Search results for: teaching and learning English

Authors: Mehrdad Shahrbanozadeh, Gholam Abbas Barani, Saeed Shojaee

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In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM.

Keywords: seepage, dam foundation, finite element method, neural network, seep 3D model

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Authors: Tiago do Carmo Nogueira, Cássio Dener Noronha Vinhal, Gélson da Cruz Júnior, Matheus Rudolfo Diedrich Ullmann

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Generating automatic image descriptions through natural language is a challenging task. Image captioning is a task that consistently describes an image by combining computer vision and natural language processing techniques. To accomplish this task, cutting-edge models use encoder-decoder structures. Thus, Convolutional Neural Networks (CNN) are used to extract the characteristics of the images, and Recurrent Neural Networks (RNN) generate the descriptive sentences of the images. However, cutting-edge approaches still suffer from problems of generating incorrect captions and accumulating errors in the decoders. To solve this problem, we propose a model based on the encoder-decoder structure, introducing a module that generates the weights according to the importance of the word to form the sentence, using the part-of-speech (PoS). Thus, the results demonstrate that our model surpasses state-of-the-art models.

Keywords: gated recurrent units, caption generation, convolutional neural network, part-of-speech

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Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

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Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Vanessa Bennemann, Gabriela Laste, Márcia Inês Goettert

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The gestational period is a phase in which the pregnant woman undergoes constant physiological and hormonal changes, which are part of the woman’s biological cycle, the development of the fetus, childbirth, and lactation. These are factors of response to the immunological adaptation of the human reproductive process that is directly related to the pregnancy’s well-being and development. Although most pregnancies occur without complications, about 15% of pregnant women will develop potentially fatal complications, implying maternal and fetal risk. Therefore, requiring specialized care for high-risk pregnant women (HRPW) with obstetric interventions for the survival of the mother and/or fetus. Among the risk factors that characterize HRPW are the women's age, gestational diabetes mellitus (GDM), autoimmune diseases, infectious diseases such as syphilis and HIV, hypertension (SAH), preeclampsia, eclampsia, HELLP syndrome, uterine contraction abnormalities, and premature placental detachment (PPD), thyroid disorders, among others. Thus, pregnancy has an impact on the thyroid gland causing changes in the functioning of the mother's thyroid gland, altering the thyroid hormone (TH) profiles and production as pregnancy progresses. Considering, throughout the gestational period, the interpretation of the results of the tests to evaluate the thyroid functioning depends on the stage in which the pregnancy is. Thyroid disorders are directly related to adverse obstetric outcomes and in child development. Therefore, the adequate release of TH is important for a pregnancy without complications and optimal fetal growth and development. Objective: Investigate the physiological effects caused by thyroid disorders in the gestational period. Methods: A search for articles indexed in PubMed, Scielo, and MDPI databases, was performed using the term “AND”, with the descriptors: Pregnancy, Thyroid. With several combinations that included: Melatonin, Thyroidopathy, Inflammatory processes, Cytokines, Anti-inflammatory, Antioxidant, High-risk pregnancy. Subsequently, the screening was performed through the analysis of titles and/or abstracts. The criteria were: including clinical studies in general, randomized or not, in the period of 10 years prior to the research, in the English literature; excluded: experimental studies, case reports, research in the development phase. Results: In the preliminary results, a total of studies (n=183) were found, (n=57) excluded, such as studies of cancer, diabetes, obesity, and skin diseases. Conclusion: To date, it has been identified that thyroid diseases can impair the fetus’s brain development. Further research is suggested on this matter to identify new substances that may have a potential therapeutic effect to aid the gestational period with thyroid diseases.

