This work presents a transparent, wideband fractal antenna which can be fabricated using silver nanoparticles (AgNPs) and screen-printing technology. The antenna features an octagonal-shaped mesh monopole patch and a mesh ground plane, both printed on a transparent polyethylene terephthalate (PET) substrate. The proposed antenna has a compact size of$\mathbf{1 0} \times \mathbf{1 1} \times \mathbf{0. 5 5} \text{mm}^{3}$. The antenna operates over a wide frequency range, from 7.6 to 20.5 GHz, covering both the X-band and Ku-band, and partially overlapping K-band with a total bandwidth of$\mathbf{1 2. 9 ~ G H z}$. The proposed antenna achieves a peak simulated realized gain of 1.97 dBi, a radiation efficiency of 93 %, and a fractional bandwidth of 91.8 %. The use of flexible and optically transparent PET substrate enables deployment on curved or see-through surfaces. With the combination of compact size, wideband performance, cost-effective fabrication, and optical transparency, the antenna shows strong potential for use in radar systems, Satellite communications, and some military and aerospace applications.

Massive amounts of data describing the genomic, transcriptomic, and epigenomic features of many different cells are produced by single-cell omics approaches. Artificial intelli- gence (AI) models are widely used to infer biological informa- tion and construct predictive models from these data due to their adaptability, scalability, and remarkable success in other disciplines. Low-dimensional representations of single-cell omics data, batch normalization, cell type classification, trajectory inference, gene regulatory network inference, and multimodal data integration have all seen a recent surge in innovation thanks to machine and deep learning. We provide a survey of recent developments in machine learning (ML) algorithms intended for analysis of single-cell omics data to aid readers in navigating this rapidly-evolving literature.

Medical technologies bolstered by AI are quickly developing into viable options for actual clinical use. Wearable, smartphones, and other mobile monitoring sensors are producing vast amounts of data that can be processed by deep learning algorithms in a variety of medical settings. Patients are eager for augmented medicine to be implemented because it will give them more control over their care and allow them to receive more tailored treatment, but doctors are hesitant to embrace the shift because they are not equipped to deal with the resulting changes in clinical practice. In addition, this phenomenon raises the questions of whether or not these cutting-edge tools should be validated by conventional clinical trials, whether or not medical schools should update their curricula to reflect the rise of digital medicine, and whether or not the ethics of constant connected monitoring should be taken into account. The purpose of this paper is to explore the current research literature and offer a holistic view of the implications of well-established clinical applications of artificial intelligence on medical professionals, hospitals, medical schools, and bioethics.

In this paper, a novel technique has been proposed to monitor water quality in real-time, using Electromagnetic (EM) sensor and machine learning techniques. The integrated multi-sensing device can measure various water quality parameters, such as Oxidation Reduction Potential (ORP), Dissolved Oxygen (DO), temperature, pH, flow, carbon dioxide gas (CO2), conductivity, pressure, etc. A Nano-VNA (Vector Network Analyzer) has provided various parameters including S11, operating at frequencies from 50kHz to 3GHz in real-time. Any change in the water samples will be recorded, analyzed, and reported. Employing machine learning techniques and developed datasets (using S11 signal), detection of the water impurities and their levels can be measured. Moreover, the setup is inexpensive, and results can be seen wirelessly on mobile or computer.

Advanced sensor and interconnect solutions are driving innovation in the wearable, wireless, and connected healthcare device industry. The implementation of 5G networks is expanding rapidly; hence, the Internet of Medical Things (IoMT) and medical technologies will require 5G antennas for wearable and wireless connectivity. This paper proposed a novel wideband Ring Antenna that is operational over a frequency band from 28GHz to 34GHz. The proposed antenna occupies a small size of (20x20x 1.5mm), and it has return loss (S11) values well below -15dB with VSWR below 3dB at most frequencies in the said band. The antenna gain reaches 8.6dBi at 34GHz and has gain values and stable radiation patterns that offer good coverage for indoor wireless communications. These features make the proposed design very suitable for wearable and wireless devices that are used for IoMT.

In this paper, we proposed four different machine learning classification models, i.e., Logistic Regression, Decision Tree, Multilayer Perceptron, and XGBoost, to predict Blood Pressure levels. Moreover, various performance metrics for each model have been calculated, such as accuracy, specificity, precision, recall, and F1 score. According to the findings and comparison of each classification model, XG-Boost achieves the highest classification accuracy of 90%. In contrast, Multilayer Perceptron, Decision Tree, and Logistic Regression achieved 87.33%, 83.83%, and 73.50% for blood pressure classifications, respectively. This study can forecast blood pressure-related diseases in the medical field.

High blood pressure (BP) or hypertension is a dangerous and deadly condition which can lead to serious disorders and high risk of heart attacks, strokes or death. Therefore, studying and monitoring blood pressure levels is highly important. In this study, we propose four distinct machine learning classification models to predict blood pressure levels. The classifiers used are: Random Forest (RF), CatBoost (CB), Support Vector Machine (SVM), and, K-Nearest Neighbors (KNN). Furthermore, several performance indicators such as accuracy, specificity, precision, recall, and F1 score have been calculated for each model. An accuracy of up to 90% was achieved for CATBoost and RF, and up to 87% and 78.33% for SVM and KNN respectively. This study was able to predict blood pressure-related disorders and cardiovascular diseases.

Electroencephalography (EEG) is a valuable instrument for acquiring brain signals from the scalp surface area that correlate to many states, one of which is epilepsy, which is defined as a central nervous system disorder that generates periods of abnormal brain activity, often known as seizures. Based on the EEG signal, frequencies are ranging from 0.1 Hz to more than 100 Hz, these signals are classified as delta, theta, alpha, beta, and gamma. This paper uses the four features extracted from EEG signal to detect Epileptic Seizures, by using a combination of both Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) mainly using MATLAB. Bonn University EEG database has been used. The data was obtained using 128 channels and is divided into five different data classes: Z, N, O, F, and S. Each dataset class contains 100 segments taken from individual channels with a period of 23.6 seconds, or in other words, each dataset class contains 4097 pulses/samples with a sampling frequency of 173.61 Hz. Important statistical features were computed such as mean, standard deviation, skewness, and kurtosis. In this work only the gamma-band of the decomposed signal is used to extract the four statistical features, two classifiers are applied one to detect epilepsy and one to detect the seizure. the epilepsy classifier is 90.3 % accurate and the seizure classifier is 98.7% accurate.