compliance.

Keywords: absolute neutrophil counts, clozapine, point of care, white blood cells

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Authors: Toyin Mary Adewumi, Cina Mosito

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Part of enabling learners with special education needs (SEN) to succeed is providing them with adequate support. Support is all activities in a school that enhance its capacity to respond to diversity by making learning contexts and lessons accessible to all learners. The paper reports findings of support provided by teachers to learners with SEN and the pockets of good practice found in the support provided by teachers to these learners in schools in the Amathole West District, Eastern Cape. A purposeful sample, comprising eight teachers, eight principals in eight schools, including one provincial and two district education officials, was selected. Thematic analysis was used for analyzing data gathered through semi-structured interviews. The results established that despite the challenges such as lack of qualifications and training in special education needs, learners with SEN received varied support from teachers which include extra exercises, extra time, special attention during break times or after school hours and homework. The study reveals pockets of good practice in some selected primary schools particularly in the poverty-stricken locations in the Amathole West District. This paper recommends adequate training for teachers for the support of learners with SEN.

Keywords: good practice, learner, special education needs, inclusion, support

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Authors: Mamta M. Barapatre, V. N. Sahare

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Since from long time two wheeled vehicle self-balancing has always been a back-breaking task for both human and robots. Leaning a bicycle driving is long time process and goes through building knowledge base for parameter decision making while balancing robots. In order to create this machine learning phase with embedded system the proposed system is designed. The system proposed aims to construct a bicycle automaton, power-driven by an electric motor, which could balance by itself and move along a specific path. This path could be wavy with bumps and varying widths. The key aim was to construct a cycle which self-balances itself by controlling its handle. In order to take a turn, the mass was transferred to the center. In order to maintain the stability, the bicycle bot automatically turned the handle and a turn. Some problems were faced by the team which were Speed, Steering mechanism through mass- distribution (leaning), Center of mass location and gyroscopic effect of its wheel. The idea proposed have potential applications in automation of transportation system and is most efficient.

Keywords: gyroscope-flywheel, accelerometer, servomotor-controller, self stability concept

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Authors: Neelam Singha

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The present work intends to analyze the system dynamics of Hashimoto’s thyroiditis with the assistance of fractional calculus. Hashimoto’s thyroiditis or chronic lymphocytic thyroiditis is an autoimmune disorder in which the immune system attacks the thyroid gland, which gradually results in interrupting the normal thyroid operation. Consequently, the feedback control of the system gets disrupted due to thyroid follicle cell lysis. And, the patient perceives life-threatening clinical conditions like goiter, hyperactivity, euthyroidism, hyperthyroidism, etc. In this work, we aim to obtain the approximate solution to the posed fractional-order problem describing Hashimoto’s thyroiditis. We employ the Adomian decomposition method to solve the system of fractional-order differential equations, and the solutions obtained shall be useful to provide information about the effect of medical care. The numerical technique is executed in an organized manner to furnish the associated details of the progression of the disease and to visualize it graphically with suitable plots.

Keywords: adomian decomposition method, fractional derivatives, Hashimoto's thyroiditis, mathematical modeling

