Search results for: frequent item sets mining

Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

Abstract:

De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

Procedia PDF Downloads 82

Authors: S. Hamid Mirmohammadi, Shahrazad Tamjidzad

Abstract:

This paper is concerned with the single-item continuous review inventory system in which demand is stochastic and discrete. The budget consumed for purchasing the ordered items is not restricted but it incurs extra cost when exceeding specific value. The unit purchasing price depends on the quantity ordered under the all-units discounts cost structure. In many actual systems, the budget as a resource which is occupied by the purchased items is limited and the system is able to confront the resource shortage by charging more costs. Thus, considering the resource shortage costs as a part of system costs, especially when the amount of resource occupied by the purchased item is influenced by quantity discounts, is well motivated by practical concerns. In this paper, an optimization problem is formulated for finding the optimal (R, Q) policy, when the system is influenced by the budget limitation and a discount pricing simultaneously. Properties of the cost function are investigated and then an algorithm based on a one-dimensional search procedure is proposed for finding an optimal (R, Q) policy which minimizes the expected system costs .

Keywords: (R, Q) policy, stochastic demand, backorders, limited resource, quantity discounts

Procedia PDF Downloads 618

Authors: Vesile Evrim, Aliyu Awwal

Abstract:

By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

Procedia PDF Downloads 359

Authors: Valentina Marutyan

Abstract:

Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

Procedia PDF Downloads 38

Authors: Galo D. Soria, Jenny S. Casame, Eddy F. Pazmino

Abstract:

In Ecuador, mining operations have significantly impacted water sources. Artisanal mining extensively relies in mercury amalgamation. Mercury is a neurotoxic substance even at low concentrations. The objective of this investigation is to exploit Hg-removal capacity of sulfur-limonene polysulfide (SLP), which is a low-cost polymer, in order to prepare granular media (sand) coated with SLP to be used in laboratory scale column-filtration systems. Preliminary results achieved 85% removal of Hg⁺⁺ from synthetic effluents using 20-cm length and 5-cm diameter columns at 119m/day average pore water velocity. During elution of the column, the SLP-coated sand indicated that Hg⁺⁺ is permanently fixed to the collector surface, in contrast, uncoated sand showed reversible retention in Hg⁺⁺ in the solid phase. Injection of 50 pore volumes decreased Hg⁺⁺ removal to 46%. Ongoing work has been focused in optimizing the synthesis of SLP and the polymer content in the porous media coating process to improve Hg⁺⁺ removal and extend the lifetime of the column-filter.

Keywords: column-filter, mercury, mining, polysulfide, water treatment

Procedia PDF Downloads 121

Authors: Yasin I. Tayem, Jamil Ahmed, Mahmood Bahzad, Abdullah Alnama, Fahad Al Asfoor, Mahmood A. Jalil, Mohammed Radhi, Ahmed Alenezi, Khalid A. J. Al-Khaja

Abstract:

Drug-drug interactions (DDIs) represent a significant cause of patient’s morbidity and mortality. The rate of DDIs is rapidly increasing worldwide with the increasing proportion of ageing population and frequent requirement of polypharmacy-prescription of multiple drugs to treat comorbidities. Prescribing physicians are responsible for checking their prescriptions for the presence and severity of DDIs. However, since a large number of new drugs are approved and marketed every year, new interactions between medications are increasingly reported. Consequently, it is no longer practical for physicians to rely only upon their previous knowledge of medicine to avoid potential DDIs. The aim of this study was to explore the perceptions of physicians working at primary healthcare centers in Bahrain towards DDIs and how they manage them during their practice. Methodology: In this cross-sectional study, physicians working at all governmental primary healthcare centers in Bahrain were invited to voluntarily, privately and anonymously respond to a self-administered questionnaire. The questionnaire aims to assess their self-reported knowledge of DDIs and how they check for them in their practice. The participants were requested to provide socio demographic data and information related to their attitudes towards DDIs including strategies they employ for detecting and managing them, and their awareness of drugs which commonly cause DDIs. At the end of the questionnaire, an open-ended item was added to allow participants to further add any comment. Findings and Conclusions: The study is going on currently, and the results and conclusions will be presented at the conference.

