Search results for: state of learning

Authors: E. O. Fakoya, B. G. Abiona, J. O. Soetan

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The study determined Perception of Agricultural Extension Agents of Private Sector Participation in Extension Services in Ogun State, Nigeria. Data were collected from 80 respondents with a well-structured questionnaire. The result of the findings showed that there is need for private sector participation in extension services (=4.313), private extension services has facilities than public extension services (=4.97). Private sector participated in extension services by: giving of loans and credits to farmers (=4.50). Major constraints identified by the respondents were: Transportation problem (=2.88) and lack of fund (=2.77) A significant relationship (P<0.05) exists between factors affecting public extension services(r = 0.641, p = 0.00) and private sector participation in extension services. It was concluded from the study that there is need for private sector to participate in extension service in order to improve productivity of the farmers.

Keywords: agricultural extension, extension agent, private sector, perception

Procedia PDF Downloads 593

Authors: Ali Kirkbas

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R peaks in an electrocardiogram (ECG) are signs of cardiac activity in individuals that reveal valuable information about cardiac abnormalities, which can lead to mortalities in some cases. This paper examines the problem of detecting R-peaks in ECG signals, which is a two-class pattern classification problem in fact. To handle this problem with a reliable high accuracy, we propose to use the common vector approach which is a successful machine learning algorithm. The dataset used in the proposed method is obtained from MIT-BIH, which is publicly available. The results are compared with the other popular methods under the performance metrics. The obtained results show that the proposed method shows good performance than that of the other. methods compared in the meaning of diagnosis accuracy and simplicity which can be operated on wearable devices.

Keywords: ECG, R-peak classification, common vector approach, machine learning

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Authors: Julio Jesus Salazar, Julio Jesus De Lama

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the fundamental objective of hydrological studies applied to the engineering field is to determine the statistically consistent volumes or water flows that, in each case, allow us to size or design a series of elements or structures to effectively manage and develop a river basin. To determine these values, there are several ways of working within the framework of traditional hydrology: (1) Study each of the factors that influence the hydrological cycle, (2) Study the historical behavior of the hydrology of the area, (3) Study the historical behavior of hydrologically similar zones, and (4) Other studies (rain simulators or experimental basins). Of course, this range of studies in a certain basin is very varied and complex and presents the difficulty of collecting the data in real time. In this complex space, the study of variables can only be overcome by collecting and transmitting data to decision centers through the Internet of things and artificial intelligence. Thus, this research work implemented the learning project of the sub-basin of the Shullcas river in the Andean basin of the Mantaro river in Peru. The sensor firmware to collect and communicate hydrological parameter data was programmed and tested in similar basins of the European Union. The Machine Learning applications was programmed to choose the algorithms that direct the best solution to the determination of the rainfall-runoff relationship captured in the different polygons of the sub-basin. Tests were carried out in the mountains of Europe, and in the sub-basins of the Shullcas river (Huancayo) and the Yauli river (Jauja) with heights close to 5000 m.a.s.l., giving the following conclusions: to guarantee a correct communication, the distance between devices should not pass the 15 km. It is advisable to minimize the energy consumption of the devices and avoid collisions between packages, the distances oscillate between 5 and 10 km, in this way the transmission power can be reduced and a higher bitrate can be used. In case the communication elements of the devices of the network (internet of things) installed in the basin do not have good visibility between them, the distance should be reduced to the range of 1-3 km. The energy efficiency of the Atmel microcontrollers present in Arduino is not adequate to meet the requirements of system autonomy. To increase the autonomy of the system, it is recommended to use low consumption systems, such as the Ashton Raggatt McDougall or ARM Cortex L (Ultra Low Power) microcontrollers or even the Cortex M; and high-performance direct current (DC) to direct current (DC) converters. The Machine Learning System has initiated the learning of the Shullcas system to generate the best hydrology of the sub-basin. This will improve as machine learning and the data entered in the big data coincide every second. This will provide services to each of the applications of the complex system to return the best data of determined flows.

