Search results for: cross-validation support vector machine

Authors: Lisa Gray, Henry Kronner, Tameca Harris-Jackson, Mimi Sodhi, Ruth Gerritsen-McKane, Donette Considine

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The MSW Peer Mentorship Program (PMP) was developed as one of several approaches to foster student success. The key purposes of the PMP are to help new graduate students transition to a graduate program, facilitate relationship building between students, grow and sustain student satisfaction, and build a strong connection to the MSW program. This pilot program also serves as an additional source of support for students during the era of the Covid-19 pandemic. Further, the long-term goals of the program are to assist in student retention. Preliminary findings suggest that both mentors and mentees enrolled in PMP find the peer mentoring relationship to have a positive impact on their graduate learning experience.

Keywords: covid-19, mentorship, peer support, student success

Procedia PDF Downloads 223

Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: James Boag

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The needs of people with a diagnosis of dementia and their carers and families are physical, psychosocial, and psychological and begin at the time of diagnosis. There is frequently a lack of emotional support and counselling. Care- giving support is required from the presentation of the first symptoms of dementia until death. Alzheimer's disease begins decades before the clinical symptoms begin to appear, and in many cases, it remains undiagnosed, or diagnosed too late for any possible interventions to have any effect. However, if an incorrect diagnosis is given, it may result in a person being treated, without effect, for a type of dementia they do not have and delaying the interventions they should have received. Being diagnosed with dementia can cause emotional distress to the person, and physical and emotional support is needed, which will become more important as the disease progresses. The severity of the patient's dementia and their symptoms has a bearing of the impact on the carer and the support needed. A lack of insight and /or a denial of the diagnosis, grief, reacting to anticipated future losses, and coping methods to maximise the disease outcome, are things that should be addressed. Because of the stigma, it is important for carers not to lose contact with family and others because social isolation leads to depression and burnout. The impact on a carer's well- being and quality of life can be influenced by the severity of the illness, its type of dementia, its symptoms, healthcare support, financial and social status, career, age, health, residential setting, and relationship to the patient. Carer burnout due to lack of support leads to people diagnosed with dementia being put into residential care prematurely. Often dementia is not recognised as a terminal illness, limiting the ability of the person diagnosed with dementia and their carers to work on advance care planning and getting access to palliative and other support. Many carers have been satisfied with the physical support they were given in their everyday life, however, it was agreed that there was an immense unmet need for psychosocial support, especially after diagnosis and approaching end of life. Providing continuity and coordination of care is important. Training is necessary for providers to understand that every case is different, and they should understand the complexities. Grief, the emotional response to loss, is suffered during the progression of the disease and long afterwards, and carers should continue to be supported after the death of the person they were caring for.

Keywords: dementia, caring, challenges, needs

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Authors: Loulwah Bokhari, Jori Nazer, Hala Sultan

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Nowadays, artificial intelligence (AI) is used as a foundation for many activities in modern computing applications at home, in vehicles, and in businesses. Many modern machines are built to carry out a specific activity or purpose. This is where the Amazon Alexa application comes in, as it is used as a virtual assistant. The purpose of this paper is to explore the use of Amazon Alexa among people and how it has improved and made simple daily tasks easier for many people. We gave our participants several questions regarding Amazon Alexa and if they had recently used or heard of it, as well as the different tasks it provides and whether it successfully satisfied their needs. Overall, we found that participants who have recently used Alexa have found it to be helpful in their daily tasks.