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Authors: Stephen Bahooshy

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Our understanding of gender inequality has advanced in recent years. Differences in pay and societal gendered behaviour expectations have been emphasized. It is acknowledged globally that gender shapes everyone’s experiences of health and social care, including access to care, use of services and products, and the interaction with care providers. NHS Digital in England collects data from local authorities on the number of carers and people with support needs and the services they access. This data does not provide a gender breakdown. Caring can have many positive and negative impacts on carers’ health and wellbeing. For example, caring can improve physical health, provide a sense of pride and purpose, and reduced stress levels for those who undertake a caring role by choice. Negatives of caring include financial concerns, social isolation, a reduction in earnings, and not being recognized as a carer or involved and consulted by health and social care professionals. Treating male and female carers differently is by definition unequitable and precludes one gender from receiving the benefits of caring whilst potentially overburdening the other with the negatives of caring. In order to explore the issue on a preliminary basis, five local authorities who provide statutory adult social care services in England were sent Freedom of Information requests in 2019. The authorities were selected to include county councils and London boroughs. The authorities were asked to provide data on the amount of money spent on care at home packages to people over 65 years, broken down by gender and carer gender for each financial year between 2013 and 2019. Results indicated that in each financial year, female carers supporting someone over 65 years received less financial support for care at home support packages than male carers. Over the six-year period, this difference equated to a £9.5k deficit in financial support received on average per female carer when compared to male carers. An example of a London borough with the highest disparity presented an average weekly spend on care at home for people over 65 with a carer of £261.35 for male carers and £165.46 for female carers. Consequently, female carers in this borough received on average £95.89 less per week in care at home support than male carers. This highlights a real and potentially detrimental disparity in the care support received to female carers in order to support them to continue to care in parts of England. More research should be undertaken in this area to better explore this issue and to understand if these findings are unique to these social care providers or part of a wider phenomenon. NHS Digital should request local authorities collect data on gender in the same way that large employers in the United Kingdom are required by law to provide data on staff salaries by gender. People who allocate social care packages of support should consider the impact of gender when allocating support packages to people with support needs and who have carers to reduce any potential impact of gender bias on their decision-making.

Keywords: caregivers, carers, gender equality, social care

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Authors: Ana Luiza Blanco, Juliana Cordeiro Carvalho, Tábatta Renata Pereira Brito, Ariene Angelini dos Santos Orlandi, Ligiana Pires Corona, Daniella Pires Nunes

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Depressive symptoms are recurrent in older adults and affect the quality of life and well-being of individuals. One of the strategies to reduce depression is social support, but studies are still needed to determine which types of social support are most effective in moderating this effect in certain populations. The objective was to identify the relationship between social support and depressive symptoms in participants of a University of the Third Age. This is a cross-sectional study. Participants were 82 individuals (≥ 50 years) who responded to the Geriatric Depression Scale - GDS and the Medical Outcomes Study - MOS. Data collection was carried out from November 2020 to May 2021. The Chi-Square and Mann Whitney tests were used, at a significance level of 5% for data analysis. Among the participants, 83.4% were female, 57.3% were age between 60 to 69 years, 83.1% studied 12 year or more and 48.1% receive from 4 to 10 minimum wages. The prevalence of depressive symptoms was 12.2%. The type of support with the highest median score was affective (100 points) and the lowest, or emotional (87.5 points). The results showed that participants without depressive symptoms had higher median scores for informational support when compared to those with depressive symptoms (p=0.029). The other types of social support were not statistically significant. The findings suggested that informational support is related to depressive symptoms in older adults. Promote informational support and educational actions in Universities of the Third Age may be an important strategy for preventing depressive symptoms and improve the quality of life of this population.

Keywords: aged, depressive symptoms, social support, university of the third age

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Authors: Nuno Biga

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Artificial Intelligence (AI) allows machines to interpret information and learn from context analysis, giving them the ability to make predictions adjusted to each specific situation. In addition to learning by performing deterministic and probabilistic calculations, the 'artificial brain' also learns through information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) that provides users with useful suggestions, namely to pursue the following operations: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time the bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed in a pilot project. Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of this information is materialised in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" that players can use during the Game. Each participant in the Virtual Assisted-BIGAMES permanently asks himself about the decisions he should make during the game in order to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, and as the participants gain a better understanding of the game, they will more easily dispense with the VA's recommendations and rely more on their own experience, capability, and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator (Serious Game Controller) is responsible for supporting the players with further analysis and the recommended action may be (or not) aligned with the previous recommendations of the VA. All the information should be jointly analysed and assessed by each player, who are expected to add “Emotional Intelligence”, a component absent from the machine learning process.