Keywords: hydrology, internet of things, machine learning, river basin

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Authors: Hajer Chaker Ben Hadj Kacem, Thouraya Slama, Néjiba El Yetim Zribi

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This paper analyzes the effects of gender, affiliation, prior work experience, social work, and vicarious learning through family role models on entrepreneurial skills development by students when they have learned through the entrepreneurship method in Tunisia. Authors suggest that these variables enhance the development of students’ entrepreneurial skills when combined with teaching through entrepreneurship. The article assesses the impact of these combinations by comparing their effects on the development of thirteen students’ entrepreneurial competencies, namely entrepreneurial mindset, core self-evaluation, entrepreneurial attitude, entrepreneurial knowledge, creativity, financial literacy, managing ambiguity, marshaling of resources, planning, teaching methods, entrepreneurial teachers, innovative employee, and Entrepreneurial intention. Authors use a two-sample independent t-test to make the comparison, and the results indicate that, when combined with teaching through the entrepreneurship method, students with prior work experience developed better six entrepreneurial skills; students with social work developed better three entrepreneurial skills, men developed better four entrepreneurial skills than women. However, all students developed their entrepreneurial skills through this practical method regardless of their affiliation and their vicarious learning through family role models.

Keywords: affiliation, entrepreneurial skills, gender, role models, social work, teaching through entrepreneurship, vicarious learning, work experience

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

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Authors: A. U. Ugwu

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Post-Apartheid South African education system had witnessed waves of curriculum reforms. Accordingly, there have been evidences of responsiveness towards local and global challenges of sustainable development over the past decade. In other words, the curriculum shows sensitivity towards issues of Sustainable Development (SD). Moreover, the paradigm of Sustainable Development Goals (SDGs) was introduced by the UNESCO in year 2015. The SDGs paradigm is essentially a vision towards actualizing sustainability in all aspects of the global society. Education for Sustainable Development (ESD) in retrospect entails teaching and learning to actualize the intended UNESCO 2030 SDGs. This paper explores how teaching and learning of ESD can be improved, by drawing from local context of the South African schooling system. Preservice natural sciences and technology teachers in their 2nd to 4th years of study at a university’s college of education in South Africa were contacted as participants of the study. Using qualitative case study research design, the study drew from the views and experiences of five (5) purposively selected participants from a broader study, aiming to closely understating how ESD is implemented pedagogically in teaching and learning. The inquiry employed questionnaires and a focus group discussion as qualitative data generation tools. A qualitative data analysis of generated data was carried out using content and thematic analysis, underpinned by interpretive paradigm. The result of analyzed data, suggests that ESD pedagogy at the location where this research was conducted is largely influenced by contextual factors. Furthermore, the result of the study shows that there is a critical need to employ/adopt local experiences or occurrences while teaching sustainable development. Certain pedagogical approaches such as the use of videos relative to local context should also be considered in order to achieve a more realistic application. The paper recommends that educational institutions through teaching and learning should implement ESD by drawing on local contexts and problems, thereby foregrounding constructivism, appreciating and fostering students' prior knowledge and lived experiences.

Keywords: context, education for sustainable development, natural sciences and technology preservice teachers, qualitative research, sustainable development goals

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Authors: Patcharawalai Jaisamut, Kamonthip Wiwattanawongsa, Ruedeekorn Wiwattanapatapee

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Supersaturation of drugs in the gastrointestinal tract is one approach to increase the absorption of poorly water-soluble drugs. The stabilization of a supersaturated state was achieved by adding precipitation inhibitors that may act through a variety of mechanisms.In this study, the effect of the natural gums, acacia, gelatin, pectin and tragacanth on curcumin supersaturation in simulated gastric fluid (SGF) (pH 1.2), fasted state simulated gastric fluid (FaSSGF) (pH 1.6), and simulated intestinal fluid (SIF) (pH 6.8)was investigated. The results indicated that all natural gums significantly increased the curcum insolubility (about 1.2-6-fold)when compared to the absence of gum, and assisted in maintaining the supersaturated drug solution. Among the tested gums, pectin at 3% w/w was the best precipitation inhibitor with a significant increase in the degree of supersaturation about 3-fold in SGF, 2.4-fold in FaSSGF and 2-fold in SIF.

