Search results for: organization learning

Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

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Authors: Sarah E. Stief, Anne Theresa Eidhoff, Markus Voeth

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Despite all progress firms are facing the increasing need to adapt and assimilate digital technologies to transform their business activities in order to pursue business development. By using new digital technologies, firms can implement major business improvements in order to stay competitive and foster new growth potentials. The corresponding phenomenon of digital transformation has received some attention in previous literature in respect to industries such as media and publishing. Nevertheless, there is a lack of understanding of the concept and its organization within firms. With the help of twenty-three in-depth field interviews with German experts responsible for their company’s digital transformation, we examined what digital transformation encompasses, how it is organized and which opportunities and challenges arise within firms. Our results indicate that digital transformation is an inevitable task for all firms, as it bears the potential to comprehensively optimize and reshape established business activities and can thus be seen as a strategy of business development.

Keywords: business development, digitalization, digital strategies, digital transformation

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Authors: Akpokiere Ugbede Roseline

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Chemistry is an activity oriented subject in which many students achievement over the years are not encouraging. Among the reasons found to be responsible for student’s poor performance in chemistry are ineffective teaching strategies. This study, therefore, sought to determine the effect of guided inquiry, guided inquiry with demonstration, and demonstration with conventional approach on pre-service teachers’ cognitive attainment and practical skills acquisition on stoichiometry and chemical reactions in practical chemistry, Two research questions and hypotheses were each answered and tested respectively. The study was a quasi-experimental research involving 50 students in each of the experimental groups and 50 students in the control group. Out of the five instruments used for the study, three were on stimulus and two on response (Test of Cognitive Attainment and Test of Practical Skills in Chemistry) instruments administered, and dataobtained were analyzed with t-test and Analysis of Variance. Findings revealed, among others, that there was a significant effect of treatments on students' cognitive attainment and on practical skills acquisition. Students exposed to guided inquiry (with/without demonstration) strategies achieved better than those exposed to demonstration with conventional strategy. It is therefore recommended, among others, that Lecturers in Colleges of Education should utilize the guided inquiry strategy for teaching concepts in chemistry.

Keywords: instructional strategy, practical chemistry, learning outcomes, pre-service teachers

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Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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Authors: B. G. Abiona, E. O. Fakoya, D. O. Alabi

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Influence of leadership roles on agricultural employees’ job satisfaction was studied. Data were from 68 randomly selected respondents. Major leadership roles include supervision of employees work (x̄=3.67), leaders were goal oriented (x̄=3.39), dissemination of information among the employees (x̄=3.35). Major employees’ satisfaction was: Employees work together with their colleagues (x̄=3.54) and also interact freely with their colleagues (x̄=3.51). Major challenges affecting employees job satisfaction were inadequate funding (x̄=3.30), irregular leave bonus (x̄=3.29), climate and weather condition (x̄=3.08) and inadequate incentive (x̄=3.02). Regression analysis showed a positive significant coefficient (P<0.05) exist between religion (p<0.05), educational status(p<0.05), year of service(p<0.05), leadership roles (p<0.005), challenges faced by respondents(P<0.05), and employees’ job satisfaction. For adequate leadership role, organization should pay attention to disbursement of training funds, availability of adequate incentive and leadership recognition.

Keywords: leadership roles, agricultural employees’, job satisfaction, institute, Nigeria

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Authors: Alisha Sinha, Laxmi Kant Sharma

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Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters.

Keywords: wildfire susceptibility mapping, LST, random forest, GEE, MODIS, climatic parameters

