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The rapid global spread of COVID-19 has sparked a significant increase in testing efforts worldwide, marking it as a pandemic. This unprecedented situation has profoundly impacted daily life, public health, and the global economy. Traditional laboratory methods, like Polymerase Chain Reaction (PCR) testing, though considered the gold standard, are time-consuming and can yield false negatives. Consequently, there arose an urgent demand for swift and accurate diagnostic techniques to identify COVID-19 cases promptly and curb the pandemic's spread. Artificial intelligence (AI) has emerged as a potent tool in conjunction with radiographic imaging to aid in detecting COVID-19. This study proposes a classification approach for identifying infectious conditions in chest X-ray images. A dataset comprising X-ray images from healthy individuals, pneumonia cases including SARS, Streptococcus, Pneumococcus, and COVID-19 patients was compiled. Leveraging the Histogram of Oriented Gradients (HOG) technique for feature extraction, the study employed machine learning algorithms such as K-Nearest Neighbors (KNN), Random Forests, and Support Vector Machines (SVM) for classification. Results demonstrated classification accuracies of 98.14%, 96.29%, and 88.89% for KNN, Random Forests, and SVM, respectively. These findings underscore promising opportunities for utilizing image analysis in the detection of COVID-19 and other respiratory illnesses, providing a robust framework for future research and clinical applications. [ABSTRACT FROM AUTHOR]

A current advancement in biomedical subject is wireless body region community. The wide variety of wearable gadgets is utilized to display the patient's health circumstance. The applications of WBAN are health surveillance systems, human sensing, defense applications, Eco-sport. WBAN operates over 2.4 GHz. The major challenges faced by WBAN applications are antenna factor degraded the bending of the antenna causes a reduction in radiation efficiency, which is further reduced by the lossy nature of human tissues. The solution to the above problem is a compact flexible antenna. A diverse number of substrates are utilized for the design of flexible antennas. A few commonly used substrates are Nanoparticle inks (silver & copper), Polyvinylidene fluoride (PVDF), Polyvinylidene fluoride-co-hexafluoro propene (PVDF-HFP), Polyvinyl fluoride (PVF), Polyethylene Terephthalate (PET). This paper presents, a design of a compact flexible substrate-based triangular patch antenna for WBAN application. Polyethylene Terephthalate (PET) substrate has been utilized to create a flexible substrate. The proposed antenna resonates over 2.4 GHz. [ABSTRACT FROM AUTHOR]

In the present research work, the slump, compressive strength, flexural strength, split tensile strength, modulus of elasticity, ultrasonic pulse velocity, density and water absorption of Ultra High Performance Concrete (UHPC) with different percentage of nano ZrO2 particles has been investigated. The cement was replaced by nano ZrO2 particles at 1 %, 2 %, 3 %, 4 % and 5 % by weight. Results of this research work clearly show that 3% nano ZrO2 was optimum. It reduces the workability and the mechanical performance of UHPC due to its smaller size, higher Specific surface area and high pozzolanic reactivity of nanoZrO2 particles. [ABSTRACT FROM AUTHOR]

The Flood debacle occurs in the most of the part due to inappropriate water system strategy in a lodging region or the unexpected increment of water level in a stream. Flood calamity regularly makes a loss of property, harms and lifecycle. This calamity is viewed as perilous to human existence, an effective countermeasure or ready framework should be carried out to advise individuals in the beginning phase for their security precautionary. This paper proposes an early notice to the public against flood catastrophes by using Global System of Mobile communication (GSM), Internet of Things (IoT) and by using GPS. Based on sensor data the relay is operated and the alert message (SMS) passed to the respective people through their mobile phones and with an alarm system. The project also utilizes GPS to identify flood affected location and to find the safer area to move the people. [ABSTRACT FROM AUTHOR]