Keywords: curcumin, solubility, supersaturation, precipitation inhibitor

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Authors: Sengul Celik, Alper Ketenci

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In Turkish Constitution about health care in Article 56, it is said that: everyone has the right to live in a healthy and balanced environment. It is the duty of the state and citizens to improve the environment, protect environmental health, and prevent environmental pollution. The state ensures that everyone lives their lives in physical and mental health; it organizes the planning and service of health institutions from a single source in order to realize cooperation by increasing savings and efficiency in human and substance power. The state fulfills this task by utilizing and supervising health and social institutions in the public and private sectors. General health insurance can be established by law for the widespread delivery of health services. To have health care is one of the basic rights of patients. After the coupe attempt in July 2016, the Government of Turkey has announced a state of emergency and issued lots of emergency decrees. By these emergency decrees, lots of people were dismissed from their jobs and lost their some basic social rights. The violations occur in social life. One of the most common observations is the discrimination by government in health care system. This study aims to put forward the violation of human rights in health care system in Turkey due to their discriminated position by an emergency decree. The study is a case study that is based on nine interviews with the people or relatives of people who lost their jobs by an emergency decree in Turkey. In this study, no personally identifiable information was obtained for the safety of individuals. Also no distinctive questions regarding the identity of individuals were asked. The interviews are obtained through internet call applications. The data were analyzed through the requirements of regular health care system in Turkey. The interviews expose that the people or the relatives of people lost their right to have regular health care. They have to pay extra amount both in clinical services and in medication treatment. The patient right to quality medical care without prejudice is violated. It was assessed that the people who are involved in emergency decree and their relatives are discriminated by government and deprived of regular medical care and supervision. Although international legal arrangements and legal responsibilities of the state have been put forward by Article 56, they are violated in practice. To prevent these kinds of violations, some measures should be taken against the deprivation in health care system especially towards the discriminated people by an emergency decree.

Keywords: emergency decree in Turkey, health care, discriminated people, patients rights

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Authors: Wei Li, Tuo Yang

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In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: Ismene Ithai Bras-Ruiz

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Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

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Authors: A. Kalizhanova, T. Shelestova

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Teacher professional development (TPD) in Kazakhstan has followed a fairly standard format for centuries, with teachers learning new information from a lecturer and being tested using multiple-choice questions. In the online world, self-access courses have become increasingly popular. Due to their extensive multimedia content, peer-reviewed assignments, adaptable class times, and instruction from top university faculty from across the world, massive open online courses (MOOCs) have found a home in Kazakhstan's system for lifelong learning. Recent studies indicate the limited use of connectivism-based tools such as discussion forums by Kazakhstani pre-service and in-service English teachers, whose professional interests are limited to obtaining certificates rather than enhancing their teaching abilities and exchanging knowledge with colleagues. This paper highlights the significance of connectivism-based tools and instruments, such as MOOCs, for the continuous professional development of pre- and in-service English teachers, facilitators' roles, and their strategies for enhancing trainees' conceptual knowledge within the MOOCs' curriculum and online learning skills. Reviewing the most pertinent papers on Connectivism Theory, facilitators' function in TPD, and connectivism-based tools, such as MOOCs, a code extraction method was utilized. Three experts, former active participants in a series of projects initiated across Kazakhstan to improve the efficacy of MOOCs, evaluated the excerpts and selected the most appropriate ones to propose the matrix of teacher professional competencies that can be acquired through MOOCs. In this paper, we'll look at some of the strategies employed by course instructors to boost their students' English skills and knowledge of course material, both inside and outside of the MOOC platform. Participants' interactive learning contributed to their language and subject conceptual knowledge and prepared them for peer-reviewed assignments in the MOOCs, and this approach of small group interaction was given to highlight the outcomes of participants' interactive learning. Both formal and informal continuing education institutions can use the findings of this study to support teachers in gaining experience with MOOCs and creating their own online courses.

