Search results for: model clone detection

Authors: J. P. Dubois, Omar M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a statistical learning tool developed to a more complex concept of structural risk minimization (SRM). In this paper, SVM is applied to signal detection in communication systems in the presence of channel noise in various environments in the form of Rayleigh fading, additive white Gaussian background noise (AWGN), and interference noise generalized as additive color Gaussian noise (ACGN). The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these advanced stochastic noise models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to conventional binary signaling optimal model-based detector driven by binary phase shift keying (BPSK) modulation. We show that the SVM performance is superior to that of conventional matched filter-, innovation filter-, and Wiener filter-driven detectors, even in the presence of random Doppler carrier deviation, especially for low SNR (signal-to-noise ratio) ranges. For large SNR, the performance of the SVM was similar to that of the classical detectors. However, the convergence between SVM and maximum likelihood detection occurred at a higher SNR as the noise environment became more hostile.

Keywords: Colour noise, Doppler shift, innovation filter, least square-support vector machine, matched filter, Rayleigh fading, Wiener filter.

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Authors: Ahmed Abbas Helmy, M. R. M. Rizk, Mohamed El-Sayed

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A model predictive controller based on recursive learning is proposed. In this SISO adaptive controller, a model is automatically updated using simple recursive equations. The identified models are then stored in the memory to be re-used in the future. The decision for model update is taken based on a new control performance index. The new controller allows the use of simple linear model predictive controllers in the control of nonlinear time varying processes.

Keywords: Adaptive control, model predictive control, dynamic matrix control, online model identification

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Authors: M. Rozanek, K. Roubik

Abstract:

The article deals with development, design and implementation of a mathematical model of the human respiratory system. The model is designed in order to simulate distribution of important intrapulmonary parameters along the bronchial tree such as pressure amplitude, tidal volume and effect of regional mechanical lung properties upon the efficiency of various ventilatory techniques. Therefore exact agreement of the model structure with the lung anatomical structure is required. The model is based on the lung morphology and electro-acoustic analogy is used to design the model.

Keywords: Model of the respiratory system, total lung impedance, intrapulmonary parameters.

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Authors: Jaedo Kim, Youngjoon Han, Hernsoo Hahn

Abstract:

The purpose of this paper is to detect human in images. This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with various sub-region size. The result shows that the accuracy level of proposed method similar to Histogram of Oriented Gradients(HOG) feature descriptor and feature extraction process is simple and faster than existing methods.

Keywords: Human detection, Projected edge descriptor, Linear SVM, Local appearance feature

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Authors: Akram A. Mustafa

Abstract:

In this paper an approaches for increasing the effectiveness of error detection in computer network channels with Pulse-Amplitude Modulation (PAM) has been proposed. Proposed approaches are based on consideration of special feature of errors, which are appearances in line with PAM. The first approach consists of CRC modification specifically for line with PAM. The second approach is base of weighted checksums using. The way for checksum components coding has been developed. It has been shown that proposed checksum modification ensure superior digital data control transformation reliability for channels with PAM in compare to CRC.

Keywords: Pulse-Amplitude Modulation, checksum, transmission, discrete.

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Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

Abstract:

Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: Fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time.

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Authors: K. Sekulová, M. Šimon

Abstract:

This article focuses on upper-extremity musculoskeletal disorders risk assessment model at workplace. In this model are used risk factors that are responsible for musculoskeletal system damage. Based on statistic calculations the model is able to define what risk of MSD threatens workers who are under risk factors. The model is also able to say how MSD risk would decrease if these risk factors are eliminated.

 

Keywords: Ergonomics, musculoskeletal disorders, occupational diseases, risk factors.

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

Abstract:

During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: Artificial intelligence, depression detection, facial emotion recognition, natural language processing, mental disorder.

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Authors: A. Uchaipichat

Abstract:

The hydro-mechanical model for unsaturated soils has been presented based on the effective stress principle taking into account effects of drying-wetting process. The elasto-plastic constitutive equations for stress-strain relations of the soil skeleton have been established. A plasticity model is modified from modified Cam-Clay model. The hardening rule has been established by considering the isotropic consolidation paths. The effect of dryingwetting process is introduced through the ¤ç parameter. All model coefficients are identified in terms of measurable parameters. The simulations from the proposed model are compared with the experimental results. The model calibration was performed to extract the model parameter from the experimental results. Good agreement between the results predicted using proposed model and the experimental results was obtained.

