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Background: The increasing number of COVID-19 patients around the world and the limited number of detection kits pose a challenge in determining the presence of the disease. Imaging modalities such as X-rays are commonly used because they are readily available and cost-effective. Deep learning has proved to be an excellent tool because of the abundance of online medical images in various medical modalities, such as X-Ray, computerized tomography (CT) Scan, and magnetic resonance imaging (MRI). A large number of medical research projects have been proposed and launched since early 2020 due to the overwhelming use of deep learning techniques in medical imaging. Methods: We have used fuzzy logic and deep learning to determine if chest X-ray images belong to people who have pneumonia related to COVID-19 and people who have interstitial pneumonias that aren't related to COVID-19. Results: In comparison to the current literature, the proposed transfer learning approach is more successful. It is possible to classify covid, viral, and bacterial pneumonia or a healthy patient using ResNet 18 Architecture's four-class classifiers. The proposed method achieved a 97% classification accuracy, 96% precision, and 98% recall in the case of COVID-19 detection using chest X-ray images, which demonstrates the importance of deep learning in medical image diagnosis. Furthermore, the results demonstrate that the proposed technique has the maximum sensitivity rate, with 97.1% ratio. Finally, with a 97.47% F1-score rate, the proposed strategy yields the highest value when compared to the others. Conclusions: DeepLearning techniques and fuzzy features resulted in an improved classification ability, with an accuracy rate of up to 97.7% using ResNet 18, which is a better value when compared to the remaining techniques. Classification of COVID-19 scans and other pneumonia cases have been done successfully by demonstrating the potential for applying such deep learning techniques in the near future.

Memory leaks are a major concern to the long running applications like servers which make the working set to grow with the program. This eventually leads to system crashing. This paper discusses a staged approach to detect leaks in firmware of remote server controller. Remote server controller monitors the server remotely with many processes running in the background. Any memory leak in the long running applications pose a threat to the performance of the system. The approach adopted here filters the processes running in the system with leaks based on time threshold in the first stage. These processes with leaks are passed to the next stage where precise memory leak detection is done using the open source dynamic instrumentation tool Valgrind. The system leverages an automated leak detection approach that invokes the leak detection process on encountering any severity in the system and generates a consolidated leak report. The proposed approach has less impact on the performance of the system and is faster compared to many available systems as there is no need to modify or re-compile the program. In addition, the automated approach offers an effective technique for detecting possible leakages in early software development phases.

Crop classification and detection of crop field boundaries empower farmers which helps the agricultural businesses to estimate crop field dimensions and yields accurately. Our research focuses on estimating crucial agricultural inputs such as seeds, pesticides, insecticides, and fertilizers to enhance overall production. The conventional method of manually identifying field boundaries is both time-consuming with labour-intensive. In contrast, our study harnesses data from diverse satellites such as Sentinel, Landsat, and MODIS, encompassing valuable land usage information. By integrating this data with machine learning algorithms, we achieve real-time monitoring of crop fields through effective classification and boundary identification. For the classification of crop fields within our study area, we recommend employing the Classification and Regression Tree (CART) algorithm. Additionally, we leverage normalized difference indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), as features for classification. We compare these features with Support Vector Machine (SVM) and Random Forest (RF) algorithms. Subsequently, we utilize the Canny edge detection technique to identify boundaries within the classified crop areas. Notably, our approach utilizes the Google Earth Engine (GEE) as a primary platform for extracting features, conducting data training, and visualizing information. The proposed algorithm yields impressive results with a high level of accuracy. Notably, the CART algorithm achieves a remarkable accuracy rate of 96.1%. Furthermore, we incorporate NDWI-based Canny edge detection into our methodology. The outcomes convincingly underscore the practicality and applicability of our research in real-world scenarios. [ABSTRACT FROM AUTHOR]

The timely identification of pulmonary embolism is of utmost importance, as the condition has the potential to be life-threatening if not promptly addressed. The assessment of the severity of a pulmonary embolism (PE) frequently necessitates a time-consuming and potentially life-threatening estimation by a medical practitioner. The primary objective of the study was to investigate the potential utility of an artificial neural network in assisting physicians with the identification and prediction of pulmonary embolism risk in patients. Deep learning algorithms are frequently employed in medical imaging to enhance image analysis due to their ability to automatically learn representations from large datasets, as opposed to relying on pre-programmed instructions. The implementation of automated systems has the potential to decrease the level of physical effort needed and enhance the efficiency of diagnostic procedures for medical professionals. Efficient training and calculation processes are crucial for the proper execution of the implementation. The Tensor Processing Units (TPUs) developed by Google are employed to expedite the process of training., with the computational tasks being executed through Google Colab, a platform offered by Google Cloud TPUs. In order to achieve outcomes comparable to human judgment, deep learning algorithms engage in reasonable assessments of data based on a predetermined logical framework. Diagnosing pulmonary embolism (PE), a potentially lethal yet curable condition, poses challenges in early detection. A distinctive convolutional neural network (CNN) model was developed and examined for the purpose of distinguishing between pulmonary embolism (PE) and computed tomography (CT) pictures. The proposed study yields a precision rate of 91.2%, showcasing an enhancement compared to current convolutional neural network (CNN) architectures that include limited trainable parameters. Furthermore, our model provides interpretability by the utilization of computed tomography (CT) images, specifically in the inferno and bone models. Our proposed deep learning model has the potential to predict the presence of PE and other associated features in current cases. [ABSTRACT FROM AUTHOR]

