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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.

This paper presents a thorough review of the main techniques used for wireless power transfer (WPT) in implantable medical devices (IMDs) with a specific focus on the techniques that employ implantable antennas for energy harvesting (electromagnetic (EM) WPT techniques). The techniques are first analysed and compared based on the IMD application, power transfer efficiency (PTE), transfer distance, implantation depth, implant size, operating frequency, and specific absorption rate (SAR). The study provides a critical analysis of the main WPT system’s as well as implantable antennas’ design parameters that control the PTE and hence the charging rate of the IMD. The investigated design parameters include the WPT TX-RX antennas’ gain, WPT-RX size, transfer distance, and the WPT TX-RX antennas’ alignment. Tutorial simulation examples are included to showcase the impact of these design parameters on the amount of power coupled to the IMD. The paper also discusses recent techniques used for improving the amount of power received by implantable antennas, and hence higher PTE and IMDs charging rate, namely, the use of implantable MIMO WPT-RX antennas to mitigate antennas misalignment and the use of metamaterial surfaces to focus the power emitted from WPT-TX antennas towards the implantable WPT-RX antennas. The findings and observations reported in this study serve as a valuable resource for designers and researchers to comprehend the effect of various WPT TX-RX antennas design parameters on PTE. The analysis and full-wave simulation examples, included in the paper, are shown very useful in understanding the challenges associated with WPT in IMDs and in proposing potential solutions.

Recent research has revealed that using machine learning systems for the analysis of genetic data could reliably detect Alzheimer’s disease. The interpretability of these models, however, has been a challenge, as they frequently provided little insight into the features that contribute to their predictions. Explainable machine learning has been presented as a solution to this problem since it enables the identification of significant attributes and gives a clearer method of making predictions. In this study, Genome-Wide Association Studies were used to recognize genetic variants associated with Alzheimer’s disease, utilizing the Alzheimer’s Disease Neuroimaging Initiative dataset and quality control methods to ensure the validity and reliability of the findings. The results indicate strong connections between certain genetic variations and Alzheimer’s disease, highlighting the potential of Genome-Wide Association Studies as a valuable tool for identifying and predicting this disease. After studying and analyzing the genetic data, machine learning algorithms are utilized to train a model to detect Alzheimer. The Support Vector Machine achieved 89% accuracy as the best-performing model. Explainable machine learning has the potential to increase the accuracy and interpretability of Alzheimer’s disease detection models, giving significant insights for both academics and physicians. The explanation of the support vector machine model reveals that rs4821510 is the most important SNP in detecting AD. On top of that, the SHAP method shows that rs429358 is an indication for Alzheimer’s disease and rs4821510 presents in the healthy ones. These findings suggest that explainable machine learning can play an important role in accurately detecting Alzheimer’s disease and identifying critical genetic markers associated with the disease.

This paper presents a critical review of modern 4G, 4G/5G, and 5G antennas, which employ PIN diodes and Varactors for polarization or frequency reconfiguration. For the reviewed 4G and 5G polarization reconfigurable antennas, a new parameter named spectrum utilization “AR/ $S_{11}$ B.W. ” is defined for the first time to predict the shared spectrum between the impedance bandwidth and the axial ratio bandwidth for circularly polarized antennas. The calculated spectrum utilization for many designs revealed that the majority of implementations failed to achieve full utilization of the available bandwidth. Besides, employing multiple PIN diodes is shown useful in improving the spectrum utilization and in supporting multiple polarization states. For the reviewed 4G, 4G/5G and 5G frequency reconfigurable antennas, the focus is mainly on comparing the available tuning range, fractional bandwidth and the Fractional Bandwidth Change (FBWC) upon tuning. The latter parameter, is defined and calculated in this work to measure the change in the FBW upon tuning. Utilizing multiple Varactors is shown promising in improving a 4G antenna’s fractional bandwidth, while combining Multiple PINs and Varactors in one design is found efficient in improving the FBWC and the tuning range in the 5G spectrum.

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.

In this paper, the impact of AlGaN barrier thickness ( $\text{t}_{\mathrm{ AlGaN}}$ ) and substrate leakage on the noise conductance and noise figure in GaN High Electron Mobility Transistor (HEMT) is investigated. The investigation and analysis in this paper are targeting the Low Noise Amplifier (LNA) applications. The noise analysis is carried out using Technology Computer-Aided Design (TCAD) physical simulator. Initially, the DC, RF, and noise simulations are validated against measurements of a GaN device. AlGaN barrier thickness ( $\text{t}_{\mathrm{ AlGaN}}$ ) is varied and its impact on the minimum noise figure (NFmin) is analyzed. It is observed that the NFmin decreases with $\text{t}_{\mathrm{ AlGaN}}$ reduction at the typical bias conditions of LNA. This observation on the impact of $\text{t}_{\mathrm{ AlGaN}}$ on the NFmin follows Fukui’s model, which states that the NFmin decreases with the increase in transconductance. In addition, the impact of the substrate material on noise performance is analyzed. The substrates used for the investigation are Silicon (Si) and Silicon Carbide (SiC). At 40 GHz, it is found that the noise conductance and the NFmin of GaN HEMT on SiC substrate is reduced by 13% and 12%, respectively, in comparison with GaN HEMT on Si substrate. This could be attributed to the lower gate-to-substrate capacitance of the GaN HEMT on SiC substrate.