Search results for: Jeewani Anupama Ginige

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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Jeewani Anupama Ginige"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Jeewani Anupama Ginige</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Comparative Evaluation of Accuracy of Selected Machine Learning Classification Techniques for Diagnosis of Cancer: A Data Mining Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Rajvir%20Kaur">Rajvir Kaur</a>, <a href="https://publications.waset.org/search?q=Jeewani%20Anupama%20Ginige"> Jeewani Anupama Ginige</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20neural%20networks" title="Artificial neural networks">Artificial neural networks</a>, <a href="https://publications.waset.org/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/search?q=cancer%0D%0Adataset" title=" cancer dataset"> cancer dataset</a>, <a href="https://publications.waset.org/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/search?q=cervical%20cancer" title=" cervical cancer"> cervical cancer</a>, <a href="https://publications.waset.org/search?q=F-score" title=" F-score"> F-score</a>, <a href="https://publications.waset.org/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=precision" title=" precision"> precision</a>, <a href="https://publications.waset.org/search?q=recall" title=" recall"> recall</a>, <a href="https://publications.waset.org/search?q=support%20vector%20machine." title=" support vector machine."> support vector machine.</a> </p> <a href="https://publications.waset.org/10008834/comparative-evaluation-of-accuracy-of-selected-machine-learning-classification-techniques-for-diagnosis-of-cancer-a-data-mining-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008834/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008834/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008834/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008834/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008834/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008834/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008834/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008834/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008834/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008834/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008834.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1553</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Scene Adaptive Shadow Detection Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohammed%20Ibrahim%20M">Mohammed Ibrahim M</a>, <a href="https://publications.waset.org/search?q=Anupama%20R."> Anupama R.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Robustness is one of the primary performance criteria for an Intelligent Video Surveillance (IVS) system. One of the key factors in enhancing the robustness of dynamic video analysis is,providing accurate and reliable means for shadow detection. If left undetected, shadow pixels may result in incorrect object tracking and classification, as it tends to distort localization and measurement information. Most of the algorithms proposed in literature are computationally expensive; some to the extent of equalling computational requirement of motion detection. In this paper, the homogeneity property of shadows is explored in a novel way for shadow detection. An adaptive division image (which highlights homogeneity property of shadows) analysis followed by a relatively simpler projection histogram analysis for penumbra suppression is the key novelty in our approach.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=homogeneity" title="homogeneity">homogeneity</a>, <a href="https://publications.waset.org/search?q=penumbra" title=" penumbra"> penumbra</a>, <a href="https://publications.waset.org/search?q=projection%20histogram" title=" projection histogram"> projection histogram</a>, <a href="https://publications.waset.org/search?q=shadow%20correction" title="shadow correction">shadow correction</a> </p> <a href="https://publications.waset.org/9116/scene-adaptive-shadow-detection-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9116/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9116/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9116/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9116/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9116/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9116/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9116/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9116/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9116/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9116/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9116.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1902</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Development of Solar Poly House Tunnel Dryer (STD) for Medicinal Plants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=N.%20C.%20Shahi">N. C. Shahi</a>, <a href="https://publications.waset.org/search?q=Anupama%20Singh"> Anupama Singh</a>, <a href="https://publications.waset.org/search?q=A.%20E.%20Kate"> A. E. Kate</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There was a scenario present day that drying of fresh fruits and vegetables by indirect solar drying by using mechanical device; hence, an effort was made to develop a small scale solar tunnel dryer (STD). Drying of spinach is carried out to analyze the performance of the dryer and to study its drying characteristics. To evaluate the performance of dryer the independent variables were selected as air flow rate, loading density and shade net while collector efficiency, drying efficiency, overall efficiency and specific energy consumption were selected as responses during performing the experiments. The spinach was dried from initial moisture content 88.21-94.04% (w.b.) to final moisture content 3.50-5.13% (w.b.). The drying time considerably reduced as compared to open sun drying of spinach as sun drying took 15 h for drying. The average collector efficiency, drying efficiency and overall efficiency were in the range 28.73-61.15%, 11.63% to 22.13%, and 7.61-14.66%, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Solar%20dryer" title="Solar dryer">Solar dryer</a>, <a href="https://publications.waset.org/search?q=collector%20efficiency" title=" collector efficiency"> collector efficiency</a>, <a href="https://publications.waset.org/search?q=drying%20efficiency" title=" drying efficiency"> drying efficiency</a>, <a href="https://publications.waset.org/search?q=spinach." title=" spinach."> spinach.</a> </p> <a href="https://publications.waset.org/10003266/development-of-solar-poly-house-tunnel-dryer-std-for-medicinal-plants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003266/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003266/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003266/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003266/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003266/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003266/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003266/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003266/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003266/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003266/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003266.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1820</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Complex-Valued Neural Network in Signal Processing: A Study on the Effectiveness of Complex Valued Generalized Mean Neuron Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Anupama%20Pande">Anupama Pande</a>, <a href="https://publications.waset.org/search?q=Ashok%20Kumar%20Thakur"> Ashok Kumar Thakur</a>, <a href="https://publications.waset.org/search?q=Swapnoneel%20Roy"> Swapnoneel Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A complex valued neural network is a neural network which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in signal processing. In Neural networks, generalized mean neuron model (GMN) is often discussed and studied. The GMN includes a new aggregation function based on the concept of generalized mean of all the inputs to the neuron. This paper aims to present exhaustive results of using Generalized Mean Neuron model in a complex-valued neural network model that uses the back-propagation algorithm (called -Complex-BP-) for learning. Our experiments results demonstrate the effectiveness of a Generalized Mean Neuron Model in a complex plane for signal processing over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error required on a Generalized Mean neural network model. Some inherent properties of this complex back propagation algorithm are also studied and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Complex%20valued%20neural%20network" title="Complex valued neural network">Complex valued neural network</a>, <a href="https://publications.waset.org/search?q=Generalized%20Meanneuron%20model" title=" Generalized Meanneuron model"> Generalized Meanneuron model</a>, <a href="https://publications.waset.org/search?q=Signal%20processing." title=" Signal processing."> Signal processing.</a> </p> <a href="https://publications.waset.org/3111/complex-valued-neural-network-in-signal-processing-a-study-on-the-effectiveness-of-complex-valued-generalized-mean-neuron-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3111/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3111/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3111/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3111/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3111/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3111/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3111/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3111/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3111/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3111/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3111.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1730</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Complex-Valued Neural Network in Image Recognition: A Study on the Effectiveness of Radial Basis Function </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Anupama%20Pande">Anupama Pande</a>, <a href="https://publications.waset.org/search?q=Vishik%20Goel"> Vishik Goel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Complex%20valued%20neural%20network" title="Complex valued neural network">Complex valued neural network</a>, <a href="https://publications.waset.org/search?q=Radial%20BasisFunction" title=" Radial BasisFunction"> Radial BasisFunction</a>, <a href="https://publications.waset.org/search?q=Image%20recognition." title=" Image recognition."> Image recognition.</a> </p> <a href="https://publications.waset.org/6242/complex-valued-neural-network-in-image-recognition-a-study-on-the-effectiveness-of-radial-basis-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6242/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6242/bibtex" target="_blank" rel="nofollow" class="btn btn-primary 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