Keywords: artificial intelligence, Echo system, machine learning, feature for feature match

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Authors: Zahra Khazaeipour, Ehsan Ahmadipour, Vafa Rahimi-Movaghar, Fereshteh Ahmadipour

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Research Objectives: Chronic pain is one of the common problems associated with spinal cord injuries (SCI), which causes many complications. Therefore, this study intended to evaluate the relationship between pain and demographic, injury characteristics, socio-economic and social support in individuals with spinal cord Injury in Iran. Design: Descriptive cross-sectional study. Setting: Brain and Spinal Cord Injury Research Center (BASIR), Tehran University of Medical Sciences, Tehran, Iran, between 2012 and 2013. Participants: The participants were 140 individuals with SCI, 101 (72%) men and 39 (28%) women, with mean age of 29.4 ±7.9 years. Main Outcome Measure: The Persian version of the Brief Pain Inventory (BPI) was used to measure the pain, and the Multidimensional Scale of Perceived Social Support (MSPSS) was used to measure social support. Results: About 50.7% complained about having pain, which 79.3% had bilateral pain. The most common locations of pain were lower limbs and back. The most quality of pain was described as aching (41.4%), and tingling (32.9%). Patients with a medium level of education had the least pain compared to high and low level of education. SCI individuals with good economic situation reported higher frequency of having pain. There was no significant relationship between pain and social support. There was positive correlation between pain and impairment of mood, normal work, relations with other people and lack of sleep (P < 0.001). Conclusion: These findings revealed the importance of socioeconomic factors such as economic situation and educational level in understanding chronic pain in people with SCI and provide further support for the bio-psychosocial model. Hence, multidisciplinary evaluations and treatment strategies are advocated, including biomedical, psychological, and psycho-social interventions.

Keywords: pain, social support, socio-economic indicators, spinal cord injury

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Authors: Somojit Saha, Rohit K. Chatterjee, Sarit K. Das, Avijit Kar

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A new force field is designed for propagation of the parametric contour into deep narrow cortical fold in the application of knowledge based reconstruction of cerebral cortex from MR image of brain. Designing of this force field is highly inspired by the Generalized Gradient Vector Flow (GGVF) model and markedly differs in manipulation of image information in order to determine the direction of propagation of the contour. While GGVF uses edge map as its main driving force, the newly designed force field uses the map of distance between zero valued pixels and their nearest non-zero valued pixel as its main driving force. Hence, it is called Zero-Non-Zero Distance (ZNZD) force field. The objective of this force field is forceful propagation of the contour beyond spurious convergence due to partial volume effect (PVE) in to narrow sulcal fold. Being function of the corresponding non-zero pixel value, the force field has got an inherent property to determine spuriousness of the edge automatically. It is effectively applied along with some morphological processing in the application of cortical reconstruction to breach the hindrance of PVE in narrow sulci where conventional GGVF fails.

Keywords: deformable model, external force field, partial volume effect, cortical reconstruction, MR image of brain

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Authors: Cao Jiayu, Lan Ximing, Huang Jingjia, Burra Venkata Durga Kumar

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The first virus to attack personal computers was born in early 1986, called C-Brain, written by a pair of Pakistani brothers. In those days, people still used dos systems, manipulating computers with the most basic command lines. In the 21st century today, computer performance has grown geometrically. But computer viruses are also evolving and escalating. We never stop fighting against security problems. Stack overflow is one of the most common security vulnerabilities in operating systems. It may result in serious security issues for an operating system if a program in it has a vulnerability with administrator privileges. Certain viruses change the value of specific memory through a stack overflow, allowing computers to run harmful programs. This study developed a mechanism to detect and respond to time whenever a stack overflow occurs. We demonstrate the effectiveness of standard machine learning algorithms and control flow enforcement techniques in predicting computer OS security using generating suspicious vulnerability functions (SVFS) and associated suspect areas (SAS). The method can minimize the possibility of stack overflow attacks occurring.

