This paper presents the development of a new complete wearable system for detecting breast tumors based on fully textile antenna-based sensors. The proposed sensor is compact and fully made of textiles so that it fits conformably and comfortably on the breasts with dimensions of 24 × 45 × 0.17 mm³ on a cotton substrate. The proposed antenna sensor is fed with a coplanar waveguide feed for easy integration with other systems. It realizes impedance bandwidth from 1.6 GHz up to 10 GHz at |S₁₁| ≤ −6 dB (VSWR ≤ 3) and from 1.8 to 2.4 GHz and from 4 up to 10 GHz at |S₁₁| ≤ −10 dB (VSWR ≤ 2). The proposed sensor acquires a low specific absorption rate (SAR) of 0.55 W/kg and 0.25 W/kg at 1g and 10 g, respectively, at 25 dBm power level over the operating band. Furthermore, the proposed system utilizes machine-learning algorithms (MLA) to differentiate between malignant tumor and benign breast tissues. Simulation examples have been recorded to verify and validate machine-learning algorithms in detecting tumors at different sizes of 10 mm and 20 mm, respectively. The classification accuracy reached 100% on the tested dataset when considering $\left|{\mathrm{S}}_{21}\right|$ parameter features. The proposed system is vision as a “Smart Bra” that is capable of providing an easy interface for women who require continuous breast monitoring in the comfort of their homes.

Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (C_o) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model (R²: 0.925) and pseudo-second-order (PSO) kinetic model (R²: 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10⁻⁵, suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions.

Wearable devices are currently of great importance in developing health management and care applications and devices especially for early breast cancer detection (BCD). This is supported by the novel innovations in materials and techniques to construct highly accurate and comfortable biosensors for wearables. In this paper, the proposed flexible sensor is integrated with a Bra to realize a wearable breast cancer detection system. The proposed antenna-based sensor is composed of a CPW monopole antenna with an overall compact size of 24 × 45 mm ² on flexible PCB Roger substrate with thickness 0.17 mm. The proposed sensors have enough bandwidth from 1.5 to 8 GHz at -6 dB reflection coefficient and conformal structure for biological structures and biocompatibility. The specific absorption rate (SAR) has been calculated and measured for the proposed sensor with a value of 0.75 W/kg at 0 dBm to ensure safety level. For testing, real shaped rubber phantoms from medical school enables the dynamic combination of breast and tumor to create test scenarios for breast cancer detection. 2×2 antenna-based sensors elements are placed around the breast phantom to gather data on scattering parameters for tumor characterization. Several simulation and measurement scenarios are presented to validate detection, optimum number of sensors to be used as well as training data for developed detection algorithms. Artificial intelligence techniques are used among which the CAT-Boost technique for scanning collected data and identifying the undesired tumor component inside the breast.

In multimedia forensics, source-based image classification problem is a well known problem. It addresses the issue of identifying those images that were captured by a specific source camera from a given set of N data images. This is possible as each camera has a unique Photo Response Non-Uniformity (PRNU) that is registered in every image captured by this camera. Classification is made by clustering images having the same PRNU. In this paper, an efficient technique is proposed to group images originated by the same camera. It is based on evaluating the similarity functions between the estimated PRNUs as well as their Hu's moment invariant information. The proposed technique has the ability to group images taken by a specific camera, even if the input images correspond to the same scene that was captured by different cameras. The classification algorithm is performed by means of a hierarchical clustering. Simulation examples are given to verify the superiority of the proposed technique compared to other techniques.