Keywords: artificial intelligence (AI), gamification, key performance indicators (KPI), machine learning, management simulators, serious games, virtual assistant

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Authors: Nurunnaher Nurunnaher

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This paper examines the impact of women’s participation in microcredit on women’s decision making in the slum areas of Dhaka city, Bangladesh. There is confusion in the literature about whether women’s empowerment is or is not a trickle down impact of poverty alleviation or household well-being, and the studies use more or less similar indicators to measure the status of household and the status of women. Studies very rarely conceptualize and operationalize the term ‘empowerment’ as the word is often used without proper care by policy makers and development practitioners instead of household wellbeing. Currently, decision making in many studies has been used as an indicator of women’s empowerment when assessing the impact of microcredit programs on women. Based on a qualitative and feminist study this paper operationalizes women’s empowerment through the development of a conceptual framework, the identification of assessment criteria and the development of proper indicators that guided the whole study. The testimonies of participants, both men and women, were the basis of exploration of women’s lived experiences, which is the most appropriate method to explore the impact of such programs on women’s empowerment. The study considers empowerment as a process that affects various levels of life and gender relationships. The study found that there is a positive change in women’s position in decision making when women have developed an independent economic base with credit money. However, predominantly, women’s decision making is shared with men with the final decision still in the men’s hands. It can be said that women’s microcredit participation has not significantly challenged the social norms, therefore it is not surprising that women who hand over credit to their husband rarely have any power in intra-household bargaining process. Nevertheless, overall it is evident that women are continuously struggling toward the freedom to have the authority over household, economic and personal matters. It is concluded that while making strategic choices or gaining empowerment requires several steps, women’s participation in decision-making has several implications on their lives and potentially challenges patriarchy.

Keywords: women, gender inequality/equality, decision making, empowerment, microcredit, slums, Dhaka, Bangladesh

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Authors: Rachid El Khoury

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Chronic pain is perhaps the most burdensome health issue facing the planet. Our understanding of the pathophysiology of chronic pain has increased substantially over the past 25 years, including but not limited to changes in the brain. However, we still do not know why chronic pain develops in some people and not in others. Most of the recent developments in pain science, that have direct relevance to clinical management, relate to our understanding of the role of the brain, the role of the immune system, or the role of cognitive and behavioral factors. Although the Biopsychosocial model of pain management was presented decades ago, the Bio-reductionist model remains, unfortunately, at the heart of many practices across professional and geographic boundaries. A large body of evidence shows that nociception is neither sufficient nor necessary for pain. Pain is a conscious experience that can certainly be, and often is, associated with nociception, however, always modulated by countless neurobiological, environmental, and cognitive factors. This study will clarify the current misconceptions of chronic pain concepts, and their misperceptions by clinicians. It will also attempt to bridge the considerable gap between what we already know on pain but somehow disregarded, the development in pain science, and clinical practice.

Keywords: chronic pain, nociception, biopsychosocial, neuroplasticity

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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: Dionne Coley B. A.

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The role of a lawyer as a mediator is to be impartial in assisting parties to arrive at a decision. This decision should be made in a voluntary and mutually acceptable manner where the mediator encourages the parties to communicate, identify their interests, assess risks and consider settlement options. One of the key components to mediation is impartiality where mediators are to have a duty to remain impartial throughout the course of mediation and uphold an “objective” demeanor with both parties. The question is whether a mediator should take on evaluative role while encouraging the parties to come to a decision. This means that the mediator would not only encourage dialogue and responses between the parties but also assess and provide an opinion on the matter. This paper submits the argument that the role of a mediator should not be one of evaluation as this does not encourage the dialogue, process or desired outcomes associated with mediation.

Keywords: evaluation, lawyer, mediation, reform

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Authors: Komang Rahayu Indrawati, Ni Wayan Sinthia Widiastuti, Ratna Dewi Santosa

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Today the career of Balinese working women has been highly developed where able to work with loyalty and high professionalism. Career for a woman is one conscious choice and a call of conscience, which provides financial support for her family. Career for women can develop their own potencies, intellectually, and socially, so women feel that their role is meaningful and beneficial for herself and others. Emotional support becomes important to understand certainly for women who have multirole like Balinese working women to meet the demands of their role and also enhancing their work motivation and the quality of work life. This research used quantitative research method with questionnaires dissemination to 120 respondents and analyzed using Multiple Regression Analysis. The purpose of this study was to see the role of emotional support for work motivation and quality of work life in working Balinese women. The results of this study showed that emotional support and work motivation give a significant role in the quality of work life on Balinese working women.

