Showing papers by "National University of Computer and Emerging Sciences published in 2011"

Application of Laplace decomposition method on semi-infinite domain

Abstract: In this article, Laplace decomposition method (LDM) is applied to obtain series solutions of classical Blasius equation. The technique is based on the application of Laplace transform to nonlinear Blasius flow equation. The nonlinear term can easily be handled with the help of Adomian polynomials. The results of the present technique have closed agreement with series solutions obtained with the help of Adomian decomposition method (ADM), variational iterative method (VIM) and homotopy perturbation method (HPM).

Watermarking of chest CT scan medical images for content authentication

Abstract: Image processing techniques have played a very significant role in the past decades in the field of medical sciences for diagnosis and treatment purposes. In some applications, medical images are divided into region of interest (ROI) and region of non-interest (RONI). Important information regarding diagnosis is contained in the ROI, so its integrity must be assured. We propose a fragile watermarking technique to ensure the integrity of the medical image that avoids the distortion of the image in ROI by embedding the watermark information in RONI. The watermark is composed of patient information, hospital logo and message authentication code computed using a hash function. Earlier encryption of watermark is performed to ensure inaccessibility of embedded data to the adversaries.

Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment

Abstract: The activity of labeling of documents according to their content is known as text categorization. Many experiments have been carried out to enhance text categorization by adding background knowledge to the document using knowledge repositories like Word Net, Open Project Directory (OPD), Wikipedia and Wikitology. In our previous work, we have carried out intensive experiments by extracting knowledge from Wikitology and evaluating the experiment on Support Vector Machine with 10- fold cross-validations. The results clearly indicate Wikitology is far better than other knowledge bases. In this paper we are comparing Support Vector Machine (SVM) and Naive Bayes (NB) classifiers under text enrichment through Wikitology. We validated results with 10-fold cross validation and shown that NB gives an improvement of +28.78%, on the other hand SVM gives an improvement of +636% when compared with baseline results. Naive Bayes classifier is better choice when external enriching is used through any external knowledge base.

Error Analysis of the Written English Essays of Pakistani Undergraduate Students: A Case Study

Abstract: The study aims to examine the errors in a corpus of 50 English essays written by 50 participants (undergraduate Pakistani students). These participants are non native speakers of English language and hail from Intermediate background with weak English writing skills. The instrument used for the study is students‟ written essays in English language. I followed Rod Ellis‟s (1994) procedural analysis of errors; collection of sample of learner language, identification of errors, description of errors, explanation of errors, and evaluation of errors in analyzing 50 English essays. The occurrences of two types of errors; Interlanguage errors and mother tongue (MT) interference errors have been compared and the results show that the percentage of the occurrences of Interlanguage errors is higher than those of errors resulting from the interference of mother tongue (MT). The study has provided an insight into language learning problems which occur when L2 learners internalize the rules of target language (TL) in its production at a particular point resulting into errors in an unknown and a more natural way. These errors serve as a useful guide for English teachers to design an effective curriculum for teaching and learning of English as a second language.

Binary PSO and random forests algorithm for PROBE attacks detection in a network

Abstract: During the past few years, huge amount of network attacks have increased the requirement of efficient network intrusion detection techniques. Different classification techniques for identifying various attacks have been proposed in the literature. In this paper we propose and implement a hybrid classifier based on binary particle swarm optimization (BPSO) and random forests (RF) algorithm for the classification of PROBE attacks in a network. PSO is an optimization method which has a strong global search capability and is used for fine-tuning of the features whereas RF, a highly accurate classifier, is used here for classification. We demonstrate the performance of our technique using KDD99Cup dataset. We also compare the performance of our proposed classifier with eight other well-known classifiers and the results show that the performance achieved by the proposed classifier is much better than the other approaches.

Critical analysis of swarm intelligence based routing protocols in adhoc and sensor wireless networks

Abstract: There are various bio inspired and evolutionary approaches including genetic programming (GP), Neural Network, Evolutionary programming (EP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) used for the routing protocols in ad hoc and sensor wireless networks. There are constraints involved in these protocols due to the mobility and non infrastructure nature of an ad hoc and sensor networks. We study in this research work a probabilistic performance evaluation frameworks and Swarm Intelligence approaches (PSO, ACO) for routing protocols. The performance evaluation metrics employed for wireless and ad hoc routing algorithms is, (a) routing overhead, (b) route optimality, and (c) energy consumption. This survey gives critical analysis of PSO and ACO based algorithms with other approaches applied for the optimization of an ad hoc and wireless sensor network routing protocols.