Keywords: pregnancy, thyroid, melatonin, high-risk pregnancy

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Authors: Leah Plocharczyk, Matthew Conner

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This paper analyzes the relatively new trend of PSE programs which seek to provide education, vocational training, and a college experience to individuals with an intellectual and developmental disability (IDD). Specifically, the paper examines the degree of interaction between PSE programs and the libraries of their college campuses. Using ThinkCollege, a clearinghouse and advocate for PSE programs, the researchers identified 293 programs throughout the country. These were all contacted with an email survey asking them about the nature of their involvement, if any, with the academic libraries on their campus. Where indicated by the responses, the libraries of PSE programs were contacted for additional information about their programming. Responses to the survey questions were tabulated and analyzed quantitatively. Written comments were analyzed for themes which were then tabulated. This paper presents the results of this study. They show obvious preferences for library programming, such as group formal instruction, individual liaisons, embedded reference, and various instructional designs. These are discussed in terms of special education principles of mainstreaming, level of restriction, training demands and cost effectiveness. The work serves as a foundation for best practices that can advance the field.

Keywords: disability studies, instructional design, universal design for learning, assessment methodology

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Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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Authors: Natalie Delia

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This academic paper investigates the transformative potential of incorporating alternative ways of knowing within the framework of Critical Studies departments. Traditional academic paradigms often prioritize empirical evidence and established methodologies, potentially limiting the scope of critical inquiry. In response to this, our research seeks to illuminate the benefits and challenges associated with integrating alternative epistemologies, such as indigenous knowledge systems, artistic expressions, and experiential narratives. Drawing upon a comprehensive review of literature and case studies, we examine how alternative ways of knowing can enrich and diversify the intellectual landscape of Critical Studies departments. By embracing perspectives that extend beyond conventional boundaries, departments may foster a more inclusive and holistic understanding of critical issues. Additionally, we explore the potential impact on pedagogical approaches, suggesting that alternative ways of knowing can stimulate alternative way of teaching methods and enhance student engagement. Our investigation also delves into the institutional and cultural shifts necessary to support the integration of alternative epistemologies within academic settings. We address concerns related to validation, legitimacy, and the potential clash with established norms, offering insights into fostering an environment that encourages intellectual pluralism. Furthermore, the paper considers the implications for interdisciplinary collaboration and the potential for cultivating a more responsive and socially engaged scholarship. By encouraging a synthesis of diverse perspectives, Critical Studies departments may be better equipped to address the complexities of contemporary issues, encouraging a dynamic and evolving field of study. In conclusion, this paper advocates for a paradigm shift within Critical Studies departments towards a more inclusive and expansive approach to knowledge production. By embracing alternative ways of knowing, departments have the opportunity to not only diversify their intellectual landscape but also to contribute meaningfully to broader societal dialogues, addressing pressing issues with renewed depth and insight.

Keywords: critical studies, alternative ways of knowing, academic department, Wallerstein

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Authors: Dave Kenneth Tayao Cayado, Carlo P. Magno, Venice Cristine Dangaran

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The present study determines whether there will be differences in the self-regulation dispositions that learners utilize when writing different types of composition. There were 7 self-regulation factors that were used to develop a scale in this study such as memory strategy, goal setting, self-evaluation, seeking assistance, learning responsibility, environmental structuring, and organizing. The scale was made specific for writing a composition. The researcher-made scale was administered to 150 participants who all came from a university in the Philippines. The participants were asked to write two compositions namely opinion essay and research introduction/review of related literature. The zero-order correlation revealed that all the factors of self-regulation are correlated with one another. However, only seeking assistance and self-evaluation are correlated with opinion essay and technical writing is not correlated to any of the self-regulation factors. However, when path analysis was used, it was shown that seeking assistance can predict opinion essay scores whereas memory strategy, self-evaluation, and organizing can predict technical writing scores.

Keywords: opinion essay, self-regulation, technical writing, writing skills

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Authors: Sakapas Saengchai, Vilasinee Jintalikhitdee, Mathinee Khongsatid, Nattapol Pourprasert

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This paper studied the participation of people of the five villages of Bang Nang Li Community in Ampawa District, Samut Songkram Province, in designing community planning. The population was 2,755 villagers from the 5 villages with 349 people sampled. The level of involvement was measured by using Likert Five Scale for: preparing readiness of local people in the community, providing information for community and self analysis and learning, designing goals and directions for community development, designing strategic plans for community projects, and operating according to the plans. All process items reported a medium level of involvement except the item of preparing readiness for local people that presented the highest mean score. A test of a correlation between personal factors and level of involvement in designing the community planning unveiled no correlation between gender, age and career. Contrarily, the findings revealed that the villagers’ educational level and community membership status had a correlation with their level of involvement in designing the community planning.