Keywords: Balinese working women, emotional support, quality of work life, work motivation

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Authors: Chusit Apirumanekul, Chayanis Krittasudthacheewa, Ratchapat Ratanavaraha, Yanyong Inmuong

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Rapid economic development in the Lower Mekong region is leading to changes in water quantity and quality. Changes in land- and forest-use, infrastructure development, increasing urbanization, migration patterns and climate risks are increasing demands for water, within various sectors, placing pressure on scarce water resources. Appropriate policies, strategies, and planning are urgently needed for improved water resource management. Over the last decade, Thailand has experienced more frequent and intense drought situations, affecting the level of water storage in reservoirs along with insufficient water allocation for agriculture during the dry season. The Huay Saibat River Basin, one of the well-known water-scarce areas in the northeastern region of Thailand, is experiencing ongoing water scarcity that affects both farming livelihoods and household consumption. Drought management in Thailand mainly focuses on emergency responses, rather than advance preparation and mitigation for long-term solutions. Despite many efforts from local authorities to mitigate the drought situation, there is yet no long-term comprehensive water management strategy, that integrates climate risks alongside other uncertainties. This paper assesses the application in the Huay Saibat River Basin, of the Robust Decision Support framework, to explore the feasibility of multiple drought management policies; including a shift in cropping season, in crop changes, in infrastructural operations and in the use of groundwater, under a wide range of uncertainties, including climate and land-use change. A series of consultative meetings were organized with relevant agencies and experts at the local level, to understand and explore plausible water resources strategies and identify thresholds to evaluate the performance of those strategies. Three different climate conditions were identified (dry, normal and wet). Other non-climatic factors influencing water allocation were further identified, including changes from sugarcane to rubber, delaying rice planting, increasing natural retention storage and using groundwater to supply demands for household consumption and small-scale gardening. Water allocation and water use in various sectors, such as in agriculture, domestic, industry and the environment, were estimated by utilising the Water Evaluation And Planning (WEAP) system, under various scenarios developed from the combination of climatic and non-climatic factors mentioned earlier. Water coverage (i.e. percentage of water demand being successfully supplied) was defined as a threshold for water resource strategy assessment. Thresholds for different sectors (agriculture, domestic, industry, and environment) were specified during multi-stakeholder engagements. Plausible water strategies (e.g. increasing natural retention storage, change of crop type and use of groundwater as an alternative source) were evaluated based on specified thresholds in 4 sectors (agriculture, domestic, industry, and environment) under 3 climate conditions. 'Business as usual' was evaluated for comparison. The strategies considered robust, emerge when performance is assessed as successful, under a wide range of uncertainties across the river basin. Without adopting any strategy, the water scarcity situation is likely to escalate in the future. Among the strategies identified, the use of groundwater as an alternative source was considered a potential option in combating water scarcity for the basin. Further studies are needed to explore the feasibility for groundwater use as a potential sustainable source.

Keywords: climate change, robust decision support, scenarios, water resources management

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Authors: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Eashwar V. Somasundaram, Raoul R. Wadhwa, Jacob G. Scott

