Search results for: K modes clustering

Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: Zdenek Silar, Martin Dobrovolny

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This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method.

Keywords: background estimation, direction of optical flow, K-means clustering, objects detection, railway crossing monitoring, velocity vectors

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Authors: Danladi Ali, Onah Festus Iloabuchi

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In this paper, GSM signal strength was measured in order to detect the type of the signal fading phenomenon using one-dimensional multilevel wavelet residual method and neural network clustering to determine the average GSM signal strength received in the study area. The wavelet residual method predicted that the GSM signal experienced slow fading and attenuated with MSE of 3.875dB. The neural network clustering revealed that mostly -75dB, -85dB and -95dB were received. This means that the signal strength received in the study is a weak signal.

Keywords: one-dimensional multilevel wavelets, path loss, GSM signal strength, propagation, urban environment

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Authors: H. Alheeh, M. Alameri, A. Al Tarabsheh

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There has been a growing interest in CMOS-based sensors technology in cameras as they afford low-power, small-size, and cost-effective imaging systems. This article describes the CMOS image sensor pixel categories and presents the design and the simulation of the 3-Transistor (3T) Active Pixel Sensor (APS) in MATLAB/Simulink tool. The analysis investigates the conversion of the light into an electrical signal for a single pixel sensing circuit, which consists of a photodiode and three NMOS transistors. The paper also proposes three modes for the pixel operation; reset, integration, and readout modes. The simulations of the electrical signals for each of the studied modes of operation show how the output electrical signals are correlated to the input light intensities. The charging/discharging speed for the photodiodes is also investigated. The output voltage for different light intensities, including in dark case, is calculated and showed its inverse proportionality with the light intensity.

Keywords: APS, CMOS image sensor, light intensities photodiode, simulation

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Kamel Taıbı, Abdelmadjid Rais

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Nickel–Aluminium ferrites with the general formula Ni Alx Fe2-x O4 (0 ≤ x ≤ 1) were studied using X-ray diffraction, Infra Red and Raman spectroscopy. XRD diffraction patterns and their Reitveld refinements show that all samples have a pure single-phase cubic spinel structure. From these patterns, the lattice parameters of these samples have been calculated and compared with those predicted theoretically. Most of the values were found to decrease with increasing Al content. Infra Red spectra showed two significant absorption bands. The high band corresponds to tetrahedral (A) sites and the lower band to octahedral [B] sites, thus confirming the single phase spinel structure. For all compositions, Raman spectra show the five active modes A1g + E1g + 3 T2g of the motion of O2- ions and both the A-site and B-site ions. The Raman frequencies trend with aluminium concentration show a blue shift for all modes consistent with the replacement of Fe3+ by lower mass Al3+. Composition dependence of the Raman frequency modes is discussed in relationship with the cations distribution among the A-sites and B-sites.

Keywords: Ni-Al ferrites, spinel structure, XRD, Raman spectroscopy

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Authors: Megha Chavda, Parth Thummar, Rahul Ghetia

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This paper presents the voltage and current control of microgrid accompanied by the synchronization of microgrid with the main utility grid in both islanded and grid-connected modes. Distributed Energy Resources (DERs) satisfy the wide-spread power demand of consumer by behaving as a micro source for a low voltage (LV) grid or microgrid. Synchronization of the microgrid with the main utility grid is done using PLL and PWM gate pulse generation technique is used for the Voltage Source Converter. Potential Function method achieves the voltage and current control of this microgrid in both islanded and grid-connected modes. A low voltage grid consisting of three distributed generators (DG) is considered for the study and is simulated in time-domain using PSCAD/EMTDC software. The simulation results depict the appropriateness of voltage and current control of microgrid and synchronization of microgrid with the medium voltage (MV) grid.