Keywords: community development, community planning, people participation, educational level

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Authors: L. R. Valencia Pérez, J. M. Peña Aguilar, A. Lamadrid Álvarez, A. Pastrana Palma, H. F. Valencia Pérez, M. Vivanco Vargas

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This work proposes a Cooperation-Competitive (Coopetitive) approach that allows coordinated work among the Secretary of Public Education (SEP), the Autonomous University of Querétaro (UAQ) and government funds from National Council for Science and Technology (CONACYT) or some other international organizations. To work on an overall knowledge transfer strategy with e-learning over the Cloud, where experts in junior high and high school education, working in multidisciplinary teams, perform analysis, evaluation, design, production, validation and knowledge transfer at large scale using a Cloud Computing platform. Allowing teachers and students to have all the information required to ensure a homologated nationally knowledge of topics such as mathematics, statistics, chemistry, history, ethics, civism, etc. This work will start with a pilot test in Spanish and initially in two regional dialects Otomí and Náhuatl. Otomí has more than 285,000 speaking indigenes in Queretaro and Mexico´s central region. Náhuatl is number one indigenous dialect spoken in Mexico with more than 1,550,000 indigenes. The phase one of the project takes into account negotiations with indigenous tribes from different regions, and the Information and Communication technologies to deliver the knowledge to the indigenous schools in their native dialect. The methodology includes the following main milestones: Identification of the indigenous areas where Otomí and Náhuatl are the spoken dialects, research with the SEP the location of actual indigenous schools, analysis and inventory or current schools conditions, negotiation with tribe chiefs, analysis of the technological communication requirements to reach the indigenous communities, identification and inventory of local teachers technology knowledge, selection of a pilot topic, analysis of actual student competence with traditional education system, identification of local translators, design of the e-learning platform, design of the multimedia resources and storage strategy for “Cloud Computing”, translation of the topic to both dialects, Indigenous teachers training, pilot test, course release, project follow up, analysis of student requirements for the new technological platform, definition of a new and improved proposal with greater reach in topics and regions. Importance of phase one of the project is multiple, it includes the proposal of a working technological scheme, focusing in the cultural impact in Mexico so that indigenous tribes can improve their knowledge about new forms of crop improvement, home storage technologies, proven home remedies for common diseases, ways of preparing foods containing major nutrients, disclose strengths and weaknesses of each region, communicating through cloud computing platforms offering regional products and opening communication spaces for inter-indigenous cultural exchange.

Keywords: Mexicans indigenous tribes, education, knowledge transfer, cloud computing, otomi, Náhuatl, language

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Authors: B. Alajmi, L. Marouf, A. S. Chaudhry

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In recent years, increasing emphasis has been placed upon issues concerning the evaluation of health care. In this regard, knowledge management has also been considered an important component of the evaluation process. KM facilitates the transfer of existing knowledge or the development of new knowledge among healthcare staff and patients. This research aimed to examine how hospitals in Kuwait employ knowledge management practices, including capturing, sharing, and generating, and the perceived impact of KM practices on performance of hospitals in Kuwait. Through adopting a quantitative survey method with 277 sample of doctors, the study found that in terms of the three major knowledge management practices – knowledge capturing, sharing, and generating – the adoption of KM practices were rated very low in the sampled hospitals in Kuwait. Hospitals paid little attention to the main activities that support the transfer of expertise among doctors in hospitals. However, as predicted by previous studies, knowledge management practices were perceived to have an impact on hospitals’ performance. Through knowledge capturing, sharing, and generating, hospitals could improve the services they provide through documenting best practices, transforming their hospitals into learning organizations in which lessons learned are captured, stored, and made available for others to learn from.

