Search results for: grey clustering method

Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

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Authors: Ari Prasetyo, Taufik Faturohman

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The COVID-19 pandemic in Indonesia is part of the ongoing worldwide pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was confirmed to have spread to Indonesia on 2 March 2020. Moreover, the government of Indonesia has been conducting a local lockdown to limit people's movement from one city to another city. Therefore, this situation has impact on business operation, especially on highway companies listed on the Indonesia stock exchange. This study evaluates and measures three companies’ financial performance and health conditions before and during the COVID-19 pandemic from 2016 – 2020. The measurement is conducted by using financial ratio analysis and the Altman Z-score method. The ratio used to measure the financial ratio analysis is taken from the decree of the Ministry of SOE’s KEP-100/MBU/2002 about the company’s health level condition. From the decree, there are eight financial ratios used such as return on equity (ROE), return on investment (ROI), current ratio, cash ratio, collection period, inventory turnover, total asset turnover, and total equity to total asset. Altman Z-score is used to calculate the financial distress condition. The result shows that the highway companies for the period 2016 – 2020 are as follows: PT Jasa Marga (Persero) Tbk (AA, BB, BB, BB, C), PT Citra Marga Nusaphala Persada Tbk (BB, AA, BB, BBB, C), and PT Nusantara Infrastructure Tbk (BB, BB, AA, BBB, C). In addition, the Altman Z-score assessment performed in 2016-2020 shows that PT Jasa Marga (Persero) Tbk was in the grey zone area for 2015-2018 and in the distress zone for 2019-2020. PT Citra Marga Nusaphala Persada Tbk was in the grey zone area for 2015-2019 and in the distress zone for 2020. PT Nusantara Infrastructure Tbk was in the grey zone area for 2015-2018 and in the distress zone for 2019-2020.

Keywords: financial performance, financial ratio, Altman Z-score, financial distress, highway company

Procedia PDF Downloads 162

Authors: A. Shirazibeheshti, T. Radwan, A. Ettefaghian, G. Wilson, C. Luca, Farbod Khanizadeh

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Anticholinergic medication has been associated with events such as falls, delirium, and cognitive impairment in older patients. To further assess this, anticholinergic burden scores have been developed to quantify risk. A risk model based on clustering was deployed in a healthcare management system to cluster patients into multiple risk groups according to anticholinergic burden scores of multiple medicines prescribed to patients to facilitate clinical decision-making. To do so, anticholinergic burden scores of drugs were extracted from the literature, which categorizes the risk on a scale of 1 to 3. Given the patients’ prescription data on the healthcare database, a weighted anticholinergic risk score was derived per patient based on the prescription of multiple anticholinergic drugs. This study was conducted on over 300,000 records of patients currently registered with a major regional UK-based healthcare provider. The weighted risk scores were used as inputs to an unsupervised learning algorithm (mean-shift clustering) that groups patients into clusters that represent different levels of anticholinergic risk. To further evaluate the performance of the model, any association between the average risk score within each group and other factors such as socioeconomic status (i.e., Index of Multiple Deprivation) and an index of health and disability were investigated. The clustering identifies a group of 15 patients at the highest risk from multiple anticholinergic medication. Our findings also show that this group of patients is located within more deprived areas of London compared to the population of other risk groups. Furthermore, the prescription of anticholinergic medicines is more skewed to female than male patients, indicating that females are more at risk from this kind of multiple medications. The risk may be monitored and controlled in well artificial intelligence-equipped healthcare management systems.