Keywords: microgrid, distributed energy resources, voltage and current control, voltage source converter, pulse width modulation, phase locked loop

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Authors: Srijoni Banerjee

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Sequences of some of the candidate genes (e.g., CPE, CDKAL1, GCKR, HSD11B1, IGF2BP2, IRS1, LPIN1, PKLR, TNF, PPARG) implicated in some of the complex disease, e.g. Type II diabetes in man has been compared with other species to investigate phylogenetic affinity. Based on mRNA sequence of these genes of 7 to 8 species, using bioinformatics tools Mega 5, Bioedit, Clustal W, distance matrix was obtained. Phylogenetic trees were obtained by NJ and UPGMA clustering methods. The results of the phylogenetic analyses show that of the species compared: Xenopus l., Danio r., Macaca m., Homo sapiens s., Rattus n., Mus m. and Gallus g., Bos taurus, both NJ and UPGMA clustering show close affinity between clustering of Homo sapiens s. (Man) with Rattus n. (Rat), Mus m. species for the candidate genes, except in case of Lipin1 gene. The results support the functional similarity of these genes in physiological and biochemical process involving man and mouse/rat. Therefore, in understanding the complex etiology and treatment of the complex disease mouse/rate model is the best laboratory choice for experimentation.

Keywords: phylogeny, candidate gene of type-2 diabetes, CPE, CDKAL1, GCKR, HSD11B1, IGF2BP2, IRS1, LPIN1, PKLR, TNF, PPARG

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Authors: KumarShashvat, ArshpreetKaur, RajeshKumar, Raman Chadha

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Wireless sensor networks have principal characteristic of having restricted energy and with limitation that energy of the nodes cannot be replenished. To increase the lifetime in this scenario WSN route for data transmission is opted such that utilization of energy along the selected route is negligible. For this energy efficient network, dandy infrastructure is needed because it impinges the network lifespan. Clustering is a technique in which nodes are grouped into disjoints and non–overlapping sets. In this technique data is collected at the cluster head. In this paper, Adaptive-PSO algorithm is proposed which forms energy aware clusters by minimizing the cost of locating the cluster head. The main concern is of the suitability of the swarms by adjusting the learning parameters of PSO. Particle Swarm Optimization converges quickly at the beginning stage of the search but during the course of time, it becomes stable and may be trapped in local optima. In suggested network model swarms are given the intelligence of the spiders which makes them capable enough to avoid earlier convergence and also help them to escape from the local optima. Comparison analysis with traditional PSO shows that new algorithm considerably enhances the performance where multi-dimensional functions are taken into consideration.

Keywords: Particle Swarm Optimization, adaptive – PSO, comparison between PSO and A-PSO, energy efficient clustering

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Zimu Li, Zheng Wan

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The Suez Canal plays an important role in global shipping networks and is one of the most frequently used waterways in the world. The 2021 canal obstruction by ship Ever Given in March 2021, however, completed blocked the Suez Canal for a week and caused significant disruption to world trade. Therefore, it is very important to quantitatively analyze the impact of the accident on the robustness of the global shipping network. However, the current research on maritime transportation networks is usually limited to local or small-scale networks in a certain region. Based on the complex network theory, this study establishes a global shipping complex network covering 2713 nodes and 137830 edges by using the real trajectory data of the global marine transport ship automatic identification system in 2018. At the same time, two attack modes, deliberate (Suez Canal Blocking) and random, are defined to calculate the changes in network node degree, eccentricity, clustering coefficient, network density, network isolated nodes, betweenness centrality, and closeness centrality under the two attack modes, and quantitatively analyze the actual impact of Suez Canal Blocking on the robustness of global shipping network. The results of the network robustness analysis show that Suez Canal blocking was more destructive to the shipping network than random attacks of the same scale. The network connectivity and accessibility decreased significantly, and the decline decreased with the distance between the port and the canal, showing the phenomenon of distance attenuation. This study further analyzes the impact of the blocking of the Suez Canal on Chinese ports and finds that the blocking of the Suez Canal significantly interferes withChina's shipping network and seriously affects China's normal trade activities. Finally, the impact of the global supply chain is analyzed, and it is found that blocking the canal will seriously damage the normal operation of the global supply chain.

Keywords: global shipping networks, ship AIS trajectory data, main channel, complex network, eigenvalue change

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Authors: Rahma Souli-Jbali, Minyar Sassi Hidri, Rahma Ben Ayed

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Grid of computing nodes has emerged as a representative means of connecting distributed computers or resources scattered all over the world for the purpose of computing and distributed storage. Since fault tolerance becomes complex due to the availability of resources in decentralized grid environment, it can be used in connection with replication in data grids. The objective of our work is to present fault tolerance in data grids with data replication-driven model based on clustering. The performance of the protocol is evaluated with Omnet++ simulator. The computational results show the efficiency of our protocol in terms of recovery time and the number of process in rollbacks.