Keywords: knowledge management, hospitals, knowledge management practices, knowledge management tools, performance

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Authors: Hamad Aldosari

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The purpose of this study is to investigate the status of sexism, or gender discrepancies, in current pedagogical and curricular practices in EFL learning higher education settings. Qualitative and quantitative analyses of both course contents and pedagogies in Saudi higher education institutions are to be discussed with reference to female/male topic presentation in dialogs and reading passages, sex-based activity types, stereotyped sex roles and the masculine generic conceptions of male superiority subliminally related in EFL curriculum and pedagogical practices, as well as the causes and effects of segregated language education practices in Saudi Arabia from a holistic vantage point of analysis. Analysis findings show that language educational practices including educational settings and segregation are gender-biased in attitude, but with regard to curriculum, sexism has not been traced. Findings also show that sexism is rampant due to socio-cultural aspects of language education rather than to religious reasons: a finding that seems to mirror the institutionalized unfair sex discrimination to the disadvantage of women in the Arabian societies at large.

Keywords: genderism, sex segregation, Saudi Arabia, EFL

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Authors: Poom Thiparapkul, Tanat Jiravansirikul, Pakpoom Thongsari

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This study explores the design of electric vehicle (EV) dashboards with a focus on user interaction. Findings from a Thai sample reveal a preference for physical buttons over touch interfaces due to their immediate feedback. Touchscreens lack this assurance, leading to potential uncertainty. Users' smartphone experiences create a learning curve that doesn't translate well to in-car touch systems. Gender-wise, females exhibit slightly longer decision times. Designing EV dashboards should consider these factors, prioritizing user experience while avoiding overreliance on smartphone principles. A successful example is Subaru XV's design, which calculates screen angles and button positions for targeted users. In summary, EV dashboards should be intuitive, minimize touch dependency, and accommodate user habits. Balancing modernity with functionality can enhance driving experiences while ensuring safety. A user-centered approach, acknowledging gender differences, will yield efficient and safe driving environments.

Keywords: user experience design, user experience, electric vehicle, dashboard design, Thailand driver.

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Authors: Khaled M. Alhawiti

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Reading capability is a major segment of language competency. On the other hand, discovering topical writings at a fitting level for outside and second language learners is a test for educators. We address this issue utilizing natural language preparing innovation to survey reading level and streamline content. In the connection of outside and second-language learning, existing measures of reading level are not appropriate to this errand. Related work has demonstrated the profit of utilizing measurable language preparing procedures; we expand these thoughts and incorporate other potential peculiarities to measure intelligibility. In the first piece of this examination, we join characteristics from measurable language models, customary reading level measures and other language preparing apparatuses to deliver a finer technique for recognizing reading level. We examine the execution of human annotators and assess results for our finders concerning human appraisals. A key commitment is that our identifiers are trainable; with preparing and test information from the same space, our finders beat more general reading level instruments (Flesch-Kincaid and Lexile). Trainability will permit execution to be tuned to address the needs of specific gatherings or understudies.

Keywords: natural language processing, trainability, syntactic simplification tools, education

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Authors: Promise Ekpo Osaine, Daniel Melesse

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Image captioning is an active area of research in the multi-modal artificial intelligence (AI) community as it connects vision and language understanding, especially in settings where it is required that a model understands the content shown in an image and generates semantically and grammatically correct descriptions. In this project, we followed a standard approach to a deep learning-based image captioning model, injecting architecture for the encoder-decoder setup, where the encoder extracts image features, and the decoder generates a sequence of words that represents the image content. As such, we investigated image encoders, which are ResNet101, InceptionResNetV2, EfficientNetB7, EfficientNetV2M, and CLIP. As a caption generation structure, we explored long short-term memory (LSTM). The CLIP-LSTM model demonstrated superior performance compared to the encoder-decoder models, achieving a BLEU-1 score of 0.904 and a BLEU-4 score of 0.640. Additionally, among the CNN-LSTM models, EfficientNetV2M-LSTM exhibited the highest performance with a BLEU-1 score of 0.896 and a BLEU-4 score of 0.586 while using a single-layer LSTM.