Keywords: anticholinergic medicines, clustering, deprivation, socioeconomic status

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Authors: Naresh Kumar, Radhika Sharma, A. K. Singh

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Acid plutonic rocks from the Khanak and Devsar areas of southwestern Haryana were investigated to understand their geochemical and petrogenetic characteristics and tectonic environments. Three dominant rock types (grey, grayish green and pink granites) are the principal geochemical features of Khanak and Devsar areas which reflect the dependencies of their composition on varied geological environment during the anorogenic magmatism. These rocks are enriched in SiO₂, Na₂O+K₂O, Fe/Mg, Rb, Zr, Y, Th, U, REE (Rare Earth Elements) enriched and depleted in MgO, CaO, Sr, P, Ti, Ni, Cr, V and Eu and exhibit a clear affinity to the within-plate granites that were emplaced in an extensional tectonic environment. Chondrite-normalized REE patterns show enriched LREE (Light Rare Earth Elements), moderate to strong negative Eu anomalies and flat heavy REE and grey and grayish green is different from pink granite which is enriched by Rb, Ga, Nb, Th, U, Y and HREE (Heavy Rare Earth Elements) concentrations. The composition of parental magma of both areas corresponds to mafic source contaminated with crustal materials. Petrogenetic modelling suggest that the acid plutonic rocks might have been generated from a basaltic source by partial melting (15-25%) leaving a residue with 35% plagioclase, 25% alkali feldspar, 25% quartz, 7% orthopyroxene, 5% biotite and 3% hornblende. Granites from both areas might be formed from different sources with different degree of melting for grey, grayish green and pink granites.

Keywords: A-type granite, anorogenic, Malani igneous suite, Khanak and Devsar

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

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Authors: Suman Som, Abhijit Maity, Sunil B. Daschakraborty, Sujit Chaudhuri, Manik Pradhan

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Background: Helicobacter pylori is a common and important human pathogen and the primary cause of peptic ulcer disease and gastric cancer. Currently H. pylori infection is detected by both invasive and non-invasive way but the diagnostic accuracy is not up to the mark. Aim: To set up an optimal diagnostic cut-off value of 13C-Urea Breath Test to detect H. pylori infection and evaluate a novel c-PDR methodology to overcome of inconclusive grey zone. Materials and Methods: All 83 subjects first underwent upper-gastrointestinal endoscopy followed by rapid urease test and histopathology and depending on these results; we classified 49 subjects as H. pylori positive and 34 negative. After an overnight, fast patients are taken 4 gm of citric acid in 200 ml water solution and 10 minute after ingestion of the test meal, a baseline exhaled breath sample was collected. Thereafter an oral dose of 75 mg 13C-Urea dissolved in 50 ml water was given and breath samples were collected upto 90 minute for 15 minute intervals and analysed by laser based high precisional cavity enhanced spectroscopy. Results: We studied the excretion kinetics of 13C isotope enrichment (expressed as δDOB13C ‰) of exhaled breath samples and found maximum enrichment around 30 minute of H. pylori positive patients, it is due to the acid mediated stimulated urease enzyme activity and maximum acidification happened within 30 minute but no such significant isotopic enrichment observed for H. pylori negative individuals. Using Receiver Operating Characteristic (ROC) curve an optimal diagnostic cut-off value, δDOB13C ‰ = 3.14 was determined at 30 minute exhibiting 89.16% accuracy. Now to overcome grey zone problem we explore percentage dose of 13C recovered per hour, i.e. 13C-PDR (%/hr) and cumulative percentage dose of 13C recovered, i.e. c-PDR (%) in exhaled breath samples for the present 13C-UBT. We further explored the diagnostic accuracy of 13C-UBT by constructing ROC curve using c-PDR (%) values and an optimal cut-off value was estimated to be c-PDR = 1.47 (%) at 60 minute, exhibiting 100 % diagnostic sensitivity , 100 % specificity and 100 % accuracy of 13C-UBT for detection of H. pylori infection. We also elucidate the gastric emptying process of present 13C-UBT for H. pylori positive patients. The maximal emptying rate found at 36 minute and half empting time of present 13C-UBT was found at 45 minute. Conclusions: The present study exhibiting the importance of c-PDR methodology to overcome of grey zone problem in 13C-UBT for accurate determination of infection without any risk of diagnostic errors and making it sufficiently robust and novel method for an accurate and fast non-invasive diagnosis of H. pylori infection for large scale screening purposes.