Design and Deployment of a Trusted Eucalyptus Cloud

Abstract: Shift from traditional software models to the Internet has been steadily gaining momentum over the last 10 years. Moving business applications to the shared utility infrastructure of the cloud with its pay-as-you-go and auto scaling features has become significantly more viable for small and medium sized businesses rather then setting up their own software and hardware infrastructure. However before clouds can reach their full potential and be wholeheartedly adopted there is a need to address the concern of privacy advocates who question the weakness of the model from being able to prevent the monitoring at will, lawfully or unlawfully of the user communication and data stored by the cloud hosting provider. Eucalyptus is an open source cloud computing software framework that implements the Cloud Service Model commonly known as Infrastructure as a Service (IaaS). The IaaS model allows users to run and control entire virtual machines on cloud Infrastructure. However one of the main privacy issues in cloud Infrastructure such as Eucalyptus is to ensure the integrity and confidentiality of user data and computation. In this paper we describe the design and deployment of a Trusted Eucalyptus cloud architecture based on remote attestation via Trusted Platform Modules (TPM). Trusted Eucalyptus guarantees users that their virtual machines execute only on cloud nodes, whose integrity is valid. Our experimental results show that Trusted Eucalyptus cloud is practical in terms of performance.

SAND Join — A skew handling join algorithm for Google's MapReduce framework

Abstract: The simplicity and flexibility of the MapReduce framework have motivated programmers of large scale distributed data processing applications to develop their applications using this framework. However, the implementations of this framework, including Hadoop, do not handle skew in the input data effectively. Skew in the input data results in poor load balancing which can swamp the benefits achievable by parallelization of applications on such parallel processing frameworks. The performance of join operation, which is the most expensive and most frequently executed operation, is severely degraded in the presence of heavy skew in the input datasets to be joined. Hadoop's implementation of the join operation cannot effectively handle such skewed joins, attributed to the use of hash partitioning for load distribution. In this work, we introduce “Skew hANDling Join” (SAND Join) that employs range partitioning instead of hash partitioning for load distribution. Experiments show that SAND Join algorithm can efficiently perform joins on the datasets that are sufficiently skewed. We also compare the performance of this algorithm with that of Hadoop's join algorithms.

Using evolutionary learning classifiers to do MobileSpam (SMS) filtering

Abstract: In recent years, we have witnessed the dramatic increase in the volume of mobile SMS (Short Messaging Service) spam. The reason is that operators - owing to fierce market competition - have introduced packages that allow their customers to send unlimited SMS in less than $1 a month. It not only degrades the service of cellular operators but also compromises security and privacy of users. In this paper, we analyze SMS spam to identify novel features that distinguishes it from benign SMS (ham). The novelty of our approach is that we intercept the SMS at the access layer of a mobile phone - in hexadecimal format - and extract two features: (1) octet bigrams, and (2) frequency distribution of octets. Later, we provide these features to a number of evolutionary and non-evolutionary classifiers to identify the best classifier for our mobile spam filtering system. We evaluate the detection rate and false alarm rate of our system - using different classifiers - on a real world dataset. The results of our experiments show that sUpervised Classifier System (UCS), by operating on the the above-mentioned features'set, achieves more than 89% detection rate and 0% false alarm rate.

On the reliability of ad hoc routing protocols for loss-and-delay sensitive applications

Abstract: In this paper, we analyze the packet delivery reliability of ad hoc routing protocols for loss-and-delay sensitive applications. Since a typical flooding-based route discovery used in ad hoc routing protocols -DSR for instance - can only discover node-disjoint paths. In this context, we first show that the reliability function of such a multipath system is concave with respect to the total number of paths. Therefore, maximum steady-state reliability may be attained by routing each packet through a small set of node-disjoint paths. Subsequently, we prove that a partially-disjoint path is more reliable than a node-disjoint path. Hence, high reliability and significant energy savings may be achieved by routing a packet through fewer partially-disjoint paths. Based on these findings, we suggest modifications to flooding-based route discovery procedure to discover partially-disjoint paths. We complement our theoretical outcomes through extensive simulations. Finally, we analyze the reliability of beacon-based routing protocols and derive an upper bound on the number of hops at which a beacon should be placed to satisfy a given packet reliability constraint.

