Search results for: Data Mining Techniques

Authors: Surender Kumar Soni, Dhirendra Pratap Singh

Abstract:

Wireless Sensor Networks can be used to monitor the physical phenomenon in such areas where human approach is nearly impossible. Hence the limited power supply is the major constraint of the WSNs due to the use of non-rechargeable batteries in sensor nodes. A lot of researches are going on to reduce the energy consumption of sensor nodes. Energy map can be used with clustering, data dissemination and routing techniques to reduce the power consumption of WSNs. Energy map can also be used to know which part of the network is going to fail in near future. In this paper, Energy map is constructed using the prediction based approach. Adaptive alpha GM(1,1) model is used as the prediction model. GM(1,1) is being used worldwide in many applications for predicting future values of time series using some past values due to its high computational efficiency and accuracy.

Keywords: Adaptive Alpha GM(1, 1) Model, Energy Map, Prediction Based Data Reduction, Wireless Sensor Networks

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Authors: Lee Bih Ni, Nurul Asyikin Binti Hassan

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This paper discusses the use of mnemonic and mathematical methods in enhancing the understanding of history. Mnemonics can help students from all levels including high school and in various disciplines including language, math and history. At the secondary level, students are exposed to various courses that require them to remember many facts that can be mastered through the application of mnemonic techniques. Researchers use narrative literature studies to illustrate the current state of art and science in the field of research focused. Researchers used narrative literature reviews to build a scientific base of knowledge. Researchers gather all the key points in the discussion, and put it here by referring to the specific field where the paper is essentially based. The findings suggest that the use of mnemonic techniques can improve the individual's memory by adding little effort. In implementing mnemonic techniques, it is important to integrate mathematics and history in the course as both are interconnected as mathematics has shaped our history and vice versa. This study shows that memory skills can actually be improved; the human mind can remember something more than expected.

Keywords: Cognitive strategy, mnemonic technique, secondary school level study, mathematical mnemonic.

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Authors: S. Logeswari, K. Premalatha

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Search is the most obvious application of information retrieval. The variety of widely obtainable biomedical data is enormous and is expanding fast. This expansion makes the existing techniques are not enough to extract the most interesting patterns from the collection as per the user requirement. Recent researches are concentrating more on semantic based searching than the traditional term based searches. Algorithms for semantic searches are implemented based on the relations exist between the words of the documents. Ontologies are used as domain knowledge for identifying the semantic relations as well as to structure the data for effective information retrieval. Annotation of data with concepts of ontology is one of the wide-ranging practices for clustering the documents. In this paper, indexing based on concept and annotation are proposed for clustering the biomedical documents. Fuzzy c-means (FCM) clustering algorithm is used to cluster the documents. The performances of the proposed methods are analyzed with traditional term based clustering for PubMed articles in five different diseases communities. The experimental results show that the proposed methods outperform the term based fuzzy clustering.

Keywords: MeSH Ontology, Concept Indexing, Annotation, semantic relations, Fuzzy c-means.

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Authors: A. Bagheri Garmarudi, M. Khanmohammadi, N. Khoddami, K. Shabani

Abstract:

Determination of nano particle size is substantial since the nano particle size exerts a significant effect on various properties of nano materials. Accordingly, proposing non-destructive, accurate and rapid techniques for this aim is of high interest. There are some conventional techniques to investigate the morphology and grain size of nano particles such as scanning electron microscopy (SEM), atomic force microscopy (AFM) and X-ray diffractometry (XRD). Vibrational spectroscopy is utilized to characterize different compounds and applied for evaluation of the average particle size based on relationship between particle size and near infrared spectra [1,4] , but it has never been applied in quantitative morphological analysis of nano materials. So far, the potential application of nearinfrared (NIR) spectroscopy with its ability in rapid analysis of powdered materials with minimal sample preparation, has been suggested for particle size determination of powdered pharmaceuticals. The relationship between particle size and diffuse reflectance (DR) spectra in near infrared region has been applied to introduce a method for estimation of particle size. Back propagation artificial neural network (BP-ANN) as a nonlinear model was applied to estimate average particle size based on near infrared diffuse reflectance spectra. Thirty five different nano TiO2 samples with different particle size were analyzed by DR-FTNIR spectrometry and the obtained data were processed by BP- ANN.