Keywords: 13C-Urea breath test, c-PDR methodology, grey zone, Helicobacter pylori

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Authors: Yangze Lan, Ruihua Xv, Feng Zhou, Yijia Shan, Longhao Zhang, Qinghui Xv

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Receiving and dispatching trains is an important part of railroad organization, and the risky evaluation of operating personnel is still reflected by scores, lacking further excavation of wrong answers and operating accidents. With natural language processing (NLP) technology, this study extracts the keywords and key phrases of 40 relevant risk events about receiving and dispatching trains and reclassifies the risk events into 8 categories, such as train approach and signal risks, dispatching command risks, and so on. Based on the historical risk data of personnel, the K-Means clustering method is used to classify the risk level of personnel. The result indicates that the high-risk operating personnel need to strengthen the training of train receiving and dispatching operations towards essential trains and abnormal situations.

Keywords: receiving and dispatching trains, natural language processing, risk evaluation, K-means clustering

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Authors: Nelson Kimeli Kemboi Yego, Juma Kasozi, Joseph Nkruzinza, Francis Kipkogei

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The study investigated the low uptake of health insurance in Kenya despite efforts to achieve universal health coverage through various health insurance schemes. Unsupervised machine learning techniques were employed to identify patterns in health insurance uptake based on sociodemographic factors among Kenyan households. The aim was to identify key demographic groups that are underinsured and to provide insights for the development of effective policies and outreach programs. Using the 2021 FinAccess Survey, the study clustered Kenyan households based on their health insurance uptake and sociodemographic features to reveal patterns in health insurance uptake across the country. The effectiveness of k-prototypes clustering, hierarchical clustering, and agglomerative hierarchical clustering in clustering based on sociodemographic factors was compared. The k-prototypes approach was found to be the most effective at uncovering distinct and well-separated clusters in the Kenyan sociodemographic data related to health insurance uptake based on silhouette, Calinski-Harabasz, Davies-Bouldin, and Rand indices. Hence, it was utilized in uncovering the patterns in uptake. The results of the analysis indicate that inclusivity in health insurance is greatly related to affordability. The findings suggest that targeted policy interventions and outreach programs are necessary to increase health insurance uptake in Kenya, with the ultimate goal of achieving universal health coverage. The study provides important insights for policymakers and stakeholders in the health insurance sector to address the low uptake of health insurance and to ensure that healthcare services are accessible and affordable to all Kenyans, regardless of their socio-demographic status. The study highlights the potential of unsupervised machine learning techniques to provide insights into complex health policy issues and improve decision-making in the health sector.

Keywords: health insurance, unsupervised learning, clustering algorithms, machine learning

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Authors: M. H. Fazel Zarandi, N. Moshahedi

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The hub location problem appears in a variety of applications such as medical centers, firefighting facilities, cargo delivery systems and telecommunication network design. The location of service centers has a strong influence on the congestion at each of them, and, consequently, on the quality of service. This paper presents a fuzzy maximal hub covering location problem (FMCHLP) in which travel costs between any pair of nodes is considered as a fuzzy variable. In order to consider the quality of service, we model each hub as a queue. Arrival rate follows Poisson distribution and service rate follows Erlang distribution. In this paper, at first, a nonlinear mathematical programming model is presented. Then, we convert it to the linear one. We solved the linear model using GAMS software up to 25 nodes and for large sizes due to the complexity of hub covering location problems, and simulated annealing algorithm is developed to solve and test the model. Also, we used possibilistic c-means clustering method in order to find an initial solution.

