Search results for: artificial reasoning

Authors: P. W. Tsai, J. W. Chen, C. W. Chen, C. Y. Chen

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In recent years, many researchers are involved in the field of fuzzy theory. However, there are still a lot of issues to be resolved. Especially on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. Besides fuzzy theory, algorithms in swarm intelligence are also a popular field for the researchers. In this paper, a concept of utilizing one of the swarm intelligence method, which is called Bacterial-GA Foraging, to find the stabilized common P matrix for the fuzzy controller system is proposed. An example is given in in the paper, as well.

Keywords: half-circle fuzzy numbers, predictions, swarm intelligence, Lyapunov method

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Authors: José Eduardo García-Mendiola

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In this work, a phenomenological approach to computational modeling of analogy processing is carried out. The paper goes through the consideration of the structure of the analogy, based on the possibility of sustaining the genesis of its elements regarding Husserl's genetic theory of association. Among particular processes which take place in order to get analogical inferences, there is one which arises crucial for enabling efficient base cases retrieval through long-term memory, namely analogical transference grounded on familiarity. In general, it has been argued that analogical reasoning is a way by which a conscious agent tries to determine or define a certain scope of objects and relationships between them using previous knowledge of other familiar domain of objects and relations. However, looking for a complete description of analogy process, a deeper consideration of phenomenological nature is required in so far, its simulation by computational programs is aimed. Also, one would get an idea of how complex it would be to have a fully computational account of the analogy elements. In fact, familiarity is not a result of a mere chain of repetitions of objects or events but generated insofar as the object/attribute or event in question is integrable inside a certain context that is taking shape as functionalities and functional approaches or perspectives of the object are being defined. Its familiarity is generated not by the identification of its parts or objective determinations as if they were isolated from those functionalities and approaches. Rather, at the core of such a familiarity between entities of different kinds lays the way they are functionally encoded. So, and hoping to make deeper inroads towards these topics, this essay allows us to consider that cognitive-computational perspectives can visualize, from the phenomenological projection of the analogy process reviewing achievements already obtained as well as exploration of new theoretical-experimental configurations towards implementation of analogy models in specific as well as in general purpose machines.

Keywords: analogy, association, encoding, retrieval

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Authors: Coriolano Salvini, Ambra Giovannelli

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The use of renewable energy sources for electric power production leads to reduced CO2 emissions and contributes to improving the domestic energy security. On the other hand, the intermittency and unpredictability of their availability poses relevant problems in fulfilling safely and in a cost efficient way the load demand along the time. Significant benefits in terms of “grid system applications”, “end-use applications” and “renewable applications” can be achieved by introducing energy storage systems. Among the currently available solutions, CAES (Compressed Air Energy Storage) shows favorable features. Small-medium size plants equipped with artificial air reservoirs can constitute an interesting option to get efficient and cost-effective distributed energy storage systems. The present paper is addressed to the design and off-design analysis of the compression system of small size CAES plants suited to absorb electric power in the range of hundreds of kilowatt. The system of interest is constituted by an intercooled (in case aftercooled) multi-stage reciprocating compressor and a man-made reservoir obtained by connecting large diameter steel pipe sections. A specific methodology for the system preliminary sizing and off-design modeling has been developed. Since during the charging phase the electric power absorbed along the time has to change according to the peculiar CAES requirements and the pressure ratio increases continuously during the filling of the reservoir, the compressor has to work at variable mass flow rate. In order to ensure an appropriately wide range of operations, particular attention has been paid to the selection of the most suitable compressor capacity control device. Given the capacity regulation margin of the compressor and the actual level of charge of the reservoir, the proposed approach allows the instant-by-instant evaluation of minimum and maximum electric power absorbable from the grid. The developed tool gives useful information to appropriately size the compression system and to manage it in the most effective way. Various cases characterized by different system requirements are analysed. Results are given and widely discussed.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), compressor design, compression system management.

