Search results for: feature clustering

Authors: Gnanadeep Gudapati, V. Annie Grace

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A broadband circular polarization (CP) feature is designed for a CPW-fed planar printed monopole antenna. A rectangle patch and an improved ground plane make up the antenna. The antenna's impedance bandwidth can be increased by adding a vertical stub and a horizontal slit in the ground plane. The measured results show that the proposed antenna has a wide 10-dB return loss bandwidth of 70.2% (4.35GHz, 3.7-8.1GHz) centered at 4.2 GHz.

Keywords: CPW-fed, circular polarised, FR4 epoxy, slit and stub

Procedia PDF Downloads 129

Authors: Ali Nouri

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Neuroeducation is one of the most exciting research fields which is continually evolving. However, there is a need to develop its theoretical bases in connection to practice. The present paper is a starting attempt in this regard to provide a space from which to think about neuroeducational theory and invoke more investigation in this area. Accordingly, a comprehensive theory of neuroeducation could be defined as grouping or clustering of concepts and propositions that describe and explain the nature of human learning to provide valid interpretations and implications useful for educational practice in relation to philosophical aspects or values. Whereas it should be originated from the philosophical foundations of the field and explain its normative significance, it needs to be testable in terms of rigorous evidence to fundamentally advance contemporary educational policy and practice. There is thus pragmatically a need to include a course on neuroeducational theory into the curriculum of the field. In addition, there is a need to articulate and disseminate considerable discussion over the subject within professional journals and academic societies.

Keywords: neuroeducation studies, neuroeducational theory, theory building, neuroeducation research

Procedia PDF Downloads 433

Authors: Bolatbek Rysbaiuly, Aliya S. Azhibekova

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Problems concerning the interpretation of the well testing results belong to the class of inverse problems of subsurface hydromechanics. The distinctive feature of such problems is that additional information is depending on the capabilities of oilfield experiments. Another factor that should not be overlooked is the existence of errors in the test data. To determine reservoir properties, some inverse problems for parabolic equations were investigated. An approach to solving the inverse problems based on the method of regularization is proposed.

Keywords: iterative approach, inverse problem, parabolic equation, reservoir properties

Procedia PDF Downloads 411

Authors: Maya Irjayanti

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Local creativity utilization has been a strategic investment to be expanded as a creative industry due to its significant contribution to the national gross domestic product. Many developed and developing countries look toward creative industries as an agenda for the economic growth. This study aims to identify the role of local potential for creative industries from various empirical studies. The method performed in this study will involve a peer-reviewed journal articles and conference papers review addressing local potential and creative industries. The literature review analysis will include several steps: material collection, descriptive analysis, category selection, and material evaluation. Finally, the outcome expected provides a creative industries clustering based on the local potential of various nations. In addition, the finding of this study will be used as future research reference to explore a particular area with well-known aspects of local potential for creative industry products.

Keywords: business, creativity, local potential, local wisdom

Procedia PDF Downloads 355

Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

Procedia PDF Downloads 67

Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

Procedia PDF Downloads 81

Authors: Abder-Rahman Ali, Adélaïde Albouy-Kissi, Manuel Grand-Brochier, Viviane Ladan-Marcus, Christine Hoeffl, Claude Marcus, Antoine Vacavant, Jean-Yves Boire

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In this paper, we present a new segmentation approach for focal liver lesions in contrast enhanced ultrasound imaging. This approach, based on a two-cluster Fuzzy C-Means methodology, considers type-II fuzzy sets to handle uncertainty due to the image modality (presence of speckle noise, low contrast, etc.), and to calculate the optimum inter-cluster threshold. Fine boundaries are detected by a local recursive merging of ambiguous pixels. The method has been tested on a representative database. Compared to both Otsu and type-I Fuzzy C-Means techniques, the proposed method significantly reduces the segmentation errors.

Keywords: defuzzification, fuzzy clustering, image segmentation, type-II fuzzy sets

Procedia PDF Downloads 462

Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

Procedia PDF Downloads 440

Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

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Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

Procedia PDF Downloads 454

Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

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Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

Procedia PDF Downloads 463

Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

Procedia PDF Downloads 160

Authors: Sachin Nagargoje

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Complete labeled data is often difficult to obtain in a practical scenario. Even if one manages to obtain the data, the quality of the data is always in question. In shopping vertical, offers are the input data, which is given by advertiser with or without a good quality of information. In this paper, an author investigated the possibility of using a very simple Semi-supervised learning approach to increase the recall of unhealthy offers (has badly written Offer Title or partial product details) in shopping vertical domain. The author found that the semisupervised learning method had improved the recall in the Smart Phone category by 30% on A=B testing on 10% traffic and increased the YoY (Year over Year) number of impressions per month by 33% at production. This also made a significant increase in Revenue, but that cannot be publicly disclosed.