Keywords: multi-modal AI systems, image captioning, encoder, decoder, BLUE score

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Authors: Annie Devault

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Mindfulness-based interventions (MBI) can be beneficial for the well-being of children. MBIs offered for children in contexts of vulnerability (poverty, neglect) report positive results in terms of emotion regulation and cognitive flexibility. Anxiety is a common issue for children living in a vulnerable context. It has a negative impact on children’s attention span, emotional regulation and self-esteem. The MBI (12 weeks) associated with this research has been developed for a total of 30 children suffering from anxiety (7 to 9 years old) and receiving services from a community center over the last seven years. The first objective is to describe in details the content of the mindfulness-based intervention. The second purpose is to document what helps and what hinders the practice of mindfulness for children living in a context of vulnerability. A special attention will be given to the importance of the way that the intervention is offered and the principles that are followed by the practitioners. Perceived effects of the intervention on children were collected through an individual semi-structured interview with each child at the end of the program. Parents were also interviewed to have their point of view on the effect of their children’s participation in the group. Anxiety was measure with the Beck youth pre-post and at follow up (2 months). Qualitative analysis of the interviews with children showed that most of them mentioned that the program helped them become calmer, more confident, less scared and more able to deal with difficult emotions. Almost all of them reported having used the material provided to them to practice at home. This result has been confirmed by parents. They reported that their child had gained confidence and were better at verbalizing emotions. Children also grew calmer, even though all anxiety was not gone. They would have liked more material to practice at home. The quantitative instrument used to measure anxiety did not corroborate the qualitative interviews about anxiety. Discussion will question the use of this questionnaire for children who have important cognitive limitations. Discussion will also report the importance of the personalized contact with children, along with other consideration, to enhance the adherence of children and parents. The MBI seems to have benefited children in different ways, which is corroborated by most parents. Since the sample was limited, we will need to continue documenting its effects with more children and parents. The major strength of this research is to have reported the subjective perspectives of children on their experience of mindfulness.

Keywords: anxiety, mindfulness, children, best practices

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Iván García-Magariño, Javier Bravo-Agapito, Marta López-Fernández

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Online judges have proven their utility in partial auto-evaluation of programming short exercises in the last decades. UnitJudge online judge has the advantage of facilitating the evaluation of separate units to provide more segregate and meaningful feedback to students in complex exercises and practices. This paper discusses the use of UnitUdge in advanced Java object-oriented programming exercises and web development practices. This later usage has been proposed by means of the Selenium Java library and classes to provide the web address. Consequently, UnitJudge is an online judge system that can be applied in several subjects, and therefore, many other students would take advantage of self-testing their exercises. This paper presents the experiments with a Java programming exercise for learning Java object-oriented classes with a generic type. Considering 10 students who voluntarily used UnitJudge, 80% successfully learned this concept, passing the judge exercise with correct results.

Keywords: online judges, programming skills, computer science education, auto-evaluation

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, Rebecca K. Rossi, Yanmin Yuan, Xianpei Li

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The fiscal growth of any country hinges on the prudent administration of water resources. The river Yamuna and Ganga are measured as the life line of India as it affords the needs for life to endure. Earth observation over remote sensing images permits the precise description and identification of ingredients on the superficial from space and airborne platforms. Multiple and heterogeneous image sources are accessible for the same geographical section; multispectral, hyperspectral, radar, multitemporal, and multiangular images. In this paper, a taxonomical learning of the fluvial geomorphological features of river Yamuna and Ganga over doon valley using color fusion of multispectral remote sensing images was performed. Experimental results exhibited that the segmentation based colorization technique stranded on pattern recognition, and color mapping fashioned more colorful and truthful colorized images for geomorphological feature extraction.

Keywords: color fusion, geomorphology, fluvial processes, multispectral images, pattern recognition

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Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