In-Execution Malware Detection Using Task Structures of Linux Processes

Abstract: In this paper, we present a novel framework -- it uses the information in kernel structures of a process -- to do run-time analysis of the behavior of an executing program. Our analysis shows that classifying a process as malicious or benign -- using the information in kernel structures of a process -- is not only very accurate but also has very low processing overheads; as a result, this lightweight framework can be incorporated within operating system kernel. To provide a proof-of-concept of our thesis, we design and implement our system as a kernel module in Linux. We perform the time series analysis of 118 parameters of Linux task structures and pre-process them to come up with a minimal features' set of 11 features. Our analysis show that these features have remarkably different values for benign and malicious processes; as a result, a number of classifiers operating on these features provide 93% detection accuracy with 0% false alarm rate within 100 milliseconds. Last but not the least, we justify that it is very difficult for a crafty attacker to evade these low-level system specific features.

Prediction of drop size distribution parameters for optical wireless communications through moderate continental fog

Abstract: Wireless optical communication links (OCL), or free space optics links involving optical ground stations are highly influenced by the earth atmosphere due to the interaction of the optical wave with particles of different size and shape. Fog, clouds, rain and snow cause significant signal attenuation, thus limiting the performance of OCL. In this paper, we consider the behavior of OCL in the troposphere under moderate continental fog conditions, which are important for both ground–ground and ground–space OCL. The impact of the droplet size distribution (DSD) of fog is investigated, by processing laser attenuation measurements carried out in Milan (Italy) and Graz (Austria). Significant differences are observed between measured and predicted attenuation when using standard values for the DSD parameters. Hence, new sets of DSD parameters are proposed to model peak, mean and median values of measured attenuation for moderate continental fog. These, in turn, can be useful to make accurate link availability predictions, thus improving the quality of service design for OCL. Copyright © 2010 John Wiley & Sons, Ltd.

Weighted MUSE for Frequent Sub-Graph Pattern Finding in Uncertain DBLP Data

Abstract: Studies shows that finding frequent sub-graphs in uncertain graphs database is an NP complete problem Finding the frequency at which these sub-graphs occur in uncertain graph database is also computationally expensive This paper focus on investigation of mining frequent sub-graph patterns in DBLP uncertain graph data using an approximation based method The frequent sub-graph pattern mining problem is formalized by using the expected support measure Here n approximate mining algorithm based Weighted MUSE, is proposed to discover possible frequent sub-graph patterns from uncertain graph data

Steam-powered sensing

Abstract: Sensornets promise to extend automated monitoring and control into industrial processes. In spite of great progress made in sensornet design, installation and operational costs can impede their widespread adoption---current practices of infrequent, manual observation are often seen as sufficient and more cost effective than automation, even for key business processes. In this paper we present two new approaches to reduce these costs, and we apply those approaches to rapidly detect blockages in steam pipelines of a production oilfield. First, we eliminate the high cost of bringing power to the field by generating electricity from heat, exploiting the high temperature of the very pipelines we monitor. We demonstrate that for temperature differences of 80 °C or more, we are able to sustain sensornet operation without grid electricity or batteries. Second, we show that non-invasive sensing can reduce the cost of sensing by avoiding sensors that pierce the pipeline and have high installation cost with interruption to production. Our system instead uses surface temperature to infer full or partial blockages in steam pipelines and full blockages in hot water pipelines. Finally, we evaluate our "steam-powered sensing" system to monitor potential blockages in steam pipeline chokes at a production oilfield. We also show the generality of our algorithm by applying it to detect water pipeline blockages in our lab. To our knowledge, this paper describes the first field-tested deployment of an industrial sensornet that employs non-solar energy harvesting.

Reversible watermarking method based on adaptive thresholding and companding technique

Abstract: Embedding and extraction of secret information as well as the restoration of the original un-watermarked image are highly desirable in sensitive applications such as military, medical, and law enforcement imaging. This paper presents a novel reversible watermarking approach for digital images using integer-to-integer wavelet transform, companding technique, and adaptive thresholding, enabling it to embed and recover the secret information as well as restore the image to its pristine state. The proposed method takes advantage of block-based watermarking and iterative optimization of threshold for companding which avoids histogram pre-and postprocessing. Consequently, it reduces the associated overhead usually required in most of the reversible watermarking techniques. As a result, it generates less distortion between the watermarked and the original image. Experimental results on regular as well as medical images show that the proposed method outperforms the existing reversible watermarking approaches reported in the literature.