Keywords: near infrared, particle size, chemometrics, neuralnetwork, nano structure.

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Authors: Ahmed S. Attia

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This paper includes a study of modern sustainable architectural techniques and elements that are originally found in vernacular and traditional architecture, particularly in the Arab region. Courtyards, Wind Catchers, and Mashrabiya, for example, are elements that have been developed in contemporary architecture using modern technology to create sustainable architecture designs. An analytical study of the topic will deal with some examples of the Islamic House in Fatimid Cairo city in Egypt, analyzing its elements and their relationship to the environment, in addition to the examples in southern Egypt (Nubba) of sustainable architecture systems, and traditional houses in Sana'a city, Yemen, using earth resources of mud bricks and other construction materials. In conclusion, a comparative study between traditional and contemporary techniques will be conducted to confirm that it is possible to achieve sustainable architecture through the use of low-technology in buildings in Arab regions.

Keywords: Islamic context, cultural environment, natural environment, Islamic House, low-technology, mud brick, vernacular and traditional architecture.

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Authors: Mohamed El Morsy, Gabriela Achtenová

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This paper presents modern vibration signalprocessing techniques for vehicle gearbox fault diagnosis, via the wavelet analysis and the Squared Envelope (SE) technique. The wavelet analysis is regarded as a powerful tool for the detection of sudden changes in non-stationary signals. The Squared Envelope (SE) technique has been extensively used for rolling bearing diagnostics. In the present work a scheme of using the Squared Envelope technique for early detection of gear tooth pit. The pitting defect is manufactured on the tooth side of a fifth speed gear on the intermediate shaft of a vehicle gearbox. The objective is to supplement the current techniques of gearbox fault diagnosis based on using the raw vibration and ordered signals. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of output joint shafts. The gearbox used for experimental measurements is the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive; a five-speed gearbox with final drive gear and front wheel differential. The results show that the approaches methods are effective for detecting and diagnosing localized gear faults in early stage under different operation conditions, and are more sensitive and robust than current gear diagnostic techniques.

Keywords: Wavelet analysis, Squared Envelope, gear faults.

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Authors: M. Bittencourt, E. K. Yanful, D. Velasquez, A. E. Jungles

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The CMLP building was developed to be a model for sustainability with strategies to reduce water, energy and pollution, and to provide a healthy environment for the building occupants. The aim of this paper is to investigate the environmental effects of energy used by this building. A LCA (life cycle analysis) was led to measure the real environmental effects produced by the use of energy. The impact categories most affected by the energy use were found to be the human health effects, as well as ecotoxicity. Natural gas extraction, uranium milling for nuclear energy production, and the blasting for mining and infrastructure construction are the processes contributing the most to emissions in the human health effect. Data comparing LCA results of CMLP building with a conventional building results showed that energy used by the CMLP building has less damage for the environment and human health than a conventional building.

Keywords: Environmental Impacts, Green buildings, Life CycleAnalysis, Sustainability

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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The gases generated in oil filled transformers can be used for qualitative determination of incipient faults. The Dissolved Gas Analysis has been widely used by utilities throughout the world as the primarily diagnostic tool for transformer maintenance. In this paper, various Artificial Intelligence Techniques that have been used by the researchers in the past have been reviewed, some conclusions have been drawn and a sequential hybrid system has been proposed. The synergy of ANN and FIS can be a good solution for reliable results for predicting faults because one should not rely on a single technology when dealing with real–life applications.

Keywords: Dissolved Gas Analysis, Artificial IntelligenceTechniques, Incipient Faults, Transformer Fault Diagnosis, andHybrid Systems.

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Authors: Giovanni Luca Masala

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Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, thalassemia.

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Authors: Yuxin Chen, Qingyun Di, C. Dinis da Gama

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In projects like waterpower, transportation and mining, etc., proving up the rock-mass structure and hidden tectonic to estimate the geological body-s activity is very important. Integrating the seismic results, drilling and trenching data, CSAMT method was carried out at a planning dame site in southwest China to evaluate the stability of a deformation. 2D and imitated 3D inversion resistivity results of CSAMT method were analyzed. The results indicated that CSAMT was an effective method for defining an outline of deformation body to several hundred meters deep; the Lung Pan Deformation was stable in natural conditions; but uncertain after the future reservoir was impounded. This research presents a good case study of the fine surveying and research on complex geological structure and hidden tectonic in engineering project.