Keywords: data grids, fault tolerance, clustering, chandy-lamport

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Authors: Muhammet Dursun Kaya, Hasan Asil

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One of the most important factors of research and development is the deep insight into the evolutions of scientific development. The state-of-the-art tools and instruments can considerably assist the researchers, and many of the world organizations have become aware of the advantages of data mining for the acquisition of the knowledge required for the unstructured data. This paper was an attempt to review the articles on the information technology published in the past five years with the aid of data mining. A clustering approach was used to study these articles, and the research results revealed that three topics, namely health, innovation, and information systems, have captured the special attention of the researchers.

Keywords: information technology, data mining, scientific development, clustering

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Authors: Ling Zhang, Weikai Li

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Recently three sufficient conditions for the three-way Candecomp/Parafac (CP) model which ensure uniqueness in one of the three modes(“uni-mode uniqueness”) are given. In this paper, we generalize these uniqueness conditions to n ≤ 3 and give a sufficient conditions for the n-way Candecomp/Parafac (CP) model, which ensure uniqueness in one of the n modes.

Keywords: uni-mode uniqueness, candecomp/parafac, n-way arrays, decomposition, tensor

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Authors: Baghdasaryan Marinka

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The current scientific and engineering interest concerning the problems of preventing the emergency manifestations of drive synchronous motors, ensuring the ore grinding technological process has been justified. The analysis of the known works devoted to the abnormal operation modes of synchronous motors and possibilities of protection against them, has shown that their application is inexpedient for preventing the impermissible displays arising in the electrical drive synchronous motors ensuring the ore-grinding process. The main energy and technological factors affecting the technical condition of synchronous motors are evaluated. An algorithm for preventing the irregular operation modes of the electrical drive synchronous motor applied in the ore-grinding technological process has been developed and proposed for further application which gives an opportunity to provide smart solutions, ensuring the safe operation of the drive synchronous motor by a comprehensive consideration of the energy and technological factors.

Keywords: synchronous motor, abnormal operating mode, electric drive, algorithm, energy factor, technological factor

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Authors: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith

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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.

Keywords: clustering, heterogeneous, stability, scalability, IoT, WSN

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Authors: Baghdasaryan Marinka

Abstract:

The particular significance of comprehensive estimation of the increase in the operation efficiency of the mill motor electromechanical system, providing the main technological process for obtaining a metallic concentrate, as well as the technical state of the system are substantiated. The works carried out in the sphere of investigating, creating, and improving the operation modes of electric drive motors and ore-grinding mills have been studied. Analytic dependences for estimating the operation modes of the ore-grinding mills aimed at improving the ore-crashing process maintenance and technical service efficiencies have been obtained. The obtained analytic dependencies establish a link between the technological and power parameters of the electromechanical system, and allow to estimate the state of the system and reveal the controlled parameters required for the efficient management in case of changing the technological parameters. It has been substantiated that the changes in the technological factors affecting the consumption power of the drive motor do not cause an instability in the electromechanical system.

Keywords: electromechanical system, estimation, operation mode, productivity, technological process, the mill filling degree

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Authors: M. Bassoui, M. Ferfra, M. Chrayagne

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This paper presents a qualitative modeling for the nonlinear B-H curve of the saturable magnetic materials for a transformer with shunts used in the power supply for the magnetron. This power supply is composed of a single phase leakage flux transformer supplying a cell composed of a capacitor and a diode, which double the voltage and stabilize the current, and a single magnetron at the output of the cell. A procedure consisting of a fuzzy clustering method and a rule processing algorithm is then employed for processing the constructed fuzzy modeling rules to extract the qualitative properties of the curve.

Keywords: B(H) curve, fuzzy clustering, magnetron, power supply

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Authors: Aya Salama

Abstract:

Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

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Authors: Binjuan Xu, Qian Zhao, Ping Jiang, Bo Guo, Zhijun Cheng, Xiaoyue Wu

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Astronautical valves are key units in engine systems of astronautical products; their reliability will influence results of rocket or missile launching, even lead to damage to staff and devices on the ground. Besides failure in engine system may influence the hitting accuracy and flight shot of missiles. Therefore high reliability is quite essential to astronautical products. There are quite a few literature doing research based on few failure test data to estimate valves’ reliability, thus this paper proposed a new method to estimate valves’ reliability, according to the corresponding tests of different failure modes, this paper takes advantage of tests data which acquired from temperature, vibration, and action tests to estimate reliability in every failure modes, then this paper has regarded these three kinds of tests as three stages in products’ process to integrate these results to acquire valves’ reliability. Through the comparison of results achieving from tests data and simulated data, the results have illustrated how to obtain valves’ reliability based on the few failure data with failure modes and prove that the results are effective and rational.