Keywords: fuzzy modeling, location, possibilistic clustering, queuing

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Authors: Ashok V. Sutagundar, Sunilkumar S. Manvi

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Energy, delay and bandwidth are the prime issues of wireless sensor network (WSN). Energy usage optimization and efficient bandwidth utilization are important issues in WSN. Event triggered data aggregation facilitates such optimal tasks for event affected area in WSN. Reliable delivery of the critical information to sink node is also a major challenge of WSN. To tackle these issues, we propose an event driven dynamic clustering and data aggregation scheme for WSN that enhances the life time of the network by minimizing redundant data transmission. The proposed scheme operates as follows: (1) Whenever the event is triggered, event triggered node selects the cluster head. (2) Cluster head gathers data from sensor nodes within the cluster. (3) Cluster head node identifies and classifies the events out of the collected data using Bayesian classifier. (4) Aggregation of data is done using statistical method. (5) Cluster head discovers the paths to the sink node using residual energy, path distance and bandwidth. (6) If the aggregated data is critical, cluster head sends the aggregated data over the multipath for reliable data communication. (7) Otherwise aggregated data is transmitted towards sink node over the single path which is having the more bandwidth and residual energy. The performance of the scheme is validated for various WSN scenarios to evaluate the effectiveness of the proposed approach in terms of aggregation time, cluster formation time and energy consumed for aggregation.

Keywords: wireless sensor network, dynamic clustering, data aggregation, wireless communication

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Authors: K. Umbleja, M. Ichino, H. Yaguchi

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In this paper, we propose a coloring method for multivariate data visualization by using parallel coordinates based on dissimilarity and tree structure information gathered during hierarchical clustering. The proposed method is an extension for proximity-based coloring that suffers from a few undesired side effects if hierarchical tree structure is not balanced tree. We describe the algorithm by assigning colors based on dissimilarity information, show the application of proposed method on three commonly used datasets, and compare the results with proximity-based coloring. We found our proposed method to be especially beneficial for symbolic data visualization where many individual objects have already been aggregated into a single symbolic object.

Keywords: data visualization, dissimilarity-based coloring, proximity-based coloring, symbolic data

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Vahid Anari, Mina Bakhshi

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Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

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Authors: Lamaa Sellami, Bechir Alaya

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Nowadays, video-on-demand (VoD) applications are becoming one of the tendencies driving vehicular network users. In this paper, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different cars included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over VANET. The proposed algorithm takes advantage of the concept of small cells and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)- Advanced network. The proposed clustering algorithm considers multiple characteristics such as the vehicle’s position and acceleration to reduce latency and packet loss. Therefore, each cluster is counted as a small cell containing vehicular nodes and an access point that is elected regarding some particular specifications.

Keywords: video-on-demand, vehicular ad-hoc network, mobility, vehicular traffic load, small cell, wireless backhaul, LTE-advanced, latency, packet loss

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Authors: M. Bahgat, H. A. Hanafy, S. Lakdawala

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Clustering phenomenon of pellets was observed frequently in shaft processes operating at higher temperatures. Clustering is a result of the growth of fibrous iron precipitates (iron whiskers) that become hooked to each other and finally become crystallized during the initial stages of metallization. If the pellet clustering is pronounced, sometimes leads to blocking inside the furnace and forced shutdown takes place. This work clarifies further the relation between metallic iron whisker growth and iron ore mineralogy. Various pellet sizes (6 – 12.0 & +12.0 mm) from three different ores (A, B & C) were (completely and partially) reduced at 985 ^oC with H₂/CO gas mixture using thermos-gravimetric technique. It was found that reducibility increases by decreasing the iron ore pellet’s size. Ore (A) has the highest reducibility than ore (B) and ore (C). Increasing the iron ore pellet’s size leads to increase the probability of metallic iron whisker formation. Ore (A) has the highest tendency for metallic iron whisker formation than ore (B) and ore (C). The reduction reactions for all iron ores A, B and C are mainly controlled by diffusion reaction mechanism.