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Authors: George Kyriakopoulos, Photini Pazartzis, Anthi Koskina, Crystalie Bourcha

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The need for securing safe access to and return from outer space, as well as ensuring the viability of outer space operations, maintains vivid the debate over the promotion of organization of space traffic through a Space Traffic Management System (STM). The proliferation of outer space activities in recent years as well as the dynamic emergence of the private sector has gradually resulted in a diverse universe of actors operating in outer space. The said developments created an increased adverse impact on outer space sustainability as the case of the growing number of space debris clearly demonstrates. The above landscape sustains considerable threats to outer space environment and its operators that need to be addressed by a combination of scientific-technological measures and regulatory interventions. In this context, recourse to recent technological advancements and, in particular, to Artificial Intelligence (AI) and machine learning systems, could achieve exponential results in promoting space traffic management with respect to collision avoidance as well as launch and re-entry procedures/phases. New technologies can support the prospects of a successful space traffic management system at an international scale by enabling, inter alia, timely, accurate and analytical processing of large data sets and rapid decision-making, more precise space debris identification and tracking and overall minimization of collision risks and reduction of operational costs. What is more, a significant part of space activities (i.e. launch and/or re-entry phase) takes place in airspace rather than in outer space, hence the overall discussion also involves the highly developed, both technically and legally, international (and national) Air Traffic Management System (ATM). Nonetheless, from a regulatory perspective, the use of AI for the purposes of space traffic management puts forward implications that merit particular attention. Key issues in this regard include the delimitation of AI-based activities as space activities, the designation of the applicable legal regime (international space or air law, national law), the assessment of the nature and extent of international legal obligations regarding space traffic coordination, as well as the appropriate liability regime applicable to AI-based technologies when operating for space traffic coordination, taking into particular consideration the dense regulatory developments at EU level. In addition, the prospects of institutionalizing international cooperation and promoting an international governance system, together with the challenges of establishment of a comprehensive international STM regime are revisited in the light of intervention of AI technologies. This paper aims at examining regulatory implications advanced by the use of AI technology in the context of space traffic management operations and its key correlating concepts (SSA, space debris mitigation) drawing in particular on international and regional considerations in the field of STM (e.g. UNCOPUOS, International Academy of Astronautics, European Space Agency, among other actors), the promising advancements of the EU approach to AI regulation and, last but not least, national approaches regarding the use of AI in the context of space traffic management, in toto. Acknowledgment: The present work was co-funded by the European Union and Greek national funds through the Operational Program "Human Resources Development, Education and Lifelong Learning " (NSRF 2014-2020), under the call "Supporting Researchers with an Emphasis on Young Researchers – Cycle B" (MIS: 5048145).

Keywords: artificial intelligence, space traffic management, space situational awareness, space debris

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: M. Sarikanat, E. Akar, I. Şen, Y. Seki, O. C. Yılmaz, B. O. Gürses, L. Cetin, O. Özdemir, K. Sever

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Chitosan is a natural, nontoxic, polyelectrolyte, cheap polymer. In this study, chitosan based electroactive polymer (CBEAP) was fabricated. Electroactive properties of this polymer were investigated at different voltages. It exhibited excellent tip displacement at low voltages (1, 3, 5, 7 V). Tip displacement was increased as the applied voltage increased. Best tip displacement was investigated as 28 mm at 5V. Characterization of CBEAP was investigated by scanning electron microscope, X-ray diffraction and tensile testing. CBEAP exhibited desired electroactive properties at low voltages. It is suitable for using in artificial muscle and various robotic applications.

Keywords: chitosan, electroactive polymer, electroactive properties

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Authors: Aisha Adam

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This study critically examines the transformative impact of automated decision-making (ADM) and artificial intelligence (AI) systems on South African labour law. As AI technologies increasingly infiltrate workplaces, existing liability frameworks face challenges in addressing the unique complexities presented by these innovations. This article explores the necessity of redefining liability to accommodate the nuanced landscape of ADM and AI within South African labour law. It emphasises the importance of ensuring responsible deployment and safeguarding the rights of workers amid evolving technological dynamics. This research investigates the central concern of fairness, bias, and discrimination in ADM and AI decision-making. Focusing on algorithmic bias and discriminatory outcomes, the paper advocates for the integration of mechanisms within the South African legal framework, particularly under the Promotion of Equality and Prevention of Unfair Discrimination Act (PEPUDA) and the Employment Equity Act (EEA). The study scrutinises the shifting dynamics of the employment relationship, calling for clear guidelines on the responsibilities and liabilities of employers, employees, and technology providers. Furthermore, the article analyses legal and policy responses to ADM and AI within South African labour law, exploring potential amendments to legislation, guidelines, and codes of practice. It assesses the role of regulatory bodies, specifically the Commission for Conciliation, Mediation, and Arbitration (CCMA), in overseeing and enforcing responsible practices in the workplace. Lastly, the research evaluates the impact of ADM and AI on human and social rights in the South African context. Emphasising the protection of constitutional rights, including fair labour practices, privacy, and equality, the study proposes remedies and safeguards. It advocates for a multidisciplinary approach involving legal, technological, and ethical considerations to redefine liability in South African labour law effectively. The article contends that a shift from accountability to responsibility is crucial for promoting fairness, antidiscrimination, and the protection of human and social rights in the age of automated decision-making. It calls for collaborative efforts among stakeholders to shape responsible practices and redefine liability in this evolving technological landscape.