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Authors: Anita Chhatwal

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Purpose: The purpose of this paper is to evaluate and investigate the scope of usage of Academic Social Networking Websites (ASNs) by the Science faculty members across universities of North India, viz. Panjab University, Punjabi University and University of Delhi, Delhi. Design/Methodology/Approach: The present study is based upon the primary data collected from 81 science faculty participants from three universities of North India. Questionnaire method was used as an instrument for survey. The study is descriptive and research-based to investigate the popular ASNs amongst the participants from three sample universities and the purpose for which they use them along with the problems they encounter while using ASNs. Findings: The findings of the study revealed that majority of the participants were using ASNs for their academic needs. It was observed that majority of the participants (78%) used ASNs to access scientific papers, while 73.8% of the participants used them to share their research publications. ResearchGate (60.5%) and Google Scholar (59.7%) were the top two most preferred and widely used ASNs by the participants. The critical analysis of the data shows that laptops (86.3%) emerged as major tools for accessing ASNs. Shortage of computers was found to be the chief obstacle in accessing ASNs by the participants. Results of the study demonstrate that 56.3% of participants suggested conduct of seminars and training as the most effective method to increase the awareness of ASNs. Research Limitations/Implications: The study in hand absorbed the 81 faculty (Assistant Professors) members from 15 Science teaching departments across three sample universities of North India. The findings of this study will help the Government of India to regulate and simultaneously make effort to develop and enhance ASNs usage among faculty, researchers, and students. The present study will add to the existing library and information science literature and will be advantageous for all the information professionals as well. Originality/Value: This study is original survey based on primary data investigate the usage of ASNs by the academia. This study will be useful for research scholars, academicians and students all over the world.

Keywords: academic social networks, awareness and usage, North India, scholarly communication, web 2.0

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Authors: Amanda Burrell, Tonia Gary, David Wright, Kumara Ward

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This paper explores the concept of academic identity as it relates to the lecture, in particular, the digitized lecture delivered to a camera, in the absence of a student audience. Many academics have the performance aspect of the role thrust upon them with little or no training. For the purpose of this study, we look at the performance of the academic identity and examine tailored film presence coaching for its contributions toward academic identity, specifically in relation to feelings of self-confidence and diminishment of discomfort or stage fright. The case is articulated through the lens of scholar-practitioners, using expert facilitated participatory action research. It demonstrates in our sample of experienced academics, all reported some feelings of uncertainty about presenting lectures to camera prior to coaching. We share how power poses and reframing fear, produced improvements in the ease and competency of all participants. We share exactly how this insight could be adapted for self-coaching by any academic when called to present to a camera and consider the relationship between this and academic identity.

Keywords: academic identity, digitized lecture, embodied learning, performance coaching

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Authors: Madhu Babu Cherukuri, Tamoghna Ghosh

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Factory supply chain management spends billions of dollars every year to procure and manage equipment spare parts. Due to technology -and processes changes some of these spares become obsolete/dead inventory. Factories have huge dead inventory worth millions of dollars accumulating over time. This is due to lack of a scientific methodology to identify them and send the inventory back to the suppliers on a timely basis. The standard approach followed across industries to deal with this is: if a part is not used for a set pre-defined period of time it is declared dead. This leads to accumulation of dead parts over time and these parts cannot be sold back to the suppliers as it is too late as per contract agreement. Our main idea is the time period for identifying a part as dead cannot be a fixed pre-defined duration across all parts. Rather, it should depend on various properties of the part like historical consumption pattern, type of part, how many machines it is being used in, whether it- is a preventive maintenance part etc. We have designed a predictive algorithm which predicts part obsolescence well in advance with reasonable accuracy and which can help save millions.

Keywords: obsolete inventory, machine learning, big data, supply chain analytics, dead inventory

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Authors: Sadeeq Launi

Abstract:

NGOs, the world over, have been recognized as part of the institutions that complement government activities in providing services to the people, particularly in respect of human development. This study examined the role played by the NGOs in human development in Bauchi State, Nigeria, between 2004 and 2013. The emphasis was on reproductive health and access to education role of the selected NGOs. All the research questions, objectives and hypotheses were stated in line with these variables. The theoretical framework that guided the study was the participatory development approach. Being a survey research, data were generated from both primary and secondary sources with questionnaires and interviews as the instruments for generating the primary data. The population of the study was made up of the staff of the selected NGOs, beneficiaries, health staff and school teachers in Bauchi State. The sample drawn from these categories were 90, 107 and 148 units respectively. Stratified random and simple random sampling techniques were adopted for NGOs staff, and Health staff and school teachers data were analyzed quantitatively and qualitatively and hypotheses were tested using Pearson Chi-square test through SPSS computer statistical package. The study revealed that despite the challenges facing NGOs operations in the study area, NGOs rendered services in the areas of health and education This research recommends among others that, both government and people should be more cooperative to NGOs to enable them provide more efficient and effective services. Governments at all levels should be more dedicated to increasing accessibility and affordability of basic education and reproductive health care facilities and services in Bauchi state through committing more resources to the Health and Education sectors, this would support and facilitate the complementary role of NGOs in providing teaching facilities, drugs, and other reproductive health services in the States. More enlightenment campaigns should be carried out by governments to sensitize the public, particularly women on the need to embrace immunization programmes for their children and antenatal care services being provided by both the government and NGOs.