Keywords: shaft furnace, cluster, metallic iron whisker, mineralogy, ferrous metallurgy

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Authors: Frodouard Minani

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Since last decade, wireless sensor networks (WSNs) have been used in many areas like health care, agriculture, defense, military, disaster hit areas and so on. Wireless Sensor Networks consist of a Base Station (BS) and more number of wireless sensors in order to monitor temperature, pressure, motion in different environment conditions. The key parameter that plays a major role in designing a protocol for Wireless Sensor Networks is energy efficiency which is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. Maximizing sensor node’s lifetime is an important issue in the design of applications and protocols for Wireless Sensor Networks. Clustering sensor nodes mechanism is an effective topology control approach for helping to achieve the goal of this research. In this paper, the researcher presents an energy efficiency protocol to prolong the network lifetime based on Energy efficient clustering algorithm. The Low Energy Adaptive Clustering Hierarchy (LEACH) is a routing protocol for clusters which is used to lower the energy consumption and also to improve the lifetime of the Wireless Sensor Networks. Maximizing energy dissipation and network lifetime are important matters in the design of applications and protocols for wireless sensor networks. Proposed system is to maximize the lifetime of the Wireless Sensor Networks by choosing the farthest cluster head (CH) instead of the closest CH and forming the cluster by considering the following parameter metrics such as Node’s density, residual-energy and distance between clusters (inter-cluster distance). In this paper, comparisons between the proposed protocol and comparative protocols in different scenarios have been done and the simulation results showed that the proposed protocol performs well over other comparative protocols in various scenarios.

Keywords: base station, clustering algorithm, energy efficient, sensors, wireless sensor networks

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Authors: Sumiko Anno, Keiji Imaoka, Takeo Tadono, Tamotsu Igarashi, Subramaniam Sivaganesh, Selvam Kannathasan, Vaithehi Kumaran, Sinnathamby Noble Surendran

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Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary over time and space. These factors have been examined separately and still require systematic clarification. The present study aimed to investigate the spatial-temporal clustering relationships between these factors and dengue outbreaks in the northern region of Sri Lanka. Remote sensing (RS) data gathered from a plurality of satellites were used to develop an index comprising rainfall, humidity and temperature data. RS data gathered by ALOS/AVNIR-2 were used to detect urbanization, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analyzed RS data and databases were integrated into geographic information systems, enabling temporal analysis, spatial statistical analysis and space-time clustering analysis. Our present results showed that increases in the number of the combination of ecological factor and socio-economic and demographic factors with above the average or the presence contribute to significantly high rates of space-time dengue clusters.

Keywords: ALOS/AVNIR-2, dengue, space-time clustering analysis, Sri Lanka

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Authors: Amanullah Lagharil, Majid Ali Laghari, M. Qasim, Jan. M., Asif Khan, M. Hassan Agheem