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Authors: Anamika Devi

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Based on Vygotsky’s cultural, historical theory and Hedegaard’s concept of transition, this study aims to investigate to propose strategies to maintain digital wellbeing of children and parents during and post COVID pandemic. Due COVID 19 pandemic, children and families have been facing new challenges and sudden changes in their everyday life. While children are juggling to adjust themselves in new circumstance of onsite and online learning settings, parents are juggling with their work-life balance. A number of papers have identified that the COVID-19 pandemic has affected the lives of many families around the world in many ways, for example, the stress level of many parents increased, families faced financial difficulties, uncertainty impacted on long term effects on their emotional and social wellbeing. After searching and doing an intensive literature review from 2020 and 2021, this study has found some scholarly articles provided solution or strategies of reducing stress levels of parents and children in this unprecedented time. However, most of them are not underpinned by proper theoretical lens to ensure they validity and success. Therefore, this study has proposed strategies that are underpinned by theoretical lens to ensure their impact on children’s and parents' emotional wellbeing during and post COVID-19 era. The strategies will highlight on activities for positive coping strategies to the best use of family values and digital technologies.

Keywords: onsite and online learning, strategies, emotional wellbeing, tips, and strategies, COVID19

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Tamer Koburtay, Radi Haloub

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Person-Organization fit theory concerns how people flourish in a workplace that is congruence with their values and other traits. This paper seeks to highlight the theoretical relevance that workplace spirituality may add to the existing theory development of the P-O fit. In specific, it aims to empirically test the emerged framework that encompasses how workplace and self-spirituality match may enhance the perceived P-O fit, and how such a fit can enhance both employees’ ethical behaviors (i.e., humanism and honesty) and spiritual leadership behaviors. Drawing on a survey of the private and public sectors in Jordan, the results reveal that increasing the match in workplace and employees’ spirituality positively enhances the perceived P-O fit. Further, ethical and spiritual behaviors were found to be positively linked with a higher P-O fit. The importance of this paper is by generating a concept (i.e., P-O spirituality fit) beyond the already vast literature on P-O fit.

Keywords: ethical behavior, leadership, P-O fit, spirituality, leadership

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Authors: Victoria L. Malakhova

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The research is devoted to multimodal content and its effectiveness in developing students’ linguistic and intercultural communicative competences as an indefeasible constituent of their future professional activity. Description of multimodal content both as a linguistic and didactic phenomenon makes the study relevant. The objective of the article is the analysis of creolized texts and the effect they have on fostering higher education students’ skills and their productivity. The main methods used are linguistic text analysis, qualitative and quantitative methods, deduction, generalization. The author studies texts with full and partial creolization, their features and role in composing multimodal textual space. The main verbal and non-verbal markers and paralinguistic means that enhance the linguo-pragmatic potential of creolized texts are covered. To reveal the efficiency of multimodal content application in English teaching, the author conducts an experiment among both undergraduate students and teachers. This allows specifying main functions of creolized texts in the process of language learning, detecting ways of enhancing students’ competences, and increasing their motivation. The described stages of using creolized texts can serve as an algorithm for work with multimodal content in teaching English as a foreign language. The findings contribute to improving the efficiency of the academic process.

Keywords: creolized text, English language learning, higher education, language and communication competences, multimodal content

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Authors: Richard White, Anne Drabble, Maureen O’Neill

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The University of the Sunshine Coast (USC) offers secondary school students in the final two years of school (Years 11 and 12, 16 – 18 years of age) an opportunity to participate in a program which provides an accelerated pathway to tertiary studies. Whilst still at secondary school, the students undertake two first year university subjects that are required subjects in USC undergraduate degree programs. The program is called Integrated Learning Pathway (ILP) and offers a range of disciplines, including business, design, drama, education, and engineering. Between 2010 and 2014, 38% of secondary students who participated in an ILP program commenced undergraduate studies at USC following completion of secondary school studies. The research reported here considers “before and after” literacy and numeracy competencies of students to determine what impact participation in the ILP program has had on their academic skills. Qualitative and quantitative data has been gathered via numeracy and literacy testing of the students, and a survey asking the students to self-evaluate their numeracy and literacy skills, and reflect on their views of these academic skills. The research will enable improved targeting of teaching strategies so that students will acquire not only course-specific learning outcomes but also collateral academic skills. This enhancement of academic skills will improve undergraduate experience and improve student retention.