Low-cost single-channel EEG based communication system for people with lock-in syndrome

Abstract: As the use of biomedical signals is incredibly increasing in both clinical and nonclinical applications They have a great deal in the development of devices that can be controlled by information inferred from thoughts One of the current hot topics for research is the Brain Computer Interface (BCI) on basis of EEG signals BCI is a technology that makes humans to control computer or other devices on basis of information inferred from thoughts BCIs have given new hopes to people who suffer from locked-in syndrome and motor disabilities by providing alternative means of communication channels The existing BCIs are Multi-channel, thus very expensive in terms of cost and processing speed, which make them difficult for domestic use The aim of this research paper is to introduce a very cheap, simple and a robust single channel BCI that could prevail in the market We propose a very low-cost EEG-based BCI that is designed to help severely disabled people communicate with others by means of text and SMS To make it simple and affordable, the number of channels is limited to one and signal is acquired through homemade silver electrodes and then fed to the computer through the soundcard for further processing and features extractions The experimental results show that the proposed system is capable enough to provide a very low cost, yet reliable, communication means and a suitable BCI for domestic use Its average accuracy is 87% The potential uses for the technology are almost limitless Instead of communication system, disabled users could have robotic wheelchair, allowing them to move and directly interact with the environments thus it can be used for clinical and nonclinical purposes

Plane Symmetric Vacuum Bianchi Type III Cosmology in f(R) Gravity

Abstract: The main purpose of this paper is to study the exact solution of Bianchi type III spacetime in the context of metric f(R) gravity. The field equations are solved by taking expansion scalar θ proportional to shear scalar σ which gives C=A n , where A and C are the metric coefficients. The physical behavior of the solution has been discussed using some physical quantities. Also, the function of the Ricci scalar is evaluated.

OG-Miner: An Intelligent Health Tool for Achieving Millennium Development Goals (MDGs) in m-Health Environments

Abstract: The latest statistics of WHO show that approxi- mately 500, 000 women die worldwide every year - the majority of them residing in developing countries - due to pregnancy related complications. The situation is so grave that UN has set a target of reducing Maternal Mortality Rate (MMR) by 75% till the year 2015 in its millennium development goals (MDGs). Therefore, the current focus of health care researchers is to advocate the use of e-health technology in developing countries that have the capability: (1) to remotely monitor patients in their homes by semiskilled health professionals, and (2) to use data mining techniques to raise alarms about high risk patients. In this paper, we develop an intelligent health tool - Obstetrics and Gynaecology (OG) OG-Miner - that presents a novel combination of data mining techniques for accurate and effective classification of high risk pregnant women. The scheme classifies four major risk factors of mortality - hypertension, hemorrhage, septicemia and obstructed labor - in a reliable, autonomous and accurate fashion. We have collected a real world data of more than 1200 patients from tertiary care hospitals and rural areas. Our tool achieves more than 98% accuracy on the collected OG dataset. Moreover, our evaluations of OG-Miner on eight other medical datasets show that its learning paradigm can be generalized to other domains as well. Last but not least, we are using OG-Miner as an integral component of a health value chain in our m-health project to autonomously filter a significant number of low risk patients in rural areas; as a result, only high risk patients are referred to specialized obstetrician in tertiary care hospitals. As a consequence, the reduced workload enables them to provide quality care to the patients.

Wavelength selection on FSO-links

Abstract: The objective of this paper is to show the latest results obtained for free space optics (FSO) within the EU COST Action IC0802 and within the European Space Agency (ESA) contract. First, the FSO technology is briefly discussed and some performance evaluation criteria for FSO are provided. Some optical signal propagation experiments through the atmosphere (including the recent investigations in satellite application for FSO) are also documented. In the main part, considerations on suitability of different optical wavelengths are brought into question. The wavelength selection is dependent on the desired application and the atmospheric effects as well as on the availability of receiver and transmitter components. Discussion on the available receiver(s) and transmitter(s) focus on advantages and costs of the different available systems. In the final part, the latest practical results (carried out within the COST Action IC0802) on modelling of the FSO channel under fog conditions and other atmospheric effects are examined.