Keywords: operating system, security, stack overflow, buffer overflow, machine learning, control-flow enforcement technology

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Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

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Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Sami M. Alshareef

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The rapid growth of smart grid technology has brought significant advancements to the power industry. However, with the increasing interconnectivity and reliance on information and communication technologies, smart grids have become vulnerable to cyber-attacks, posing significant threats to the reliable operation of power systems. Among the critical components of smart grids, the Automatic Generation Control (AGC) system plays a vital role in maintaining the balance between generation and load demand. Therefore, protecting the AGC system from cyber threats is of paramount importance to maintain grid stability and prevent disruptions. Traditional security measures often fall short in addressing sophisticated and evolving cyber threats, necessitating the exploration of innovative approaches. Machine learning, with its ability to analyze vast amounts of data and learn patterns, has emerged as a promising solution to enhance AGC system security. Therefore, this research proposal aims to address the challenges associated with detecting and mitigating cyber-attacks on AGC in smart grids by leveraging machine learning techniques on automatic generation control of two-area power systems. By utilizing historical data, the proposed system will learn the normal behavior patterns of AGC and identify deviations caused by cyber-attacks. Once an attack is detected, appropriate mitigation strategies will be employed to safeguard the AGC system. The outcomes of this research will provide power system operators and administrators with valuable insights into the vulnerabilities of AGC systems in smart grids and offer practical solutions to enhance their cyber resilience.

Keywords: machine learning, cyber-attacks, automatic generation control, smart grid

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Kate Parkinson

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A Family Group Conference (FGC) is a family-led decision-making process through which a family/kinship group, rather than the professionals involved, is asked to develop a plan for the care or the protection of children in the family. In England and Wales, FGCs are used in 76% of local authorities and in recent years, have tended to be used in cases where the local authority are considering the court process to remove children from their immediate family, to explore kinship alternatives to local authority care. Some local authorities offer the service much earlier, when families first come to the attention of children's social care, in line with research that suggests the earlier an FGC is held, the more likely they are to be successful. Family plans that result from FGCs are different from professional plans in that they are unique to a family and, as a result, reflect the diversity of families. Despite the fact that FGCs are arguable the most researched area of social work globally, there is a dearth of research that examines the nature of family plans and their substance. This paper presents the findings of a documentary analysis of 42 Early Help FGC plans from local authorities in England, with the aim of exploring the level and type of support that family members offer at a FGC. A thematic analysis identified 5 broad areas of support: Practical Support, Building Relationships, Child-care Support, Emotional Support and Social Support. In the majority of cases, family members did not want or ask for any formal support from the local authority or other agencies. Rather, the families came together to agree a plan of support, which was within the parameters of the resources that they as a family could provide. Perhaps then the role of the Early Help professional should be one of a facilitating and enabling role, to support families to develop plans that address their own specific difficulties, rather than the current default option, which is to either close the case because the family do not meet service thresholds or refer to formal support if they do, which may offer very specific support, have rigid referral criteria, long waiting lists and may not reflect the diverse and unique nature of families. FGCs are argued to be culturally appropriate social work practices in that they are appropriate for families from a range of cultural backgrounds and can be adapted to meet particular cultural needs. Furthermore, research on the efficacy of FGCs at an Early Help Level has demonstrated that Early Help FGCs have the potential to address difficulties in family life and prevent the need for formal support services, which are potentially stigmatising and do not reflect the uniqueness and diversity of families. The paper concludes with a recommendation for the use of FGCs across Early Help Services in England and Wales.

Keywords: family group conferences, family led decision making, early help, prevention

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Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Hao Li, Guy Y. Cornejo Maceda, Yiqing Li, Jianguo Tan, Marek Morzynski, Bernd R. Noack

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We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich database of machine learning control (MLC) optimizing a feedback law for a cost function in the plant. The proposed methodology provides (1) insights into the control landscape, which maps control laws to performance, including extrema and ridge-lines, (2) a catalogue of representative flow states and their contribution to cost function for investigated control laws and (3) visualization of the dynamics. Key enablers are classification and feature extraction methods of machine learning. The analysis is successfully applied to the stabilization of a mixing layer with sensor-based feedback driving an upstream actuator. The fluctuation energy is reduced by 26%. The control replaces unforced Kelvin-Helmholtz vortices with subsequent vortex pairing by higher-frequency Kelvin-Helmholtz structures of lower energy. These efforts target a human interpretable, fully automated analysis of MLC identifying qualitatively different actuation regimes, distilling corresponding coherent structures, and developing a digital twin of the plant.