Keywords: academic skills enhancement, accelerated pathways, improved teaching, student retention

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Authors: Shilpagauri Prasad Ganpule

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The teaching of drama is considered as the most significant and noteworthy area in an ESL classroom. Diverse innovative techniques can be used to make the teaching of drama worthwhile and interesting. The paper presents the different innovative techniques that can be used while teaching Henrik Ibsen’s A Doll’s House [2007]. The innovative techniques facilitate students’ understanding and comprehension of the text. The use of the innovative techniques makes them explore the dramatic text and uncover a multihued arena of meanings hidden in it. They arouse the students’ interest and assist them overcome the difficulties created by the second language. The diverse innovative techniques appeal to the imagination of the students and increase their participation in the classroom. They help the students in the appreciation of the dramatic text and make the teaching learning situation a fruitful experience for both the teacher and students. The students successfully overcome the problem of L2 comprehension and grasp the theme, story line and plot-structure of the play effectively. The innovative techniques encourage a strong sense of participation on the part of the students and persuade them to learn through active participation. In brief, the innovative techniques promote the students to perform various tasks and expedite their learning process. Thus the present paper makes an attempt to present varied innovative techniques that can be used while teaching drama. It strives to demonstrate how the use of innovative techniques improve and enhance the students’ understanding and appreciation of Ibsen’s A Doll’s House [2007].

Keywords: ESL classroom, innovative techniques, students’ participation, teaching of drama

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Authors: Roshan Jelal, Charles Mbohwa

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In today’s world, internal fraud remains one of the most challenging problems within companies worldwide and despite investment in controls and attention given to the problem, the instances of internal fraud has not abated. To the contrary it appears that internal fraud is on the rise especially in the wake of the economic downturn. Leadership within companies believes that the more sophisticated the controls employed the less likely it would be for employees to pilfer. This is a very antiquated view as investment in controls may not be enough to curtail internal fraud; however, ensuring that a company drives the correct culture and behaviour within the organisation is likely to yield desired results. This research aims to understand how creating a strong ethical culture and embedding the principle of good corporate governance impacts on levels of internal fraud with an organization (a South African Bank).

Keywords: internal fraud, corporate governance, ethics, reserve bank, the King Code

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Authors: Claire Xu, Welton Wang, Manasvi Pinnaka, Anqi Pan, Michael Han

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Hospitalizations account for one-third of the total health care spending in the US. Early risk detection and intervention can reduce this high cost and increase the satisfaction of both patients and physicians. Due to the lack of awareness of the potential arising risks in home environment, the opportunities for patients to seek early actions of clinical visits are dramatically reduced. This research aims to offer a highly personalized remote patients monitoring and risk assessment AI framework to identify the potentially preventable hospitalization for both acute as well as chronic diseases. A hybrid-AI framework is trained with data from clinical setting, patients surveys, as well as online databases. 20+ risk factors are analyzed ranging from activities, biometric info, demographic info, socio-economic info, hospitalization history, medication info, lifestyle info, etc. The AI model yields high performance of 87% accuracy and 88 sensitivity with 20+ features. This hybrid-AI framework is proven to be effective in identifying the potentially preventable hospitalization. Further, the high indicative features are identified by the models which guide us to a healthy lifestyle and early intervention suggestions.

Keywords: hospitalization prevention, machine learning, remote patient monitoring, risk prediction

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Authors: G. Singer, M. Golan

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Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Precious M. Tshabalala, Thula S. Dlamini, Frikkie Liebenberg, Johann Kirsten

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Numerous studies have been conducted, and the evidence has been unambiguous showing that investing in agricultural research and development increases productivity. Continued investments in agricultural research have led to the development of over 26 successful plum cultivars since 1980 at the Agricultural Research Council’s (ARC) Infruitec/Nietvoorbij in South Africa, and more continue to be developed to meet the specific needs of both producers and consumers. Yet very little is known about the returns on any of these research initiatives. The objective of the study was determine the economic impact of plum research investments at the ARC-the main plum breeding research organization in the country. The rate of return to plum research is estimated by estimating parameters in plum production and expressing research investment as an explanatory variable. The marginal rate of return is then determined to be 14.23 per cent. The rate of return to investment being this high is indicative of an under investment in plum research.