Keywords: Drying-wetting process, Effective stress, Elastoplasticmodel, Unsaturated soils

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Authors: Tze Sian Pui, Tushar Bansal, Patthara Kongsuphol, Sunil K. Arya

Abstract:

We present a label-free biosensor based on electrochemical impedance spectroscopy for the detection of proinflammatory cytokine Tumor Necrosis Factor (TNF-α). Secretion of TNF-α has been correlated to the onset of various diseases including rheumatoid arthritis, Crohn-s disease etc. Gold electrodes were patterned on a silicon substrate and self assembled monolayer of dithiobis-succinimidyl propionate was used to develop the biosensor which achieved a detection limit of ~57fM. A linear relationship was also observed between increasing TNF-α concentrations and chargetransfer resistance within a dynamic range of 1pg/ml – 1ng/ml.

Keywords: Tumor necrosis factor, electrochemical impedance spectroscopy, label free, self assembled monolayer

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Authors: M. Elhaj, M. Almrabet, M. Rgeai, I. Ehtiwesh

Abstract:

A Comparison and evaluation of the different condition monitoring (CM) techniques was applied experimentally on RC e.g. Dynamic cylinder pressure and crankshaft Instantaneous Angular Speed (IAS), for the detection and diagnosis of valve faults in a two - stage reciprocating compressor for a programme of condition monitoring which can successfully detect and diagnose a fault in machine. Leakage in the valve plate was introduced experimentally into a two-stage reciprocating compressor. The effect of the faults on compressor performance was monitored and the differences with the normal, healthy performance noted as a fault signature been used for the detection and diagnosis of faults. The paper concludes with what is considered to be a unique approach to condition monitoring. First, each of the two most useful techniques is used to produce a Truth Table which details the circumstances in which each method can be used to detect and diagnose a fault. The two Truth Tables are then combined into a single Decision Table to provide a unique and reliable method of detection and diagnosis of each of the individual faults introduced into the compressor. This gives accurate diagnosis of compressor faults.

Keywords: Condition Monitoring, Dynamic Pressure, Instantaneous Angular Speed, Reciprocating Compressor.

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Authors: Çiğdem Bakır, Mecit Yüzkat

Abstract:

With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software used in the study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: Preprocessing of the data used, feature detection and classification. We tried to determine the success of our study with different accuracy metrics and the results were presented comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: Decision tree, water quality, water pollution, machine learning.

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Authors: Wiriya Loongyai, Kiettisak Promphet, Nilubol Kangsukul, Ratchawat Noppha

Abstract:

Polymerase chain reaction (PCR) assay and conventional microbiological methods were used to detect bacterial contamination of egg shells and egg content in different commercial housing systems, open house system and evaporative cooling system. A PCR assay was developed for direct detection using a set of primers specific for the invasion by A gene (invA) of Salmonella spp. PCR detected the presence of Salmonella in 2 samples of shell egg from the evaporative cooling system, while conventional cultural methods detected no Salmonella from the same samples.

Keywords: egg content, egg shell, invA gene, PCR, Salmonellaspp.

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Authors: M. Govindarajan

Abstract:

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: Data mining, ensemble, radial basis function, support vector machine, accuracy.

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Authors: Ainul Akmar Mokhtar, Masdi Muhammad, Hilmi Hussin, Mohd Amin Abdul Majid

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A reliability, availability and maintainability (RAM) model has been built for acid gas removal plant for system analysis that will play an important role in any process modifications, if required, for achieving its optimum performance. Due to the complexity of the plant, the model was based on a Reliability Block Diagram (RBD) with a Monte Carlo simulation engine. The model has been validated against actual plant data as well as local expert opinions, resulting in an acceptable simulation model. The results from the model showed that the operation and maintenance can be further improved, resulting in reduction of the annual production loss.

