Search results for: smart training

Authors: Hidehiko Okada

Abstract:

The author previously proposed an extension of differential evolution. The proposed method extends the processes of DE to handle interval numbers as genotype values so that DE can be applied to interval-valued optimization problems. The interval DE can employ either of two interval models, the lower and upper model or the center and width model, for specifying genotype values. Ability of the interval DE in searching for solutions may depend on the model. In this paper, the author compares the two models to investigate which model contributes better for the interval DE to find better solutions. Application of the interval DE is evolutionary training of interval-valued neural networks. A result of preliminary study indicates that the CW model is better than the LU model: the interval DE with the CW model could evolve better neural networks. 

Keywords: Evolutionary algorithms, differential evolution, neural network, neuroevolution, interval arithmetic.

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Authors: H. Mashhady, B. Lotfi, M. Doosti, M. Fatollahi

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An exploration of the related literature reveals that all instruction methods aim at training autonomous learners. After the turn of second language pedagogy toward learner-oriented strategies, learners’ needs were more focused. Yet; the historical, social and political aspects of learning were still neglected. The present study investigates the notion of autonomous learning and explains its various facets from a pedagogical point of view. Furthermore; different elements, fields and scopes of autonomous learning will be explored. After exploring different aspects of autonomy, it is postulated that liberatory autonomy is highlighted since it not only covers social autonomy but also reveals learners’ capabilities and human potentials. It is also recommended that learners consider different elements of autonomy such as motivation, knowledge, confidence, and skills.

Keywords: Critical pedagogy, social autonomy, academic learning, cultural notions.

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Authors: A. Ellen Kalinga, R. B. Burchard Bagile, Lena Trojer

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Information and Communications Technologies (ICT) has been integrated in education in many developing and developed countries alike, but the use of ICT in Tanzanian schools is dismal. Many Tanzanian secondary schools have no computers. The few schools with computers use them primarily for secretarial services and computer literacy training. The Tanzanian education system at other levels like secondary school level has to undergo substantial transformation, underscored by the growing application of new information and communication technology. This paper presents the e-readiness survey result from secondary schools in Tanzania. The paper also suggests how Tanzania can make use of the few present ICT resources to support and improve teaching and learning functions to improve performance and acquisition of knowledge by using e-Learning Management System (e-LMS).

Keywords: e-Learning, ICT, Object-Oriented, Participatorydesign.

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Authors: Weidong Xia, Malgorzata Kolotylo, Xuan Tan

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This study examines the key factors that influence general practitioners’ learning and transfer of specialized arthritis knowledge and self-care techniques to patients during normal patient visits. Drawing on the theory of planed behavior and using matched survey data collected from general practitioners before and after training sessions provided by specialized orthopedic physicians, the study suggests that the general practitioner’s intention to use and transfer learned knowledge was influenced mainly by intrinsic motivation, organizational learning culture and absorptive capacity, but was not influenced by extrinsic motivation. The results provide both theoretical and practical implications.

Keywords: Empirical study, healthcare knowledge management, patient self-care, physician knowledge transfer.

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Authors: Kazunari Meguro, Shinichi Motomura, Takao Kawamura, Kazunori Sugahara

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In this paper, backup and recovery technique for Peer to Peer applications, such as a distributed asynchronous Web-Based Training system that we have previously proposed. In order to improve the scalability and robustness of this system, all contents and function are realized on mobile agents. These agents are distributed to computers, and they can obtain using a Peer to Peer network that modified Content-Addressable Network. In the proposed system, although entire services do not become impossible even if some computers break down, the problem that contents disappear occurs with an agent-s disappearance. As a solution for this issue, backups of agents are distributed to computers. If a failure of a computer is detected, other computers will continue service using backups of the agents belonged to the computer.

Keywords: Distributed Multimedia Systems, e-Learning, P2P, Mobile Agent

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Authors: Maria E. Eggink

Abstract:

Innovation plays an important role in economic growth and development. Evolutionary economics has entrepreneurs at the centre of the innovation system, but includes all other participants as contributors to the performance of the innovation system. Education and training institutions, one of the participants in the innovation system, contributes in different ways to human capital. The gap in literature on the competence building as part of human capital in the analysis of innovation systems is addressed in this paper. The Mpumalanga Province of South Africa is used as a case study. It was found that the absence of a university, the level of education, the quality and performance in the education sector and the condition of the education infrastructure have not been conducive to learning.