Keywords: Agricultural research investments, productivity and rate of return, plum

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Authors: Nadjet Brahmia

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The growing needs of displacements led advanced countries in this field install new specific transport systems, able to palliate any deficiencies, especially when regular public transport does not adequately meet the requests of users. In this context, on-demand transportation (ODT) are very efficient; they rely on techniques based on the location of trip generators which should be assured effectively with the use of operators responsible of the advance reservation, planning and organization, and studying the different ODT criteria (organizational, technical, geographical, etc.). As the advanced countries in the field of transport, some developing countries are involved in the adaptation of the new technologies to reduce the deficit in their communication system. This communication presents the study of an ODT implementation in the west of Algeria, by developing the Geomatics side of the study. This part requires the use of specific systems (such as GIS, RDBMS…), so we developed the process through an application in an environment of mobility by using the computer tools dedicated to the management of the entities related to the transport field.

Keywords: ODT, geomatics, GIS, transport systems

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: J. Erina, I. Erins

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The goals of the present research are to estimate Six Sigma implementation in Latvian commercial banks and to identify the perceived benefits of its implementation. To achieve the goals, the authors used a sequential explanatory method. To obtain empirical data, the authors have developed the questionnaire and adapted it for the employees of Latvian commercial banks. The questions are related to Six Sigma implementation and its perceived benefits. The questionnaire mainly consists of closed questions, the evaluation of which is based on 5 point Likert scale. The obtained empirical data has shown that of the two hypotheses put forward in the present research Hypothesis 1 has to be rejected, while Hypothesis 2 has been partially confirmed. The authors have also faced some research limitations related to the fact that the participants in the questionnaire belong to different rank of the organization hierarchy.

Keywords: six sigma, quality, commercial banking sector, latvian

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

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Authors: Mwamba Mwamini Ruth

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For many authors,the percentage of married or in union persons using family planning methods has increased significantly since the 1960s, despite this progress, important differences across régions are observer.These différences become even greater,to present a paradox,when studying the issue in smallest territorial entities in developing countries.In line with the above,the general objective of this research is to investigate into "knowledge , attitude and practice"of households from a basic territorial entity,here in"Murara Sector"(in the city of Goma, province of North Kivu,Democratic Republic of Congo,Africa)on family planning (as defined and provisioned by the four World Health Organization-WHO key texts on the matter)

Keywords: DRC, family planning methods, information technology, Murara

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Authors: Juhanita Jiman

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This paper aims to demonstrate how androgogical approach can help educators to facilitate animation students with better understanding of their acquired technical knowledge and skills while introducing them to crucial content and ethical values. In this borderless world, it is important for the educators to know that they are dealing with young adults who are heavily influenced by their surroundings. Naturally, educators are not only handling academic issues, they are also burdened with social obligations. Appropriate androgogical approach can be beneficial for both educators and students to tackle these problems. We used to think that teaching pedagogy is important at all level of age. Unfortunately, pedagogical approach is not entirely applicable to university students because they are no longer children. Pedagogy is a teaching approach focusing on children, whereas andragogy is specifically focussing on teaching adults and helping them to learn better. As adults mature, they become increasingly independent and responsible for their own actions. In many ways, the pedagogical model is not really suitable for such developmental changes, and therefore, produces tension, dissatisfaction, and resistance in individual student. The ever-changing technology has resulted in animation students to be very competitive in acquiring their technical skills, making them forget and neglecting the importance of the core values of a story. As educators, we have to guide them not only to excel in achieving knowledge, skills and technical expertise but at the same time, show them what is right or wrong and encourage them to inculcate moral values in their work.