Keywords: connectivism-based tools, teacher professional development, massive open online courses, facilitators, Kazakhstani context

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Authors: Emad A. M. Osman, Ahmed M. Abu-Bakr

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Compacted granular fill under shallow foundation is one of the oldest, cheapest, and easiest techniques to improve the soil characteristics to increase the bearing capacity and decrease settlement under footing. There are three main factors affecting the design of soil replacement to gain these advantages. These factors are the type of replaced soil, characteristics, and thickness. The first two factors can be easily determined by laboratory and field control. This paper emphasizes on how to determine the thickness accurately for footing under centric axial load by limit state design method. The advantages of the method are the way of determining the thickness (independent of experience) and it takes into account the replaced and original or underneath soil characteristics and reaches the goals of replaced soils economically.

Keywords: design of soil replacement, LSD method, soil replacement, soil improvement

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Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

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This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

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Authors: Ezgiamn Abraha Hagos

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The primary objective of this study was to assess the existential concerns and related manifestations of higher learning students by investigating their perception of meaningful life and evaluating their purpose in life. In addition, this study was aimed at assessing the manifestations of existential pain among the students. Data was procured using Purpose in Life test (PIL), Well-being Manifestation Measure Scale (WBMMS), and focus group discussion. The total numbers of participants was 478, of which 299 were males and the remaining 179 females. They were selected using a simple random sampling technique. Data was analyzed using two ways. SPSS-version 20 was used to analyze the quantitative part, and narrative modes were utilized to analyze the qualitative data. The research finding revealed that students are involved in risk taking behaviors like alcohol ingestion, drug use, Khat (chat) chewing, and unsafe sex. In line with this it is found out that life in campus was perceived as temporary and as a result the sense of hedonism was prevalent at any cost. Of course, the most important thing for the majority of the students was to know about the purpose of life. Regarding WBMMS, there was no statistically significant difference among males and females and with the exception of the sub-scale of happiness; in all the sub-scales the mean is low. At last, assisting adolescents to develop holistically in terms of body, mind, and spirit is recommended.

Keywords: existential concerns, higher learning institutions, Ethiopia, Aksum University

Procedia PDF Downloads 433

Authors: Wei Zhao

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The pandemic crisis disrupted normal classroom practices so that the constraints of the traditional practice became apparent. This turns out to be new opportunities for technology-based learning and teaching. However, how the technology supports the preschool teachers go through this sudden crisis and how preschool teachers conceived of the use of technology, appropriate and design technological artifacts as a mediator of knowledge construction in order to suit young children’s literacy level are rarely explored. This study addresses these issues by looking at the influence of a web-supported teacher community on changes/shifts in preschool teachers’ epistemological beliefs and practices. This teachers’ professional development community was formulated before the pandemic time and developed virtually throughout the home-based learning caused by Covid-19. It served as a virtual and asynchronous community for those teachers to collaboratively plan for and conduct online lessons using the knowledge-building approach for the purpose of sustaining children’s learning curiosity and opening up new learning opportunities during the lock-down period. The knowledge-building approach helps to increase teachers’ collective responsibility to collaboratively work on shared educational goals in the teacher community and awareness of noticing new ideas or innovations in their classroom. Based on the data collected across five months during and after the lock-down period and the activity theory, results show a dynamic interplay between the evolution of the community culture, the growth of teacher community and teachers’ identity transformation and professional development. Technology is useful in this regard not only because it transforms the geographical distance and new gathering guidelines after the outbreak of pandemic into new ways of communal communication and collaboration. More importantly, while teachers selected, monitored and adapted the technology, it acts as a catalyst for changes in teachers’ old teaching practices and epistemological dispositions.