Keywords: semi-supervised learning, clustering, recall, coverage

Procedia PDF Downloads 103

Authors: Sung-mo Seo, Chang-u Chae

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The Product Carbon Footprint Labeling has been run for more than four years by the Ministry of Environment and there are number of products labeled by KEITI, as for declaring products with their carbon emission during life cycle stages. There are several categories for certifying products by the characteristics of usage. Building products which are applied to a building as combined components. In this paper, current status of PCF labeling has been compared with LCI DB for data composition. By this comparative analysis, we suggest carbon labeling development.

Keywords: carbon labeling, LCI DB, building materials, life cycle assessment

Procedia PDF Downloads 407

Authors: Lidia N. Hernández-Piña, Carlos R. Jaimez-González

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Interoperability in distributed systems is an important feature that refers to the communication of two applications written in different programming languages. This paper presents a serializer and a de-serializer of PHP objects to and from XML, which is an independent library written in the PHP programming language. The XML generated by this serializer is independent of the programming language, and can be used by other existing Web Objects in XML (WOX) serializers and de-serializers, which allow interoperability with other object-oriented programming languages.

Keywords: interoperability, PHP object serialization, PHP to XML, web objects in XML, WOX

Procedia PDF Downloads 213

Authors: Morteza Giti

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As online shopping is becoming increasingly popular, computing customer lifetime value for better knowing the customers is also gaining more importance. Two distinct factors that can affect the value of a customer in the context of online shopping is the number of returned orders and payment method. Returned orders are those which have been shipped but not collected by the customer and are returned to the store. Payment method refers to the way that customers choose to pay for the price of the order which are usually two: Pre-pay and Cash-on-delivery. In this paper, a novel model called RFMSP is presented to calculated the customer lifetime value, taking these two parameters into account. The RFMSP model is based on the common RFM model while adding two extra parameter. The S represents the order status and the P indicates the payment method. As a case study for this model, the purchase history of customers in an online shop is used to compute the customer lifetime value over a period of twenty months.

Keywords: RFMSP model, AHP, customer lifetime value, k-means clustering, e-commerce

Procedia PDF Downloads 300

Authors: Fawad Zaidi

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This paper discusses a variety of challenges and solutions involved with creating computer games and the issues faced by the software engineers working in this field. This review further investigates the articles coverage of project scope and the problem of feature creep that appears to be inherent with game development. The paper tries to answer the following question: Is this a problem caused by a shortage, or bad software engineering practices, or is this outside the control of the software engineering component of the game production process?

Keywords: software engineering, computer games, software applications, development

Procedia PDF Downloads 460

Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

Procedia PDF Downloads 27

Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

Procedia PDF Downloads 318

Authors: Oumaima Ben Abderrahim, Mohamed Houcine Elhedhili, Leila Saidane

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In this paper, we propose a trust management system based on clustering architecture for the social internet of things called TMBCO-SIOT. The proposed model integrates numerous factors such as direct and indirect trust; transaction factor; precaution factor; and social modeling of trust. The novelty of our approach can be summed up in two aspects. The first aspect concerns the architecture based on the community of interest (CoT) where each community is headed by an administrator (admin). However, the second aspect is the trust management system that tries to prevent On-Off attacks and mitigates dishonest recommendations using the k-means algorithm and guarantor things. The effectiveness of the proposed system is proved by simulation against malicious nodes.

Keywords: IoT, trust management system, attacks, trust, dishonest recommendations, K-means algorithm

Procedia PDF Downloads 195

Authors: Yuan-lin Liu, Ye Li, Tian Xia

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There are some shortcomings in Chinese existing taxi management modes. This paper suggests to establish the third-party comprehensive information management platform and put forward an evaluation model based on credit. Four indicators are used to evaluate the drivers’ credit, they are passengers’ evaluation score, driving behavior evaluation, drivers’ average bad record number, and personal credit score. A weighted clustering method is used to achieve credit level evaluation for taxi drivers. The management of taxi industry is based on the credit level, while the grade of the drivers is accorded to their credit rating. Credit rating determines the cost, income levels, the market access, useful period of license and the level of wage and bonus, as well as violation fine. These methods can make the credit evaluation effective. In conclusion, more credit data will help to set up a more accurate and detailed classification standard library.