Keywords: awareness, drug interactions, health centres, physicians

Procedia PDF Downloads 219

Authors: Arie Pujiwati, Kengo Nakamura, Noriaki Watanabe, Takeshi Komai

Abstract:

Coal mining is well known to cause considerable environmental impacts, including trace element contamination of soil. This study aimed to assess the trace element (As, Cd, Co, Cu, Ni, Pb, Sb, and Zn) contamination of soil in the vicinity of coal mining activities, using the case study of Asam-asam River basin, South Kalimantan, Indonesia, and to assess the human health risk, incorporating total and bioavailable (water-leachable and acid-leachable) concentrations. The results show the enrichment of As and Co in soil, surpassing the background soil value. Contamination was evaluated based on the index of geo-accumulation, I_geo and the pollution index, PI. I_geo values showed that the soil was generally uncontaminated (I_geo ≤ 0), except for elevated As and Co. Mean PI for Ni and Cu indicated slight contamination. Regarding the assessment of health risks, the Hazard Index, HI showed adverse risks (HI > 1) for Ni, Co, and As. Further, Ni and As were found to pose unacceptable carcinogenic risk (risk > 1.10^-5). Farming, settlement, and plantation were found to present greater risk than coal mines. These results show that coal mining activity in the study area contaminates the soils by particular elements and may pose potential human health risk in its surrounding area. This study is important for setting appropriate countermeasure actions and improving basic coal mining management in Indonesia.

Keywords: coal mine, risk, trace elements, soil

Procedia PDF Downloads 235

Authors: Muthukumarasamy Govindarajan

Abstract:

Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

Procedia PDF Downloads 115

Authors: Ndivhudzannyi Rembuluwani, Francis Dacosta, Emmanuel Mhlongo

Abstract:

The small-scale rock aggregate sector of the mining industry is a major source of employment for a significant number of people, particularly in remote rural areas, where alternative livelihoods are rare. It contributes to local economy by generating income and producing major and essential materials for the building, construction, and other industries. However, the sector is confronted with many challenges that hamper productivity and growth. The problems that confront this sector includes: health and safety, environmental impacts, low production and low adherence to mining legislations. This study investigated the challenges confronting selected small-scale rock aggregate mines in the Vhembe District of Limpopo province of South Africa, assesses the health, safety, low production and environmental impacts associated with aggregate production and to develop an integrated approach of addressing the multi-faceted challenges.

Keywords: health and safety, legislative framework, productivity, rock aggregate, small-scale mining

Procedia PDF Downloads 466

Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

Abstract:

The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error.

Keywords: rough sets, gene expression programming, rough neural networks, classification

Procedia PDF Downloads 351

Authors: Hadiza Lawan Ismail

Abstract:

Academic writing in Nigeria is fraught with a lot of grammatical errors which brings backward to education specifically at the tertiary institution level. This paper deals with the use of English quantifiers in academic writing, with particular emphasis on the use of ‘MANY.’ NCEI students of the Federal College of Education, Kano were used as the case study. The paper attempts to highlight the problems that arise due to incorrect use of quantifiers as well as identifying the causes of difficulties in the use of English quantifiers by some NCE1 students. To achieve this objective, the data was collected through sentence writing test by testing the students’ use of quantifiers, using only one quantifier as the variable of the study, which is MANY. In analyzing the data, the sentence writing tests are analyzed item by item and the scores of the correct responses as well as the wrong responses are converted into percentage forms. The findings revealed that students have difficulty in remembering and grasping the grammatical restrictions that control the use of English quantifiers specifically MANY; mother tongue also affects the use of quantifiers by some NCE1 students to the extent that they use one word to represent about three or four English quantifiers. The causes of difficulty in the use of English quantifiers by the students are attributed to poor background and inadequate use of English language and quantifiers, because we cannot use quantifiers alone and get the desired meaning without putting them in a sentence.