Keywords: activity theory, changes in epistemology and practice, knowledge building, web-based teachers’ professional development community

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Authors: K. Doi, T. Nishimura, H. Fujimoto

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The number of people who become visually impaired and do not have sufficient tactile experiences has increased by various disease. Especially, many acquired visually impaired persons due to accidents, disorders, and aging cannot adequately read Braille. It is known that learning Braille requires a great deal of time and the acquisition of various skills. In our previous studies, we reported one of the problems in learning Braille. Concretely, the standard Braille size is too small for Braille beginners. And also we are short of the objective data regarding easily readable Braille size. Therefore, it is necessary to conduct various experiments for evaluating Braille size that would make learning easier for beginners. In this study, for the purpose of investigating easy-to-read conditions of vertical and horizontal dot spacing for beginners, we conducted one Braille reading experiment. In this our experiment, we prepared test pieces by use of our original Braille printer with controlling function of Braille size. We specifically considered Braille beginners with acquired visual impairments who were unfamiliar with Braille. Therefore, ten sighted subjects with no experience of reading Braille participated in this experiment. Size of vertical and horizontal dot spacing was following conditions. Each dot spacing was 2.0, 2.3, 2.5, 2.7, 2.9, 3.1mm. The subjects were asked to read one Braille character with controlled Braille size. The results of this experiment reveal that Braille beginners can read Braille accurately and quickly when both vertical and horizontal dot spacing are 3.1 mm or more. This knowledge will be helpful data in considering Braille size for acquired visually impaired persons.

Keywords: paper-based Braille, vertical and horizontal dot spacing, readability, acquired visual impairment, Braille beginner

Procedia PDF Downloads 183

Authors: Gona Ayuba, Isiaka Mohammed, Kotom Mohammed Baba, Mohammed Aabubakar Maikasuwa

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The study examined the technical efficiency and its determinants of cattle fattening enterprises in Kebbi state, Nigeria. Data were collected from a sample of 160 fatteners between June 2010 and June 2011 using the multistage random sampling technique. Translog stochastic frontier production function was employed for the analysis. Results of the analysis show that technical efficiency indices varied from 0.74 to 0.98%, with a mean of 0.90%, indicating that there was no wide gap between the efficiency of best technical efficient fatteners and that of the average fattener. The result also showed that fattening experience and herd size influenced the level of technical efficiency at 1% levels. It is recommended that credit agencies should ensure that credit made available to the fatteners is monitored to ensure appropriate utilization.

Keywords: technical efficiency, determinants, cattle, fattening enterprises

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Anil Nis, Nilufer Ozyurt Zihnioglu

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The object of the study is to investigate fresh state properties of the steel fiber reinforced self-compacting concrete (SFR-SCC). Three different steel fibers; straight (Vf:0.5%), hooked-end long (Vf:0.5% and 1%) and hybrid fibers (0.5%short+0.5%long) were used in the research aiming to obtain flow properties of non-fibrous self-compacting concrete. Fly ash was used as a supplementary with an optimum dosage of 30% of the total cementitious materials. Polycarboxylic ether based high-performance concrete superplasticizer was used to get high flowability with percentages ranging from 0.81% (non-fibrous SCC) to 1.07% (hybrid SF-SCC) of the cement weight. The flowability properties of SCCs were measured via slump flow and V-funnel tests; passing ability properties of SCCs were measured with J-Ring, L-Box, and U-Box tests. Workability results indicate that small increase on the superplasticizer dosages compensate the adverse effects of steel fibers on flowability properties of SSC. However, higher dosage fiber addition has a negative effect on passing ability properties, causing blocking of the mixes. In addition, compressive strength, tensile strength, and four point bending results were given. Results indicate that SCCs including steel fibers have superior performances on tensile and bending strength of concrete. Crack bridging capability of steel fibers prevents concrete from splitting, yields higher deformation and energy absorption capacities than non-fibrous SCCs.

Keywords: fiber reinforced self-compacting concrete, fly ash, fresh state properties, steel fiber