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The Nagar Parkar area in southeastern Sindh is a part of the Thar Desert adjacent to the Runn of Kutchh, and covers 480 km2. It contains exposures of a variety of igneous rocks referred to as the Nagar Parkar Igneous Complex. The complex comprises rocks belonging to at least six phases of magmatism, from oldest to youngest: 1) amphibolitic basement rocks, 2) riebeckite-aegirine grey granite, 3) biotite-hornblende pink granite, 4) acid dykes, 5) rhyolite “plugs”, and basic dykes (Jan et al., 1997). The last three of these are not significant in volume. Radiometric dates are lacking but the grey and pink granites are petrographically comparable to the Siwana and Jalore plutons, respectively, emplaced in the Malani volcanic series. Based on these similarities and proximity, the phase 2 to 6 bodies in the Nagar Parkar may belong to the Late Proterozoic (720–745 Ma) Malani magmatism that covers large areas in western Rajasthan. Khan et al. (2007) have reported a 745 ±30 – 755 ±22 Ma U-Th-Pb age on monazite from the pink granite. The grey granite is essentially composed of perthitic feldspar (microperthite, mesoperthite), quartz, small amount of plagioclase and, characteristically, sodic minerals such as riebeckite and aegirine. A few samples lack aegirine. Fe-Ti oxide and minute, well-developed crystals of zircon occur in almost all the studied samples. Tourmaline, fluorite, apatite and rutile occur in only some samples and astrophyllite is rare. Allanite, sphene and leucoxene occur as minor accessories along with local epidote. The pink granite is mostly leucocratic, but locally rich in biotite (up to 7 %). It is essentially made up of microperthite and quartz, with local microcline, and minor plagioclase (albite-oligoclase). Some rocks contain sufficient oligoclase and can be called adamellite or quartz mozonite. Biotite and hornblende are main accessory minerals along with iron oxide, but in a few samples are without hornblende. Fayalitic olivine, zircon, sphene, apatite, tourmaline, fluorite, allanite and cassiterite occur as sporadic accessory minerals. Epidote, carbonate, sericite and muscovite are produced due to the alteration of feldspar. This work concerns the major element geochemistry and comparison of the principal granitic rocks of Nagar Parkar. According to the scheme of De La Roche et al. (1980), majority of the grey and pink granites classify as alkali granite, 20 % as granite and 10 % as granodiorite. When evaluated on the basis of Shand's indices (after Maniar and Piccoli, 1989), the grey and pink granites span all three fields (peralkaline, metaluminous and peraluminous). Of the analysed grey granites, 67 % classify as peralkaline, 20 % as peraluminous and 10 % as metaluminous, while 50 % of pink granites classify as peralkaline, 30 % metaluminous and 20 % peraluminous.

Keywords: petrography, nagar parker, granites, geological sciences

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Authors: Tammeen Siraj, Wenxing Zhou, Terry Huang, Mohammad Al-Amin

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The high resolution inline inspection (ILI) tool is used extensively in the pipeline industry to identify, locate, and measure metal-loss corrosion anomalies on buried oil and gas steel pipelines. Corrosion anomalies may occur singly (i.e. individual anomalies) or as clusters (i.e. a colony of corrosion anomalies). Although the ILI technology has advanced immensely, there are measurement errors associated with the sizes of corrosion anomalies reported by ILI tools due limitations of the tools and associated sizing algorithms, and detection threshold of the tools (i.e. the minimum detectable feature dimension). Quantifying the measurement error in the ILI data is crucial for corrosion management and developing maintenance strategies that satisfy the safety and economic constraints. Studies on the measurement error associated with the length of the corrosion anomalies (in the longitudinal direction of the pipeline) has been scarcely reported in the literature and will be investigated in the present study. Limitations in the ILI tool and clustering process can sometimes cause clustering error, which is defined as the error introduced during the clustering process by including or excluding a single or group of anomalies in or from a cluster. Clustering error has been found to be one of the biggest contributory factors for relatively high uncertainties associated with ILI reported anomaly length. As such, this study focuses on developing a consistent and comprehensive framework to quantify the measurement errors in the ILI-reported anomaly length by comparing the ILI data and corresponding field measurements for individual and clustered corrosion anomalies. The analysis carried out in this study is based on the ILI and field measurement data for a set of anomalies collected from two segments of a buried natural gas pipeline currently in service in Alberta, Canada. Data analyses showed that the measurement error associated with the ILI-reported length of the anomalies without clustering error, denoted as Type I anomalies is markedly less than that for anomalies with clustering error, denoted as Type II anomalies. A methodology employing data mining techniques is further proposed to classify the Type I and Type II anomalies based on the ILI-reported corrosion anomaly information.

Keywords: clustered corrosion anomaly, corrosion anomaly assessment, corrosion anomaly length, individual corrosion anomaly, metal-loss corrosion, oil and gas steel pipeline

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Authors: Nada Slimane, Foued Theljani, Faouzi Bouani

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The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark.