Keywords: academic writing, English quantifiers, grammatical restrictions, tertiary institution students

Procedia PDF Downloads 323

Authors: Waheeda Jabbar, Ali Gul, Rida Noor, Sania Kurd, Saba Gulzar

Abstract:

Hundreds of miners die from mining accidents each year due to poisonous gases found underground mining areas. This paper proposed an idea to protect the precious lives of mining workers. A supervising system is designed which is based on ZigBee wireless technique along with the smart protective helmets to detect real-time surveillance and it gives early warnings on presence of different poisonous gases in order to save mineworkers from any danger caused by these poisonous gases. A wireless sensor network is established using ZigBee wireless technique by integrating sensors on the helmet, apart from this helmet have embedded heartbeat sensor to detect the pulse rate and be aware of the physical or mental strength of a mineworker to increase the potential safety. Radio frequency identification (RFID) technology is used to find the location of workers. A ZigBee based base station is set-upped to control the communication. The idea is implemented and results are verified through experiment.

Keywords: Arduino, gas sensor (MQ7), RFID, wireless ZigBee

Procedia PDF Downloads 421

Authors: Arista Mayang Sari Slamet

Abstract:

This study was conducted to see the shape or behavior that shows the attitude of confidence in the practice of debate on science program students in Senior High School. This research is a descriptive qualitative study by explaining the forms of behavior of each indicator (there are ten indicators) confidence of Santrock. Data collection using interviews with Indonesian language teachers, direct observation, and documents. In this study, it was found that there is one item that is not visible indicator of the high school students of class X, which is the fourth item ‘Sitting with others in social activities’. This is caused by the forum examined are debating forum, so there is a social activity can’t be seen. The result of this study there are two students who do not show the behavior of confidence, their name is Dea and Audria (from the pro team). This indicates that the head of a pro team dominated the debate. The time for the debate is 45 minutes. Therefore all students in both of team can’t demonstrate their debate skill. In each team is only dominated by one student. The most common forms of confidence behavior are expressing opinion, look at the other person (speaker), and keeping eye contact with the other person. This indicates that the attitude of confidence by looking at the other person makes them more confident about their opinion. The most uncommon indicators is to direct or instruct to the other person. This shows that the attitude of self-confidence shown by the students isn’t lead.

Keywords: confidence, debate, senior high school, Jakarta

Procedia PDF Downloads 142

Authors: Freddy Munzhelele

Abstract:

Setting: the empirical research has shown that the life cycle theory has an impact on the firms’ financing decisions, particularly the dividend pay-outs. Accordingly, the life cycle theory posits that as a firm matures, it gets to a level and capacity where it distributes more cash as dividends. On the other hand, the young firms prioritise investment opportunities sets and their financing; thus, they pay little or no dividends. The research on firms’ financing decisions also demonstrated, among others, the adoption of trade-off and pecking order theories on the dynamics of firms capital structure. The trade-off theory talks to firms holding a favourable position regarding debt structures particularly as to the cost and benefits thereof; and pecking order is concerned with firms preferring a hierarchical order as to choosing financing sources. The case of life cycle hypothesis explaining the financial managers’ decisions as regards the firms’ capital structure dynamics appears to be an interesting link, yet this link has been neglected in corporate finance research. If this link is to be explored as an empirical research, the financial decision-making alternatives will be enhanced immensely, since no conclusive evidence has been found yet as to the dynamics of capital structure. Aim: the aim of this study is to examine the impact of life cycle theory on the capital structure dynamics trade-off and pecking order theories of firms listed in retail, industrial and mining sectors of the JSE. These sectors are among the key contributors to the GDP in the South African economy. Design and methodology: following the postpositivist research paradigm, the study is quantitative in nature and utilises secondary data obtainable from the financial statements of sampled firm for the period 2010 – 2022. The firms’ financial statements will be extracted from the IRESS database. Since the data will be in panel form, a combination of the static and dynamic panel data estimators will used to analyse data. The overall data analyses will be done using STATA program. Value add: this study directly investigates the link between the life cycle theory and the dynamics of capital structure decisions, particularly the trade-off and pecking order theories.