Keywords: machine learning control, mixing layer, feedback control, model-free control

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Authors: Thevaranchany Sivaskaran

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Women entrepreneurs are the key players in the society and their contributions is highly highlighted to enhance economic stability in the country. In Sri Lanka, especially in North and East provinces people badly affected by war. Most of them are widows and women headed families. Due to this changing environment, Educational opportunities, and the support of NGO’s Most of the women have started their business and become entrepreneurs. Even though existing family setup and social setup entrepreneurial women are overburdened and difficult to balance their business and family roles. The research has been conducted on the experiences of women entrepreneurs with the family role support and work-life balance within the small and micro- enterprise sector in Jaffna, Srilanka. This study aims to identify that what extent the role of family support will be the tool to balancing work and life effectively and, secondly, the main challenges they face in achieving work-life balance. This is done by drawing on literatures including those on work-life balance, small-and micro enterprises, and entrepreneurship theories. To find out this objective, the data were collected from 50 entrepreneurs among the members of Jaffna women chamber in each GS division basis (cluster random sampling). A qualitative methodological technique and semi-structured interviews were used to collect the data for the case study on these entrepreneurs. The results indicate that the majority of entrepreneurs do not enjoy a sense of work-life balance because most of them are women headed family and they need to work hard to generate financial profit for the benefit of family. The motivation for them to work in this way is to provide basic needs. Results confirmed for others that support of husbands is very important. Mostly, emotional support (belief and empowerment) is exposed; however, getting financial contribution seems to be highly appreciated. More responsibilities which spouses were ready to take over regarding the home responsibilities (that is, childcare) should also not be neglected in the system of support to their entrepreneurial wives. Although, more important for all, women with children appreciated other members and spouses help and assistance to a higher extent. Results showed that majority of women who started their own business feel that in the first year of ope-ration the emotional support of family members was more important.

Keywords: family support, work life balance, women entrepreneurs, Jaffna District, Sri Lanka

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Authors: Petra Palic, Maruska Vizek

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We provide an in-depth analysis of interdependence of asset returns and volatilities in developed and developing countries. The analysis is split into three parts. In the first part, we use multivariate GARCH model in order to provide stylized facts on cross-market volatility spillovers. In the second part, we use a generalized vector autoregressive methodology developed by Diebold and Yilmaz (2009) in order to estimate separate measures of return spillovers and volatility spillovers among sovereign bond, equity, foreign exchange and commodity markets. In particular, our analysis is focused on cross-market return, and volatility spillovers in 19 developed and developing countries. In order to estimate named spillovers, we use daily data from 2008 to 2017. In the third part of the analysis, we use a generalized vector autoregressive framework in order to estimate total and directional volatility spillovers. We use the same daily data span for one developed and one developing country in order to characterize daily volatility spillovers across stock, bond, foreign exchange and commodities markets.

Keywords: cross-market spillovers, sovereign bond markets, equity markets, value at risk (VAR)

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Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

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Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

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Authors: Thomas Nally, Jane Ireland, Roxanne Khan, Philip Birch

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Intimate Partner Violence (IPV), a significant societal problem, affects individuals worldwide. However, the strategies victims use to keep safe are under-researched. IPV is significantly under-reported, and services often are not able to be accessed by all victims. Thus they are likely to use their own strategies to manage their victimization before being able to seek support. Two studies were completed to understand these strategies. A systematic review of the literature and study completed with professionals who work with victims was undertaken to understand this area. In study one, a systematic review of the literature (n=61 papers), were analyzed using Thematic Analysis. The results indicated that victims use a large array of behaviors to increase their sense of safety and coping with emotions but also experience significant barriers to help-seeking. In study 2, sixty-nine professionals completed a measure exploring the likelihood and effectiveness of various victim strategies regarding increasing their sense of safety. Strategies included in the measure were obtained from those identified in study 1. Findings indicated that professionals perceived victims of IPV to be more likely to employ safety strategies and coping behaviors that may be ineffective but not help-seeking behaviors. Further, the responses were analyzed using Cluster Analysis. Safety strategies resulted in five clusters; perpetrator-directed strategies, prevention strategies, cognitive reappraisal, safety planning and avoidance strategies. Help-Seeking resulted in six clusters; information or practical support, abuse-related support, emotional support, secondary support and informal support. Finally, coping resulted in four clusters; emotional coping, self-directed coping, thought recording/change and cognitive coping. Both studies indicate that victims may use a variety of strategies to manage their safety besides seeking help. Professionals working with victims, using a strength-based approach, should understand what is used and is effective for victims who are unable to leave the relationships or access external support.