Keywords: andragogy, animation, artistic contents, productive learning environment

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Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Ertuğrul Ferhat Yilmaz, Ali Riza Perçin

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In the business enterprises implemented modern business enterprise principles, the most important issues are increasing the performance of workers and getting maximum income. Through the twentieth century, rapid development of the sectors of data processing and communication and because of the free trade politics arising of multilateral business enterprises have canceled the economical borders and changed the local rivalry into the spherical rivalry. In this rivalry conditions, the business enterprises have to work active and productive in order to continue their existences. The employees worked at business enterprises have formed the most important factor of product. Therefore, the business enterprises inferring the importance of the human factors in order to increase the profit have used “the performance evolution system” to increase the success and development of the employees. The evolution of the performance is aimed to increase the manpower productive by using the employees in an active way. Furthermore, this system assists the wage politics implemented in business enterprise, determining the strategically plans in business enterprises through the short and long terms, being promoted and determining the educational needs of employees, making decisions as dismissing and work rotation. It requires a great deal of effort to catch the pace of change in the working realm and to keep up ourselves up-to-date. To get the quality in people,to have an effect in workplace depends largely on the knowledge and competence of managers and prospective managers. Therefore,managers need to use the performance evaluation systems in order to base their managerial decisions on sound data. This study aims at finding whether the organizations effectively use performance evaluation systms,how much importance is put on this issue and how much the results of the evaulations have an effect on employees. Whether the organizations have the advantage of competition and can keep on their activities depend to a large extent on how they effectively and efficiently use their employees.Therefore,it is of vital importance to evaluate employees' performance and to make them better according to the results of that evaluation. The performance evaluation system which evaluates the employees according to the criteria related to that organization has become one of the most important topics for management. By means of those important ends mentioned above,performance evaluation system seems to be a tool that can be used to improve the efficiency and effectiveness of organization. Because of its contribution to organizational success, thinking performance evaluation on the axis of efficiency shows the importance of this study on a different angle. In this study, we have explained performance evaluation system ,efficiency and the relation between those two concepts. We have also analyzed the results of questionnaires conducted on the textile workers in Edirne city.We have got positive answers from the questions about the effects of performance evaluation on efficiency.After factor analysis ,the efficiency and motivation which are determined as factors of performance evaluation system have the biggest variance (%19.703) in our sample. Thus, this study shows that objective performance evaluation increases the efficiency and motivation of employees.

Keywords: performance, performance evolution system, productivity, Edirne region

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

Procedia PDF Downloads 76

Authors: Hoora Dehghani, Meisam Shahbazi, Reza Zare

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During the previous decade, a significant evolution has occurred in the number of private educational centers and, accordingly, the increase in the number of providers and students of these centers around the world. The number of language teaching institutes in Iran that are considered private educational sectors is also growing exponentially as the request for learning foreign languages has extremely increased in recent years. This fact caused competition among the institutions in improving better services tailored to the students’ demands to win the competition. Along with the growth in the industry of education, higher education institutes should apply the marketing-related concepts and view students as customers because students’ outlooks are similar to consumers with education. Studying the influential factors in the selection of an institute has multiple benefits. Firstly, it acknowledges the institutions of the students’ choice factors. Secondly, the institutions use the obtained information to improve their marketing methods. It also helps institutions know students’ outlooks that can be applied to expand the student know-how. Moreover, it provides practical evidence for educational centers to plan useful amenities and programs, and use efficient policies to cater to the market, and also helps them execute the methods that increase students’ feeling of contentment and assurance. Thus, this study explored the influencing factors in the selection of a language learning institute by language learners and examined and compared the importance among the varying age groups and genders. In the first phase of the study, the researchers selected 15 language learners as representative cases within the specified age ranges and genders purposefully and interviewed them to explore the comprising elements in their language institute selection process and analyzed the results qualitatively. In the second phase, the researchers identified elements as specified items of a questionnaire, and 1000 English learners across varying educational contexts rated them. The TOPSIS method was used to analyze the data quantitatively by representing the level of importance of the items for the participants generally and specifically in each subcategory; genders and age groups. The results indicated that the educational quality, teaching method, duration of training course, establishing need-oriented courses, and easy access were the most important elements. On the other hand, offering training in different languages, the specialized education of only one language, the uniform and appropriate appearance of office staff, having native professors to the language of instruction, applying Computer or online tests instead of the usual paper tests respectively as the least important choice factors in selecting a language institute. Besides, some comparisons among different groups’ ratings of choice factors were made, which revealed the differences among different groups' priorities in choosing a language institute.