Keywords: life cycle theory, trade-off theory, pecking order theory, capital structure, JSE listed firms

Procedia PDF Downloads 41

Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

Abstract:

The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

Procedia PDF Downloads 114

Authors: Yusif Habib

Abstract:

The Volta River Basin (VRB) is a transboundary resource shared by Six (6) the West African States. It’s utilization spans across irrigation, hydropower generation, domestic/household water use, transportation, industrial processing, among others. Simultaneously, mineral resources such as gold are mined within the VRB catchment. Typically, the extraction/mining operation is earth-surface excavation; known as Artisanal and Small-scale mining. We developed a conceptual framework in the context of Water-Energy-Food (WEF) Nexus to delineate the trade-offs and synergies between the mineral extractive operation’s impact on Agricultural systems, specifically, cereal crops (e.g. Maize, Millet, and Rice) and the environment (water and soil quality, deforestation, etc.) on the VRB. Thus, the study examined the trade-offs and synergies through the WEF nexus lens to explore the extent of an eventual overarching mining preference for gold exploration with high economic returns as opposed to the presumably low yearly harvest and household income from food crops production to inform intervention prioritization. Field survey (household, expert, and stakeholder consultation), bibliometric analysis/literature review, scenario, and simulation models, including land-use land cover (LULC) analyses, were conducted. The selected study area(s) in Ghana was the location where the mineral extractive operation’s presence and impact are widespread co-exist with the Agricultural systems. Overall, the study proposes mechanisms of the virtuous cycle through FEW Nexus instead of the presumably existing vicious cycle to inform decision making and policy implementation.

Keywords: agriculture, environmental sustainability, gold Mining, synergies, trade-off, water-energy-food nexus

Procedia PDF Downloads 134

Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

Abstract:

Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

Procedia PDF Downloads 82

Authors: Z. Abdullah, A. R. Hamdan

Abstract:

This paper is meant to analyze the ranking of University of Malaysia Terengganu, UMT’s website in the World Wide Web. There are only few researches have been done on comparing the ranking of universities’ websites so this research will be able to determine whether the existing UMT’s website is serving its purpose which is to introduce UMT to the world. The ranking is based on hub and authority values which are accordance to the structure of the website. These values are computed using two web-searching algorithms, HITS and SALSA. Three other universities’ websites are used as the benchmarks which are UM, Harvard and Stanford. The result is clearly showing that more work has to be done on the existing UMT’s website where important pages according to the benchmarks, do not exist in UMT’s pages. The ranking of UMT’s website will act as a guideline for the web-developer to develop a more efficient website.

Keywords: algorithm, ranking, website, web structure mining

Procedia PDF Downloads 486

Authors: Khaled Benyounes

Abstract:

Determination of the rock mechanical properties such as unconfined compressive strength UCS, Young’s modulus E, and tensile strength by the Brazilian test Rtb is considered to be the most important component in drilling and mining engineering project. Research related to establishing correlation between strength and physical parameters of rocks has always been of interest to mining and reservoir engineering. For this, many rock blocks of limestone were collected from the quarry located in Meftah(Algeria), the cores were crafted in the laboratory using a core drill. This work examines the relationships between mechanical properties and some physical properties of limestone. Many empirical equations are established between UCS and physical properties of limestone (such as dry bulk density, velocity of P-waves, dynamic Young’s modulus, alteration index, and total porosity). Others correlations UCS-tensile strength, dynamic Young’s modulus-static Young’s modulus have been find. Based on the Mohr-Coulomb failure criterion, we were able to establish mathematical relationships that will allow estimating the cohesion and internal friction angle from UCS and indirect tensile strength. Results from this study can be useful for mining industry for resolve range of geomechanical problems such as slope stability.

Keywords: limestone, mechanical strength, Young’s modulus, porosity

Procedia PDF Downloads 427

Authors: Ahmed Sami Hammami, Mohamed Jellazi

Abstract:

Background: The aim of the study is to evaluate the magnitude of different psychological outcomes among Tunisian health care professionals (HCP) during the COVID-19 pandemic and to identify the associated factors. Methods: HCP completed a cross-sectional questionnaire from April 4th to April, 28th 2020. The survey collected demographic information, factors that may interfere with the psychological outcomes, behavior changes and mental health measurements. The latter was assessed through 3 scales; the 7-item questions Insomnia Severity Index, the 2-item Patient Health Questionnaire and the 2-item Generalized Anxiety Disorder. Multivariable logistic regression was conducted to identify factors associated with psychological outcomes. Results: A total of 503 HCP successfully completed the survey; among those, n=493 consented to enroll in the study, 411 [83.4%] were physicians, 323 [64.2%] were women and 271 [55%] had a second-line working position. A significant proportion of HCP had anxiety 35.7%, depression 35.1% and insomnia 23.7%. Females, those with psychiatric history and those using public transport exhibited the highest proportions for overall symptoms compared to other groups e.g., depression among females vs. males: 44,9% vs. 18,2%, P=0.00. Those with a previous medical history and nurses, had more anxiety and insomnia compared to other groups e.g. anxiety among nurses vs. interns/residents vs. attending 45,1% vs 36,1% vs 27,5%; p=0.04. Multivariable logistic regression showed that female gender was a risk factor for all psychological outcomes e.g. female sex increased the odds of anxiety by 2.86; 95% confidence interval [CI], 1, 78-4, 60; P=0.00, whereas having a psychiatric history was a risk factor for both anxiety and insomnia. (e.g. for insomnia OR=2,86; 95% [CI], 1,78-4,60; P=0.00), Having protective equipment was associated with lower risk for depression (OR=0,41; 95% CI, 0,27-0,62; P=0.00) and anxiety. Physical activity was also protective against depression and anxiety (OR=0,41, 95% CI, 0,25-0,67, P=0.00). Conclusion: Psychological symptoms are usually undervalued among HCP, though the COVID-19 pandemic played a major role in exacerbating this burden. Prompt psychological support should be endorsed and simple measures such as physical activity and ensuring the necessary protection are paramount to improve mental health outcomes and the quality of care provided to patients.

Keywords: COVID-19 pandemic, health care professionals, mental health, protective factors, psychological symptoms, risk factors

Procedia PDF Downloads 171

Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

Abstract:

With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

Procedia PDF Downloads 331

Authors: Daphne Sobolev, Niklas Voege

Abstract:

During the last three decades, consumer ethics perceptions have attracted the attention of a large number of researchers. Nevertheless, little is known about the effect of the cognitive and situational contexts of the decision on ethics judgments. In this paper, the interrelationship between consumers’ ethics perceptions, legality evaluations and mind-sets are explored. Legality evaluations represent the cognitive context of the ethical judgments, whereas mind-sets represent their situational context. Drawing on moral development theories and priming theories, it is hypothesized that both factors are significantly related to consumer ethics perceptions. To test this hypothesis, 289 participants were allocated to three mind-set experimental conditions and a control group. Participants in the mind-set conditions were primed for aggressiveness, politeness or awareness to the negative legal consequences of breaking the law. Mind-sets were induced using a sentence-unscrambling task, in which target words were included. Ethics and legality judgments were assessed using consumer ethics and internet ethics questionnaires. All participants were asked to rate the ethicality and legality of consumer actions described in the questionnaires. The results showed that consumer ethics and legality perceptions were significantly correlated. Moreover, including legality evaluations as a variable in ethics judgment models increased the predictive power of the models. In addition, inducing aggressiveness in participants reduced their sensitivity to ethical issues; priming awareness to negative legal consequences increased their sensitivity to ethics when uncertainty about the legality of the judged scenario was high. Furthermore, the correlation between ethics and legality judgments was significant overall mind-set conditions. However, the results revealed conflicts between ethics and legality perceptions: consumers considered 10%-14% of the presented behaviors unethical and legal, or ethical and illegal. In 10-23% of the questions, participants indicated that they did not know whether the described action was legal or not. In addition, an asymmetry between the effects of aggressiveness and politeness priming was found. The results show that the legality judgments and mind-sets interact with consumer ethics perceptions. Thus, they portray consumer ethical judgments as dynamical processes which are inseparable from other cognitive processes and situational variables. They highlight that legal and ethical education, as well as adequate situational cues at the service place, could have a positive effect on consumer ethics perceptions. Theoretical contribution is discussed.

Keywords: consumer ethics, legality judgments, mind-set, priming, aggressiveness

Procedia PDF Downloads 272

Authors: Emilija Tribocka

Abstract:

This contribution presents parts of an ongoing study of a diachronic relationship between two weak verb classes in Norwegian, the a-class (cf. the paradigm of ‘throw’: kasta – kastar – kasta – kasta) and the e-class (cf. the paradigm of ‘buy’: kjøpa – kjøper – kjøpte – kjøpt). The study investigates inflection class shifts between the two classes with Old Norse, the ancestor of Modern Norwegian, as a starting point. Examination of inflection in 38 verbs in four chosen dialect areas (106 places of attestations) demonstrates that the shifts from the a-class to the e-class are widespread to varying degrees in three out of four investigated areas and are more common than the shifts in the opposite direction. The diachronic productivity of the e-class is unexpected for several reasons. There is general agreement that type frequency is an important factor influencing productivity. The a-class (53% of all weak verbs) was more type frequent in Old Norse than the e-class (42% of all weak verbs). Thus, given the type frequency, the expansion of the e-class is unexpected. Furthermore, in the ‘core’ areas of expanded e-class inflection, the shifts disregard phonological principles creating forms with uncomfortable consonant clusters, e.g., fiskte instead of fiska, the preterit of fiska ‘fish’. Later on, these forms may be contracted, i.e., fiskte > fiste. In this contribution, two factors influencing the shifts are presented: phonological form and token frequency. Verbs with the stem ending in a consonant cluster, particularly when the cluster ends in -t, hardly ever shift to the e-class. As a matter of fact, verbs with this structure belonging to the e-class in Old Norse shift to the a-class in Modern Norwegian, e.g., ON e-class verb skipta ‘change’ shifts to the a-class. This shift occurs as a result of the lack of morpho-phonological transparency between the stem and the preterit suffix of the e-class, -te. As there is a phonological fusion between the stem ending in -t and the suffix beginning in -t, the transparent a-class inflection is chosen. Token frequency plays an important role in the shifts, too, in some dialects. In one of the investigated areas, the most token frequent verbs of the ON e-class remain in the e-class (e.g., høyra ‘hear’, leva ‘live’, kjøpa ‘buy’), while less frequent verbs may shift to the a-class. Furthermore, the results indicate that the shift from the a-class to the e-class occurs in some of the most token frequent verbs of the ON a-class in this area, e.g., lika ‘like’, lova ‘promise’, svara ‘answer’. The latter is unexpected as frequent items tend to remain stable. This study presents a case of unexpected productivity, demonstrating that minor patterns can grow and outdo major patterns. Thus, type frequency is not the only factor that determines productivity. The study addresses the role of phonological form and token frequency in the spread of inflection patterns.

Keywords: inflection class, productivity, token frequency, phonological form

Procedia PDF Downloads 36

Authors: Chiou-Fang Liou

Abstract:

Cancer has been one of the most life-threaten diseases worldwide for 30+ years. The influences of cancer illness include symptoms from cancer itself along with its treatments. The quality of life among patients diagnosed with cancer during cancer treatments has been conceptualized within four domains: Functional Well-Being, Social Well-Being, Physical Well-Being, and Emotional Well-Being. Patients with cancer often need to make adjustments to face all the challenges. The middle-range theory of Resourcefulness and Quality of life has been applied to explore factors contributing to cancer patients’ needs. Resourcefulness is defined as sets of skills that can be learned and consisted of Person and Social Resourcefulness. Empirical evidence also supported a possible relationship between Resourcefulness and Quality of Life. However, little is known about the extent to which the two concepts are related to each other. This study, therefore, applied a multivariate technique, Canonical Correlation Analysis, to identify the relationship between the two sets of variables with multi-dimensional measures, the Resourcefulness and Quality of Life in Cancer patients receiving treatments. After IRB approval, this multi-centered study took place at two medical centers in the Central Region of Taiwan. Sample A total of 186 patients with various cancer diagnoses and either receiving radiation therapy or chemotherapy consented to and answered questionnaires. The Import findings of the Generalized F test identified two typical sets with several linear relations and explained a total of 79.1% of the total variance. The first typical set found Personal Resourcefulness negatively related to Social Well-being, Functional being, Emotional Well-being, and Physical, in that order. The second typical set found Social Resourcefulness negatively related to Functional Well-being and Physical-being yet positively related to Social Well-being and Emotional Well-being. Discussion and Conclusion, The results of this presented study supported the statistically significant relationship between two sets of variables that are consistent with the theory. In addition, the results are considerably important in cancer patients receiving cancer treatments.