Keywords: censored data, temperature tests, valves, vibration tests

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Authors: M. Karthigayan, M. Rizon, Sazali Yaacob, R. Nagarajan, M. Muthukumaran, Thinaharan Ramachandran, Sargunam Thirugnanam

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In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate.

Keywords: ellipse fitness function, genetic algorithm, emotion recognition, fuzzy clustering

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Authors: M. Abbasi Layegh, S. Haghipour, K. Athari, R. Khosravi, M. Tafkikialamdari

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An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study.

Keywords: radif of Mirzâ Ábdollâh, Gushe, mel frequency cepstral coefficients, fuzzy c-mean clustering algorithm, k-nearest neighbors (KNN), gaussian mixture model (GMM), hidden markov model (HMM), support vector machine (SVM)

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Authors: Manpreet Kaur, Amita Rani, Sanjay Kumar

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The Internet of Things (IoT), a cyber-physical paradigm, allows a large number of devices to connect and send the sensory data in the network simultaneously. This tremendous amount of data generated leads to very high network load consequently resulting in network congestion. It further amounts to frequent loss of useful information and depletion of significant amount of nodes’ energy. Therefore, there is a need to control congestion in IoT so as to prolong network lifetime and improve the quality of service (QoS). Hence, we propose a two-level clustering based routing algorithm considering congestion score and packet priority metrics that focus on minimizing the network congestion. In the proposed Priority based Congestion Control (PBCC) protocol the sensor nodes in IoT network form clusters that reduces the amount of traffic and the nodes are prioritized to emphasize important data. Simultaneously, a congestion score determines the occurrence of congestion at a particular node. The proposed protocol outperforms the existing Packet Discard Network Clustering (PDNC) protocol in terms of buffer size, packet transmission range, network region and number of nodes, under various simulation scenarios.

Keywords: internet of things, cyber-physical systems, congestion control, priority, transmission rate

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Authors: Azita Ramezani, Atousa Ramezani

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In the ever-escalating landscape of windows malware the necessity for pioneering defense strategies turns into undeniable this study introduces an avant-garde approach fusing the capabilities of clustering random forests and support vector machines SVM to combat the intricate web of cyber threats our fusion model triumphs with a staggering accuracy of 98.67 and an equally formidable f1 score of 98.68 a testament to its effectiveness in the realm of windows malware defense by deciphering the intricate patterns within malicious code our model not only raises the bar for detection precision but also redefines the paradigm of cybersecurity preparedness this breakthrough underscores the potential embedded in the fusion of diverse analytical methodologies and signals a paradigm shift in fortifying against the relentless evolution of windows malicious threats as we traverse through the dynamic cybersecurity terrain this research serves as a beacon illuminating the path toward a resilient future where innovative fusion models stand at the forefront of cyber threat defense.

Keywords: fusion models, cyber threat defense, windows malware, clustering, random forests, support vector machines (SVM), accuracy, f1-score, cybersecurity, malicious code detection

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Authors: Sanpachai Huvanandana, Settapong Malisuwan, Soparwan Tongyuak, Prust Pannachet, Anong Phoepueak, Navneet Madan

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Many regions of Thailand are prone to flooding due to tropical climate. A commonly increasing precipitation in this continent results in risk of flooding. Many efforts have been implemented such as drainage control system, multiple dams, and irrigation canals. In order to decide where the drainages, dams, and canal should be appropriately located, the flooding risk area should be determined. This paper is aimed to identify the appropriate features that can be used to classify the flooding risk area in Thailand. Several features have been analyzed and used to classify the area. Non-supervised clustering techniques have been used and the results have been compared with ten years average actual flooding area.