Keywords: Education institutions, human capital, innovation systems, Mpumalanga Province.

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Authors: Z. Zainuddin, N. Mahat, Y. Abu Hassan

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Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.

Keywords: Backpropagation, Dynamic Adaptation Methods, Local Adaptive Techniques, Neural networks.

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Authors: Eric Jiang

Abstract:

As a popular rank-reduced vector space approach, Latent Semantic Indexing (LSI) has been used in information retrieval and other applications. In this paper, an LSI-based content vector model for text classification is presented, which constructs multiple augmented category LSI spaces and classifies text by their content. The model integrates the class discriminative information from the training data and is equipped with several pertinent feature selection and text classification algorithms. The proposed classifier has been applied to email classification and its experiments on a benchmark spam testing corpus (PU1) have shown that the approach represents a competitive alternative to other email classifiers based on the well-known SVM and naïve Bayes algorithms.

Keywords: Feature Selection, Latent Semantic Indexing, Text Classification, Vector Space Model.

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Authors: P. Hedbávný, M. Kalichová

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The contribution deals with problem of take-off phase of back somersault with twisting with various numbers of twists along longitudinal body axis. The aim was to evaluate the changes in angles during transition phase from back handspring to back somersault using 3D kinematic analysis of the somersaults. We used Simi Motion System for the 3D kinematic analysis of the observed gymnastic element performed by Czech Republic female representative and 2008 Summer Olympic Games participant. The results showed that the higher the number of twists, the smaller the touchdown angle in which the gymnasts lands on the pad in the beginning of take-off phase. In back somersault with one twist (180°) the average angle is 54°, in 1080° back somersault the average angle is 45.9°. These results may help to improve technical training of sports gymnasts.

Keywords: back somersault with twisting, biomechanicalanalysis, take-off

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Authors: Omid Gervei, Ahmad Ayatollahi, Navid Gervei

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In this paper we present an approach for 3D face recognition based on extracting principal components of range images by utilizing modified PCA methods namely 2DPCA and bidirectional 2DPCA also known as (2D) 2 PCA.A preprocessing stage was implemented on the images to smooth them using median and Gaussian filtering. In the normalization stage we locate the nose tip to lay it at the center of images then crop each image to a standard size of 100*100. In the face recognition stage we extract the principal component of each image using both 2DPCA and (2D) 2 PCA. Finally, we use Euclidean distance to measure the minimum distance between a given test image to the training images in the database. We also compare the result of using both methods. The best result achieved by experiments on a public face database shows that 83.3 percent is the rate of face recognition for a random facial expression.

Keywords: 3D face recognition, 2DPCA, (2D) 2 PCA, Rangeimage

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.

Keywords: Dialogue management, response generation, reinforcement learning, deep learning, evaluation.

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Authors: R. Rajeswari, N. Kamaraj

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In this paper, Wavelet based ANFIS for finding inter turn fault of generator is proposed. The detector uniquely responds to the winding inter turn fault with remarkably high sensitivity. Discrimination of different percentage of winding affected by inter turn fault is provided via ANFIS having an Eight dimensional input vector. This input vector is obtained from features extracted from DWT of inter turn faulty current leaving the generator phase winding. Training data for ANFIS are generated via a simulation of generator with inter turn fault using MATLAB. The proposed algorithm using ANFIS is giving satisfied performance than ANN with selected statistical data of decomposed levels of faulty current.

Keywords: Winding InterTurn fault, ANN, ANFIS, and DWT.

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Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

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In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: Cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning.