Keywords: cancer, canonical correlation analysis, quality of life, resourcefulness

Procedia PDF Downloads 51

Authors: Atiya Mahmood, Mubasharah Hanif, Ghazala Butt, Mehwish Zahoor Ahmed

Abstract:

Acne vulgaris affects millions of adults.Despite extensive research, its food related etiology remains elusive. Objective:To assess the correlation between dietary intake and acne through a case-control survey of 300,15-25 year old respondents living in Pakistan. 50 acne patients and 150 age-and ethnicity-matched controls completed a questionnaire.Cases and controls were separated using SPSS-22 and univariate analysis was performed using the chi-square test. p value < 0.05 was considered statistically significant. We used adjusted odds ratios to assess the strength of associations with 95% confidence intervals. Most of the respondents were females(91.3%).Most(48.7%)acne patients were 20-25 yearsold.Acne severity was mild in 50%,moderate in 34%,severe in14%and very severe in 2%.Frequent low-fat foods(p<0.001)(OR=3.22),fat intake(p=0.03)(OR = 1.629),sweet snacks i.e. biscuits and candies etc. (p=0.013) (OR=1.9254), soft drinks(p= 0.045)(OR= 1.9091),butter(p<0.001) (OR= 1.8185),dairy products(p=0.043)(OR=0.624),salty foods(p= 0.011)(OR=1.961),chocolate (p=0.028)(OR=1.669), were associated with increased acne risk.No association was found with consumption of fried foods, desserts, fruit juices, raw fruit, fast food, vegetables, cheese, soy products, salt, and corn. Increased butter and chocolate consumption were linked to more severe forms of acne(p=0.049 and p=0.005 respectively).Most respondents (n=218) considered themselves to have healthy eating habits, indicating they were not educated about the nutritional aspects of acne treatment.Certain food item intake was significantly higher in acne patients to give an association between the two. Further studies must be conducted to develop a causative relationship. Nutrition aawareness is critical to reduce acne.

Keywords: correlation between dietary components and acne, dietary components, acne, nutrition

Procedia PDF Downloads 36

Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi

Abstract:

Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, Diagnostic errors, Ontology, Pneumonia

Procedia PDF Downloads 163

Authors: Rorlinda Yusof, Siti Aishah Hassan, Afifah Mohamad Radzi, Mohd Hakimie Zainal Abidin, Amran Rasli, Inderbir Sandhu

Abstract:

The purpose of this study is to examine the self-identities of gifted and talented students and the relationship between self-identity and academic accomplishment. A random sample of 300 students enrolled in a secondary education programme at the Pusat GENIUS@pintar Negara was chosen as respondents of a 151-item holistic-identity component development tool. The validity of the instrument was assessed using Principal Components Analysis and Factor Analysis via an inter-Item Correlation Matrix (Loading values 0.44 to 0.86), which resulted in the formation of eight dimensions. The Cronbach's Alpha was calculated to determine the instrument's reliability (the overall result was 0.98). The results showed that students' holistic-identity profiles were relatively high (mean=4.09, standard deviation=0.449). In addition, spiritual identity received the greatest mean score (4.34) out of the eight components of identity investigated, while leadership identity received the lowest mean score (3.88). A conceptual framework for Islamic school leadership is recommended to implement spiritual values without differentiation to harmonize spiritual and intellectual intelligence among all the students. Some benchmarking studies with other centres for gifted and talented students are recommended for further research.

Keywords: holistic self-identity, academic achievement, self-development programme, counselling services, gifted and talented students

Procedia PDF Downloads 85

Authors: Nik Azmah Nik Yusuff, Faridah Hassan Basri, Rosnidar Mansor

Abstract:

The purpose of the study is to find out the effectiveness of using Graphic calculators (GC) with Calculator Based Laboratory 2 (CBL2) in teaching and learning of form four biology for these topics: Nutrition, Respiration and Dynamic Ecosystem. Sixty form four science stream students were the participants of this study. The participants were divided equally into the treatment and control groups. The treatment group used GC with CBL2 during experiments while the control group used the ordinary conventional laboratory apparatus without using GC with CBL2. Instruments in this study were a set of pre-test and post-test and a questionnaire. T-Test was used to compare the student’s biology achievement while a descriptive statistic was used to analyze the outcome of the questionnaire. The findings of this study indicated the use of GC with CBL2 in biology had significant positive effect. The highest mean was 4.43 for item stating the use of GC with CBL2 had saved collecting experiment result’s time. The second highest mean was 4.10 for item stating GC with CBL2 had saved drawing and labelling graphs. The outcome from the questionnaire also showed that GC with CBL2 were easy to use and save time. Thus, teachers should use GC with CBL2 in support of efforts by Malaysia Ministry of Education in encouraging technology-enhanced lessons.