Keywords: intimate partner violence, help-seeking, professional support, victims, victim coping, victim safety

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Authors: Thirasak Panyaphirawat, Pairoj Sapsmarnwong, Teeratas Pornyungyuen

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This paper presents the findings of an experimental investigation of important machining parameters for the horizontal boring tool modified to mouth with a horizontal lathe machine to bore an overlength workpiece. In order to verify a usability of a modified tool, design of experiment based on Taguchi method is performed. The parameters investigated are spindle speed, feed rate, depth of cut and length of workpiece. Taguchi L9 orthogonal array is selected for four factors three level parameters in order to minimize surface roughness (Ra and Rz) of S45C steel tubes. Signal to noise ratio analysis and analysis of variance (ANOVA) is performed to study an effect of said parameters and to optimize the machine setting for best surface finish. The controlled factors with most effect are depth of cut, spindle speed, length of workpiece, and feed rate in order. The confirmation test is performed to test the optimal setting obtained from Taguchi method and the result is satisfactory.

Keywords: design of experiment, Taguchi design, optimization, analysis of variance, machining parameters, horizontal boring tool

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Ciwang Teyra, A. H. Y. Lai

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Introduction: Indigenous LGBTQ individuals face with significant life stress such as racial and gender discrimination and microaggressions, which may lead to negative impacts of their mental health. Although studies relevant to Taiwanese indigenous LGBTQpeople gradually increase, most of them are primarily conceptual or qualitative in nature. This research aims to fulfill the gap by offering empirical quantitative evidence, especially investigating the impact of racial and gender microaggressions on mental health among Taiwanese indigenous LGBTQindividuals with an intersectional perspective, as well as examine whether social support can help them to cope with microaggressions. Methods: Participants were (n=200; mean age=29.51; Female=31%, Male=61%, Others=8%). A cross-sectional quantitative design was implemented using data collected in the year 2020. Standardised measurements was used, including Racial Microaggression Scale (10 items), Gender Microaggression Scale (9 items), Social Support Questionnaire-SF(6 items); Patient Health Questionnaire(9-item); and Generalised Anxiety Disorder(7-item). Covariates were age, gender, and perceived economic hardships. Structural equation modelling (SEM) was employed using Mplus 8.0 with the latent variables of depression and anxiety as outcomes. A main effect SEM model was first established (Model1).To test the moderation effects of perceived social support, an interaction effect model (Model 2) was created with interaction terms entered into Model1. Numerical integration was used with maximum likelihood estimation to estimate the interaction model. Results: Model fit statistics of the Model 1:X2(df)=1308.1 (795), p<.05; CFI/TLI=0.92/0.91; RMSEA=0.06; SRMR=0.06. For Model, the AIC and BIC values of Model 2 improved slightly compared to Model 1(AIC =15631 (Model1) vs. 15629 (Model2); BIC=16098 (Model1) vs. 16103 (Model2)). Model 2 was adopted as the final model. In main effect model 1, racialmicroaggressionand perceived social support were associated with depression and anxiety, but not sexual orientation microaggression(Indigenous microaggression: b = 0.27 for depression; b=0.38 for anxiety; Social support: b=-0.37 for depression; b=-0.34 for anxiety). Thus, an interaction term between social support and indigenous microaggression was added in Model 2. In the final Model 2, indigenous microaggression and perceived social support continues to be statistically significant predictors of both depression and anxiety. Social support moderated the effect of indigenous microaggression of depression (b=-0.22), but not anxiety. All covariates were not statistically significant. Implications: Results indicated that racial microaggressions have a significant impact on indigenous LGBTQ people’s mental health. Social support plays as a crucial role to buffer the negative impact of racial microaggression. To promote indigenous LGBTQ people’s wellbeing, it is important to consider how to support them to develop social support network systems.