Keywords: CSAMT Surveying, Deformation Stability.

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Authors: Venkata Reddy Kota

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These days, the industrial trend is moving away from heavy and bulky passive components to power converter systems that use more and more semiconductor elements. Also, it is difficult to connect the traditional converters to the high and medium voltage. For these reasons, a new family of multilevel inverters has appeared as a solution for working with higher voltage levels. Different modulation topologies like Sinusoidal Pulse Width Modulation (SPWM), Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) are available for multilevel inverters. In this work, different hybrid modulation techniques which are combination of fundamental frequency modulation and multilevel sinusoidal-modulation are compared. The main characteristic of these modulations are reduction of switching losses with good harmonic performance and balanced power loss dissipation among the device. The proposed hybrid modulation schemes are developed and simulated in Matlab/Simulink for cascaded H-bridge inverter. The results validate the applicability of the proposed schemes for cascaded multilevel inverter.

Keywords: Hybrid PWM techniques, Cascaded Multilevel Inverters, Switching loss minimization.

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: Data mining, Korean linguistic feature, literary fiction, relationship extraction.

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Authors: Saad Mutashar Abbas, M. A. Hannan, S. A. Samad, A. Hussain

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This paper presented a proposed design for transcutaneous inductive powering links. The design used to transfer power and data to the implanted devices such as implanted Microsystems to stimulate and monitoring the nerves and muscles. The system operated with low band frequency 13.56 MHZ according to industrial- scientific – medical (ISM) band to avoid the tissue heating. For external part, the modulation index is 13 % and the modulation rate 7.3% with data rate 1 Mbit/s assuming Tbit=1us. The system has been designed using 0.35-μm fabricated CMOS technology. The mathematical model is given and the design is simulated using OrCAD P Spice 16.2 software tool and for real-time simulation the electronic workbench MULISIM 11 has been used. The novel circular plane (pancake) coils was simulated using ANSOFT- HFss software.

Keywords: Implanted devices, ASK techniques, Class-E power amplifier, Inductive powering and low-frequency ISM band.

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: Collapsible soil, relative subsidence, dielectric permittivity, moisture content.

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Authors: Sandesh Achar

Abstract:

Cloud computing security is a broad term that covers a variety of security concerns for organizations that use cloud services. Multi-cloud service providers must consider several factors when addressing security for their customers, including identity and access management, data at rest and in transit, egress and ingress traffic control, vulnerability and threat management, and auditing. This paper explores each of these aspects of cloud security in detail and provides recommendations for best practices for multi-cloud service providers. It also discusses the challenges inherent in securing a multi-cloud environment and offers solutions for overcoming these challenges. By the end of this paper, readers should have a good understanding of the various security concerns associated with multi-cloud environments in the context of today’s modern cyber threats and how to address them.

Keywords: Multi-cloud service, SOC, system organization control, data loss prevention, DLP, identity and access management, IAM.

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Authors: Laureano Rodríguez, Gladys Casas, Ricardo Grau, Yailen Martínez

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The classical temporal scan statistic is often used to identify disease clusters. In recent years, this method has become as a very popular technique and its field of application has been notably increased. Many bioinformatic problems have been solved with this technique. In this paper a new scan fuzzy method is proposed. The behaviors of classic and fuzzy scan techniques are studied with simulated data. ROC curves are calculated, being demonstrated the superiority of the fuzzy scan technique.

Keywords: Scan statistic, fuzzy scan, simulating study

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Authors: N. Matveev, A. Pervov

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Local utilities often face problems of local industrial wastes, storm water disposal due to existing strict regulations. For many local industries, the problem of wastewater treatment and discharge into surface reservoirs can’t be solved through the use of conventional biological treatment techniques. Current discharge standards require very strict removal of a number of impurities such as ammonia, nitrates, phosphate, etc. To reach this level of removal, expensive reagents and sorbents are used. The modern concept of rational water resources management requires the development of new efficient techniques that provide wastewater treatment and reuse. As RO membranes simultaneously reject all dissolved impurities such as BOD, TDS, ammonia, phosphates etc., they become very attractive for the direct treatment of wastewater without biological stage. To treat wastewater, specially designed membrane "open channel" modules are used that do not possess "dead areas" that cause fouling or require pretreatment. A solution to RO concentrate disposal problem is presented that consists of reducing of initial wastewater volume by 100 times. Concentrate is withdrawn from membrane unit as sludge moisture. The efficient use of membrane RO techniques is connected with a salt balance in water system. Thus, to provide high ecological efficiency of developed techniques, all components of water supply and wastewater discharge systems should be accounted for.

