A Review of Lunar Communications and Antennas: Assessing Performance in the Context of Propagation and Radiation.

Over the previous two decades, a notable array of space exploration missions have been initiated with the primary aim of facilitating the return of both humans and robots from Earth to the moon. The significance of these endeavors cannot be emphasized enough as numerous entities, both public and private, from across the globe have invested substantial resources into this pursuit. Researchers have committed their efforts to addressing the challenges linked to lunar communication. Even with all of these efforts, only a few of the many suggested designs for communication and antennas on the moon have been evaluated and compared. These designs have also not been shared with the scientific community. To bridge this gap in the existing body of knowledge, this paper conducts a thorough review of lunar surface communication and the diverse antenna designs employed in lunar communication systems. This paper provides a summary of the findings presented in lunar surface communication research while also outlining the assorted challenges that impact lunar communication. Apart from various antenna designs reported in this field, based on their intended usage, two additional classifications are introduced: (a) mission-based antennas-utilized in actual lunar missions-and (b) research-based antennas-employed solely for research purposes. Given the critical need to comprehend and predict lunar conditions and antenna behaviors within those conditions, this review holds immense significance. Its relevance is particularly pronounced in light of the numerous upcoming lunar missions that have been announced.

To predict the left ventricular endocardial scar tissue pattern using Radon descriptor-based machine learning.

OBJECTIVE: Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated Left ventricle (LV) segmentation to find the LV endocardial wall, performed morphological operations, and marked the region of the scar tissue on the endocardial wall of LV. Motivated by a Radon descriptor-based machine learning approach; the patches of 17 patients from Computer tomography (CT) images of the heart were used and categorized into "endocardial Scar tissue" and "normal tissue" groups. The ten feature vectors are extracted from patches using Radon descriptors and fed into a traditional machine learning model. RESULTS: The decision tree has shown the best performance with 98.07% accuracy. This study is the first attempt to provide a Radon transform-based machine learning method to distinguish patterns between "endocardial Scar tissue" and "normal tissue" groups. Our proposed research method could be potentially used in advanced interventions.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Concatenated Xception-ResNet50 - A novel hybrid approach for accurate skin cancer prediction.

Skin cancer is a malignant disease that affects millions of people around the world every year. It is an invasive disease characterised by an abnormal proliferation of skin cells in the body that multiply and spread through the lymph nodes, killing the surrounding tissue. The number of skin cancer cases is on the rise due to lifestyle changes and sun-seeking behaviour. As skin cancer is a deadly disease, early diagnosis and grading are crucial to save lives. In this work, state-of-the-art AI approaches are applied to develop a unique deep learning model that integrates Xception and ResNet50. This network achieves maximum accuracy by combining the properties of two robust networks. The proposed concatenated Xception-ResNet50 (X-R50) model can classify skin tumours as basal cell carcinoma, melanoma, melanocytic nevi, dermatofibroma, actinic keratoses and intraepithelial carcinoma, vascular and non-cancerous benign keratosis-like lesions. The performance of the proposed method is compared with a DeepCNN and other state-of-the-art transfer learning models. The Human Against Machine (HAM10000) dataset assesses the suggested method's performance. For this study, 10,500 skin images were used. The model is trained and tested with the sliding window technique. The proposed concatenated X-R50 model is cutting-edge, with a 97.8% prediction accuracy. The performance of the model is also validated by a statistical hypothesis test using analysis of variance (ANOVA). The reported approach is both accurate and efficient and can help dermatologists and clinicians detect skin cancer at an early stage of the clinical process.