Keywords: microaggressions, intersectionality, indigenous population, mental health, social support

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Authors: Mohd Shazad, Kamal Kumar Gupta

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Aedes aegypti (Diptera: Culicidae), commonly known as tiger mosquito is the vector of dengue fever, yellow fever, chikungunya and zika virus. In the absence of any effective vaccine against these diseases, control the mosquito population is the only promising mean to prevent the diseases. Currently used chemical insecticides cause environmental contamination, high mammalian toxicity and hazards to non-target organisms, insecticide resistance and vector resurgence. Present research work aimed to explore the potentials of phytochemicals present in the Ocimum sanctum in management of mosquito population. The leaves of Ocimum were extracted with ethanol by ‘cold extraction method’. 0-24h old fourth instar larvae of Aedes aegypti were treated with the extract of concentrations 50ppm, 100ppm, 200ppm and 400ppm for 24h. Survival, growth and development of the treated larvae were evaluated. The adults emerged from the treated larvae were used for the reproductive fitness studies. Our results indicate 77.2% mortality in the larvae exposed to 400 ppm. At lower doses, although there was no significant reduction in the survival after 24h however, it decreased during subsequent days of observations. In control experiments, no mortality was observed. It was also observed that the larvae survived after treatment showed severe growth and developmental abnormalities. There was significant increase in larval duration. In control, fourth instar moulted into pupa after 3 days while larvae treated with 400 ppm extract were moulted after 4.6 days. Larva-pupa intermediates and the pupa-adult intermediates were observed in many cases. The adults emerged from the treated larvae showed impaired mating and oviposition behaviour. The females exhibited longer preoviposition period, reduced oviposition rate and decreased egg output. GCMS analysis of the ethanol extract revealed presence of JH mimics and intermediates of JH biosynthetic pathway. Potentials of Ocimum sanctum in integrated vector management programme of Aedes aegypti were discussed.

Keywords: Aedes aegypti, Ocimum sanctum, oviposition, survival

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Authors: Nasser Almonawer

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Timely reaction to an evolving global business environment and volatile market conditions necessitates system and process flexibility, which in turn demands agile and adaptable architecture and a steady infusion of affordable new technologies. On the contrary, a large number of organizations utilize systems characterized by inflexible and obsolete legacy architectures. To effectively respond to the dynamic contemporary business environments, such architectures must be migrated to robust and modular open architectures. To this end, this paper proposes an integrated decision support system for a seamless migration to open systems. The proposed decision support system (DSS) integrates three well-established quantitative and qualitative decision-making models—namely, the Delphi method, Analytic Hierarchy Process (AHP) and Goal Programming (GP) to (1) assess risks and establish evaluation criteria; (2) formulate migration strategy and rank candidate systems; and (3) allocate resources among the selected systems.