Keywords: access to education, human development, NGOs, reproductive health

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Authors: Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

Abstract:

The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, image classifiers to detect CSSVD-infected cacao plants are presented in this study. The classifiers are based on VGG16, ResNet50 and Vision Transformer (ViT). The image classifiers are evaluated on a recently released and publicly accessible KaraAgroAI Cocoa dataset. The best performing image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. These results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.

Keywords: CSSVD, image classification, ResNet50, vision transformer, KaraAgroAI cocoa dataset

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

Abstract:

The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Duygu Dere, Mert Ergeneci, Kaan Gokcesu

Abstract:

Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data.

Keywords: adaptive data processing, behavioral finance , convex optimization, online learning, soft minimum thresholding

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Authors: Junqi Zou

Abstract:

As one of the world’s most tech-ready countries, Singapore has initiated the Smart Nation plan to harness the full power and potential of digital technologies to transform the way people live and work, through the more efficient government and business processes, to make the economy more productive. The key evolutions of digital technology transformation in healthcare and the increasing deployment of Internet of Things (IoTs), Big Data, AI/cognitive, Robotic Process Automation (RPA), Electronic Health Record Systems (EHR), Electronic Medical Record Systems (EMR), Warehouse Management System (WMS in the most recent decade have significantly stepped up the move towards an information-driven healthcare ecosystem. The advances in information technology not only bring benefits to patients but also act as a key force in changing management accounting in healthcare sector. The aim of this study is to investigate the impacts of digital technology transformation on Singapore’s healthcare sector from a management accounting perspective. Adopting a Balanced Scorecard (BSC) analysis approach, this paper conducted an exploratory case study of a newly launched Singapore public hospital, which has been recognized as amongst the most digitally advanced healthcare facilities in Asia-Pacific region. Specifically, this study gains insights on how the new technology is changing healthcare organizations’ management accounting from four perspectives under the Balanced Scorecard approach, 1) Financial Perspective, 2) Customer (Patient) Perspective, 3) Internal Processes Perspective, and 4) Learning and Growth Perspective. Based on a thorough review of archival records from the government and public, and the interview reports with the hospital’s CIO, this study finds the improvements from all the four perspectives under the Balanced Scorecard framework as follows: 1) Learning and Growth Perspective: The Government (Ministry of Health) works with the hospital to open up multiple training pathways to health professionals that upgrade and develops new IT skills among the healthcare workforce to support the transformation of healthcare services. 2) Internal Process Perspective: The hospital achieved digital transformation through Project OneCare to integrate clinical, operational, and administrative information systems (e.g., EHR, EMR, WMS, EPIB, RTLS) that enable the seamless flow of data and the implementation of JIT system to help the hospital operate more effectively and efficiently. 3) Customer Perspective: The fully integrated EMR suite enhances the patient’s experiences by achieving the 5 Rights (Right Patient, Right Data, Right Device, Right Entry and Right Time). 4) Financial Perspective: Cost savings are achieved from improved inventory management and effective supply chain management. The use of process automation also results in a reduction of manpower costs and logistics cost. To summarize, these improvements identified under the Balanced Scorecard framework confirm the success of utilizing the integration of advanced ICT to enhance healthcare organization’s customer service, productivity efficiency, and cost savings. Moreover, the Big Data generated from this integrated EMR system can be particularly useful in aiding management control system to optimize decision making and strategic planning. To conclude, the new digital technology transformation has moved the usefulness of management accounting to both financial and non-financial dimensions with new heights in the area of healthcare management.

Keywords: balanced scorecard, digital technology transformation, healthcare ecosystem, integrated information system

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