A novel fast and robust digital image watermarking using Bee Algorithm

Abstract: In today's era of digital information, protection of intellectual property rights is of great concern Watermarking is one technique which is used to protect against illicit copy and distribution of digital information In this paper we propose a novel technique for embedding watermarks in digital images that takes inspiration from the Bee Algorithm The Bee Algorithm is a fairly new evolutionary algorithm that exploits the food foraging behavior of honey bees which enables them to search for best quality flower patches over large distances Our Algorithm mimics this behavior of bees to embed watermark in the wavelet domain such that the resultant image is highly imperceptible as well as robust Simulation results show that our proposed technique consumes far less time in comparison to other classical evolutionary algorithms

A hybrid artificial immune system (AIS) model for power aware secure Mobile Ad Hoc Networks (MANETs) routing protocols

Abstract: Securing ad hoc routing protocols for MANETs is a significant challenge due to number of reasons: (1) mobility results in continuously changing network topology - the premise of stable self or non-self is void, (2) the proposed security solution must be lightweight so that it can be deployed on resource constrained mobile nodes, and (3) the solution should provide high detection accuracy and low false positive rate. The major contribution of this paper is a hybrid AIS model - combining the relevant features of classical self/non-self paradigm with the emerging danger theory paradigm - that has the capability to meet the above-mentioned challenges of the MANET environment. As a case study, we use our hybrid model to develop a power aware security framework for BeeAdHoc- a well-known bio-inspired routing protocol. We have realized our framework in ns-2 simulator. We have also developed an attacker framework in ns-2 that has the capability to launch a number of Byzantine attacks on BeeAdHoc. The results of our experiments show that our proposed framework meets all its requirements: (1) the adaptive learning because of changing self/non-self, (2) high detection accuracy and low false positive rate, (3) lightweight in terms of processing and communication overheads, and (4) better or comparable performance compared with non-secure versions of existing state-of-the-art MANET routing protocols -DSR and AODV. We have also compared our hybrid AIS model with self/non-self, danger theory and a conventional anomaly detection system to show its merits over these schemes. Finally, we propose an extension of the framework for securing DSR.

Adaptive Automated Teller Machines — Part II

Abstract: Nowadays, the banking sector is increasingly relying on Automated Teller Machines (ATMs) in order to provide services to its customers. Although thousands of ATMs exist across many banks and different locations, the GUI and content of a typical ATM interface remains, more or less, the same. For instance, any ATM provides typical options for withdrawal, electronic funds transfer, viewing of mini-statements etc. However, such a static interface might not be suitable for all ATM customers, e.g., some users might not prefer to view all the options when they access the ATM, or to view specific withdrawal amounts less than, say, ten thousand. Hence, it becomes important to data mine the ATM transactions in order to extract and understand useful patterns concerning the customers' behaviors. In this work, we aim to address this requirement. This paper is the second one (Part II) in a series of two papers (Part I and Part II). In Part I, we have described the selection and pre-processing of an ATM transaction dataset (from an international bank based in Kuwait). We have also described its conversion into the MXML format, in order to data mine it through the ProM tool. In this paper, we import this MXML file into ProM and apply diverse types of data mining algorithms on it. Our results reveal that customers perform money-withdrawing transaction most frequently. Also, it is possible to design adaptive ATM interfaces which cater for the ATM terminal (location) at which the withdrawal is being made, the time of this withdrawal, the number of customers accessing the terminal at this time, and the range of money withdrawn in this time.

User Privacy Issues in Eucalyptus: A Private Cloud Computing Environment

Abstract: The highly scalable nature of Cloud Computing enables its users to utilize distributed computational resources and access large amounts of data using different interfaces. Cloud entities including cloud users, service providers and business partners share the available resources at different levels of technological operations. However, the Cloud Computing framework is inherently susceptible to a great number of security threats due to the amalgamation of different computing technologies that make it a complex architecture. Among all the potential threats, those targeting the users' data are significantly important and must be thwarted in precedence to facilitate effective cloud functionality. This paper focuses on the potential threats to users' cloud resident data and metadata and suggests possible solutions to prevent these threats. We have used UEC (Ubuntu Enterprise Cloud) Eucalyptus, which is a popular open source cloud computing software, widely used by the research community. In this work, we have simulated some of the potential attacks to users' data and metadata stored in Eucalyptus database files in order to provide the intended reader with the requisite information to be able to anticipate the grave consequences of violation of cloud users' data privacy.