Keywords: Acid gas removal plant, RAM model, Reliabilityblock diagram

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Authors: U. Rubins, Z. Marcinkevics, K.Volceka

Abstract:

The cardiovascular system has become the most important subject of clinical research, particularly measurement of arterial blood flow. Therefore correct determination of arterial diameter is crucial. We propose a novel, semi-automatic method for artery lumen detection. The method is based on Gaussian probability function. Usability of our proposed method was assessed by analyzing ultrasound B-mode CFA video sequences acquired from eleven healthy volunteers. The correlation coefficient between the manual and semi-automatic measurement of arterial diameter was 0.996. Our proposed method for detecting artery boundary is novel and accurate enough for the measurement of artery diameter.

Keywords: Ultrasound, boundary detection, artery diameter, curve fitting.

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Authors: Roberto Sabatini, Alessandro Gardi, Mark A. Richardson

Abstract:

The availability of powerful eye-safe laser sources and the recent advancements in electro-optical and mechanical beam-steering components have allowed laser-based Light Detection and Ranging (LIDAR) to become a promising technology for obstacle warning and avoidance in a variety of manned and unmanned aircraft applications. LIDAR outstanding angular resolution and accuracy characteristics are coupled to its good detection performance in a wide range of incidence angles and weather conditions, providing an ideal obstacle avoidance solution, which is especially attractive in low-level flying platforms such as helicopters and small-to-medium size Unmanned Aircraft (UA). The Laser Obstacle Avoidance Marconi (LOAM) system is one of such systems, which was jointly developed and tested by SELEX-ES and the Italian Air Force Research and Flight Test Centre. The system was originally conceived for military rotorcraft platforms and, in this paper, we briefly review the previous work and discuss in more details some of the key development activities required for integration of LOAM on UA platforms. The main hardware and software design features of this LOAM variant are presented, including a brief description of the system interfaces and sensor characteristics, together with the system performance models and data processing algorithms for obstacle detection, classification and avoidance. In particular, the paper focuses on the algorithm proposed for optimal avoidance trajectory generation in UA applications.

Keywords: LIDAR, Low-Level Flight, Nap-of-the-Earth Flight, Near Infra-Red, Obstacle Avoidance, Obstacle Detection, Obstacle Warning System, Sense and Avoid, Trajectory Optimisation, Unmanned Aircraft.

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Authors: Yong-Gyu Lee, Gilwon Yoon

Abstract:

Bleeding in the digestive duct is an important diagnostic parameter for patients. Blood in the endoscopic image can be determined by investigating the color tone of blood due to the degree of oxygenation, under- or over- illumination, food debris and secretions, etc. However, we found that how to pre-process raw images obtained from the capsule detectors was very important. We applied various image process methods suitable for the capsule endoscopic image in order to remove noises and unbalanced sensitivities for the image pixels. The results showed that much improvement was achieved by additional pre-processing techniques on the algorithm of determining bleeding areas.

Keywords: blood detection, capsule endoscopy, image processing.

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Authors: Haibo Hu, Dan Yang, Chunxiao Ye, Chunlei Fu, Ren Li

Abstract:

High quality requirements analysis is one of the most crucial activities to ensure the success of a software project, so that requirements verification for software system becomes more and more important in Requirements Engineering (RE) and it is one of the most helpful strategies for improving the quality of software system. Related works show that requirement elicitation and analysis can be facilitated by ontological approaches and semantic web technologies. In this paper, we proposed a hybrid method which aims to verify requirements with structural and formal semantics to detect interactions. The proposed method is twofold: one is for modeling requirements with the semantic web language OWL, to construct a semantic context; the other is a set of interaction detection rules which are derived from scenario-based analysis and represented with semantic web rule language (SWRL). SWRL based rules are working with rule engines like Jess to reason in semantic context for requirements thus to detect interactions. The benefits of the proposed method lie in three aspects: the method (i) provides systematic steps for modeling requirements with an ontological approach, (ii) offers synergy of requirements elicitation and domain engineering for knowledge sharing, and (3)the proposed rules can systematically assist in requirements interaction detection.

Keywords: Requirements Engineering, Semantic Web, OWL, Requirements Interaction Detection, SWRL.

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Authors: Jaroslav Polec, Viliam Hirner, Michal Martinovič, Kvetoslava Kotuliaková

Abstract:

In this paper we present a novel error model for packet loss and subsequent error description. The proposed model simulates the error performance of wireless communication link. The model is designed as two independent Markov chains, where the first one is used for packet generation and the second one generates correctly and incorrectly transmitted bits for received packets from the first chain. The statistical analyses of real communication on the wireless link are used for determination of model-s parameters. Using the obtained parameters and the implementation of the generator, we collected generated traffic. The obtained results generated by proposed model are compared with the real data collection.