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Authors: Sumanta Daw, Gopal Chandra Saha

Abstract:

The present study was conducted to explore the basic pulmonary functions which may generally vary according to the bio-physical characteristics including age, height, body weight, and environment etc. of the sports performers. Regular and specific training exercises also change the characteristics of an athlete’s prowess and produce a positive effect on the physiological functioning, mostly upon cardio-pulmonary efficiency and thereby improving the body mechanism. The objective of the present study was to compare the differences in cardio-respiratory functions between aquatics and track and field performers. As cardio-respiratory functions are influenced by pulse rate and blood pressure (systolic and diastolic), so both of the factors were also taken into consideration. The component selected under cardio-respiratory functions for the present study were i) FEVI/FVC ratio (forced expiratory volume divided by forced vital capacity ratio, i.e. the number represents the percentage of lung capacity to exhale in one second) ii) FVC1 (this is the amount of air which can force out of lungs in one second) and iii) FVC (forced vital capacity is the greatest total amount of air forcefully breathe out after breathing in as deeply as possible). All the three selected components of the cardio-respiratory efficiency were measured by spirometry method. Pulse rate was determined manually. The radial artery which is located on the thumb side of our wrist was used to assess the pulse rate. Blood pressure was assessed by sphygmomanometer. All the data were taken in the resting condition. 36subjects were selected for the present study out of which 18were water polo players and rest were sprinters. The age group of the subjects was considered between 18 to 23 years. In this study the obtained data inform of digital score were treated statistically to get result and draw conclusions. The Mean and Standard Deviation (SD) were used as descriptive statistics and the significant difference between the two subject groups was assessed with the help of statistical ‘t’-test. It was found from the study that all the three components i.e. FEVI/FVC ratio (p-value 0.0148 < 0.01), FVC1 (p-value 0.0010 < 0.01) and FVC (p-value 0.0067 < 0.01) differ significantly as water polo players proved to be better in terms of cardio-respiratory functions than sprinters. Thus study clearly suggests that the exercise training as well as the medium of practice arena associated with water polo players has played an important role to determine better cardio respiratory efficiency than track and field athletes. The outcome of the present study revealed that the lung function in land-based activities may not provide much impact than that of in water activities.

Keywords: Cardio-respiratory efficiency, spirometry, water polo players, sprinters.

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Authors: Lilac A. E. Al-Safadi

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This paper proposes an auto-classification algorithm of Web pages using Data mining techniques. We consider the problem of discovering association rules between terms in a set of Web pages belonging to a category in a search engine database, and present an auto-classification algorithm for solving this problem that are fundamentally based on Apriori algorithm. The proposed technique has two phases. The first phase is a training phase where human experts determines the categories of different Web pages, and the supervised Data mining algorithm will combine these categories with appropriate weighted index terms according to the highest supported rules among the most frequent words. The second phase is the categorization phase where a web crawler will crawl through the World Wide Web to build a database categorized according to the result of the data mining approach. This database contains URLs and their categories.

Keywords: Information Processing on the Web, Data Mining, Document Classification.

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Authors: B. Singh, A. Singh, R. C. Yadav

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Utmost care of human related issues are essentially required for sustainable growth of micro, small and medium enterprises (MSMEs) of India, as these MSMEs are contributing enormously to socio-economic development of country. In this research, aspects related to human factors and functioning of MSMEs of India were studied. The investigation, based on a survey of 84 MSMEs of India cited that the enterprises are mostly employing unskilled labor whose wages are less with poor training. In spite of reported minor accidents, attention towards safety is poorly paid. To meet-out the production target, MSMEs generally employ over-time and payment towards this overtime is sometimes missing. Hence, honest and humanitarian attention for better human resources is needed to improve the performance and competitiveness of MSMEs of India.

Keywords: Human factors, Small and medium enterprises, Working culture.

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Authors: Jiří Barta

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The paper deals with simulation programs of spread of harmful substances. Air pollution has a direct impact on the quality of human life and environmental protection is currently a very hot topic. Therefore, the paper focuses on the simulation of release of harmful substances. The first part of article deals with perspectives and possibilities of implementation outputs of simulations programs into the system which is education and of practical training of the management staff during emergency events in the frame of critical infrastructure. The last part shows the practical testing and evaluation of simulation programs. Of the tested simulations software been selected Symos97. The tool offers advanced features for setting leakage. Gradually allows the user to model the terrain, location, and method of escape of harmful substances.