Brain tumor is a serious life-threatening disease which occurs due to peculiar growth of cells or tissues present in brain. In recent times it is becoming a considerable cause of death of many people. The seriousness of this tumor growing in brain is very huge when compared to all other varieties of cancers and tumors. Hence, to save the affected people detection of the tumor and proper treatment should be done instantaneously without any delay. In this new age of technology, Machine Learning (ML) and Deep Learning (DL) models can be utilized to identify the tumor at early stages more precisely so that proper medication can be given to the affected person which will help in curing them. This paper proposes two different machine learning models to identify the brain tumor by analysing the Magnetic Resonance Image (MRI) scans of the brain. Both unsupervised and supervised learning models were implemented to detect the tumors in brain. Fuzzy C means is used as a part of unsupervised learning model, it is a data clustering algorithm in which entire data set is grouped into predefined number of clusters with every data point belonging to every cluster to a specific degree of membership value. In this approach tumor region is treated as one cluster and healthy brain is another cluster. Moving forward, as a part of supervised learning, transfer learning approach is implemented for classifying whether the given input MRI scan consists of tumor or not. Visual Geometric Group (VGG-19) model was used which is a 19-layer deep pre-trained neural network architecture for better accuracy and results. All the models were developed using python in jupyter notebook. [ABSTRACT FROM AUTHOR]

The monitoring of solar ultraviolet radiation from a space platform is now considered essential for a wide range of fields including solar physics, atmospheric science and astrobiology. 4H-Silicon Carbide (4H-SiC) is a superior alternative to conventional materials like silicon for the fabrication of UV detectors for adverse space conditions due to its inherent radiation hardness and visible-blind nature. This paper describes the space qualification and deployment of indigenously developed Pd/4H-SiC Schottky UV detectors in a low-earth orbit (LEO) and their on-board performance. Two SiC UV detectors were flown as a rad-hard sun detection sensor in the nanosatellite INS-2TD. The sensor has carried out solar UV flux observations continuously since its launch in February 2022 and the data gathered during the first seven months of flight is discussed in this paper. [ABSTRACT FROM AUTHOR]

It is established that exercise will increase immunity against covid-19. This data can be statistically verified by employing a Support Vector Machine model and a K-Means method. The number of push-ups taken per day/week and the occurrence covid-19 infection can be modeled into a Support Vector Machine algorithm. Similarly the minutes of jogging per day and breathing difficulty occurrences can be modeled into a K-Means algorithm. The training data set will give a predictive model to assess the importance of exercise against covid-19. [ABSTRACT FROM AUTHOR]

4H-SiC, by virtue of its intrinsic properties, is a very promising semiconductor material for fabricating rad-hard UV detectors suitable for harsh radiation environments. This paper aims to investigate the radiation tolerance of indigenously developed Pd/4H-SiC Schottky detectors, in order to determine their feasibility for space applications. 4H-SiC detectors of active area 1 x 1 mm² were irradiated with electrons of energy 10 MeV at fluence of 2x1013 e-/cm² and gamma rays from a Co- 60 source with a total dose of 1 Mrad. The impact of these irradiations on electro-optical characteristics of the devices was studied by analyzing the changes in electrical parameters like reverse saturation current (Is), ideality factor (n), barrier height (ϕB), effective doping concentration (Neff) derived from I-V and C-V characteristics as well as in the UV spectral responsivity (i.e., from 248 to 365 nm) of the irradiated detectors. The electron irradiated device showed negligible change in I-V and C-V characteristics whereas its UV spectral responsivity at the peak wavelength of 290 nm reduced by 48.7 %. Gamma irradiated device displayed a noticeable variation in its electrical characteristics and 15.8 % reduction in the spectral responsivity (optical characteristics) at the peak wavelength. The results show that the radiation hardness of 4H-SiC detectors is better than that of conventional semiconductor ones, making it a more appealing choice as radiation detectors in space systems. [ABSTRACT FROM AUTHOR]