Optimization of deep learning features for age-invariant face recognition

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods

FIDChain: Federated Intrusion Detection System for Blockchain-Enabled IoT Healthcare Applications

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

Tunable Multi-Channels Bandpass InGaAsP Plasmonic Filter Using Coupled Arrow Shape Cavities

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

A deep learning framework for accurate diagnosis of colorectal cancer using histological images

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

Detection of Kidney Cancer Using Circularly Polarized Patch Antenna Array

The use of a circularly polarized patch antenna array to detect kidney cancer by microwave techniques is proposed in this paper. A four-element linear antenna array is designed and fabricated at the ISM frequency of 2.4 GHz. The dimensions of the antenna array are 200 mm $\times78$ mm. The single element is a square patch with side length of 30 mm. The distance between patches is chosen to be 20 mm which ensures that mutual coupling between any two adjacent patches is less than 20 dBs. The substrate is a FR-4 material of relative permittivity 4.3 and thickness 1.6 mm. The circular polarization has an axial ratio of 0.8 dB at 2.4 GHz. The bandwidth at S11 = −10 dB is 7.23 %. Renal system phantom consisting of kidney cortex, renal capsule, ureter, adrenal gland, muscle, fat, and skin is used. Four stages of renal cancer tumors are considered depending upon the tumor size in each stage. The presence of a tumor causes an increase in the reflection coefficient (S11) and a shift in resonance frequency, which can be used to identify cancer. The increase in reflection coefficient and the shift in resonance frequency are calculated for each stage of the cancer tumors. The shift in resonance frequency for the early stages is too small. Therefore, detection depends mainly on the increase in S11. The shift in resonance frequency and increase in S11 are large for advanced stages of the tumor, which makes detection easier. Computed specific absorption rate (SAR) is found to be less than the safety levels, which means this technique is safe to use. Overall, this work suggests a new simple detection technique of kidney cancer. The advantages of this technique are: safe, compact, fast, inexpensive, comfortable examination, non-invasive, and finally non- ionizing radiation during measurement