The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep ...CNN based multi-phase mitosis detection framework "MP-MitDet" for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier "MitosRes-CNN" to filter false mitoses. The performance of the proposed "MitosRes-CNN" is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the ...competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam’s numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In the present study, a novel application of nature-inspired heuristics is presented for problems in nonlinear circuit analysis using neural networks, particle swarm optimization (PSO), and ...interior-point algorithm (IPA) as well as integrated approach PSO–IPA. The governing system models of resistor–capacitor circuits with nonlinear capacitance as well as resistor–inductor circuits with nonlinear inductance are mathematically modeled through competency of neural networks and weights of these networks are trained for global search with PSO hybrid with IPA for speedy refinements. The designed technique is applied on a number of scenarios by taking different values of resistance, current, voltage inductance, and capacitance parameters in nonlinear electrical circuit models. Comparative study with Adams numerical solvers having matching of the order 10
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and consistently attaining near-optimal gauges of performance indices based on root-mean-squared error, Theil’s inequality coefficient, and Nash–Sutcliffe efficiency metrics validate and verify the efficacy of the scheme.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. ...Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This paper presents machine learning techniques for short-term wind speed prediction by exploiting their computational intelligence capabilities such as random forest regression (RFR), support vector ...regression (SVR), radial basis function neural networks (RBFNN), and long short-term memory (LSTM) on various wind farm datasets located in Pakistan. Initially, predictions are obtained by employing baseline regressors, RFR and RBFNN in terms of error indices. Later, a stacked LSTM forecast engine (SLFE) has been proposed to improve the efficacy, accuracy, and prediction capability of the forecast engine by using leaky rectified linear units (Leaky ReLU) as kernel function. SLFE has been implemented and tested on the datasets acquired from four different wind farms having a temporal locality of 10 min for short-term wind speed prediction. Furthermore, an ensemble of stacked LSTM with RFR, SVR, and RBFNN has also been developed for the comparison. The strength of the SLFE has been evaluated in terms of various performance measures such as mean absolute error (MAE), root mean squared error (RMSE),
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Score, and explained variance score (EVS). The efficacy of the proposed models is evaluated in terms of performance metrics, MAE, RMSE,
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Score and EVS, 0.043, 0.065, 0.813, and 0.814, respectively, demonstrating the worth of the forecast engine. Additionally, statistical one-way ANOVA is also carried out with multiple independent executions of the proposed algorithm to demonstrate the robustness, efficiency, and reliability of the model.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In this work, novel application of integrated computational heuristics is presented for computational fluid mechanics problem arising in the study of heat transfer and thermal radiation in two-phase ...magnetohydrodynamic (MHD) fluid flow model involving nanoparticles using the accurate approximation ability of neural networks hybrid with global exploration of genetic algorithm aided with local search exploitation of sequential quadratic programming. The networks are designed and arbitrarily combined to formulate mean squared error-based objective function for solving and governing nonlinear nanofluidic system. The designed methodology is evaluated to study the dynamics of the system by means of velocities, temperature and concentration profiles for prevailing factors based on variation in Reynolds and Schmidt numbers, as well as, rotation, radiation, magnetic, thermophoretic and Brownian parameters. The pragmatic worth of the scheme is established through statistical inferences in terms of accuracy, convergence and complexity metrics.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The strength of evolutionary computational heuristic paradigms is exploited for parameter estimation of power signal modeling problems by incorporating differential evolution (DE), genetic algorithms ...(GAs) and pattern search (PS) methodologies. The objective function of power signal harmonics is constructed by utilizing the power of approximation theory in mean squared error sense. The stiff optimization task of signal harmonics is performed with heuristic solvers DE, GAs and PS that provide efficacy, fast convergence rate and avoid getting trapped in local minima. Statistics reveal that DE outperforms its counterparts in terms of accuracy, robustness and complexity measures.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
ABSTRACT Lyme borreliosis is caused by Borrelia burgdorferi sensu leto. This disease has been recently reported in Pakistan but the efficacy of different antibiotics against it has not been tested. ...Therefore, this study was aimed at evaluating the efficacy of different antibiotics in curing this disease under field conditions and ascertaining the role of C-reactive protein as a prognostic agent in Borrelia-positive dogs. For this purpose, twenty-four dogs of different breeds were used in the study. These dogs were found Borrelia positive on PCR and equally divided into four groups (n=6). Group A was treated with doxycycline @ 10mg/Kg, B with azithromycin @ 20mg/Kg and C with clindamycin @ 11mg/Kg, and D with amoxicillin @2mg/Kg. All drugs were administered orally in different forms, i.e., tablet, capsule, or suspension as per the availability. Five clinical biomarkers including fever, anorexia, arthritis, lameness, and lymphadenopathy were used to declare the status of the disease at days zero, 7th, 14th, 21st, and 28th. Serum samples were also collected on the same days for determining C-reactive protein (CRP) values. The results of clinical biomarkers showed that doxycycline cured the dogs immediately when checked on the 7th day followed by amoxicillin on the 21st day; whereas dogs treated with azithromycin and clindamycin did not show promising results. Besides, results of CRP showed that doxycycline had significantly (p < 0.05) greater efficacy than Azithromycin till the end of the trial and clindamycin till the 21st day. However, the comparative efficacy of doxycycline and amoxicillin showed a significantly (p < 0.05) greater efficacy of the former till the 7th day. These results have proved that doxycycline is the best drug, followed by amoxicillin, to treat dogs suffering from Lyme borreliosis. Moreover, it is also ascertained that CRP can be effectively used to monitor the prognosis of Lyme borreliosis in pet dogs. These results will be helpful for the pet clinicians and veterinarians working to diagnose, treat, and monitor Lyme borreliosis under field conditions.
COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting ...the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study presents a novel implementation of evolutionary heuristics through backtracking search optimization algorithm (BSA) for accurate, efficient and robust parameter estimation of power signal ...models. The mathematical formulation of fitness function is accomplished by exploiting the approximation theory in mean squared errors between actual and estimated responses, as well as, true and approximated decision variables. Variants of BSA-based meta-heuristics are applied for parameter estimation problem of power signals for identification of amplitude, frequency and phase parameters for different scenarios of noise variation. Analysis of performance evaluation for BSAs is conducted through exhaustive statistical observations in terms of mean weight deviation, root mean square error and Thiel inequality coefficient-based assessment metrics, as well as, ANOVA tests for statistical significance.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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