Keywords: automated decision-making, artificial intelligence, labour law, vicarious liability

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Authors: Halim Djerroud, Arab Ali Cherif

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We assume that conscious functions are implemented automatically. In other words that consciousness as well as the non-consciousness aspect of human thought, planning, and perception, are produced by biologically adaptive algorithms. We propose that the mechanisms of consciousness can be produced using similar adaptive algorithms to those executed by the mechanism. In this paper, we propose a computational model of consciousness, the ”Global Abstraction Workspace” which is an internal environmental modelling perceived as a multi-agent system. This system is able to evolve and generate new data and processes as well as actions in the environment.

Keywords: artificial consciousness, cognitive architecture, global abstraction workspace, multi-agent system

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Authors: Fatima Faiza Ahmed, Syed Farrukh Hussain

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The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems.

Keywords: adaptable e-learning, HTMLParser, information extraction, semantic web

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Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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Authors: Aliya K. Salahova

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Scientific thinking is a fundamental cognitive skill that plays a crucial role in preparing young minds for an increasingly complex world. This study explores the connection between scientific thinking and the ability to ask questions in children aged 5-9. The research aims to identify and assess how questioning ability serves as an indicator of scientific thinking development in this age group. A longitudinal investigation was conducted over a span of 240 weeks, involving 72 children from diverse backgrounds. The participants were divided into an experimental group, engaging in weekly STEM activities, and a control group with no STEM involvement. The development of scientific thinking was evaluated through a comprehensive assessment of questioning skills, hypothesis formulation, logical reasoning, and problem-solving abilities. The findings reveal a significant correlation between the ability to ask questions and the level of scientific thinking in children aged 5-9. Participants in the experimental group exhibited a remarkable improvement in their questioning ability, which positively influenced their scientific thinking growth. In contrast, the control group, devoid of STEM activities, showed minimal progress in questioning skills and subsequent scientific thinking development. This study highlights the pivotal role of questioning ability as a key indicator of scientific thinking in young children. The results provide valuable insights for educators and researchers, emphasizing the importance of fostering and nurturing questioning skills to enhance scientific thinking capabilities from an early age. The implications of these findings are crucial for designing effective educational interventions to promote scientific curiosity and critical thinking in the next generation of scientific minds.

Keywords: scientific thinking, education, STEM, intervention, psychology, pedagogy, collaborative learning, longitudinal study

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Authors: Sharifa Alduraibi, Abir El Sadik, Ahmed Elzainy, Alaa Alduraibi, Ahmed Alsolai

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Introduction: Problem-based learning (PBL) is an active student-centered educational modality, influenced by the students' interest that required continuous motivation to improve their engagement. The new era of professional information technology facilitated the utilization of educational multimedia, such as videos, soundtracks, and photographs promoting students' learning. The aim of the present study was to introduce multimedia-enriched PBL scenarios for the first time in college of medicine, Qassim University, as an incentive for better students' engagement. In addition, students' performance and satisfaction were evaluated. Methodology: Two multimedia-enhanced PBL scenarios were implemented to the third years' students in the urinary system block. Radiological images, plain CT scan, and X-ray of the abdomen and renal nuclear scan correlated with their pathological gross photographs were added to the scenarios. One week before the first sessions, pre-recorded orientation videos for PBL tutors were submitted to clarify the multimedia incorporated in the scenarios. Other two traditional PBL scenarios devoid of multimedia demonstrating the pathological and radiological findings were designed. Results and Discussion: Comparison between the formative assessments' results by the end of the two PBL modalities was done. It revealed significant increase in students' engagement, critical thinking and practical reasoning skills during the multimedia-enhanced sessions. Students' perception survey showed great satisfaction with the new strategy. Conclusion: It could be concluded from the current work that multimedia created technology-based teaching strategy inspiring the student for self-directed thinking and promoting students' overall achievement.