Keywords: choice factors, EFL institute selection, english learning, need analysis, TOPSIS

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Authors: Mei Xuan Tan, Eng Ying Bong

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Academic discussion during and after instructional teaching is an integral part of learning. Such discussion between the instructor and student or peer-to-peer discussion can be in several different forms. It could be face-to-face discussion, via email and use of online discussion forum. In this study, the effectiveness of using WhatsApp for academic discussion for a first year half-credit Chemistry course was examined. This study was run over two years with two different batches of students. Participation in the study was voluntary and student volunteers were recruited within the first week of the term. The activity in the WhatsApp group was monitored by two instructors teaching the course. At the end of the course, the students participated in an online survey to evaluate their experience of using WhatsApp for academic discussion. There were a total of 26 questions. The survey had a total of 4 sections with regards to the use of WhatsApp for academic discussion: 1) Familiarity with WhatsApp, 2) Effectiveness of using WhatsApp for discussion, 3) Challenges and 4) Overall experience. The main purpose of using an IM platform for academic discussion was to encourage after-class discussion amongst the students. 32% of the participants had used other online platform, such as Piazza and forums in Learning Management System (LMS), for after-class academic discussion with their instructors and peers. This was a low percentage considering that some courses use such online platform as their main forum amongst instructors and students. At the end of our study, over 83% of the participants felt that WhatsApp was a more effective platform compared to other online forum. One interesting finding was the effect of WhatsApp discussion on face-to-face interaction with instructors. 28% of the students agreed that the use of WhatsApp as a discussion forum had encouraged them to approach their instructors during or after class. 51% of students answered neutral. This could be interpreted that the use of WhatsApp had not affected the frequent (or lack of) face-to-face interaction with their instructors. A second survey question, similar but phrased differently from the first, was also asked to evaluate the aspect of face-to-face interaction with instructors. 34% disagreed that the use of WhatsApp had reduced the frequency of face-to-face interaction. This could imply that the frequency remained the same or might have increased. The 38% who agreed to a decrease in face-to-face interaction have either asked the questions in WhatsApp or had their questions answered by a query from another student in the group chat. These outcomes suggested that the use of technology aided and complemented face-to-face interaction between instructors and students. The study also looked at the challenges of using WhatsApp for academic discussion. Some challenges included difficulty in referring back to previous discussion and students finding some discussions irrelevant to them. In conclusion, the use of IM platform for academic discussion was desirable for the students, but it should not be the only channel as face-to-face consultation and online forum for lengthy discussion are still important for after-class learning of students.

Keywords: chemistry, pedogogy, technological tools, undergraduate

Procedia PDF Downloads 134

Authors: Nasir Sulman

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In Pakistan, the inclusion of persons with disabilities in higher education institutions has significantly been increased with every passing year and anyone can observe a sizeable number of these students in each faculty. The study executes to conduct a baseline survey for measuring faculty understanding about the special needs, experiences of students with disabilities and support provided by university administration in order to teach these students effectively. The researcher has used mixed methods and the University of Karachi was selected through non-probability-based sampling method. This university is one of the largest universities in Pakistan where more than 40,000 students have been enrolled. Data was gathered through a questionnaire and focused group discussion from three stakeholders including students with disabilities, faculty members and members of the university administration. The key findings show that students with disabilities experience a number of problems related to accommodating their special needs. However, the most encouraging factors identified are the attitude, support, and motivation they received from various faculty members and university administration. On the basis of the findings of the study the researcher has prepared a faculty guidebook and established a ‘Model Learning Assistance Centre for Students with Disabilities’ in the Department of Special Education, University of Karachi. Both these efforts will be helpful for improving the support services for students with disabilities to strengthen the existing laws, policies, and practices in institutions of higher education.

Keywords: persons with disabilities, higher education, learning assistance center, faculty guidebook

Procedia PDF Downloads 147