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The use of radiomics in measuring geometric properties of tumor images such as size, surface area, and volume has been invaluable in assessing cancer diagnosis, treatment, and prognosis. In addition to analyzing geometric properties, radiomics would benefit from measuring topological properties using persistent homology. Intuitively, features uncovered by persistent homology may correlate to tumor structural features. One example is necrotic cavities (corresponding to 2D topological features), which are markers of very aggressive tumors. We develop a data pipeline in R that clusters tumors images based on persistent homology is used to identify meaningful clinical distinctions between tumors and possibly new relationships not captured by established clinical categorizations. A preliminary analysis was performed on 16 Magnetic Resonance Imaging (MRI) breast tissue segments downloaded from the 'Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis' (I-SPY TRIAL or ISPY1) collection in The Cancer Imaging Archive. Each segment represents a patient’s breast tumor prior to treatment. The ISPY1 dataset also provided the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status data. A persistent homology matrix up to 2-dimensional features was calculated for each of the MRI segmentation. Wasserstein distances were then calculated between all pairwise tumor image persistent homology matrices to create a distance matrix for each feature dimension. Since Wasserstein distances were calculated for 0, 1, and 2-dimensional features, three hierarchal clusters were constructed. The adjusted Rand Index was used to see how well the clusters corresponded to the ER/PR/HER2 status of the tumors. Triple-negative cancers (negative status for all three receptors) significantly clustered together in the 2-dimensional features dendrogram (Adjusted Rand Index of .35, p = .031). It is known that having a triple-negative breast tumor is associated with aggressive tumor growth and poor prognosis when compared to non-triple negative breast tumors. The aggressive tumor growth associated with triple-negative tumors may have a unique structure in an MRI segmentation, which persistent homology is able to identify. This preliminary analysis shows promising results in the use of persistent homology on tumor imaging to assess the severity of breast tumors. The next step is to apply this pipeline to other tumor segment images from The Cancer Imaging Archive at different sites such as the lung, kidney, and brain. In addition, whether other clinical parameters, such as overall survival, tumor stage, and tumor genotype data are captured well in persistent homology clusters will be assessed. If analyzing tumor MRI segments using persistent homology consistently identifies clinical relationships, this could enable clinicians to use persistent homology data as a noninvasive way to inform clinical decision making in oncology.

Keywords: cancer biology, oncology, persistent homology, radiomics, topological data analysis, tumor imaging

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Authors: Walid Abouzeid, Mohamed Shadad, Mostafa Farid, Magdy El Hawary

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The most frequent treatable vascular abnormality of the spinal canal is spinal dural arteriovenous fistulae (SDAVFs), which cause progressive para- or quadriplegia mostly affecting elderly males. SDAVFs are present in the thoracolumbar region. The main goal of treatment must be to obliterate the shunting zone via superselective embolization with the usage of a liquid embolic agent. This study aims to evaluate endovascular technique as a safe and efficient approach for the treatment SDAVFs, especially with long-term follow-up clinical outcomes. Study Design: A retrospective clinical case study. From May 2010 to May 2017, 15 patients who had symptoms attributed to SDAVFs underwent the operation in the Departments of Neurosurgery in Suhag, Tanta, and Al-Azhar Universities and Interventional Radiology, Ain Shams University. All the patients had varying degrees of progressive spastic paraparesis with and without sphincteric disturbances. Endovascular embolization was used in all cases. Fourteen were males, with ages ranging from 45 to 74 years old. After the treatment, good outcome was found in five patients (33.3%), a moderate outcome was delineated in six patients (40 %), and four patients revealed a poor outcome (26.7%). Spinal AVF could be treated safely and effectively by the endovascular approach. Generally, there is no correlation between the disappearance of MRI abnormalities and significant clinical improvement. The preclinical state of the patient is directly proportional to the clinical outcome. Due to unexpected responses, embolization should be attempted even the patient is in a bad clinical condition.

Keywords: spine, arteriovenous, fistula, endovascular, embolization

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Authors: Markou Vasiliki, Serdaris Panagiotis

