Papers by Sameem Abdul Kareem
World-altering features of semantic web service description languages
Investigating Data Allocation in Parallel Database Systems
Editing, reproduction and distribution of the digital multimedia are becoming extremely easier an... more Editing, reproduction and distribution of the digital multimedia are becoming extremely easier and faster with the existence of the internet and the availability of pervasive and powerful multimedia tools. . However these advances have brought the problem of ownership or unauthorized tampering of images. Digital watermarking has emerged as a possible method to tackle these issues. In this paper, a digital watermarking algorithm using wavelet transformed is proposed. Before embedding is done, the cover image after undergoing wavelet decomposition is shuffled. Gaussian sequence related to the watermark signal are then embedded into each of the image wavelet coefficient The extraction of the watermark bit, requires knowledge of the original cover image. The experimental results demonstrate that the watermark with the proposed algorithm satisfied imperceptibility and robust against lossy compression and addition of Gaussian noise.
The best feature of artificial intelligent Feed Forward Neural Network (FFNN) classification mode... more The best feature of artificial intelligent Feed Forward Neural Network (FFNN) classification models is learning of input data through their weights. Data preprocessing and pre-training are the contributing factors in developing efficient techniques for low training time and high accuracy of classification. In this study, we investigate and review the powerful preprocessing functions of the FFNN models. Currently initialization of the weights is at random which is the main source of problems. Multilayer auto-encoder networks as the latest technique like other related techniques is unable to solve the problems. Weight Linear Analysis (WLA) is a combination of data pre-processing and pretraining to generate real weights through the use of normalized input values. The FFNN model by using the WLA increases classification accuracy and improve training time in a single epoch without any training cycle, the gradient of the mean square error function, updating the weights. The results of comparison and evaluation show that the WLA is a powerful technique in the FFNN classification area yet.
Materialized View Selection Using Vector Evaluated Genetic Algorithm
Malaysian Journal of Computer …, 2009
Conversational systems or chatterbots converse/chat by learning from their interactions with user... more Conversational systems or chatterbots converse/chat by learning from their interactions with users. To do this the systems must have an adaptive knowledge base that can be updated by the systems themselves. RONE is a tele-text based conversational system. RONE's knowledge base is built using SQL and accessed using the main Java application. Additionally, RONE uses conjunctions and prepositions as markers to expedite the dissemination and storage of information which helps him learn. In this paper, we describe the approach RONE uses to break up new information for learning purposes -the principle technique introduced here being the storage of information in a format to answer all the possible questions directly without inference. We also look at other conversation based learning approaches and their limitations. Further, we compare RONE's performance against some contemporary conversational systems and provide evidence of the relative superior informational accuracy of RONE's responses to user interrogation. RONE's better performance is noteworthy because it is relative to systems which are Loebner Prize medal winners.
Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm
PLOS ONE, 2015
Global warming is attracting attention from policy makers due to its impacts such as floods, extr... more Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks-hence, reducing global warming. The policy implications are discussed in the paper.
Fluctuation free approximations are used everywhere matrix representation is directly or indirect... more Fluctuation free approximations are used everywhere matrix representation is directly or indirectly involved. The idea is simple: The matrix representation of a function operator, whose action on its operand is the multiplication by a function, is equivalent ...
Journal of the Association for Information Science and Technology, 2014
This research creates an architecture for investigating the existence of probable lexical diverge... more This research creates an architecture for investigating the existence of probable lexical divergences between articles, categorized as Institute for Scientific Information (ISI) and non-ISI, and consequently, if such a difference is discovered, to propose the best available classification method. Based on a collection of ISI-and non-ISI-indexed articles in the areas of business and computer science, three classification models are trained. A sensitivity analysis is applied to demonstrate the impact of words in different syntactical forms on the classification decision. The results demonstrate that the lexical domains of ISI and non-ISI articles are distinguishable by machine learning techniques. Our findings indicate that the support vector machine identifies ISIindexed articles in both disciplines with higher precision than do the Naïve Bayesian and K-Nearest Neighbors techniques.
Lecture Notes in Electrical Engineering, 2014
This paper presents a conceptual framework for selection of data mining technique based on the 8 ... more This paper presents a conceptual framework for selection of data mining technique based on the 8 selection criteria's: optimization capability, computation complexity, flexibility, interpretability, scalability, ease of problem encoding, autonomy, and accessibility. The framework is suitable for choosing appropriate technique for application in a particular task of data mining. The paper has set the stage for further research work.
Fourth International Conference on Intelligent Systems, Modelling and Simulation, 2013
Most image retrieval methods are specialized for image based matching and retrieval based on low-... more Most image retrieval methods are specialized for image based matching and retrieval based on low-level features. However, humans perceive facial images and compare their similarities using high-level features , such as the description and ranking of facial traits including gender, race, and age. In this research, a new proposed method is used for semantic features selection and representation to be described by the user directly through appropriate verbal descriptions using the natural language concepts.In this manner, the research has strategized to bridge the semantic gap between the low-level image features and high-level semantic concepts. A total of 100 respondents participated in selecting and weighting the semantic features of the human face based on the level of importance of each feature. The semantic features were also integrated directly with the eigenfaces and color histogram features for facial image searching and retrieval to enhance retrieval accuracy for the user. Euclidean distance was used for features classes integration and classification purposes. The proposed human facial image retrieval has been evaluated through several experiments using the precision and recall methods. The results have indicated high accuracies, which are considered a significant improvement compared with low-level features based facial image retrieval techniques.