Keywords: multimedia, pathology and radiology images, problem-based learning, videos

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Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Farhan J. M. Al-Behadili, Vineeta Bilgi, Miyuki Taniguchi, Junxi Li, Wei Xu

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Mediterranean fruit fly (Ceratitis capitata) is one of the most destructive pests of fruits and vegetables. Medfly originated from Africa and spread in many countries, and is currently an endemic pest in Western Australia. Medfly has been recorded from over 300 plant species including fruits, vegetables, nuts and its main hosts include blueberries, citrus, stone fruit, pome fruits, peppers, tomatoes, and figs. Global trade of fruits and other farm fresh products are suffering from the damages of this pest, which prompted towards the need to develop more effective ways to control these pests. The available quarantine treatment technologies mainly include chemical treatment (e.g., fumigation) and non-chemical treatments (e.g., cold, heat and irradiation). In recent years, with the loss of several chemicals, it has become even more important to rely on non-chemical postharvest control technologies (i.e., heat, cold and irradiation) to control fruit flies. Cold treatment is one of the most potential trends of focus in postharvest treatment because it is free of chemical residues, mitigates or kills the pest population, increases the strength of the fruits, and prolongs storage time. It can also be applied to fruits after packing and ‘in transit’ during lengthy transport by sea during their exports. However, limited systematic study on cold treatment of Medfly stages in artificial diets was reported, which is critical to provide a scientific basis to compare with previous research in plant products and design an effective cold treatment suitable for exported plant products. The overall purpose of this study was to evaluate and understand Medfly responses to cold treatments. Medfly stages were tested. The long-term goal was to optimize current postharvest treatments and develop more environmentally-friendly, cost-effective, and efficient treatments for controlling Medfly. Cold treatment with different exposure times is studied to evaluate cold eradication treatment of Mediterranean fruit fly (Ceratitis capitata), that reared on carrot diet. Mortality is important aspect was studied in this study. On the other hand, study effects of exposure time on mortality means of medfly stages.

Keywords: cold treatment, fruit fly, Ceratitis capitata, carrot diet, temperature effects

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%.

Keywords: ADALINE, neural network, gradient decent, process mining, resource behaviour, polynomial regression model

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Authors: Safa Abdussalam

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Adolescence is a period of storm and stress. It is a transitional period. Adolescents undergo a lot of changes physically, emotionally and mentally during adolescence. Physical changes include puberty, sexual maturation, changes in height, weight, hormonal changes, changes in body image, changes in brain and in sexuality. Changes also occur in their cognition. According to Piaget’s theory, adolescent enter formal operational stage and engage in hypothetical-deductive reasoning. Main characteristic of adolescent cognition is adolescent egocentrism: imaginary audience and personal fable. One of the most common struggle majority of adolescents face is the conflict between parent and adolescent. They often complain that parents do not understand them/their situation. Common topics of conflict include identity crisis, issues with personal freedom and issues over personal preferences. Conflict resolution refers to solving conflicts in a healthy way. There is a lack of resources in dealing with such conflicts creatively. To deal with parent-adolescent conflict, a conflict resolution board game is designed. The board game consists of tokens, dice, 10 conflict situation cards and two conflict resolution sheets. Purpose of using a board game is to help adolescents understand the conflict situations and resolutions in a fun, creative and interactive way. It can be used for self-help or even therapists can use it in their clinical practice. The study aims to assess the effectiveness of the board game in dealing with the conflict. Experimental design will be used. Samples include 15 adolescents belonging to age group 10-19. Samples will be divided into two groups: Experimental group and control group. A pre-test and post-test will be conducted. The board game will be demonstrated to the experimental group. Results will be obtained after statistical analysis. Board games are a great way to be used with children and adolescents.