Keywords: Computer Simulation, Symos97, spread, simulation software, harmful substances.

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Authors: Al-bdour, A. Ali., Ellisha Nasruddin., Soh Keng Lin

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This study attempts to investigate the relationship between internal CSR practices and organizational commitment based on the social exchange theory (SET). Specifically, we examine the impact of five dimensions of internal CSR practices on organizational commitment: health and safety, human rights, training and education, work life balance and workplace diversity. The proposed model was tested on a sample of 336 frontline employees within the banking sector in Jordan. Results showed that all internal CSR dimensions are significantly and positively related to affective and normative commitment. In addition, the findings of this study indicate that all internal CSR dimensions did not have a significant relationship with continuance commitment. Limitations of the study, directions for future research, and implications of the findings are discussed.

Keywords: Internal CSR, organizational commitment, Jordan, banking sector.

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Authors: Aldrin R. Logdat

Abstract:

Philosophy offers a systematic approach to reflective, logical, and innovative thinking, which involves a thorough investigation of various concepts. On the other hand, national development encompasses a country’s ability to embrace its unique identity and take charge of its future. Education, meanwhile, provides individuals with enlightening experiences and specialized training in different fields. The aim of this paper is to demonstrate how philosophy and education can serve as catalysts for national progress. Given the Philippines' diverse socio-cultural, religious, ethnic, and political backgrounds, this study seeks to answer two crucial questions: Can the country achieve development despite its differences? If that is the case, what are the critical elements propelling this advancement? By means of historical interpretation and philosophical analysis, this article posits that philosophy and education are essential components in driving the country's progress in the era following the pandemic.

Keywords: Philosophy, national progress, educational discourse, pandemic.

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Authors: Xingyu Pan, Laure Tougne

Abstract:

For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: Coin, detection, character recognition, topology.

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Authors: Ayat E. Ali, Christeen R. Aziz, Merna A. Helmy, Mohammed M. Malek, Sherif H. El-Gohary

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Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.

Keywords: K-nearest neighbor, face detection, vitiligo, bone deformity.

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Authors: Wajdi Bellil, Chokri Ben Amar, Adel M. Alimi

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This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn - R from scattered samples (xi; y = f(xi)) i=1....n, where first, we have little a priori knowledge on the unknown function f: it lives in some infinite dimensional smooth function space and second the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate f as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.

Keywords: Beta wavelets networks, RBF neural network, training algorithms, MSE, 1-D, 2D function approximation.

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Authors: Reza Bazargan Lari, Mohammad H. Fattahi

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Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for preprocessing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based preprocessing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.

Keywords: Wavelet, de-noising, predictability, time series fractal analysis, valid length, ANN.

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Authors: Kleanthis Prekas, Maria Rangoussi, Savvas Vassiliadis, George Prekas

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Intelligent tutoring systems constitute an evolution of computer-aided educational software. We present here the modules of an intelligent tutoring system for Automatic Control, developed in our department. Through the software application developed,students can perform complete automatic control laboratory experiments, either over the departmental local area network or over the Internet. Monitoring of access to the system (local as well as international), along with student performance statistics, has yielded strongly encouraging results (as of fall 2004), despite the advanced technical content of the presented paradigm, thus showing the potential of the system developed for education and for training.

Keywords: Automatic control, tutoring system, Internet access, laboratory experiments.

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Authors: M. Aslan, A. Karaaslan, S. Selcuk

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The research was conducted in order to determine the organizational socialization levels of nurses working in hospitals in the form of a descriptive study. The research population was composed of nurses employed in public and private sector hospitals in the province of Konya with 0-3 years of professional experience in the hospitals (N=1200); and the sample was composed of 495 nurses that accepted to take part in the study voluntarily. Statistical evaluation of data was conducted in SPSS.16 software. The results of the study revealed that the total score taken by nurses at the organizational socialization scale was 262.95; and this was close to the maximum score. Particularly the departmental socialization sub-dimension proved to be higher in comparison to the other two dimensions (organization socialization and task socialization). Statistically meaningful differences were found in the levels of organization socialization in relation to the status of organizational orientation training, level of education and age group.