Keywords: decision support systems, open systems architecture, analytic hierarchy process (AHP), goal programming (GP), delphi method

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Koon Weng Lau, Chee Dhang Chen, Abdul Aziz Azidah, Mohd Sofian-Azirun

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Recently, Sarawak state of Malaysia encounter an outbreak of dengue fever. Aedes albopictus has incriminated as one of the important vectors of dengue transmission. Without an effective vaccine, approaches to control or prevent dengue will be a focus on the vectors. The control of Aedes mosquitoes is still dependent on the use of chemical insecticides and insecticide resistance represents a threat to the effectiveness of vector control. This study was conducted to determine the resistance status of 11 active ingredients representing four major insecticide classes: DDT, dieldrin, malathion, fenitrothion, bendiocarb, propoxur, etofenprox, deltamethrin, lambda-cyhalothrin, cyfluthrin, and permethrin. Standard WHO test procedures were conducted to determine the insecticide susceptibility. Aedes albopictus collected from Kapit (resistance ratio, RR = 1.04–3.02), Kuching (RR = 1.17–4.61), and Sibu (RR = 1.06–3.59) exhibited low resistance toward all insecticides except dieldrin. This study reveled that dieldrin is still effective against Ae. albopictus, followed by fenitrothion, cyfluthrin, and deltamethrin. In conclusion, Ae. albopictus in Sarawak exhibited different resistance levels toward various insecticides and alternative solutions should be implemented to prevent further deterioration of the condition.

Keywords: Aedes albopictus, dengue, insecticide resistance, Malaysia

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Authors: Khouloud Safa Azoud, Süleyman Baştürk

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In the pursuit of cleaner and more sustainable urban environments, advanced technologies play a critical role in evolving sanitation systems. This paper presents two distinct advancements in automated cleaning machines designed to improve urban sanitation. The first advancement is a semi-automatic road surface cleaning machine that integrates human labor with solar energy to enhance environmental sustainability and adaptability, especially in regions with limited access to electricity. By reducing carbon emissions and increasing operational efficiency, this approach offers significant potential for urban sanitation enhancement. The second advancement is a multifunctional semi-automatic street cleaning machine equipped with a camera, Arduino programming, and GPS for an autonomous operation aimed at addressing cost barriers in developing countries. Prioritizing low energy consumption and cost-effectiveness, this machine provides versatile cleaning solutions adaptable to various environmental conditions. By integrating solar energy with autonomous operating systems and careful design, these developments represent substantial progress in sustainable urban sanitation, particularly in developing regions.

Keywords: automated cleaning machines, solar energy integration, operational efficiency, urban sanitation systems

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Authors: Martha E. Ndeley

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Operation management is the key element within the manufacturing process. However, during this process, there are a number of unforeseen disruptions that causes the process to a standstill which are, machine breakdown, employees absenteeism, improper scheduling. When this happens, it forces the shop flow to a rescheduling process and these strategy reschedules only a limited part of the initial schedule to match up with the pre-schedule at some point with the objective to create a new schedule that is reliable which in the long run gets disrupted. In this work, we have developed feedback control system that minimizes any form of disruption before the impact becomes severe, the model was tested in a paper manufacturing industries and the results revealed that, if the disruption is minimized at the initial state, the impact becomes unnoticeable.

Keywords: disruption, machine, absenteeism, scheduling

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Authors: Levi Corallo

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Typed language made by humans on computers and cell phones has made a significant distinction from previous modes of written language exchanges. While acronyms remain one of the most predominant markings of typed language, another and perhaps more recent revolution in the way humans communicate has been with the use of symbols or glyphs, primarily Emojis—globally introduced on the iPhone keyboard by Apple in 2008. This paper seeks to analyze the use of symbols in typed communication from both a linguistic and machine learning perspective. The Unicode system will be explored and methods of encoding will be juxtaposed with the current machine and human perception. Topics in how typed symbol usage exists in conversation will be explored as well as topics across current research methods dealing with Emojis like sentiment analysis, predictive text models, and so on. This study proposes that sequential analysis is a significant feature for analyzing unicode characters in a corpus with machine learning. Current models that are trying to learn or translate the meaning of Emojis should be starting to learn using bi- and tri-grams of Emoji, as well as observing the relationship between combinations of different Emoji in tandem. The sociolinguistics of an entire new vernacular of language referred to here as ‘typed language’ will also be delineated across my analysis with unicode glyphs from both a semantic and technical perspective.

Keywords: unicode, text symbols, emojis, glyphs, communication

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