Keywords: adolescent, adolescence, parent-child conflict, conflict resolution

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Authors: Kerols Seif Said Botros

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The relationship between development and human rights has been debated for a long time. Various principles, from the right to development to development-based human rights, are applied to understand the dynamics between these two concepts. Despite the measures calculated, the connection between enhancement and human rights remains vague. Despite, the connection between these two opinions and the need to strengthen human rights have increased in recent years. It will then be examined whether the right to sustainable development is acceptable or not. In various human rights instruments and this is a good vibe to the request cited above. The book then cites domestic and international human rights treaties, as well as jurisprudence and regulations defining human rights institutions, to support this view.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Vipin Kumar Verma, Neeta Sehgal, Om Prakash

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Cyprinus carpio has a wide spread occurrence in the lakes and rivers of Europe and Asia. Heavy losses in natural environment due to anthropogenic activities, including pollution as well as pathogenic diseases have landed this fish in IUCN red list of vulnerable species. The significance of a suitable diet in preserving the health status of fish is widely recognized. In present study, artificial feed supplemented with leaves of two weed plants, Eichhornia crassipes and Ricinus communis were evaluated for their role on the fish immune system. To achieve this objective fish were acclimatized to laboratory conditions (25 ± 1 °C; 12 L: 12D) for 10 days prior to start of experiment and divided into 4 groups: non-challenged (negative control= A), challenged [positive control (B) and experimental (C & D)]. Group A, B were fed with non-supplemented feed while group C & D were fed with feed supplemented with 5% Eichhornia crassipes and 5% Ricinus communis respectively. Supplemented feeds were evaluated for their effect on growth, health, immune system and disease resistance in fish when challenged with Vibrio harveyi. Fingerlings of C. carpio (weight, 2.0±0.5 g) were exposed with fresh overnight culture of V. harveyi through bath immunization (concentration 2 Χ 105) for 2 hours on 10 days interval for 40 days. The growth was monitored through increase in their relative weight. The rate of mortality due to bacterial infection as well as due to effect of feed was recorded accordingly. Immune response of fish was analyzed through differential leucocyte count, percentage phagocytosis and phagocytic index. The effect of V. harveyi on fish organs were examined through histo-pathological examination of internal organs like spleen, liver and kidney. The change in the immune response was also observed through gene expression analysis. The antioxidant potential of plant extracts was measured through DPPH and FRAP assay and amount of total phenols and flavonoids were calculates through biochemical analysis. The chemical composition of plant’s methanol extracts was determined by GC-MS analysis, which showed presence of various secondary metabolites and other compounds. Investigation revealed immuno-modulatory effect of plants, when supplemented with the artificial feed of fish.

Keywords: immuno-modulation, gc-ms, Cyprinus carpio, Eichhornia crassipes, Ricinus communis

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Authors: Pandurang Balwant, Ashutosh Mishra, Jyothi V., Abhay Soni, Padmakar C., Rafat Quamar, Ramesh J.

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The sea water intrusion in the coastal aquifers has become one of the major environmental concerns. Although, it is a natural phenomenon but, it can be induced with anthropogenic activities like excessive exploitation of groundwater, seacoast mining, etc. The geological and hydrogeological conditions including groundwater heads and groundwater pumping pattern in the coastal areas also influence the magnitude of seawater intrusion. However, this problem can be remediated by taking some preventive measures like rainwater harvesting and artificial recharge. The present study is an attempt to identify suitable sites for rainwater harvesting in salt intrusion affected area near coastal aquifer of Jafrabad town, Amreli district, Gujrat, India. The physico-chemical water quality results show that out of 25 groundwater samples collected from the study area most of samples were found to contain high concentration of Total Dissolved Solids (TDS) with major fractions of Na and Cl ions. The Cl/HCO3 ratio was also found greater than 1 which indicates the salt water contamination in the study area. The geophysical survey was conducted at nine sites within the study area to explore the extent of contamination of sea water. From the inverted resistivity sections, low resistivity zone (<3 Ohm m) associated with seawater contamination were demarcated in North block pit and south block pit of NCJW mines, Mitiyala village Lotpur and Lunsapur village at the depth of 33 m, 12 m, 40 m, 37 m, 24 m respectively. Geospatial techniques in combination of Analytical Hierarchy Process (AHP) considering hydrogeological factors, geographical features, drainage pattern, water quality and geophysical results for the study area were exploited to identify potential zones for the Rainwater Harvesting. Rainwater harvesting suitability model was developed in ArcGIS 10.1 software and Rainwater harvesting suitability map for the study area was generated. AHP in combination of the weighted overlay analysis is an appropriate method to identify rainwater harvesting potential zones. The suitability map can be further utilized as a guidance map for the development of rainwater harvesting infrastructures in the study area for either artificial groundwater recharge facilities or for direct use of harvested rainwater.