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Authors: Saraswati Suna

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The outbreak of the COVID-19 epidemic and the subsequent lockdown have significantly impacted India's political, structural, and economic systems and a rising gap between the rich and the disadvantaged, upper and lower caste. For Dalit women, such forms of subjugation were followed by socioeconomic uncertainty due to the pandemic's economic shutdown and labour oppressions. Dalit women have been the victims of the most oppression among the nation's underprivileged groups. Dalit women undergo systemic oppression at the hands of the state, caste, class, gender, and religious hegemons historically. Dalit women hold a subordinate position within the gender to their male counterparts and caste to their upper-caste counterparts. This paper examines how Brahminical patriarchy and state power severely affected Dalit/Adivasi women during COVID-19 in Odisha, India. In order to understand caste-based sexual violence, a total of five cases have been analysed from newspapers. Findings revealed that Covid-19 appears to have a significant physical, psychological, and economic impact on Dalit women. The intention of sexual harassment and rape perpetrated by upper caste men is to maintain power and patriarchal culture in society. Dalit women are economically, socially, and culturally marginalised, which effectively exacerbates the sense of impunity by perpetrators of violence against Dalit women. This issue requires special attention to end atrocities against Dalit women. Dalit women become the target of rape, sexual assault, and murder. Sexual violence against Dalit women cannot be fully explained without linkage to caste, gender, and power. Dominant caste comes through caste privilege-socio-economic and politically; these factors contribute to sexual violence against Dalit women. The findings revealed that state police manipulate sexual violence, and in so doing, they create and deny access to both services to get justice. This article has argued that understanding Brahminical culture and the legal impacts of state police on Dalit women's identity requires a nuanced analysis.

Keywords: COVID-19, dalit women, sexual violence, brahminical patriarchy, power

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Authors: Enock E. Goler, Emmanuel H. Kwon-Ndung, Gbenga F. Akomolafe, Terna T. Paul, Markus Musa, Joshua I. Waya, James H. Okogbaa

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An ethno-medicinal survey of plants used in treating various diseases and ailments was carried out in the study area of Nasarawa State, North Central Nigeria to obtain information on their uses and potentials. The ethno-medicinal survey was administered through structured questionnaires among local inhabitants from areas with high plant density and diversity within the various Local Government Areas of the State. A total of 84 (Eighty four) plant species belonging to 45 (Forty five) families were found to be useful in treatment of various ailments such as diabetes, measles, fever, asthma, jaundice, pneumonia, sexually transmitted diseases (STDs), aches, diarrhea, cough, arthritis, yellow fever, typhoid, erectile dysfunction and excessive bleeding. Different parts of the plant such as the roots, leaves and stems are used in preparing herbal remedies which could be from dry or freshly collected plants. The main methods of preparation are decoction or infusion, while in some cases the plant parts used are consumed directly. Residents in the study areas find the herbal remedy cheaper and more accessible and claimed that there are no side effects compared to orthodox medicine. This study has confirmed the need towards the conscious conservation of plant genetic resources in order to ensure sustained access to these ethno-medicinal plant materials.

Keywords: ethno-medicinal, Nasarawa, plants, survey

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Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: N. Belas, O. Belaribi, S. Aggoun, K. Bendani, N. Bouhamou, A. Mebrouki

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This experimental work is a part of a long research on the valorization of the dam dredged sediments issued from Fergoug Dam (Mascara-West Algeria). These sediments have to be subjected to thermal treatment to become reactive with the cement and thus to obtain an artificial pozzolana. It is therefore a question of developing the calcined mud as substitutable material in part to the cement used in the composition of self compacting concrete. The objective of the present work is to highlight its influence on the behavior of self compacting concrete compared to that of the natural pozzolana and this, in fresh and hardened states. The study is being conducted on three SCC, the first using 20% in volume of natural pozzolana, the second with 20 % of calcined mud and the third for the sake of comparison is made with cement only. The first results showed the possibility of obtaining SCC with calcined mud complying with the AFGC recommendations having a good mechanical behavior which makes interesting its development as construction materials.

Keywords: dam, fresh state, hardened state mud, sediments, self compacting concrete, valorization

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Authors: Revoti Prasad Bora, Nikita Katyal

Abstract:

Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, Cannibalization, promotion, Baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression

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Authors: Maha Jazouli

Abstract:

Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition

Procedia PDF Downloads 194

Authors: Arpit Patil, Atharav Bhagwat, Rajas Bhope, Pramod Bide

Abstract:

This research integrates IoT and ML to predict heart failure risks, utilizing the Framingham dataset. IoT devices gather real-time physiological data, focusing on heart rate dynamics, while ML, specifically Random Forest, predicts heart failure. Rigorous feature selection enhances accuracy, achieving over 90% prediction rate. This amalgamation marks a transformative step in proactive healthcare, highlighting early detection's critical role in cardiovascular risk mitigation. Challenges persist, necessitating continual refinement for improved predictive capabilities.