Keywords: Wireless channel, error model, Markov chain, Elliot model, Gilbert model, generator, IEEE 802.11.

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Authors: Tomoya Okubo, Kuninori Nakamura, Shin-ichi Mayekawa

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In this research, a latent class vector model for pairwise data is formulated. As compared to the basic vector model, this model yields consistent estimates of the parameters since the number of parameters to be estimated does not increase with the number of subjects. The result of the analysis reveals that the model was stable and could classify each subject to the latent classes representing the typical scales used by these subjects.

Keywords: finite mixture models, latent class analysis, Thrustone's paired comparison method, vector model

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Authors: T. N. Mac, B. Shahbodaghkhan, N. Khalili

Abstract:

A new elastic-viscoplastic (EVP) constitutive model is proposed for the analysis of time-dependent behavior of clay. The proposed model is based on the bounding surface plasticity and the concept of viscoplastic consistency framework to establish continuous transition from plasticity to rate dependent viscoplasticity. Unlike the overstress based models, this model will meet the consistency condition in formulating the constitutive equation for EVP model. The procedure of deriving the constitutive relationship is also presented. Simulation results and comparisons with experimental data are then presented to demonstrate the performance of the model.

Keywords: Bounding surface, consistency theory, constitutive model, viscosity.

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Authors: H. Heidari-Bafroui, B. Ribeiro, A. Charbaji, C. Anagnostopoulos, M. Faghri

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In this paper, we report the development of a portable and inexpensive infrared lightbox for improving the detection limits of paper-based phosphate devices. Commercial paper-based devices utilize the molybdenum blue protocol to detect phosphate in the environment. Although these devices are easy to use and have a long shelf life, their main deficiency is their low sensitivity based on the qualitative results obtained via a color chart. To improve the results, we constructed a compact infrared lightbox that communicates wirelessly with a smartphone. The system measures the absorbance of radiation for the molybdenum blue reaction in the infrared region of the spectrum. It consists of a lightbox illuminated by four infrared light-emitting diodes, an infrared digital camera, a Raspberry Pi microcontroller, a mini-router, and an iPhone to control the microcontroller. An iPhone application was also developed to analyze images captured by the infrared camera in order to quantify phosphate concentrations. Additionally, the app connects to an online data center to present a highly scalable worldwide system for tracking and analyzing field measurements. In this study, the detection limits for two popular commercial devices were improved by a factor of 4 for the Quantofix devices (from 1.3 ppm using visible light to 300 ppb using infrared illumination) and a factor of 6 for the Indigo units (from 9.2 ppm to 1.4 ppm) with repeatability of less than or equal to 1.2% relative standard deviation (RSD). The system also provides more granular concentration information compared to the discrete color chart used by commercial devices and it can be easily adapted for use in other applications.

Keywords: Infrared lightbox, paper-based device, phosphate detection, smartphone colorimetric analyzer.

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Authors: Archana Panchapakesan Iyer, Taha Abdullah Kumosani

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Many high-risk pathogens that cause disease in humans are transmitted through various food items. Food-borne disease constitutes a major public health problem. Assessment of the quality and safety of foods is important in human health. Rapid and easy detection of pathogenic organisms will facilitate precautionary measures to maintain healthy food. The Polymerase Chain Reaction (PCR) is a handy tool for rapid detection of low numbers of bacteria. We have designed gene specific primers for most common food borne pathogens such as Staphylococci, Salmonella and E.coli. Bacteria were isolated from food samples of various food outlets and identified using gene specific PCRs. We identified Staphylococci, Salmonella and E.coli O157 using gene specific primers by rapid and direct PCR technique in various food samples. This study helps us in getting a complete picture of the various pathogens that threaten to cause and spread food borne diseases and it would also enable establishment of a routine procedure and methodology for rapid identification of food borne bacteria using the rapid technique of direct PCR. This study will also enable us to judge the efficiency of present food safety steps taken by food manufacturers and exporters.

Keywords: food borne pathogens, PCR, food safety, rapiddetection.

