Breast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized healthcare and can serve as a promising tool for early diagnosis. The present study aims to develop a machine-learning model to classify breast cancer and to provide explanations for the model results. This could improve the understanding of the diagnosis and treatment of breast cancer by identifying the most important features of breast cancer tumors and the way they affect the classification task. The best-performing machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. Hence, this asserts the importance and effectiveness of the proposed approach. The present research explains the model behavior using model-agnostic methods, demonstrating that the bare nuclei feature in the Wisconsin breast cancer dataset and the area’s worst feature Wisconsin diagnostic breast cancer dataset are the most important factors in determining breast cancer malignancy. The work provides extensive insights into the particular characteristics of the diagnosis of breast cancer and suggests possible directions for expected investigation in the future into the fundamental biological mechanisms that underlie the disease’s onset. The findings underline the potential of machine learning to enhance breast cancer diagnosis and therapy planning while emphasizing the importance of interpretability and transparency in artificial intelligence-based healthcare systems.

Recognition of the modulation scheme is the intermediate step between signal detection and demodulation of the received signal in communication networks. Automatic modulation recognition (AMR) plays a central role in many applications, especially in the military and security sectors. In general, several properties of the received signal are extracted and employed for AMR. Selecting the appropriate features has a significant impact on increasing the efficiency of AMR. In this paper, we implement and compare digital modulation recognition via multi-layer perceptrons (MLP), radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT), and naïve Bayes (NB) algorithms. In addition, the optimal parameters of each model are obtained by utilizing a genetic algorithm (GA). A series of studies are conducted in this work in order to determine the efficiency of different algorithms in identifying modulated signals with commonly used digital modulations. Numerous computer simulations are performed in the presence of additive white Gaussian noise (AWGN) with a signal-to-noise ratio (SNR) ranging from -10 dB to 30 dB. The simulation results and comparisons with previous studies demonstrate that applying the proposed algorithms along with the selected features leads to a significant enhancement in the accuracy and speed of the automatic determination of the digital modulation types at low SNRs. In addition, the convergence rates of the models are enhanced.

In the contemporary era, blind source separation has emerged as a highly appealing and significant research topic within the field of signal processing. The imperative for the integration of blind source separation techniques within the context of beyond fifth-generation and sixth-generation networks arises from the increasing demand for reliable and efficient communication systems that can effectively handle the challenges posed by high-density networks, dynamic interference environments, and the coexistence of diverse signal sources, thereby enabling enhanced signal extraction and separation for improved system performance. Particularly, audio processing presents a critical domain where the challenge lies in effectively handling files containing a mixture of human speech, silence, and music. Addressing this challenge, speech separation systems can be regarded as a specialized form of human speech recognition or audio signal classification systems that are leveraged to separate, identify, or delineate segments of audio signals encompassing human speech. In various applications such as volume reduction, quality enhancement, detection, and identification, the need arises to separate human speech by eliminating silence, music, or environmental noise from the audio signals. Consequently, the development of robust methods for accurate and efficient speech separation holds paramount importance in optimizing audio signal processing tasks. This study proposes a novel three-way neural network architecture that incorporates transfer learning, a pre-trained dual-path recurrent neural network, and a transformer. In addition to learning the time series associated with audio signals, this network possesses the unique capability of direct context-awareness for modeling the speech sequence within the transformer framework. A comprehensive array of simulations is meticulously conducted to evaluate the performance of the proposed model, which is benchmarked with seven prominent state-of-the-art deep learning-based architectures. The results obtained from these evaluations demonstrate notable advancements in multiple objective metrics. Specifically, our proposed solution showcases an average improvement of 4.60% in terms of short-time objective intelligibility, 14.84% in source-to-distortion ratio, and 9.87% in scale-invariant signal-to-noise ratio. These extraordinary advancements surpass those achieved by the nearest rival, namely the dual-path recurrent neural network time-domain audio separation network, firmly establishing the superiority of our proposed model’s performance.

