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In the medical profession, recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality. The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans. Automatic (computerized) illness detection in medical imaging has found you the emergent region in several medical diagnostic applications. Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio. The brain tumor is one of the most common causes of death. Researchers have already proposed various models for the classification and detection of tumors, each with its strengths and weaknesses, but there is still a need to improve the classification process with improved efficiency. However, in this study, we give an in-depth analysis of six distinct machine learning (ML) algorithms, including Random Forest (RF), Naïve Bayes (NB), Neural Networks (NN), CN2 Rule Induction (CN2), Support Vector Machine (SVM), and Decision Tree (Tree), to address this gap in improving accuracy. On the Kaggle dataset, these strategies are tested using classification accuracy, the area under the Receiver Operating Characteristic (ROC) curve, precision, recall, and F1 Score (F1). The training and testing process is strengthened by using a 10-fold crossvalidation technique. The results show that SVM outperforms other algorithms, with 95.3% accuracy.

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The mass production of cost-effective, large area, defect-free and high crystal quality graphene sheets with a high yield is a challenging task. In order to investigate the mechanisms involved, we report on the synthesis of graphene sheets by a homemade atmospheric pressure thermal plasma jet system, which is a single-step and less time-consuming technique. The samples were prepared by using pure Ar gas and a mixture of Ar and N2. The microstructure of the synthesized graphene sheets was characterized with the help of Raman spectroscopy, field emission scanning electron microscopy (FE-SEM) and Fourier transform infrared (FTIR) spectroscopy. The appearance of G and 2D peaks in the Raman spectrum confirmed the formation of graphene. Moreover, we observed that the addition of nitrogen increased the production of the graphene sheets but compromised the quality of those graphene sheets by increasing their structural defects. The morphology of the synthesized samples studied via FE-SEM images showed that the sheets were composed of multilayers. FTIR spectra show the presence of C=C and a hydroxyl group directly bonded to the aromatic hydrocarbon.

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