Comparative Study on DFD to UML Diagrams Transformations

Abstract: Most of legacy systems use nowadays were modeled and documented using structured approach. Expansion of these systems in terms of functionality and maintainability requires shift towards object-oriented documentation and design, which has been widely accepted by the industry. In this paper, we present a survey of the existing Data Flow Diagram (DFD) to Unified Modeling language (UML) transformation techniques. We analyze transformation techniques using a set of parameters, identified in the survey. Based on identified parameters, we present an analysis matrix, which describes the strengths and weaknesses of transformation techniques. It is observed that most of the transformation approaches are rule based, which are incomplete and defined at abstract level that does not cover in depth transformation and automation issues. Transformation approaches are data centric, which focuses on data-store for class diagram generation. Very few of the transformation techniques have been applied on case study as a proof of concept, which are not comprehensive and majority of them are partially automated.

Malicious users' circle detection in social network based on spatio-temporal co-occurrence

Abstract: Online social networks have witnessed massive increase from the point of view of users during last decade. However, it is also becoming center of attraction for spammers. It is a complex problem to trace spammers on a large scale. Since spammers communicate covertly so by analyzing simple graph of social network, they cannot be identified. In order to find the circle of people involved in the malicious messaging, we associate people on the basis of their spatio-temporal co-occurrence i.e. people frequently communicating with each other. In this paper, we associate people on the basis of their spatio-temporal co-occurrence and find the users involved in malicious communications.

A comparison of two suffix tree-based document clustering algorithms

Abstract: Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, focuses in this domain shifted from traditional vector based document similarity for clustering to suffix tree based document similarity, as it offers more semantic representation of the text present in the document. In this paper, we compare and contrast two recently introduced approaches to document clustering based on suffix tree data model. The first is an Efficient Phrase based document clustering, which extracts phrases from documents to form compact document representation and uses a similarity measure based on common suffix tree to cluster the documents. The second approach is a frequent word/word meaning sequence based document clustering, it similarly extracts the common word sequence from the document and uses the common sequence/ common word meaning sequence to perform the compact representation, and finally, it uses document clustering approach to cluster the compact documents. These algorithms are using agglomerative hierarchical document clustering to perform the actual clustering step, the difference in these approaches are mainly based on extraction of phrases, model representation as a compact document, and the similarity measures used for clustering. This paper investigates the computational aspect of the two algorithms, and the quality of results they produced.

Teaching problem solving effectively

Abstract: To teach students problem solving effectively it is important to guide them properly through the process of problem solving. Most of the programming lab exercises lack emphasis on practicing the process of problem solving. Based on our experience, we have suggested a guideline to design lab exercises. In which we emphasize on defining detailed steps to guide students through the process of problem solving. We have proven through experiments that lab exercises designed with guidelines provided to solve the problem are very effective way of teaching problem solving skills.

Power efficient scheduling heuristics for energy conservation in computational grids

Abstract: Computation grids and computational clouds are becoming increasingly popular in the organizations which require massive computational capabilities Building such infrastructures makes a lucrative business case, thanks to availability of cheap hardware components and affordable software Maintaining computational grids or cloud, however, require careful planning as in such dedicated environments, round-the-clock availability of workstations is very crucial Ensuring uninterrupted availability, not only demands mechanism for failover redundancy but also results in constant power drainage The tradeoff between the cost and the performance is the constant dilemma that the operations of the data centers face today In this paper, we propose various heuristics for power-aware scheduling algorithms for scheduling jobs with dependent tasks onto the computational grid We formulate the problem as a multi-objective function which results in various cost-performance tradeoffs each lying within the solution boundary

Dividend Policy and Earnings Management: An Empirical Study of Pakistani Listed Companies

Abstract: Dividend policy is one of the widely addressed topics in financial management. It is an important duty of a financial manager to formulate the company's dividend policy that is in the best interest of the company. Many a time financial managers are involved in earnings management practices with the intention of adjusting dividends. The present study has been carried out to scrutinize the effect of earnings management on dividend policy. The researchers have taken the data of 86 listed companies for the year 2004 to 2009. The researchers have measured the dividend policy by using dividend payout ratio while Modified Cross Sectional Jones Model (1995) has been employed to measure the earnings management. The results of the common effect model show that there is not any significant relationship among earnings management and dividend policy. Moreover, smaller companies are paying more dividends as compared to larger companies. This study reveals that involvement of managers is not for dividend policy. There might be some other motives behind the earnings management.