Keywords: Nurses, Newcomers, Organizational Socialization.

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline which ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

Keywords: Machine learning, healthcare, classification, explainability.

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Authors: Amado, M. P., Amado, A., Poggi, F., Correia de Freitas, J.

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Today is widely understood that global energy consumption patterns are directly related to the urban expansion and development process. This expansion is based on the natural growth of human activities and has left most urban areas totally dependent on fossil fuel derived external energy inputs. This status-quo of production, transportation, storage and consumption of energy has become inefficient and is set to become even more so when the continuous increases in energy demand are factored in. The territorial management of land use and related activities is a central component in the search for more efficient models of energy use, models that can meet current and future regional, national and European goals.

In this paper a methodology is developed and discussed with the aim of improving energy efficiency at the municipal level. The development of this methodology is based on the monitoring of energy consumption and its use patterns resulting from the natural dynamism of human activities in the territory and can be utilized to assess sustainability at the local scale. A set of parameters and indicators are defined with the objective of constructing a systemic model based on the optimization, adaptation and innovation of the current energy framework and the associated energy consumption patterns. The use of the model will enable local governments to strike the necessary balance between human activities and economic development and the local and global environment while safeguarding fairness in the energy sector.

Keywords: Solar urban planning, solar smart city, urban development, energy efficiency.

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Authors: Chiung-Hui Chen

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With advancements in science and technology, the concept of the Internet of Things (IoT) has gradually developed. The development of the intelligent environment adds intelligence to objects in the living space by using the IoT. In the smart environment, when multiple users share the living space, if different service requirements from different users arise, then the context-aware system will have conflicting situations for making decisions about providing services. Therefore, the purpose of establishing a communication and negotiation mechanism among objects in the intelligent environment is to resolve those service conflicts among users. This study proposes developing a decision-making methodology that uses “Event Agents” as its core. When the sensor system receives information, it evaluates a user’s current events and conditions; analyses object, location, time, and environmental information; calculates the priority of the object; and provides the user services based on the event. Moreover, when the event is not single but overlaps with another, conflicts arise. This study adopts the “Multiple Events Correlation Matrix” in order to calculate the degree values of incidents and support values for each object. The matrix uses these values as the basis for making inferences for system service, and to further determine appropriate services when there is a conflict.

Keywords: Internet of things, intelligent object, event agents, negotiation mechanism, degree of similarity.

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Authors: G. Mokryani, M. R. Haghifam, J. Esmaeilpoor

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Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Competitive Neural Network used for classification. Ferroresonance data and other transients was obtained by simulation using EMTP program. Using Daubechies wavelet transform signals has been decomposed till six levels. The energy of six detailed signals that obtained by wavelet transform are used for training and trailing Competitive Neural Network. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.

Keywords: Competitive Neural Network, Ferroresonance, EMTP program, Wavelet transform.

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Authors: Dongwan Kang, Joohyung Oh, Chaetae Im

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Advancement of communication technologies and smart devices in the recent times is leading to changes into the integrated wired and wireless communication environments. Since early days, businesses had started introducing environments for mobile device application to their operations in order to improve productivity (efficiency) and the closed corporate environment gradually shifted to an open structure. Recently, individual user's interest in working environment using mobile devices has increased and a new corporate working environment under the concept of BYOD is drawing attention. BYOD (bring your own device) is a concept where individuals bring in and use their own devices in business activities. Through BYOD, businesses can anticipate improved productivity (efficiency) and also a reduction in the cost of purchasing devices. However, as a result of security threats caused by frequent loss and theft of personal devices and corporate data leaks due to low security, companies are reluctant about adopting BYOD system. In addition, without considerations to diverse devices and connection environments, there are limitations in detecting abnormal behaviors, such as information leaks, using the existing network-based security equipment. This study suggests a method to detect abnormal behaviors according to individual behavioral patterns, rather than the existing signature-based malicious behavior detection, and discusses applications of this method in BYOD environment.

Keywords: BYOD, Security, Anomaly Behavior Detection.

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