Keywords: biology experiments, Calculator-Based Laboratory 2 (CBL2), graphic calculators, Malaysia Secondary School, teaching/learning

Procedia PDF Downloads 382

Authors: Guan-Ying Chen, Han-Tsung Liou, Shou-Ren Hu

Abstract:

To address traffic congestion problems and encourage the use of public transportation systems in the Taipei metropolitan area, the Taipei City Government and the New Taipei City Government implemented a monthly ticket policy on April 16, 2018. This policy offers unlimited rides on the Taipei MRT, Taipei City Bus, New Taipei City Bus, Danhai Light Rail, and Public Bike (YouBike) on a monthly basis. Additionally, both city governments replaced the smart card discount policy with a new frequent flyer discount program (referred to as the loyal customer program) on February 1, 2020, introducing a differential pricing policy. Specifically, the more frequently the Taipei MRT system is used, the greater the discounts users receive. To analyze the impact of the Taipei public transport monthly ticket policy and the frequent user discount program on the passenger volume of the Taipei MRT system and the transferring behaviors of MRT users, this study conducts a trip-chain analysis using transaction data from Taipei MRT smart cards between September 2017 and December 2020. To achieve these objectives, the study employs four indicators: 1) number of passengers, 2) average number of rides, 3) average trip distance, and 4) instances of multiple consecutive rides. The study applies the t-test and Mann-Kendall trend test to investigate whether the proposed indicators have changed over time due to the implementation of the discount policy. Furthermore, the study examines the travel behaviors of passengers who use monthly tickets. The empirical results of the study indicate that the implementation of the Taipei public transport monthly ticket policy has led to an increase in the average number of passengers and a reduction in the average trip distance. Moreover, there has been a significant increase in instances of multiple consecutive rides, attributable to the unlimited rides offered by the monthly tickets. The impact of the frequent user discount program on changes in MRT passengers is not as pronounced as that of the Taipei public transportation monthly ticket policy. This is partly due to the fact that the frequent user discount program is only applicable to the Taipei MRT system, and the passenger volume was greatly affected by the COVID-19 pandemic. The findings of this research can serve as a reference for Taipei MRT Corporation in formulating its fare strategy and can also provide guidance for the Taipei and New Taipei City Governments in evaluating differential pricing policies for public transportation systems.

Keywords: frequent user discount program, mass rapid transit, monthly ticket, smart card

Procedia PDF Downloads 44

Authors: Vivian W. L. Tsang, Karen Lockhart, Samuel Spiegel, Annalee Yassi

Abstract:

Background: Workers in the informal economy often incur exposure to well-documented occupational health hazards. Insufficient attention has been afforded to rigorously evaluating intervention programs to reduce the risks, especially in artisanal and small-scale gold mining (ASGM). Objectives: This systematic review, conducted as part of the World Health Organization’s Global Plan of Action for Workers’ Health, sought to assess the state of knowledge on occupational health programs and interventions for the informal artisanal and small-scale gold mining (ASGM) sector, an occupation which directly employs at least 50 million people. Methods: We used a comprehensive search strategy for four well-known databases relevant to health outcomes: PubMed, Engineering Village, OVID Medline, and Web of Science, and employed the PRISMA framework for our analysis. Findings: Ten studies met the inclusion criteria of a primary study focused on assessing the impact of interventions addressing occupational health concerns in ASGM. There were no studies evaluating or even identifying comprehensive occupational health and safety programs for this sector, although target interventions addressing specific hazards exist. Major areas of intervention –education and introduction of mercury-reducing/eliminating technology were identified, and the challenges and limitations of each intervention taken into the assessment. Even for these, however, there was a lack of standardization for measuring outcome or impact, let alone long-term health outcomes for miners and mining communities. Conclusion: There is an urgent need for research on comprehensive occupational health programs addressing the array of hazards faced by artisanal and small-scale miners.

Keywords: informal economy, artisanal and small-scale gold mining, occupational health, health and safety, workplace safety

Procedia PDF Downloads 122