The recognition of modulation types in received signals is essential for signal detection and demodulation, with broad applications in telecommunications, defense, and wireless communications. This paper introduces a pioneering approach to automatic modulation recognition (AMR) through the development of a highly optimized long short-term memory (LSTM) network. The proposed framework is engineered to capture intricate temporal dependencies within modulated signals, leveraging a gated architecture that effectively mitigates the vanishing gradient problem. This innovation markedly improves recognition accuracy, particularly in low-SNR conditions where traditional methods are often limited. A defining contribution of this work is the introduction of a novel, adaptive temporal-spectral feature learning mechanism, which seamlessly integrates both temporal and spectral characteristics of the signal. This paradigm eliminates the need for manual feature extraction, enhances interpretability, and significantly boosts classification efficiency. Furthermore, the proposed framework is designed for low-complexity deployment, ensuring its scalability and suitability for next-generation wireless networks and real-time communication systems. The proposed architecture is capable of distinguishing between seven modulation classes: BASK, 4-ASK, BFSK, 4-FSK, BPSK, 4-PSK, and 16-QAM. Performance is evaluated across a broad range of signal-to-noise ratios (SNR), from -10 dB to +30 dB, through extensive simulations. Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), naïve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as recurrent neural networks (RNNs). Comparative analysis reveals that the proposed framework outperforms conventional machine learning techniques, with accuracy improvements ranging from 1.77% to 34.03% over the best- and worst-performing methods. Additionally, the proposed model achieves a performance gain of 2.02% over the deep learning (DL)-based RNN, further highlighting its superior capability in AMR.

The advancement of wireless communication toward beyond fifth-generation (B5G) and sixth-generation (6G) standards demands intelligent and scalable signal processing capable of accurate modulation classification under dynamic and noisy channel conditions. Automatic modulation classification (AMC) is essential for spectrum awareness, interference mitigation, and autonomous decision-making. While deep learning (DL) models have improved AMC performance, many conventional architectures face limitations due to sequential processing, high complexity, and reduced robustness in low signal-to-noise ratio (SNR) environments. To address these limitations, this paper presents a novel attention-enhanced hybrid AMC framework that synergistically integrates specialized convolutional layers for efficient temporal feature extraction with a compact transformer encoder for global sequence modeling. The proposed architecture is meticulously tailored to capture the amplitude-phase dynamics inherent in in-phase/quadrature (I/Q) modulated signals while maintaining lightweight design principles. Furthermore, the proposed architecture employs state-of-the-art computational optimizations, including gradient scaling, mixed precision training, and adaptive moment estimation with fixed weight decay (AdamW), to enhance energy efficiency and accelerate convergence. Unlike conventional DL architectures, the proposed technique introduces an efficient parallelized sequence processing strategy that significantly reduces training complexity while preserving robust adaptability across diverse conditions. In contrast to existing hybrid or purely sequential architectures, this design attains high classification fidelity without the need for complex graph-based structures or stacked attention mechanisms, thereby enhancing both model interpretability and practical deployment feasibility. Extensive benchmarking is conducted employing the RadioML 2018.01A dataset, comprising 24 diverse modulation types across various SNR levels, simulating real-world impairments such as fading, oscillator drift, and noise. The proposed framework is rigorously compared against six representative models—recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks-transformer graph neural network (CTGNet), MobileViT, and DeepsigNet—across multiple evaluation criteria. Experimental results consistently validate the effectiveness of the proposed framework, which surpasses leading architectures in accuracy under both static and dynamic (i.e., Jakes-fading) channel conditions. The model achieves an average accuracy improvement of 14.86% over benchmark methods and delivers up to a 38% reduction in inference latency. The study further includes comprehensive ablation analysis, statistical validation, and computational benchmarking, confirming the proposed model as a robust, efficient, and scalable solution for next-generation AMC applications.