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Authors: JooHyung Oh, Dongwan Kang, Sekwon Kim, ChaeTae Im

Abstract:

Currently, there has been a 3G mobile networks data traffic explosion due to the large increase in the number of smartphone users. Unlike a traditional wired infrastructure, 3G mobile networks have limited wireless resources and signaling procedures for complex wireless resource management. And mobile network security for various abnormal and malicious traffic technologies was not ready. So Malicious or potentially malicious traffic originating from mobile malware infected smart devices can cause serious problems to the 3G mobile networks, such as DoS and scanning attack in wired networks. This paper describes the DoS security threat in the 3G mobile network and proposes a detection technology.

Keywords: 3G, WCDMA, DoS, Security Threat

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Authors: Aswir Premadi, Mohammad Syuhaimi Ab. Rahman, Mohamad Najib Moh. Saupe, KasmiranJumari

Abstract:

An optical fault monitoring in FTTH-PON using ACS is demonstrated. This device can achieve real-time fault monitoring for protection feeder fiber. In addition, the ACS can distinguish optical fiber fault from the transmission services to other customers in the FTTH-PON. It is essential to use a wavelength different from the triple-play services operating wavelengths for failure detection. ACS is using the operating wavelength 1625 nm for monitoring and failure detection control. Our solution works on a standard local area network (LAN) using a specially designed hardware interfaced with a microcontroller integrated Ethernet.

Keywords: ACS, monitoring tool, FTTH-PON.

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Authors: Zaw Tun, Aung Htein Maw

Abstract:

The nature of wireless ad hoc and sensor networks make them very attractive to attackers. One of the most popular and serious attacks in wireless ad hoc networks is wormhole attack and most proposed protocols to defend against this attack used positioning devices, synchronized clocks, or directional antennas. This paper analyzes the nature of wormhole attack and existing methods of defending mechanism and then proposes round trip time (RTT) and neighbor numbers based wormhole detection mechanism. The consideration of proposed mechanism is the RTT between two successive nodes and those nodes- neighbor number which is needed to compare those values of other successive nodes. The identification of wormhole attacks is based on the two faces. The first consideration is that the transmission time between two wormhole attack affected nodes is considerable higher than that between two normal neighbor nodes. The second detection mechanism is based on the fact that by introducing new links into the network, the adversary increases the number of neighbors of the nodes within its radius. This system does not require any specific hardware, has good performance and little overhead and also does not consume extra energy. The proposed system is designed in ad hoc on-demand distance vector (AODV) routing protocol and analysis and simulations of the proposed system are performed in network simulator (ns-2).

Keywords: AODV, Wormhole attacks, Wireless ad hoc andsensor networks

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Authors: Alaa Al Hawari, Mohammad Khader, Tarek Zayed, Osama Moselhi

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Ground Penetrating Radar (GPR) is one of the most effective electromagnetic techniques for non-destructive non-invasive subsurface features investigation. Water leak from pipelines is the most common undesirable reason of potable water losses. Rapid detection of such losses is going to enhance the use of the Water Distribution Networks (WDN) and decrease threatens associated with water mains leaks. In this study, GPR approach was developed to detect leaks by implementing an appropriate imaging analyzing strategy based on image refinement, reflection polarity and reflection amplitude that would ease the process of interpreting the collected raw radargram image.

Keywords: Water Networks, Leakage, Water pipelines, Ground Penetrating Radar.

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Authors: Rana Khalid Naeem, Asif Mansoor

Abstract:

The hydrologic time series data display periodic structure and periodic autoregressive process receives considerable attention in modeling of such series. In this communication long term record of monthly waste flow of Lyari river is utilized to quantify by using PAR modeling technique. The parameters of model are estimated by using Frances & Paap methodology. This study shows that periodic autoregressive model of order 2 is the most parsimonious model for assessing periodicity in waste flow of the river. A careful statistical analysis of residuals of PAR (2) model is used for establishing goodness of fit. The forecast by using proposed model confirms significance and effectiveness of the model.

Keywords: Diagnostic checks, Lyari river, Model selection, Monthly waste flow, Periodicity, Periodic autoregressive model.

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Authors: Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo

Abstract:

With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.

Keywords: Bipartite graph, clustering, one-mode projection, web proxy detection.

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