House Price Prediction using Machine Learning Algorithms

House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decision tree is a graphical representation of all possible solutions. Polynomial regression can be easily fitted to a wide variety of curves. Regression and classification issues are resolved with random forests .Among the given algorithm, Random forest provides better accuracy of about 89% for given dataset

COVID -19 Predictions using Transfer Learning based Deep Learning Model with Medical Internet of Things

Early detection of COVID-19 may help medical expert for proper treatment plan and infection control. Internet of Things (IoT) has vital improvement in many areas including medical field. Deep learning also provide tremendous improvement in the field of medical. We have proposed a Transfer learning based deep learning model with medical Internet of Things for predicting COVID-19 from X-ray images. In the proposed method, the X ray images of patient are stored in cloud storage using internet for wide access. Then, the images are retrieved from cloud and Transfer learning based deep learning models namely VGG-16, Inception, Alexnet, Googlenet and Convolution neural Network models are applied on the X-rays images for predicting COVID 19, Normal and pneumonia classes. The pre-trained models helps to the effectiveness of deep learning accuracy and reduced the training time. The experimental analysis show that VGG -16 model gives accuracy of 99% for detecting COVID19 than other models

AUTOMATIC BRAIN TUMOUR SEGMENTATION OF MAGNETIC RESONANCE IMAGES (MRI) BASED ON REGION OF INTEREST (ROI)

Segmentation is one of techniques used for classifying brain tissues in Magnetic Resonance Image (MRI) for identifying anatomical structures in the brain. The automated brain tumour segmentation remains challenging and computationally intensive because tumour appears in different size and intensity. In this paper, we have proposed a method for fast and automatic segmentation of tumour from Region of Interest (ROI) identified in MRI. ROI is a smaller portion of the image containing tumour. In the first step, tumour slices are identified using bilateral asymmetry property of the brain. In the second step, the ROI is identified using quadtree decomposition and similarity detection based on coefficient computed with gray level intensity histograms. In the third step, only the ROI is segmented using spectral clustering method rather than considering the whole image. Experimental results on real-world datasets are carried and compared with the recent existing works which show better results in terms of accuracy and less processing time for segmentatio

Composite Polymer Electrolytes Encompassing Metal Organic Frame Works: A New Strategy for All-Solid-State Lithium Batteries

Magnesium-benzene tricarboxylate metal organic framework (Mg-BTC MOF)-incorporated composite polymer electrolytes (CPE) composed of poly(ethylene oxide) (PEO) and lithium bistrifluoromethane sulfonylimide (LiTFSI) were prepared by a simple hot-press technique. The incorporation of Mg-BTC MOF in the polymeric matrix has significantly enhanced the ionic conductivity of CPE up to two orders magnitudes even at 0 °C. It also improved the thermal stability, compatibility, and elongation-at-break of the polymeric membrane. The all-solid-state lithium polymer cell composed of Li/ CPE/LiFePO4 has delivered a stable discharge capacity of 110 mAh g−1 at 70 °C with a current rate of 1-C, which is higher than that of those reported earlier. The appealing properties such as high ionic conductivity, better compatibility, and stable cycling qualify this membrane as electrolyte for all-solid-state lithium batteries for elevated temperature application

Chitin-Incorporated Poly(ethylene oxide)-Based Nanocomposite Electrolytes for Lithium Batteries

Nanocomposite polymer electrolytes (NCPE), with different proportions of poly(ethylene oxide)/LiClO4/ chitin were prepared by a hot press method. Nanochitin, a biopolymer, poly(�-(1f4)-N acetyl-D-glucosamine) was incorporated as a filler in poly(ethylene oxide) (PEO). The ionic conductivity of the composite polymer electrolytes was enhanced by one order upon addition of nanochitin. The lithium transference number, tLi +, was increased from 0.24 to 0.51 upon chitin addition. The membranes were subjected to scanning electron microscopy, thermogravimetric-differential thermal analysis, differential scanning calorimetry, ionic conductivity, and Fourier transform infrared (FTIR) spectroscopy analysis. The free volume Vf was probed by positron annihilation lifetime spectroscopy studies at 30 °C. Li/NCPE/Li symmetric cells were assembled, and the thickness of the solid electrolyte interface as a function of time was analyzed. This paper also describes FTIR spectroscopic studies of the interface between lithium metal and NCPE, which suggests that the surface chemistry of lithium electrodes in contact with NCPE is dominated by compounds with C-N-Li and C-O-Li bonding

Metal-organic frameworks based membrane as a permselective separator for lithium-sulfur batteries

Although lithium-sulfur batteries possess five-fold higher theoretical capacity than the state-of-the-art lithium-ion batteries, the migration of polysulfide between the electrodes remains as a problem area. In order to overcome this issue, numerous strategies have been adopted. Herein, we introduce a novel 1,3,5 benzene tricarboxylate-manganese (Mn-BTC) metal organic framework (MOF) coated-Celgard (2320) separator which acts as permselective in a Li-S cell. The Li-S cell with coated membrane exhibited higher discharge capacity than the uncoated one. The diffusion of polysulfides is successfully blocked by the separator due to the repulsive ionic forces provided by the COOe that is present in the periphery of Mn-BTC MOF which was confirmed by XPS and XRD analyse