Keywords: clustering, diagnosis, Kalman Filtering, k-means, regularized regression

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Authors: Jainendra Singh, Zaheeruddin

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A typical wireless sensor networks (WSNs) consists of several tiny and low-power sensors which use radio frequency to perform distributed sensing tasks. The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While routing protocols are striving to save energy by acting on sensor nodes, recent studies show that network lifetime can be enhanced by further involving sink mobility. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we propose an energy- efficient homogenous clustering (EHC) technique. In this technique, the decision of each sensor is based on their residual energy and an estimate of how many of its neighboring cluster heads (CHs) will benefit from it being a CH. We, also explore the routing algorithm in clustered WSNs. We show that the proposed schemes significantly outperform current approaches in terms of packet delay, hop count and energy consumption of WSNs.

Keywords: wireless sensor network, energy efficiency, clustering, routing

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Authors: Manverpreet Kaur, Amarpreet Singh

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The demand of Vehicular ad hoc networks is increasing day by day, due to offering the various applications and marvelous benefits to VANET users. Clustering in VANETs is most important to overcome the connectivity problems of VANETs. In this paper, we proposed a new clustering technique Enhanced cluster based connectivity maintenance in vehicular ad hoc network. Our objective is to form long living clusters. The proposed approach is grouping the vehicles, on the basis of the longest list of neighbors to form clusters. The cluster formation and cluster head selection process done by the RSU that may results it reduces the chances of overhead on to the network. The cluster head selection procedure is the vehicle which has closest speed to average speed will elect as a cluster Head by the RSU and if two vehicles have same speed which is closest to average speed then they will be calculate by one of the new parameter i.e. distance to their respective destination. The vehicle which has largest distance to their destination will be choosing as a cluster Head by the RSU. Our simulation outcomes show that our technique performs better than the existing technique.

Keywords: VANETs, clustering, connectivity, cluster head, intelligent transportation system (ITS)

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Authors: Asma Omri, Noureddine Benothman, Sofiane Sayahi, Fethi Tlili, Hichem Besbes

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Cycling, a popular mode of transportation, can also be perilous due to cyclists' vulnerability to collisions with vehicles and obstacles. This paper presents an innovative cyclist safety system based on radar technology designed to offer real-time collision risk warnings to cyclists. The system incorporates a low-power radar sensor affixed to the bicycle and connected to a microcontroller. It leverages radar point cloud detections, a clustering algorithm, and a supervised classifier. These algorithms are optimized for efficiency to run on the TI’s AWR 1843 BOOST radar, utilizing a coarse classification approach distinguishing between cars, trucks, two-wheeled vehicles, and other objects. To enhance the performance of clustering techniques, we propose a 2-Level clustering approach. This approach builds on the state-of-the-art Density-based spatial clustering of applications with noise (DBSCAN). The objective is to first cluster objects based on their velocity, then refine the analysis by clustering based on position. The initial level identifies groups of objects with similar velocities and movement patterns. The subsequent level refines the analysis by considering the spatial distribution of these objects. The clusters obtained from the first level serve as input for the second level of clustering. Our proposed technique surpasses the classical DBSCAN algorithm in terms of geometrical metrics, including homogeneity, completeness, and V-score. Relevant cluster features are extracted and utilized to classify objects using an SVM classifier. Potential obstacles are identified based on their velocity and proximity to the cyclist. To optimize the system, we used the View of Delft dataset for hyperparameter selection and SVM classifier training. The system's performance was assessed using our collected dataset of radar point clouds synchronized with a camera on an Nvidia Jetson Nano board. The radar-based cyclist safety system is a practical solution that can be easily installed on any bicycle and connected to smartphones or other devices, offering real-time feedback and navigation assistance to cyclists. We conducted experiments to validate the system's feasibility, achieving an impressive 85% accuracy in the classification task. This system has the potential to significantly reduce the number of accidents involving cyclists and enhance their safety on the road.

