Sameem Abdulkareem

RETRACTED ARTICLE: Static hand gesture recognition using neural networks

AbstractThis paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.

RETRACTED ARTICLE: Human---computer interaction using vision-based hand gesture recognition systems: a survey

Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified into two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human--computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.

Fingerprint image enhancement and recognition algorithms: a survey

Fingerprint systems have received a great deal of research and attracted many researchers’ effort since they provide a powerful tool for access control and security and for practical applications. A literature review of the techniques used to extract the features of fingerprint as well as recognition techniques is given in this paper. Some of the reviewed research articles have used traditional methods such as recognition techniques, whereas the other articles have used neural networks methods. In addition, fingerprint techniques of enhancement are introduced.

Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

BackgroundMachine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.ResultsIn the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis.ConclusionsThe results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies.

Long-term survival of nasopharyngeal carcinoma patients treated with adjuvant chemotherapy subsequent to conventional radical radiotherapy

PURPOSE To assess the long-term survival of patients with nasopharyngeal carcinoma (NPC) who were treated with conventional radical radiotherapy (RT) followed by adjuvant chemotherapy. METHODS AND MATERIALS Ninety-one newly diagnosed patients with Stage III and IV (American Joint Committee on Cancer, 1988) NPC, seen at the University of Malaya Medical Center, Kuala Lumpur, Malaysia between January 1992 and May 1997, were treated with RT followed by adjuvant chemotherapy. The tumor dose was 70 Gy delivered in 35 fractions, 5 fractions weekly. Three cycles of chemotherapy, each consisting of 5-fluorouracil, 1 g/m(2)/d on Days 1-4 and cisplatin 100 mg/m(2) on Day 1, were administered 3 weeks after RT completion. Thirty-six patients had Stage II, 10 had Stage III, and 45 had Stage IV disease (AJCC 1997 staging system). RESULTS After a median follow-up of 61 months, the 5-year overall survival rate for all 91 patients was 80.1%, the disease-free survival rate was 76%, and the locoregional control rate was 85%. The 3-year overall survival rate for Stage II was 94.3%; it was 80% for Stage III and 79.8% for Stage IV (p = 0.0108). The 3-year DFS rate for Stage II was 90%; it was 80% for Stage II and 65% for Stage IV. The rate of distant failure for Stage IV was 8.9%. CONCLUSION Radical RT followed by adjuvant chemotherapy was effective in our patients with locoregionally advanced NPC. The long-term results appear encouraging, even for patients with Stage IV disease. This single institution experience deserves further investigation in prospective trials.

Computational intelligence techniques with application to crude oil price projection: a literature survey from 2001–2012

This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to re-direct their attention and resources in order to keep on searching for an optimum solution.

Orthogonal Wavelet Support Vector Machine for Predicting Crude Oil Prices

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 interactive security monitoring system

Over the years an increasing demand for an automated security system begins to emerge. Many applications that help in protecting life and properties are being developed. Most of them are aimed at improving the work of security personnel and security agencies. However, security is a responsibility of everyone not only the security agencies or security personnel alone. This paper present an interactive security monitoring system based on passive infrared motion detection sensor, which will capture the image of any intruding persons and share it to the entire people that are using the system on both Android platform and in an online portal display. The people on the system can communicate with each other and post information to a commonly accessible board in the online system to discuss any issues or to see if anyone recognizes the felons/intruder on the images. Images of interest can then be transmitted to law enforcement authorities. This could be use in anywhere that needs to be protected against intruder. It will be best use in kindergarten, primary school and or in a neighborhood. That is why we call it neighborhood watch security system (NWSS). Preliminaries evaluation indicated an accurate image captured in a real time with an avoidance of false alarm.

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

Background 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. Methods/Findings 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. Conclusion 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.

A Framework for Selecting the Optimal Technique Suitable for Application in a Data Mining Task

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.