Keywords: flood area clustering, geographical information system, flood features

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Authors: Somayeh Nezami

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The management of financial and human resources of police in metropolitans requires many information and exact plans to reduce a rate of crime and increase the safety of the society. Geographical Information Systems have an important role in providing crime maps and their analysis. By using them and identification of crime hot spots along with spatial presentation of the results, it is possible to allocate optimum resources while presenting effective methods for decision making and preventive solutions. In this paper, we try to explain and compare between some of the methods of hot spots analysis such as Mode, Fuzzy Mode and Nearest Neighbour Hierarchical spatial clustering (NNH). Then the spots with the highest crime rates of drug smuggling for one province in Iran with borderline with Afghanistan are obtained. We will show that among these three methods NNH leads to the best result.

Keywords: GIS, Hot spots, nearest neighbor hierarchical spatial clustering, NNH, spatial analysis of crime

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Amit Verma, Pardeep Kaur

Abstract:

In software development organizations, Component based Software engineering (CBSE) is emerging paradigm for software development and gained wide acceptance as it often results in increase quality of software product within development time and budget. In component reusability, main challenges are the right component identification from large repositories at right time. The major objective of this work is to provide efficient algorithm for storage and effective retrieval of components using neural network and parameters based on user choice through clustering. This research paper aims to propose an algorithm that provides error free and automatic process (for retrieval of the components) while reuse of the component. In this algorithm, keywords (or components) are extracted from software document, after by applying k mean clustering algorithm. Then weights assigned to those keywords based on their frequency and after assigning weights, ANN predicts whether correct weight is assigned to keywords (or components) or not, otherwise it back propagates in to initial step (re-assign the weights). In last, store those all keywords into repositories for effective retrieval. Proposed algorithm is very effective in the error correction and detection with user base choice while choice of component for reusability for efficient retrieval is there.

Keywords: component based development, clustering, back propagation algorithm, keyword based retrieval

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Authors: Mohamad Amin Amini, Mehdi Poursha

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Nonlinear response history analysis (NL-RHA) is a robust analytical tool for estimating the seismic demands of structures responding in the inelastic range. However, because of its conceptual and numerical complications, the nonlinear static procedure (NSP) is being increasingly used as a suitable tool for seismic performance evaluation of structures. The conventional pushover analysis methods presented in various codes (FEMA 356; Eurocode-8; ATC-40), are limited to the first-mode-dominated structures, and cannot take higher modes effect into consideration. Therefore, since more than a decade ago, researchers developed enhanced pushover analysis procedures to take higher modes effect into account. The main objective of this study is to propose an enhanced invariant lateral displacement distribution to take higher modes effect into consideration in performing a displacement-based pushover analysis, whereby a set of laterally applied displacements, rather than forces, is monotonically applied to the structure. For this purpose, the effect of different parameters such as the spectral displacement of ground motion, the modal participation factor, and the effective modal participating mass ratio on the lateral displacement distribution is investigated to find the best distribution. The major simplification of this procedure is that the effect of higher modes is concentrated into a single invariant lateral load distribution. Therefore, only one pushover analysis is sufficient without any need to utilize a modal combination rule for combining the responses. The invariant lateral displacement distribution for pushover analysis is then calculated by combining the modal story displacements using the modal combination rules. The seismic demands resulting from the different procedures are compared to those from the more accurate nonlinear response history analysis (NL-RHA) as a benchmark solution. Two structures of different heights including 10 and 20-story special steel moment resisting frames (MRFs) were selected and evaluated. Twenty ground motion records were used to conduct the NL-RHA. The results show that more accurate responses can be obtained in comparison with the conventional lateral loads when the enhanced modal lateral displacement distributions are used.

Keywords: displacement-based pushover, enhanced lateral load distribution, higher modes effect, nonlinear response history analysis (NL-RHA)

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Authors: Rekioua D., Mohammedi A., Rekioua T., Mehleb Z.

Abstract:

Photovoltaic water pumping systems is considered as one of the most promising areas in photovoltaic applications, the economy and reliability of solar electric power made it an excellent choice for remote water pumping. Two conventional techniques are currently in use; the first is the directly coupled technique and the second is the battery buffered photovoltaic pumping system. In this paper, we present different performances of a three operation modes of photovoltaic pumping system. The aim of this work is to determine the effect of different parameters influencing the photovoltaic pumping system performances, such as pumping head, System configuration and climatic conditions. The obtained results are presented and discussed.

Keywords: batteries charge mode, photovoltaic pumping system, pumping head, submersible pump

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