作者：
Sujatha. KChandrashaker reddy. BPonmagal. R.SSu-Qun CaoProfessor
EEE Dept. Dr.MGR Educational and Research Institute Chennai Tamil Nadu India Asst. Prof.
ECE Dept. NallaNarasimha Reddy Educational Societies Group of Institutions Hyderabad India Professor
Dept. of CSE SRM Institute of Science and Technology Tamil Nadu India Professor
Faculty of Electronic Information Engineering Huaiyin Institute of Technology China
The longest part of the large intestine or colon is called as rectum. The uncontrolled division of cells in the colon forming a polyp at initial stage is called as colorectal cancer. Initially the polyp formed may be ...
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The longest part of the large intestine or colon is called as rectum. The uncontrolled division of cells in the colon forming a polyp at initial stage is called as colorectal cancer. Initially the polyp formed may be benign and later it may turn out to be malignant. The advancements in the field of medicine has made the screening techniques to be an effective tool in identification of the colon cancer. Early detection of the colon cancer may lead to complete cure of the disease normally, but it is very difficult to diagnose at the start. It is because, it does not show any symptoms at initial stages. Colonoscopy, a type of computed tomography (CT) is usually recommended for the patients to detect the colon cancer and it is usually a painful test conduct to identify the disease. To relieve the patients from the suffering, image processing algorithms like Weighted Adaptive Scalable Invariant Transform (WASIT) is used for extraction of features like location, orientation and scale are used as inputs to train the Artificial Neural Network (ANN) using Back Propagation algorithm (BPA). The optimal set of weights are obtained by adjusting the weights for BPA hybrid with Genetic algorithm (GA) and fire fly algorithm (FFA). The Likelihood Ratio (LR+) is found to optimal for the BPA tuned with FFA and is inferred to be in the range of 1.5 to 4 which varies with a deviation of ±0.4% from the nominal value.
The paper presents the research work carried out to predict the 28-day compressive strength of concrete with supplementary materials such as ash (fly ash, bottom ash) and silica fume using various data mining techniqu...
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The paper presents the research work carried out to predict the 28-day compressive strength of concrete with supplementary materials such as ash (fly ash, bottom ash) and silica fume using various data mining techniques. It mimics the decision-making ability of humans in imprecise and incomplete information situations. The model developed consists of 7 input parameters i.e., contents of cement, fine aggregates, coarse aggregates, silica fume, ash, water to cement ratio, superplasticizers, and one output parameter that is compressive strength at 28 days. The models used i.e., Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN) and ANN-fire fly algorithm (ANN-FFA) to estimate the compressive strength (MPa) at 28 days. The model developed is completely based on experimental data obtained from creditable literature available. The result of modeling techniques suggests that ANN-FFA based model works better than the other modeling techniques used in this study with Mean Square Error = 1.8099, Root Mean Square Error = 2.6584, and Coefficient of Correlation = 0.9370 with the testing dataset. Hence, these computational techniques suggest that it can be used to estimate the compressive strength of concrete at any stage. The sensitivity study concludes that RF is more sensitive to the absence of important parameters and less sensitive to the lack of less important parameters. Sensitivity analysis results using RF model suggest that Silica fume (kg/m(3)) is the most important parameter for estimate the compressive strength of concrete using this data set.
Mobile ad-hoc networks (MANETs) are wireless networks comprising of small, battery powered mobile devices/nodes. All these nodes communicate cooperatively without fixed infrastructure and able to operate alone or in c...
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Mobile ad-hoc networks (MANETs) are wireless networks comprising of small, battery powered mobile devices/nodes. All these nodes communicate cooperatively without fixed infrastructure and able to operate alone or in coordination with wired infrastructure by using gateway nodes. In this work, the performance of optimized dynamic source routing protocol (DSR) is investigated forMANETs. To find the optimal paths between the communicating nodes, traditional DSR algorithm is modified by using the fire fly algorithm. In recent times a population based method named as fireflyalgorithm is stimulated by the surveillance of real firefly and its brightness behaviour. So fireflyalgorithm is used for the proposed method on MANET which improves the DSR routing performance with well-organized packets transfer from the source to destination node. Optimal route is found based on link quality, node mobility and end to end delay. Simulations are conducted with 25 nodes and the performance of the traditional DSR, link quality based DSR for selecting a route and proposed fireflyalgorithm for optimal route finding are compared by the parameters such as throughput, end to end delay, number of retransmitted packets and the number of hops to the destination.
This paper presents a novel approach for synthesizing compact linear arrays with low side lobes for specified beam widths using Biogeography Based Optimization (BBO). BBO is applied to synthesize compact arrays with l...
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ISBN:
(纸本)9781479915736;9781479921751
This paper presents a novel approach for synthesizing compact linear arrays with low side lobes for specified beam widths using Biogeography Based Optimization (BBO). BBO is applied to synthesize compact arrays with low side lobes for a given beam width and element spacing. The element spacing is based on minimal spacing criteria to avoid coupling between the elements. The simulation is performed for a 20 element array with different beam widths and spacing criteria. Results obtained are compared with other algorithms such as Modified Cuckoo Search (MCS) and fire-flyalgorithms (FA). especially in terms of size reduction and side lobe reduction.
A new robust power oscillation damper (POD) design for a doubly fed induction generator based-wind turbine is proposed in this paper. The POD structure is specified by the second-order lead/lag compensator with single...
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ISBN:
(纸本)9781479905461;9781479905454
A new robust power oscillation damper (POD) design for a doubly fed induction generator based-wind turbine is proposed in this paper. The POD structure is specified by the second-order lead/lag compensator with single input signal. The parameters optimization of POD is formulated based on a mixed H-2/H-infinity control using linear matrix inequalities. The POD parameters are optimized by the fireflyalgorithm so that the damping performance against system disturbances and the robustness under system uncertainties are satisfied. Simulation results in a single machine infinite bus confirm the superior robustness of the proposed POD over the conventional POD.
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