Enlarging Test Collections of Composite Semantic Services
IJCN, 2012
Frontiers of Computer Science, 2013
2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation, 2012
Due to the significant increase in the already huge collection of digital images that we have tod... more Due to the significant increase in the already huge collection of digital images that we have today, it has become imperative to find efficient methods for the archival and retrieval of these images. In this research, a content basedhuman facial image detection and retrieval model is proposed for retrieving facial images of humans based on their visual content from an image database. The research proposes a technique of face segmentation based on which a new method of features extraction from the human face is devised. The capability and effectiveness of the color space models (RGB, HSV, and HSI) on facial image retrieval technique are also investigated. Eigenfaces features are used as a domain specific visual content to extract the characteristic feature images of the human facial images, while the color histogram of the facial image is used as a general visual content. Viola-Jones face detection method is employed to obtain the location, extent and dimensions of each face. Moreover, for the measurement of distance and classification purposes, Euclidean distance is utilized. The sample image database consists of 1500 local facial images of one hundred and fifty participants from the University of Malaya (UM), Kuala Lumpur, and some of their friends and families outside the UM. Several experiments based on precision and recall approach were conducted to evaluate the proposed methods. The retrieval result of the facial image given by the proposed method showed excellent improvement comparing to those achieved when using the traditional method of visual features extraction.
Facial Image Retrieval based on Eigenfaces and Semantic Features
Proceedings of the First International Conference on Advances in Computer and Information Technology, 2012
2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 2012
An essential part of the diagnosis and treatment of leukemia is the visual examination of the pat... more An essential part of the diagnosis and treatment of leukemia is the visual examination of the patient's peripheral blood smear under the microscope. Morphological changes in the white blood cells are commonly used to determine the nature of the malignant cells, namely blasts. Manual techniques are labor intensive slow, subjected to error and costly. A computerized system can be used as an aiding tool for the specialist in order to improve and accelerate the morphological analysis process. This paper presents and application of feature extraction, selection and cell classification to the recognition and differentiation of normal lymphocytes versus abnormal lymphoblast cells on the image of peripheral blood smears. This is considered as a very useful procedure in the initial treatment process of leukemia patients. A computerized recognition system has been developed, and the results of its numerical verification are presented and discussed. The methodology demonstrates that the application of pattern recognition is a powerful tool for the differentiation of normal lymphocytes and acute lymphoblastic leukemia, leading to the improvement in the early effective treatment for leukemia.
2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation, 2012
A main challenge facing the law-enforcement and intelligence-gathering environment is accurately ... more A main challenge facing the law-enforcement and intelligence-gathering environment is accurately and efficiently analyzing the huge volumes of data. Analyzing crime data can be difficult because of recognizing key features and transactions among the large amounts of data, of which only a small section is relevant to illegal process. An intelligent forensic system (IFS) is a powerful medium that enables investigators, who may lack extensive training as data analysts, to assess large databases efficiently and rapidly. Computers can process thousands of transactions in seconds, saving time and manpower. Computers are also less prone to errors than humans. This research goal is to develop a computer-based system for crime data mining and analyzing multimedia data using physical evidence (fingerprints, DNA, dental, face or personal effects, etc.) in large databases to support intelligent forensic study.
Lecture Notes in Electrical Engineering, 2013
Previous studies mainly used radial basis, sigmoid, polynomial, linear, and hyperbolic functions ... more Previous studies mainly used radial basis, sigmoid, polynomial, linear, and hyperbolic functions as the kernel function for computation in the neurons of conventional support vector machine (CSVM) whereas orthogonal wavelet requires less number of iterations to converge than these listed kernel functions. We proposed an orthogonal wavelet support vector machine (OSVM) model for predicting the monthly prices of West Texas Intermediate crude oil prices. For evaluation purposes, we compared the performance of our results with that of the CSVM, and multilayer perceptron neural network (MLPNN). It was found to perform better than the CSVM, and the MLPNN. Moreover, the number of iterations, and time computational complexity of the OSVM model is less than that of CSVM, and MLPNN. Experimental results suggest that the OSVM is effective, robust, and can efficiently be used for crude oil price prediction. Our proposal has the potentials of advancing the prediction accuracy of crude oil prices, which makes it suitable for building intelligent decision support systems.
Automatic method for bone age assessment based on combined method
2014 International Conference on Computer and Information Sciences (ICCOINS), 2014
ABSTRACT Bone age assessment (BAA) is a method of evaluating the level of skeletal maturation in ... more ABSTRACT Bone age assessment (BAA) is a method of evaluating the level of skeletal maturation in children. Generally, it is applied manually by comparing an X-ray of a left hand-wrist with a standard samples as atlas in the clinical procedure. The manual methods are prone to variability of observation, time consuming and limited to objective decisions. These are big motivations for automatic method for bone age assessment. This study aims to develop an automated method for BAA based on combined method. This method tries to overcome the problems of conducting BAA in manual methods. A survey questionnaire is used as the part of research methodology to identify the BAA method used by radiologists and to extract the factors that effect on determination of bone age in the Faculty of Medicine, University of Malaya Medical Centre (UMMC).
Failure Recovery of Composite Semantic Services using Expiration Times
Lecture Notes in Electrical Engineering, 2013
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Papers by Sameem Abdul Kareem