Keywords: Reverse osmosis, stormwater treatment, openchannel module, wastewater reuse.

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Authors: Angelina A. Tzacheva, Jaishree Ranganathan

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Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.

Keywords: Actionable pattern discovery, education, emotion, data mining.

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Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha

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Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization (MSSVQ), which is a hybrid of Multi, switched, split vector quantization techniques. The spectral distortion performance, computational complexity, and memory requirements of MSSVQ are compared to split vector quantization (SVQ), multi stage vector quantization(MSVQ) and switched split vector quantization (SSVQ) techniques. It has been proved from results that MSSVQ has better spectral distortion performance, lower computational complexity and lower memory requirements when compared to all the above mentioned product code vector quantization techniques. Computational complexity is measured in floating point operations (flops), and memory requirements is measured in (floats).

Keywords: Linear predictive Coding, Multi stage vectorquantization, Switched Split vector quantization, Split vectorquantization, Line Spectral Frequencies (LSF).

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Authors: R. Poonkuzhali, M. Y. Sanavullah, M. R. Gurupriya

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Opportunistic routing is used, where the network has the features like dynamic topology changes and intermittent network connectivity. In Delay tolerant network or Disruption tolerant network opportunistic forwarding technique is widely used. The key idea of opportunistic routing is selecting forwarding nodes to forward data packets and coordination among these nodes to avoid duplicate transmissions. This paper gives the analysis of pros and cons of various opportunistic routing techniques used in MANET.

Keywords: Expected Transmission Count (ETX), Opportunistic routing, Proactive Source Routing (PSR), throughput.

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Authors: Mina Alibeigi, Sattar Hashemi, Ali Hamzeh

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Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. In this paper, we propose a new unsupervised feature selection method which will remove redundant features from the original feature space by the use of probability density functions of various features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several datasets derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both classification accuracy and the number of selected features.

Keywords: Feature, Feature Selection, Filter, Probability Density Function

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Authors: Nhu Van NGUYEN, Jean-Marc OGIER, Salvatore TABBONE, Alain BOUCHER

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The state-of-the-art Bag of Words model in Content- Based Image Retrieval has been used for years but the relevance feedback strategies for this model are not fully investigated. Inspired from text retrieval, the Bag of Words model has the ability to use the wealth of knowledge and practices available in text retrieval. We study and experiment the relevance feedback model in text retrieval for adapting it to image retrieval. The experiments show that the techniques from text retrieval give good results for image retrieval and that further improvements is possible.

Keywords: Relevance feedback, bag of words model, probabilistic model, vector space model, image retrieval

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Authors: H. Apaza, L. Chévez, H. Loro

Abstract:

Plastic and microplastic pollution in human food chain is a big problem for human health that requires more elaborated techniques that can identify their presences in different kinds of food. Hyperspectral imaging technique is an optical technique than can detect the presence of different elements in an image and can be used to detect plastics and microplastics in a scene. To do this statistical techniques are required that need to be evaluated and compared in order to find the more efficient ones. In this work, two problems related to the presence of plastics are addressed, the first is to detect and identify pieces of plastic immersed in almond seeds, and the second problem is to detect and quantify microplastic in almond flour. To do this we make use of the analysis hyperspectral images taken in the range of 900 to 1700 nm using 4 unmixing techniques of hyperspectral imaging which are: least squares unmixing (LSU), non-negatively constrained least squares unmixing (NCLSU), fully constrained least squares unmixing (FCLSU), and scaled constrained least squares unmixing (SCLSU). NCLSU, FCLSU, SCLSU techniques manage to find the region where the plastic is found and also manage to quantify the amount of microplastic contained in the almond flour. The SCLSU technique estimated a 13.03% abundance of microplastics and 86.97% of almond flour compared to 16.66% of microplastics and 83.33% abundance of almond flour prepared for the experiment. Results show the feasibility of applying near-infrared hyperspectral image analysis for the detection of plastic contaminants in food.

Keywords: Food, plastic, microplastic, NIR hyperspectral imaging, unmixing.