Keywords: analytical hierarchy process, groundwater quality, rainwater harvesting, seawater intrusion

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Malathi Balakrishnan, Cheng Lee Ooi, Chander Vengadasalam

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The purpose of this study is to explore a case study of trainee teachers’ learning experience on innovating traditional games during the traditional game carnival. It explores issues arising from multiple case studies of trainee teachers learning experiences in innovating traditional games. A qualitative methodology was adopted through observations, semi-structured interviews and reflective journals’ content analysis of trainee teachers’ learning experiences creating and implementing innovative traditional games. Twelve groups of 36 trainee teachers who registered for Sports and Physical Education Management Course were the participants for this research during the traditional game carnival. Semi structured interviews were administrated after the trainee teachers learning experiences in creating innovative traditional games. Reflective journals were collected after carnival day and the content analyzed. Inductive data analysis was used to evaluate various data sources. All the collected data were then evaluated through the Nvivo data analysis process. Inductive reasoning was interpreted based on the Self Determination Theory (SDT). The findings showed that the trainee teachers had positive game participation experiences, game knowledge about traditional games and positive motivation to innovate the game. The data also revealed the influence of themes like cultural significance and creativity. It can be concluded from the findings that the organized game carnival, as a requirement of course work by the Institute of Teacher Training Malaysia, was able to enhance teacher trainers’ innovative thinking skills. The SDT, as a multidimensional approach to motivation, was utilized. Therefore, teacher trainers may have more learning experiences using the SDT.

Keywords: learning experiences, innovation, traditional games, trainee teachers

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Authors: Vimal Kumar Dewangan, T. S. Sampath Kumar, Mukesh Doble, Viju Daniel Varghese

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The apatite-based, i.e., calcium-deficient hydroxyapatite (CDHAp) bone cement is well-known potential bone graft/substitute in orthopaedics due to its similar chemical composition with natural bone minerals. Therefore, an easy approach was attempted to fabricate the apatite-based (CDHAp) bone cement with improved injectability, bioresorbability, and macroporosity. In this study, the desired bone cement was developed by mixing the solid phase (consisting of wet chemically synthesized nanocrystalline hydroxyapatite and commercially available (synthetic) tricalcium phosphate) and the liquid phase (consisting of cement binding accelerator with few biopolymers in a dilute acidic solution) along with a liquid porogen as polysorbate or a solid porogen as mannitol (for comparison) in an optimized liquid-to-powder ratio. The fabricated cement sets within clinically preferred setting time (≤20 minutes) are better injectable (>70%) and also stable at ~7.3-7.4 (physiological pH). The CDHAp phased bone cement was resulted by immersing the fabricated after-set cement in phosphate buffer solution and other similar artificial body fluids and incubated at physiological conditions for seven days, confirmed through the X-ray diffraction and Fourier transform-infrared spectroscopy analyses. The so-formed synthetic apatite-based bone cement holds the acceptable compressive strength (within the range of trabecular bone) with average interconnected pores size falls in a macropores range (~50-200μm) inside the cement, verified by scanning electron microscopy (SEM), mercury intrusion porosimetry and micro-CT analysis techniques. Also, it is biodegradable (degrades ~19-22% within 10-12 weeks) when incubated in artificial body fluids under physiological conditions. The biocompatibility study of the bone cement, when incubated with MG63 cells, shows a significant increase in the cell viability after 3rd day of incubation compared with the control, and the cells were well-attached and spread completely on the surface of the bone cement, confirmed through SEM and fluorescence microscopy analyses. With this all, we can conclude that the developed synthetic macroporous apatite-based bone cement may have the potential to become promising material used as a trabecular bone substitute.

Keywords: calcium deficient hydroxyapatite, synthetic apatite-based bone cement, injectability, macroporosity, trabecular bone substitute

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Authors: Jubaida Gulshan Ara, Zebun Nasreen Ahmed