Keywords: credit, mobile internet, e-hailing taxi, management mode, weighted cluster

Procedia PDF Downloads 296

Authors: L. Belkhiri, L. Mouni, A. Tiri, T.S. Narany

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In the present study, the self-organizing map (SOM) clustering technique was applied to identify homogeneous clusters of hydrochemical parameters in El Milia plain, Algeria, to assess the quality of groundwater for potable and agricultural purposes. The visualization of SOM-analysis indicated that 35 groundwater samples collected in the study area were classified into three clusters, which showed progressive increase in electrical conductivity from cluster one to cluster three. Samples belonging to cluster one are mostly located in the recharge zone showing hard fresh water type, however, water type gradually changed to hard-brackish type in the discharge zone, including clusters two and three. Ionic ratio studies indicated the role of carbonate rock dissolution in increases on groundwater hardness, especially in cluster one. However, evaporation and evapotranspiration are the main processes increasing salinity in cluster two and three.

Keywords: groundwater quality, self-organizing maps, drinking water, irrigation water

Procedia PDF Downloads 236

Authors: Miranda E. Karban

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A distinctive pattern of occipital squama convexity, known as the occipital bun or chignon, has traditionally been considered a derived Neandertal trait. However, some early modern and extant Homo sapiens share similar occipital bone morphology, showing pronounced internal and external occipital squama curvature and paralambdoidal flattening. It has been posited that these morphological patterns are homologous in the two groups, but this claim remains disputed. Many developmental hypotheses have been proposed, including assertions that the chignon represents a developmental response to a long and narrow cranial vault, a narrow or flexed basicranium, or a prognathic face. These claims, however, remain to be metrically quantified in a large subadult sample, and little is known about the feature’s developmental, functional, or evolutionary significance. This study assesses patterns of chignon development and covariation in a comparative sample of extant human growth study cephalograms. Cephalograms from a total of 549 European-derived North American subjects (286 male, 263 female) were scored on a 5-stage ranking system of chignon prominence. Occipital squama shape was found to exist along a continuum, with 34 subjects (6.19%) possessing defined chignons, and 54 subjects (9.84%) possessing very little occipital squama convexity. From this larger sample, those subjects represented by a complete radiographic series were selected for metric analysis. Measurements were collected from lateral and posteroanterior (PA) cephalograms of 26 subjects (16 male, 10 female), each represented at 3 longitudinal age groups. Age group 1 (range: 3.0-6.0 years) includes subjects during a period of rapid brain growth. Age group 2 (range: 8.0-9.5 years) includes subjects during a stage in which brain growth has largely ceased, but cranial and facial development continues. Age group 3 (range: 15.9-20.4 years) includes subjects at their adult stage. A total of 16 landmarks and 153 sliding semi-landmarks were digitized at each age point, and geometric morphometric analyses, including relative warps analysis and two-block partial least squares analysis, were conducted to study covariation patterns between midsagittal occipital bone shape and other aspects of craniofacial morphology. A convex occipital squama was found to covary significantly with a low, elongated neurocranial vault, and this pattern was found to exist from the youngest age group. Other tested patterns of covariation, including cranial and basicranial breadth, basicranial angle, midcoronal cranial vault shape, and facial prognathism, were not found to be significant at any age group. These results suggest that the chignon, at least in this sample, should not be considered an independent feature, but rather the result of developmental interactions relating to neurocranial elongation. While more work must be done to quantify chignon morphology in fossil subadults, this study finds no evidence to disprove the developmental homology of the feature in modern humans and Neandertals.

Keywords: chignon, craniofacial covariation, human cranial development, longitudinal growth study, occipital bun

Procedia PDF Downloads 172

Authors: Michal Krecichwost, Zauzanna Miodonska, Pawel Badura

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The diagnosis of sigmatism is mostly based on the observation of articulatory organs. It is, however, not always possible to precisely observe the vocal apparatus, in particular in the oral cavity of the patient. Speech processing can allow to objectify the therapy and simplify the verification of its progress. In the described study the methodology for classification of incorrectly pronounced phoneme [s] is proposed. The recordings come from adults. They were registered with the speech recorder at the sampling rate of 44.1 kHz and the resolution of 16 bit. The database of pathological and normative speech has been collected for the study including reference assessments provided by the speech therapy experts. Ten adult subjects were asked to simulate a certain type of stigmatism under the speech therapy expert supervision. In the recordings, the analyzed phone [s] was surrounded by vowels, viz: ASA, ESE, ISI, SPA, USU, YSY. Thirteen MFCC (mel-frequency cepstral coefficients) and RMS (root mean square) values are calculated within each frame being a part of the analyzed phoneme. Additionally, 3 fricative formants along with corresponding amplitudes are determined for the entire segment. In order to aggregate the information within the segment, the average value of each MFCC coefficient is calculated. All features of other types are aggregated by means of their 75th percentile. The proposed method of features aggregation reduces the size of the feature vector used in the classification. Binary SVM (support vector machine) classifier is employed at the phoneme recognition stage. The first group consists of pathological phones, while the other of the normative ones. The proposed feature vector yields classification sensitivity and specificity measures above 90% level in case of individual logo phones. The employment of a fricative formants-based information improves the sole-MFCC classification results average of 5 percentage points. The study shows that the employment of specific parameters for the selected phones improves the efficiency of pathology detection referred to the traditional methods of speech signal parameterization.

