Prostate cancer (PRC) is the major reason of mortality globally. Early recognition and classification of PRC become essential to enhance the quality of healthcare services. A newly established deep learning (DL) and machine learning (ML) approach with different optimization tools can be employed to classify accurately of PRC accurately using microarray gene expression data (GED). Though the microarray data structures are important to diagnosing different kinds of diseases, the optimum hyperparameter tuning of the DL models poses a major challenge to achieving maximum classification performance. To resolve these issues, this study develops a new Gene Expression Data Analysis using Artificial Intelligence for Prostate Cancer Diagnoses (GEDAAI-PCD) technique. The proposed GEDAAI-PCD technique examines the GED for the identification of PRC. To accomplish this, the GEDAAI-PCD technique initially normalizes the GED into a uniform format. In addition, the long short-term memory-deep belief network (LSTM-DBN) model was applied for PRC classification purposes. The wild horse optimization (EWHO) system was utilized as a hyperparameter tuning strategy to optimize the performance of the LSTM-DBN model. The experimental assessment of the GEDAAI-PCD system occurs on open open-accessed gene expression database. The experimental outcomes emphasized the supremacy of the GEDAAI-PCD method on PRC classification.

Breast cancer (BC) is closely linked with the maximum mortality rate for cancer detection across the globe and has become a predominant public health issue. Earlier detection might increase the possibility of survival and successful treatment. However, it is a time-consuming and very challenging task that depends on the diagnostician’s experience. For patients and their prognosis, it is essential that BC cancer can be automatically detected by the analysis of histopathological images. Conventional feature extraction method extracts some lower-level features of images, and preceding knowledge is essential for selecting suitable features that could be heavily impacted by human beings. The deep learning (DL) technique extracts higher-level abstract features from an image automatically. Therefore, this study develops a new Ensemble Learning with Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification (ELSOSA-BCC) technique on Histopathological Images. In the ELSOSA-BCC technique, the noise is removed using Gabor filtering (GF). In addition, the ELSOSA-BCC technique employs the EfficientNet-B0 model for feature extraction and optimal hyperparameter tuning using the SOS algorithm. Finally, the ensemble learning-based classification process is performed by three classifiers namely deep stacked autoencoder (DSAE), kernel extreme learning machine (KELM), and bidirectional long short-term memory (BiLSTM). In this study, ELSOSA-BCC simulation values are tested on a medical dataset. ELSOSA-BCC has been shown to perform better than other models in the experimental results.

Channel equalization is remaining a challenge for the researcher. Especially for the non-linear channel as well as the extremely dispersive channel, an effective channel equalizer is required. It is common knowledge that non-linear channel equalizers based on the neural networks (NN) outperform adaptive filter-based linear equalizers. To train NN equalizers, gradient-descent-based approaches like the back-propagation algorithm are often utilized, although they have drawbacks such as trapping of local minima, slower convergence, and compassion to log in. In this work, we presented a novel training strategy using a fuzzy firefly algorithm (FFA) for channel equalization. By using proper network topology and parameters, the suggested training system offers stronger exploitation and exploration skills, as well as the ability to solve the local minima issue. The performance of the equalizer can be analyzed by estimating two parameters i.e. MSE and BER. To exhibit the suggested technique’s resilience in performance, the burst error situation was used, and the outcomes showed that the strategy is more effective in managing such situations than previous methods. The outcomes of the proposed method are presented through simulation, Furthermore, it proved that the suggested method validates a wide range of SNR, and also it outperforms the existing NN-based equalizers.

The transmission of high-speed data over communication channels is the function of digital communication systems. Due to linear and nonlinear distortions, data transmitted through this process is distorted. In a communication system, the channel is the medium through which signals are transmitted. The useful signal received at the receiver becomes corrupted because it is associated with noise, ISI, CCI, etc. The equalizers function at the front end of the receiver to eliminate these factors, and they are designed to make them work efficiently with proper network topology and parameters. In the case of highly dispersive and nonlinear channels, it is well known that neural network-based equalizers are more effective than linear equalizers, which use finite impulse response filters. An alternative approach to training neural network-based equalizers is to use metaheuristic algorithms. Here, in this work, to develop the symmetry-based efficient channel equalization in wireless communication, this paper proposes a modified form of bat algorithm trained with ANN for channel equalization. It adopts a population-based and local search algorithm to exploit the advantages of bats’ echolocation. The foremost initiative is to boost the flexibility of both the variants of the proposed algorithm and the utilization of proper weight, topology, and the transfer function of ANN in channel equalization. To evaluate the equalizer’s performance, MSE and BER can be calculated by considering popular nonlinear channels and adding nonlinearities. Experimental and statistical analyses show that, in comparison with the bat as well as variants of the bat and state-of-the-art algorithms, the proposed algorithm substantially outperforms them significantly, based on MSE and BER.