Keywords: cardiovascular diseases, internet of things, machine learning, cardiac risk assessment, heart failure prediction, early detection, cardio data analysis

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Authors: Alper Akin, Ibrahim Aydogdu

Abstract:

This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teaching-learning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame

Procedia PDF Downloads 548

Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

Abstract:

Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

Procedia PDF Downloads 79

Authors: Sebastianus Menggo

Abstract:

The aim of communicative language teaching is to enable learners to communicate in the target language. Each learner is required to perform the micro and macro components in each utterance produced. Utterances produced must be in line with the understanding of competence and performance of each speaker. These are inter-depended. Competence and performance are obliged to be appeared proportionally in creating the utterances. The representative of competence and performance reflects the linguistics identity of a speaker in providing sentences in each certain language community. Each lexicon spoken may lead that interlocutor in comprehending the intentions utterances given. However proportional performance of both components in an utterance needed to be further elaborated. Finding appropriate gap between competence and performance components in a communicative competence must be supported positive response given by the learners.The learners’ inability to keep communicative competence proportionally is caused by inside and outside factors. The inside factors are certain lacks such as lack of self-confidence and lack of motivation which could make students feel ashamed to produce utterances, scared to make mistakes, and have no enough confidence. Knowing learner’s English learning motivation is an urgent variable to be considered in creating conducive atmosphere classroom which will raise the learners to do more toward the achievement of communicative competence. Meanwhile, the outside factor is related with the teacher. The teacher should be able to recognize the students’ problem in creating conducive atmosphere in the classroom that will raise the students’ ability to be an English speaker qualified. Moreover, the aim of this research is to know and describe the English learning motivation affecting students’ communicative competence of 48 students of XI grade of science program at catholic senior of Saint Ignasius Loyola Labuan Bajo, West Flores, Indonesia. Correlation design with purposive procedure applied in this research. Data were collected through questionnaire, interview, and students’ speaking achievement document. Result shows the description of motivation significantly affecting students’ communicative competence.

Keywords: communicative, competence, English, learning, motivation

Procedia PDF Downloads 204

Authors: Muhammad Saqib Manzoor, Camille Dickson-Deane, Prashan Karunaratne

Abstract:

Cobb-Douglas production (utility) function is a fundamental function widely used in economics teaching and research. The key reason is the function's characteristics to describe the actual production using inputs like labour and capital. The characteristics of the function like returns to scale, marginal, and diminishing marginal productivities are covered in the introductory units in both microeconomics and macroeconomics with a 2-dimensional static visualisation of the function. However, less insight is provided regarding three-dimensional surface, changes in the curvature properties due to returns to scale, the linkage of the short-run production function with its long-run counterpart and marginal productivities, the level curves, and the constraint optimisation. Since (freshman) learners have diverse prior knowledge and cognitive skills, the existing “one size fits all” approach is not very helpful. The aim of this study is to bridge this gap by introducing technological intervention with interactive animations of the three-dimensional surface and sequential unveiling of the characteristics mentioned above using Python software. A small classroom intervention has helped students enhance their analytical and visualisation skills towards active and authentic learning of this topic. However, to authenticate the strength of our approach, a quasi-Delphi study will be conducted to ask domain-specific experts, “What value to the learning process in economics is there using a 2-dimensional static visualisation compared to using a 3-dimensional dynamic visualisation?’ Here three perspectives of the intervention were reviewed by a panel comprising of novice students, experienced students, novice instructors, and experienced instructors in an effort to determine the learnings from each type of visualisations within a specific domain of knowledge. The value of this approach is key to suggesting different pedagogical methods which can enhance learning outcomes.

Keywords: cobb-douglas production function, quasi-Delphi method, effective teaching and learning, 3D-visualisations

Procedia PDF Downloads 151