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Authors: R. Latif, W. Jenkal, A. Toumanari, A. Hatim

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This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques.

Keywords: Hybrid technique, ADTF, DWT, tresholding, ECG signal.

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Authors: Muhammad Raza Ali, Tim Morris

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Skin color is an important visual cue for computer vision systems involving human users. In this paper we combine skin color and optical flow for detection and tracking of skin regions. We apply these techniques to gesture recognition with encouraging results. We propose a novel skin similarity measure. For grouping detected skin regions we propose a novel skin region grouping mechanism. The proposed techniques work with any number of skin regions making them suitable for a multiuser scenario.

Keywords: Bayesian tracking, chromaticity space, optical flowgesture recognition

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Authors: Ganiyu Amuda Yusuf, Sarajul Fikri Mohamed

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The procurement and cost management approach adopted for mechanical and electrical (M&E) services in Malaysian construction industry have been criticized for its inefficiency. The study examined early cost estimating practices adopted for mechanical and electrical services (M&E) in Malaysia so as to understand the level of compliance of the current techniques with best practices. The methodology adopted for the study is a review of bidding documents used on both completed and on – going building projects awarded between 2008 – 2010 under 9th Malaysian Plan. The analysis revealed that, M&E services cost cannot be reliably estimated at pre-contract stage; the bidding techniques adopted for M&E services failed to provide uniform basis for contractors to submit tender; detailed measurement of items were not made which could complicate post contract cost control and financial management. The paper concluded that, there is need to follow a structured approach in determining the pre-contract cost estimate for M&E services which will serve as a virile tool for post contract cost control.

Keywords: Cost Management, Mechanical and Electrical Services, Procurement, Standard Method of Measurement

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Authors: Murat Ayyıldız, Oya Kalıpsız, Sırma Yavuz

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Software project effort estimation is frequently seen as complex and expensive for individual software engineers. Software production is in a crisis. It suffers from excessive costs. Software production is often out of control. It has been suggested that software production is out of control because we do not measure. You cannot control what you cannot measure. During last decade, a number of researches on cost estimation have been conducted. The metric-set selection has a vital role in software cost estimation studies; its importance has been ignored especially in neural network based studies. In this study we have explored the reasons of those disappointing results and implemented different neural network models using augmented new metrics. The results obtained are compared with previous studies using traditional metrics. To be able to make comparisons, two types of data have been used. The first part of the data is taken from the Constructive Cost Model (COCOMO'81) which is commonly used in previous studies and the second part is collected according to new metrics in a leading international company in Turkey. The accuracy of the selected metrics and the data samples are verified using statistical techniques. The model presented here is based on Multi-Layer Perceptron (MLP). Another difficulty associated with the cost estimation studies is the fact that the data collection requires time and care. To make a more thorough use of the samples collected, k-fold, cross validation method is also implemented. It is concluded that, as long as an accurate and quantifiable set of metrics are defined and measured correctly, neural networks can be applied in software cost estimation studies with success

Keywords: Software Metrics, Software Cost Estimation, Neural Network.

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Authors: Nader Ghareeb, R¨udiger Schmidt

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Minimizing the weight in flexible structures means reducing material and costs as well. However, these structures could become prone to vibrations. Attenuating these vibrations has become a pivotal engineering problem that shifted the focus of many research endeavors. One technique to do that is to design and implement an active control system. This system is mainly composed of a vibrating structure, a sensor to perceive the vibrations, an actuator to counteract the influence of disturbances, and finally a controller to generate the appropriate control signals. In this work, two different techniques are explored to create two different mathematical models of an active control system. The first model is a finite element model with a reduced number of nodes and it is called a super-element. The second model is in the form of state-space representation, i.e. a set of partial differential equations. The damping coefficients are calculated and incorporated into both models. The effectiveness of these models is demonstrated when the system is excited by its first natural frequency and an active control strategy is developed and implemented to attenuate the resulting vibrations. Results from both modeling techniques are presented and compared.

Keywords: Finite element analysis, super-element, state-space model.

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Authors: H. Shareef, A. Mohamed, S. A. Khalid, Aziah Khamis

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This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation. 

Keywords: Artificial intelligence, Power tracing, Artificial neural network, ANFIS, Power system deregulation.

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: Data Estimation, link data, machine learning, road network.

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