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The consumer behavior and decision-making process of consumers is a process that is affected by the factor of uncertainty. The onslaught of the Covid 19 pandemic has changed the consumer decision-making process in many ways. This change can be seen both in the buying process (how and where they shop) but also in the types of goods and services they are looking for. In addition, due to the mainly economic uncertainty that came from this event, but also the effects on both society and the economy in general, new consumer behaviors were created. Traditional forms of shopping are no longer a primary choice, consumers have turned to digital channels such as e-commerce and social media to fulfill needs. The purpose of this particular article is to examine how much the consumer's decision-making process has been affected after the pandemic and if consumer behavior has changed. An online survey was conducted to examine the change in decision making. Essentially, the demographic factors that influence the decision-making process were examined, as well as the social and economic factors. The research is divided into two parts. The first part included a literature review of the research that has been carried out to identify the factors, and the second part where the empirical investigation was carried out using a questionnaire and was done electronically with the help of Google Forms. The questionnaire was divided into several sections. They included questions about consumer behavior, but mainly about how they make decisions today, whether those decisions have changed due to the pandemic, and whether those changes are permanent. Also, for decision-making, goods were divided into essential products, high-tech products, transactions with the state and others. Αbout 500 consumers aged between 18 and 75 participated in the research. The data was processed with both descriptive statistics and econometric models. The results showed that the consumer behavior and decision-making process has changed. Now consumers widely use the internet for shopping, consumer behaviors and consumer patterns have changed. Social and economic factors play an important role. Income, gender and other factors were found to be statistically significant. In addition, it is worth noting that the percentage who made purchases during the pandemic through the internet for the first time was remarkable and related to age. Essentially, the arrival of the pandemic caused uncertainty for individuals, mainly financial, and this affected the decision-making process. In addition, shopping through the internet is now the first choice, especially among young people, and it seems that it is about to become established.

Keywords: consumer behavior, decision making, COVID-19, Greece, behavior change

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Authors: Kemi Olalekan Oduntan

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This paper was carried out in order to empirical, and descriptively analysis how interest rate and foreign exchange rate liberalization influence investment decision in Nigeria. The study spanned through the period of 1985 – 2014, secondary data were restricted to relevant variables such as investment (Proxy by Gross Fixed Capital Formation) saving rate, interest rate and foreign exchange rate. Theories and empirical literature from various scholars were reviews in the paper. Ordinary Least Square regression method was used for the analysis of data collection. The result of the regression was critically interpreted and discussed. It was discovered for empirical finding that tax investment decision in Nigeria is highly at sensitive rate. Hence, all the alternative hypotheses were accepted while the respective null hypotheses were rejected as a result of interest rate and foreign exchange has significant effect on investment in Nigeria. Therefore, impact of interest rate and foreign exchange rate on the state of investment in the economy cannot be over emphasized.

Keywords: interest rate, foreign exchange liberalization, investment decision, economic growth

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Authors: Vânia Costa, Nuno De Sá Teixeira, Ana C. Santos, Eduardo Santos

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Financial literacy and numeracy have been regarded as paramount for rational household decision making in the increasing complexity of financial markets. However, financial decisions are often made under sub-optimal circumstances, including cognitive overload. The present study aims to clarify how financial literacy and numeracy, taken as relevant expert knowledge for financial decision-making, modulate possible effects of cognitive load. Participants were required to perform a choice between a sure loss or a gambling pertaining a financial investment, either with or without a competing memory task. Two experiments were conducted varying only the content of the competing task. In the first, the financial choice task was made while maintaining on working memory a list of five random letters. In the second, cognitive load was based upon the retention of six random digits. In both experiments, one of the items in the list had to be recalled given its serial position. Outcomes of the first experiment revealed no significant main effect or interactions involving cognitive load manipulation and numeracy and financial literacy skills, strongly suggesting that retaining a list of random letters did not interfere with the cognitive abilities required for financial decision making. Conversely, and in the second experiment, a significant interaction between the competing mnesic task and level of financial literacy (but not numeracy) was found for the frequency of choice of a gambling option. Overall, and in the control condition, both participants with high financial literacy and high numeracy were more prone to choose the gambling option. However, and when under cognitive load, participants with high financial literacy were as likely as their illiterate counterparts to choose the gambling option. This outcome is interpreted as evidence that financial literacy prevents intuitive risk-aversion reasoning only under highly favourable conditions, as is the case when no other task is competing for cognitive resources. In contrast, participants with higher levels of numeracy were consistently more prone to choose the gambling option in both experimental conditions. These results are discussed in the light of the opposition between classical dual-process theories and fuzzy-trace theories for intuitive decision making, suggesting that while some instances of expertise (as numeracy) are prone to support easily accessible gist representations, other expert skills (as financial literacy) depend upon deliberative processes. It is furthermore suggested that this dissociation between types of expert knowledge might depend on the degree to which they are generalizable across disparate settings. Finally, applied implications of the present study are discussed with a focus on how it informs financial regulators and the importance and limits of promoting financial literacy and general numeracy.