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Urban flood and drought has been a major problem of Dhaka city, particularly in recent years. Continuous increase of the city built up area, and limiting rainwater infiltration zone, are thought to be the main causes of the problem. Proper rainwater management, even at the individual plot level, might bring significant improvement in this regard. As residential use pattern occupies a significant portion of the city surface, the scope of rainwater harvesting (RWH) in residential buildings can be investigated. This paper reports on a research which explored the scope of rainwater harvesting in residential plots, with multifamily apartment buildings, in Dhaka city. The research investigated the basics of RWH, contextual information, i.e., hydro-geological, meteorological data of Dhaka city and the rules and legislations for residential building construction. The study also explored contemporary rainwater harvesting practices in the local and international contexts. On the basis of theoretical understanding, 21 sample case-studies, in different phases of construction, were selected from seven different categories of plot sizes, in different residential areas of Dhaka city. Primary data from the 21 case-study buildings were collected from a physical survey, from design drawings, accompanied by a questionnaire survey. All necessary secondary data were gathered from published and other relevant sources. Collected primary and secondary data were used to calculate and analyze the RWH needs for each case study, based on the theoretical understanding. The main findings have been compiled and compared, to observe residential development trends with regards to building rainwater harvesting system. The study has found that, in ‘Multifamily Apartment Building’ of Dhaka city, storage, and recharge structure size for rainwater harvesting, increases along with occupants’ number, and with the increasing size of the plot. Hence, demand vs. supply ratio remains almost the same for different sizes of plots, and consequently, the size of the storage structure increases significantly, in large-scale plots. It has been found that rainwater can meet only 12%-30% of the total restricted water demand of these residential buildings of Dhaka city. Therefore, artificial groundwater recharge might be the more suitable option for RWH, than storage. The study came up with this conclusion that, in multifamily residential apartments of Dhaka city, artificial groundwater recharge might be the more suitable option for RWH, than storing the rainwater on site.

Keywords: Dhaka city, rainwater harvesting, residential plots, urban flood

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: M. Pramila Devi, N. V. N. Indra Kiran

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Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed.

Keywords: control chart pattern recognition, neural network, backpropagation, generalization, early stopping

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Authors: Normah Sulaiman, Yong Zhi Kang, Nor Hayati Hussain, Abdul Rehman Khalid

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Segara Ninda owned by Din Ku Meh, the governor of the province Satul, a Malay man with a big role liaising with Thailand. This mansion is part of the legacy he left behind among other properties in George Town, Penang, besides his family. The island’s geographical location is strategic which has benefitted it through important trade routes for Europe, Middle, East, India, and China in the past. Due to this reasoning, various architectural styles were introduced in Penang; Late Straits Eclectic style is one of the forms of the Colonial Architectural style widely spread as vernacular shophouses in George Town. Segara Ninda is located among the mixture of nouveau-riche, historical and heritage sites at the most important street; Penang Road, which dated back to the late 18th century. This paper examines the strait eclectic style that Segara Ninda encompasses. Acknowledging the mixture of colonial architecture in Georgetown, we argue that the mansion faces challenging issues in conservation processes to be vindicated. This is reflected by analysing the spatial layout, visual elements quality, and its activity through interviews with the occupants of the mansion. The focus will be on the understanding of building form, features, and functions; respecting the architectural spaces and their activity. The methodology applied is to promote our understanding of the mix of culture that the mansion holds through documentation, observation and measuring exercises. This offers a positional interpretation of the mix of culture that the mansion holds. This conservation effort will further contribute exposure to the public and recognize it in the society as its essence is a deficiency character to the existing built environment.

Keywords: eclectic, heritage, spatial organization, culture

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Authors: Dinesh N. Kurup, Celine Perriera

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The economic development of any country is based on the policies adopted by the government from time to time. If these policies are framed by the opinion of the people of the country, there is need for having strong knowledge base, right from the school level. There should be emphasis to provide in education, an ability to take informed decisions regarding socio-scientific issues. It would be better to adopt this practice in high school classrooms to build capacity among future citizens. This study is an attempt to provide a different approach of teaching and learning in classrooms at the high school level in Indian schools for providing opportunity for informed decision making regarding nuclear power use. A unit of work based on the 5E instructional model about the use of nuclear energy is used to build knowledge base and find out the effectiveness in terms of its influence for taking decisions as a future citizen. A sample of 120 students from three high schools using different curricula and teaching and learning methods were chosen for this study. This research used a design based research method. A pre and post questionnaire based on the theory of reasoned action, structured observations, focus group interviews and opportunity for decision making were used during the intervention. The data analysed qualitatively and quantitatively, and the qualitative data were coded into categories based on responses. The results of the study show that students were able to make informed decisions and could give reasons for their decisions. They were enthusiastic in formulating policy making based on their knowledge base and have strong held views and reasoning for their choice.

Keywords: informed decision making, socio-scientific issues, nuclear energy use, policy making

Procedia PDF Downloads 292