Keywords: 2-level clustering, coarse classification, cyclist safety, warning system based on radar technology

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Authors: G. Rajyalakhmi, C. Karthik, Gerson Desouza, Rimmie Duraisamy

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In this present work, metal matrix composites with combination of aluminium with (Sic/Al2O3) were fabricated using stir casting technique. The objective of the present work is to optimize the process parameters of Wire Electric Discharge Machining (WEDM) composites. Pulse ON Time, Pulse OFF Time, wire feed and sensitivity are considered as input process parameters with responses Material Removal Rate (MRR), Surface Roughness (SR) for optimization of WEDM process. Taguchi L18 Orthogonal Array (OA) is used for experimentation. Grey Relational Analysis (GRA) is coupled with Taguchi technique for multiple process parameters optimization. ANOVA (Analysis of Variance) is used for finding the impact of process parameters individually. Finally confirmation experiments were carried out to validate the predicted results.

Keywords: parametric optimization, particulate reinforced metal matrix composites, Taguchi-grey relational analysis, WEDM

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Authors: Yen Chia-Ju, Cheng Ding-Ruei

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This study took case office building as subject and explored the responsive work order repair request of facilities and equipment in offices and public areas by gray theory, with the purpose of providing for future related office building owners, executive managers, property management companies, mechanical and electrical companies as reference for deciding and assessing forecast model. Important conclusions of this study are summarized as follows according to the study findings: 1. Grey Relational Analysis discusses the importance of facilities repair number of six categories, namely, power systems, building systems, water systems, air conditioning systems, fire systems and manpower dispatch in order. In terms of facilities maintenance importance are power systems, building systems, water systems, air conditioning systems, manpower dispatch and fire systems in order. 2. GM (1,N) and regression method took maintenance hours as dependent variables and repair number, leased area and tenants number as independent variables and conducted single month forecast based on 12 data from January to December 2011. The mean absolute error and average accuracy of GM (1,N) from verification results were 6.41% and 93.59%; the mean absolute error and average accuracy of regression model were 4.66% and 95.34%, indicating that they have highly accurate forecast capability.

Keywords: rey theory, forecast model, Taipei 101, office buildings, property management, facilities, equipment

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Authors: I Dewa Gede Arya Putra

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Principal Component Analysis (PCA) is a mathematical procedure that uses orthogonal transformation techniques to change a set of data with components that may be related become components that are not related to each other. This can have an impact on clustering wind speed characteristics in Indonesia. This study uses data daily wind speed observations of the Site Meteorological Station network for 30 years. Multicollinearity tests were also performed on all of these data before doing clustering with PCA. The results show that the four main components have a total diversity of above 80% which will be used for clusters. Division of clusters using Ward's method obtained 3 types of clusters. Cluster 1 covers the central part of Sumatra Island, northern Kalimantan, northern Sulawesi, and northern Maluku with the climatological pattern of wind speed that does not have an annual cycle and a weak speed throughout the year with a low-speed ranging from 0 to 1,5 m/s². Cluster 2 covers the northern part of Sumatra Island, South Sulawesi, Bali, northern Papua with the climatological pattern conditions of wind speed that have annual cycle variations with low speeds ranging from 1 to 3 m/s². Cluster 3 covers the eastern part of Java Island, the Southeast Nusa Islands, and the southern Maluku Islands with the climatological pattern of wind speed conditions that have annual cycle variations with high speeds ranging from 1 to 4.5 m/s².

Keywords: PCA, cluster, Ward's method, wind speed

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Authors: Morteza Giti

Abstract:

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

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

Abstract:

Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Akshay Agrawal, Anand K. Srivastava

Abstract:

The increasing amount of XML datasets available to casual users increases the necessity of investigating techniques to extract knowledge from these data. Data mining is widely applied in the database research area in order to extract frequent correlations of values from both structured and semi-structured datasets. The increasing availability of heterogeneous XML sources has raised a number of issues concerning how to represent and manage these semi structured data. In recent years due to the importance of managing these resources and extracting knowledge from them, lots of methods have been proposed in order to represent and cluster them in different ways.

Keywords: XML, similarity measure, clustering, cluster quality, semantic clustering

Procedia PDF Downloads 350