Keywords: decision making, cognitive load, financial literacy, numeracy

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Authors: Wesam Hamed

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This paper contributes to the existing literature by investigating the impact of political risks on the capital adequacy ratio in the banking sector of Gulf Cooperation Council (GCC) countries, which is the first attempt for this nexus to the best of our knowledge. The dynamic panel data model (System‐GMM) showed that political risks significantly decrease the capital adequacy ratio in the banking sector. For this purpose, we used political risks, bank-specific, profitability, and macroeconomic variables that are utilized from the data stream database for the period 2005-2017. The results also actively support the “too big to fail” hypothesis. Finally, the robustness results confirm the conclusions derived from the baseline System‐GMM model.

Keywords: capital adequacy ratio, system GMM, GCC, political risks

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

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Authors: Marina Diković

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By giving personal opinions, suggestions and criticism through e-democracy, young people can reinforce the adoption of decisions which they have an impact on. The purpose of this research was to examine the opinion of university students about the possibility of their decision-making by using information and communication technology (ICT). The questionnaire examined young people's values and behaviour associated with e-democracy and the related decision-making. Students are most active online when it comes to finding information connected with their academic responsibilities, but less frequently take part in democratic processes in society, both at the national and local level. E-democracy as a tool can be learned in programmes of Human Rights Education and Citizenship Education. 

Keywords: active citizens, e-democracy, information and communication technology (ICT), university students

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Sihem Oujdi, Hafida Belbachir

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Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.

Keywords: C4.5 algorithm, decision trees, S-CART, spatial data mining

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Authors: Marzieh Moattari Mitra Abbasi, Masoud Mousavinasab, Poorahmad

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Background: Nurses in their daily practice need to communicate with patients and their families as well as health professional team members. Effective communication contributes to patients’ satisfaction which is a fundamental outcome of nursing practice. There are some evidences in support of patients' dissatisfaction with nurses’ performance in communication process. Therefore improving nurses’ communication skills is a necessity for nursing scholars and nursing administrators. Objective: The aim of the present study was to evaluate the effect of a 2-days workshop on nurses’ competencies and performances in communication in a central hospital located in the sought of Iran. Materials and Method: This is a randomized controlled trial which comprised of a convenient sample of 70 eligible nurses, working in a central hospital. They were randomly divided into 2 experimental and control groups. Nurses’ competencies was measured by an Objective Structured Clinical Examination (OSCE) and their performance was measured by asking eligible patients hospitalized in the nurses work setting during a one month period to evaluate nurses' communication skills before and 2 months after intervention. The experimental group participated in a 2 day workshop on communication skills. Content included in this workshop were: the importance of communication (verbal and non verbal), basic communication skills such as initiating the communication, active listening and questioning technique. Other subjects were patient teaching, problem solving, and decision making, cross cultural communication and breaking bad news. Appropriate teaching strategies such as brief didactic sessions, small group discussion and reflection were applied to enhance participants learning. The data was analyzed using SPSS 16. Result: A significant between group differences was found in nurses’ communication skills competencies and performances in the posttest. The mean scores of the experimental group was higher than that of the control group in the total score of OSCE as well as all stations of OSCE (p<0.003). Overall posttest mean scores of patient satisfaction with nurse's communication skills and all of its four dimensions significantly differed between the two groups of the study (p<0.001). Conclusion: This study shows that the education of nurses in communication skills, improves their competencies and performances. Measurement of Nurses’ communication skills as a central component of efficient nurse patient relationship by valid and reliable methods of evaluation is recommended. Also it is necessary to integrate teaching of communication skills in continuing nursing education programs. Trial Registration Number: IRCT201204042621N11

Keywords: communication skills, simulation, performance, competency, objective structure, clinical evaluation

Procedia PDF Downloads 215