Keywords: computer-aided pronunciation evaluation, sibilants, sigmatism diagnosis, speech processing

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

Procedia PDF Downloads 377

Authors: Bakhtiar Ul Haq, R. Ahmed, A. Shaari, Mazmira Binti Mohamed, Nisar Ali

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The capability of exploiting the electronic charge and spin properties simultaneously in a single material has made diluted magnetic semiconductors (DMS) remarkable in the field of spintronics. We report the designing of DMS based on zinc-blend ZnO doped with Cr impurity. The full potential linearized augmented plane wave plus local orbital FP-L(APW+lo) method in density functional theory (DFT) has been adapted to carry out these investigations. For treatment of exchange and correlation energy, generalized gradient approximations have been used. Introducing Cr atoms in the matrix of ZnO has induced strong magnetic moment with ferromagnetic ordering at stable ground state. Cr:ZnO was found to favor the short range magnetic interaction that reflect the tendency of Cr clustering. The electronic structure of ZnO is strongly influenced in the presence of Cr impurity atoms where impurity bands appear in the band gap.

Keywords: ZnO, density functional theory, diluted agnetic semiconductors, ferromagnetic materials, FP-L(APW+lo)

Procedia PDF Downloads 407

Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: magnetic resonance image (MRI), c-means model, image segmentation, information entropy

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Authors: Fadhl Qutaish

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This paper examines the pharyngealization spread in one of the Yemeni Arabic dialects, namely, Ibbi Arabic (IA). It investigates how pharyngealized sounds spread their acoustic features onto the neighboring vowels and change their default features. This feature has been investigated quietly well in MSA but still has to be deeply studied in the different dialect of Arabic which will bring about a clearer picture of the similarities and the differences among these dialects and help in mapping them based on the way this feature is utilized. Though the studies are numerous, no one of them has illustrated how far in the multi-syllabic word the spread can be and whether it takes a steady or gradient manner. This study tries to fill this gap and give a satisfactory explanation of the pharyngealization spread in Ibbi Dialect. This study is the first step towards a larger investigation of the different dialects of Yemeni Arabic in the future. The data recorded are represented in minimal pairs in which the trigger (pharyngealized or the non-pharyngealized sound) is in the initial or final position of monosyllabic and multisyllabic words. A group of 24 words were divided into four groups and repeated three times by three subjects which will yield 216 tokens that are tested and analyzed. The subjects are three male speakers aged between 28 and 31 with no history of neurological, speaking or hearing problems. All of them are bilingual speakers of Arabic and English and native speakers of Ibbi-Dialect. Recordings were done in a sound-proof room and praat software was used for the analysis and coding of the trajectories of F1 and F2 for the low vowel /a/ to see the effect of pharyngealization on the formant trajectory within the same syllable and in other syllables of the same word by comparing the F1 and F2 formants to the non-pharyngealized environment. The results show that pharyngealization spread is gradient (progressively and regressively). The spread is reflected in the gradual raising of F1 as we move closer towards the trigger and the gradual lowering of F2 as well. The results of the F1 mean values in tri-syllabic words when the trigger is word initially show that there is a raise of 37.9 HZ in the first syllable, 26.8HZ in the second syllable and 14.2HZ in the third syllable. F2 mean values undergo a lowering of 239 HZ in the first syllable, 211.7 HZ in the second syllable and 176.5 in the third syllable. This gradual decrease in the difference of F2 values in the non-pharyngealized and pharyngealized context illustrates that the spread is gradient. A similar result was found when the trigger is word-final which proves that the spread is gradient (progressively and regressively.

Keywords: pharyngealization, Yemeni Arabic, Ibbi dialect, pharyngealization spread

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Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

Abstract:

To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

Procedia PDF Downloads 131

Authors: Alam Ali, Ashok Kumar Pathak

Abstract:

Nowadays, copulas have attracted significant attention for modeling multivariate observations, and the foremost feature of copula functions is that they give us the liberty to study the univariate marginal distributions and their joint behavior separately. The copula parameter apprehends the intrinsic dependence among the marginal variables, and it can be estimated using parametric, semiparametric, or nonparametric techniques. This work aims to compare the coverage rates between an Elliptical and an Archimedean family of copulas via a fully parametric estimation technique.

Keywords: elliptical copula, archimedean copula, estimation, coverage rate

Procedia PDF Downloads 48