In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

This paper proposed an improved structure of Proportional Integral Derivative (PID) controller called as Integral Proportional Derivative (I-PD), applied for Automatic Generation Control (AGC) of Multi-Source Interconnected Power System (IPS). The parameters of the proposed controller are optimized with a newly developed, powerful, nature-inspired meta-heuristic technique known as Fitness Dependent Optimizer (FDO). To show the efficacy of the proposed controller and the technique used, they have been tested on three different system models. Initially, a two-equal area of diverse source generation including reheat-thermal, gas, and hydro power system is considered. In the second scenario, the same power system model is used with addition of two non-linearities; Generation Rate Constraint (GRC) and Governor Dead Band (GDB). Lastly, multiple non-linearities including Governor Dead Band (GDB), Time Delay (TD), Generation Rate Constraint (GRC), and Boiler Dynamics (BD) have been considered to make the initial system more realistic and practical. The outcome from the proposed techniques is also compared with some recently meta-heuristic algorithms such as Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). From the results, it has been perceived that the proposed technique shows superior performance in respect of Overshoot (Osh), Undershoot (Ush) and Settling Time (Ts) of the system frequency.

Lung nodules are vital indicators for the presence of lung cancer. An early detection enhances the survival rate of the patient by starting treatment at the right time. The detection and classification of malignancy in Computed Tomography (CT) images is a very time-consuming and difficult task for radiologists which lead the researchers to develop algorithms for Computer-Aided Diagnosis (CAD) systems to mitigate this burden. The performance of CAD systems is continuously improving by using various deep learning techniques for screening of lung cancer. In this paper, we proposed transferable texture Convolutional Neural Networks (CNN) to improve the classification performance of pulmonary nodules in CT scans. An Energy Layer (EL) is incorporated in our scheme, which extracts texture features from the convolutional layer. The inclusion of EL reduces the number of learnable parameters of the network, which further reduces the memory requirements and computational complexity. The proposed model has only three convolutional layers and one EL, instead of pooling layer. Overall proposed CNN architecture comprises of nine layers for automatic feature extraction and classification of pulmonary nodule candidates as malignant or benign. Furthermore, the pre-trained model of proposed CNN is also used to handle the smaller dataset classification problem by using transfer learning. This work has been evaluated on publicly available LIDC-IDRI and the LUNGx Challenge database through different evaluation matrices, such as; the accuracy, specificity, error rate and AUC. The proposed model is trained by six-fold cross-validation and achieved an accuracy score of 96.69%±0.72% with only 3.30%±0.72% error rate. Whereas, the measured AUC and recall is 99.11%±0.45% and 97.19%±0.57%, respectively. Moreover, we also tested our proposed technique on the MNIST dataset and achieved state-of-the-art results in terms of accuracy and error rate.

This paper presents a Fractional Order Integral-Proportional Derivative (FOI-PD) controller for Automatic Generation Control (AGC) of two-area Interconnected Power System (IPS) with six multiple generations units in a restructured environment. Further, the two-area IPS is composed of multiple non-linearities with Time Delay (TD), Boiler Dynamic (BD), Governor Dead Zone/Band (GDZ/GDB) and Generation Rate Constraint (GRC). The gains of the proposed controller are optimized by a most recent powerful meta-heuristic algorithm known as Improved-Fitness Dependent Optimizer (I-FDO). The efficiency of the proposed approach is compared with other techniques such as Firefly Algorithm (FA), Fitness Dependent Optimizer (FDO) and Teaching Learning Based Optimization (TLBO) algorithms. Further, to enhance the performance of the system, Redox Flow Batteries (RFB) is incorporated in each area and Thyristor Controlled Series Compensator (TCSC) in the tie-line of the power system. Results reveal that our proposed approach performs superior in terms of less Overshoot (Os), Settling time (Ts) and Undershoot (Us). Robustness of the proposed controller is verified by changing system parameters within a range of ± (25) %.

The exact lung cancer identification is a critical problem that has attracted the researchers’ attention. The practice of multiview single image and segmentation has been widely used for the last 2 years to improve the identification of lung cancer disease. The utilization of machine learning (ML) and deep learning (DL) techniques can significantly expedite the process of cancer detection and stage classification, enabling researchers to study a larger number of patients in a shorter time frame and at a reduced cost applying the image segmentation approach herein, the multiresolution rigid registration mechanism is applied to enhance the segmentation further. Techniques like principle component averaging and discrete wavelet transform are verified for the image fusion development. To review the performance of the suggested technique, the image database resource initiative‐based lungs image database consortium is tested in this paper which includes 4,682 computed tomography scan images of 61 patients with nodules sizes from 3 to 30 mm. According to the study finding, the outperformed results of our model are obtained in terms of feature mutual information, and peak signal‐to‐noise ratio, which were recorded at 0.80 and 19.25, respectively. Moreover, the detection and stages of cancer (STG‐1, STG‐2, STG‐3, and STG‐4) of lung nodules are also assessed by using the ResNet‐18 convolutional neural network classifier. With only 1.8 FP/scan, the achieved accuracy and sensitivity for detection are 98.2% and 96.4%, respectively. The study’s findings show that our proposed strategy outperforms existing models significantly. Therefore, the proposed models have the potential to be implemented in clinical settings to provide support to doctors in the early diagnosis of cancer, while minimizing the occurrence of false positives in scans.

Automatic Generation Control (AGC) delivers a high quality electrical energy to energy consumers using efficient and intelligent control systems ensuring nominal operating frequency and organized tie-line power deviation. Subsequently, for the AGC analysis of a two-area interconnected hydro-gas-thermal-wind generating unit, a novel Fractional Order Integral-Tilt Derivative (FOI-TD) controller is proposed that is fine-tuned by a powerful meta-heuristic optimization technique referred as Improved-Fitness Dependent Optimizer (I-FDO) algorithm. For more realistic analysis, various constraints, such as Boiler Dynamics (BD), Time Delay (TD), Generation Rate Constraint (GRC), and Governor Dead Zone (GDZ) having non-linear features are incorporated in the specified system model. Moreover, a comparative analysis of I-FDO algorithm is performed with state-of-the-art approaches, such as FDO, teaching learning based optimization, and particle swarm optimization algorithms. Further, the proposed I-FDO tuned controller is compared with Fractional Order Tilt Integral Derivative (FOTID), PID, and Integral-Tilt Derivative (I-TD) controllers. The performance analysis demonstrates that proposed FOI-TD controller provides better performance and show strong robustness by changing system parameters and load condition in the range of  ± 50%, compared to other controllers.