Recently, there has been considerable growth in the internet of things (IoT)-based healthcare applications; however, they suffer from a lack of intrusion detection systems (IDS). Leveraging recent technologies, such as machine learning (ML), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this paper, FIDChain IDS is proposed using lightweight artificial neural networks (ANN) in a federated learning (FL) way to ensure healthcare data privacy preservation with the advances of blockchain technology that provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, which prevents poisoning attacks and provides full transparency and immutability over the distributed system with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the BoT-IoT dataset. The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as intensive care unit (ICU) in healthcare systems. Testing the FIDChain with different datasets (CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99) reveals that the BoT-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.

This article presents a highly sensitive and thermally stable photonic crystal (PhC) biosensor designed for accurate cancer-cell detection. The proposed sensor features a square-lattice of silicon rods (radius 0.1 µm) with a photonic bandgap spanning 1.2–2.1 µm. It includes two line-defect waveguides for input and output, and a uniquely engineered Eye-shaped cavity that holds the analyte as embedded rods. These rods are strategically arranged along the Eye-shaped boundary and the central area resembling an iris, facilitating accurate detection through resonance wavelength shifts triggered by changes in the refractive index. The biosensor demonstrates excellent transmission efficiency (69.7%–99.9%), high sensitivity (236–243 nm/RIU), and a strong quality factor (15,764–87,070), ensuring sharp and clearly defined resonance peaks. A key advantage of the design is its linear response to refractive index variations, which enhances detection accuracy and supports reliable real-time biosensing. Moreover, the sensor maintains stable performance across a wide temperature range (25 °C to 75 °C) and exhibits robust tolerance to fabrication variations. These features validate the biosensor’s precision for biomedical diagnostics.

In this paper, a textile two-port multi-input multi-output (MIMO) half-circle antenna with corrugated borders is designed and fully analyzed for nearfield ultra-wideband biomedical diagnostic applications. The orthogonal polarization technique is introduced for mutual coupling reduction, signal quality, and imaging accuracy improvement. A low pass filter with significant out-of-band rejection is inserted between the two MIMO structural elements to improve the isolation from element to element. For simple system integration, A 50 ohms feed coplanar waveguide is suggested for each element. The entire antenna is constructed using textile materials, with the radiating element composed of conductor fabric. The substrate, on the other hand, is crafted from cotton material which possesses a relative permittivity of and a tan δ value of 0.025. The size of each antenna element has low-profile dimensions of 40 × 40 × 0.3 $${mm}^{3}$$mm 3 . The measured results obtained demonstrate an extremely broad bandwidth extends from 2.5 GHz to 12 GHz. Omnidirectional radiation pattern is obtained, with 6 dBi maximum gain and 96% efficiency. In order to ensure flexibility, a range of bending conditions were analyzed, and the durability was assessed through wash ability testing. Safety was confirmed by measuring the specific absorption rate. Furthermore, time domain and MIMO performance assessments were performed to ensure its suitability for imaging systems.

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.

A new design for a tunable multi-channel plasmonic bandpass filter was numerically investigated using the two-dimensional finite element method (2D-FEM). The proposed multi-channel plasmonic bandpass filter consists of a metal-insulator-metal waveguide (MIM-WG) and double-sided arrow-shaped cavities. Silver (Ag) and a non-linear optical medium (InGaAsP) are used in the designed filter. InGaAsP fills the bus waveguide and arrow-shaped cavities. The refractive index of InGaAsP is sensitive to the incident light intensity, therefore the resonance wavelengths can be controlled. Utilizing different incident light intensities (such as 10¹⁷ v²/m² and 2 × 10¹⁷ v²/m²) on the InGaAsP, the filter wavelengths can be tuned over a range from 600 nm to 1200 nm. The proposed filter with a confinement area of 0.5 μm² can be used in wavelength division multiplexing (WDM), photonic systems, coloring filters, sensing, and 5G+ communication.