Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately. The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model.
Error control schemes became a necessity in network-on-chip (NoC) to improve reliability as the on-chip interconnect errors increase with the continuous shrinking of geometry. Accordingly, many researchers are trying to present multi-bit error correction coding schemes that perform a high error correction capability with the simplest design possible to minimize area and power consumption. A recent work, Multi-bit Error Correcting Coding with Reduced Link Bandwidth (MECCRLB), showed a huge reduction in area and power consumption compared to a well-known scheme, namely, Hamming product code (HPC) with Type-II HARQ. Moreover, the authors showed that the proposed scheme can correct 11 random errors which is considered a high
... Show MoreChronic lymphocytic leukaemia (CLL) patients display a highly variable clinical course, with progressive acquisition of drug resistance. We sought to identify aberrant epigenetic traits that are enriched following exposure to treatment that could impact patient response to therapy.
Epigenome-wide analysis of DNA methylation was performed for 20 patients at two timepoints during treatment. The prognostic significance of differentially methylated regions (DMRs) was assessed in independent cohorts of 139 and 1
Objectives: To assess the performance of a novel resin-modified glass-ionomer cement (pRMGIC) bonded to various tooth tissues after two-time intervals. Methods: 192 sound human molars were randomly assigned to 3 groups (n = 64): sound enamel, demineralised enamel, sound dentine. Sixty-four teeth with natural carious lesions including caries-affected dentine (CAD) were selected. All substrates were prepared, conditioned and restored with pRMGIC (30% ethylene glycol methacrylate phosphate (EGMP, experimental), Fuji II LC (control), Fuji IX, and Filtek™ Supreme with Scotchbond ™ Universal Adhesive. Shear bond strength (SBS) was determined after 24 h and three months storage in SBF at 37C. The debonded surfaces were examined using stereomi
... Show MoreEncasing glass fiber reinforced polymer (GFRP) beam with reinforced concrete (RC) improves stability, prevents buckling of the web, and enhances the fire resistance efficiency. This paper provides experimental and numerical investigations on the flexural performance of RC specimens composite with encased pultruded GFRP I-sections. The effect of using shear studs to improve the composite interaction between the GFRP beam and concrete was explored. Three specimens were tested under three-point loading. The deformations, strains in the GFRP beams, and slippages between the GFRP beams and concrete were recorded. The embedded GFRP beam enhanced the peak loads by 65% and 51% for the composite specimens with and without shear connectors,
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Objective(s): The study aims to measure the effectiveness of the program on removing dead tissue for burn patients by testing the nurses before the program in addition to testing them again after implementing the educational program.
Methodology: The study is quantitative in nature (one experimental) and will employ pre- and post-testing techniques between October 17, 2020 and March 20, 2022. A non-probability (purposive) sample of 24 nurses working in the Azadi Teaching Hospital's Burns and Plastic Surgery Center was chosen. The experimental survey of nursing practice, a literature review, scientific records, and previous research were all taken into considerat
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