Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps’ segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.

Air contamination is today the world’s toughest problem for the environment and public health. Air contamination has a detrimental effect on the wellbeing of humans, the atmosphere and the biodiversity. Factory air pollution continues caused by leaked toxic gases, vehicle waste and increasing accumulation of unhealthy gases and particulate matter. Particular materials are among the principal parameters of elevated air emissions, including real-time air quality monitoring to be monitored and analysed to determine correctly in a prompt manner. A standalone air quality control system with many parameters in operation in real-time is introduced in this article: CO2, temperature, humidity, air quality PM 2.5 and carbon monoxide. In reality, in every sector, the Internet of Things is commonly used and is also a key factor in air quality management. The Internet of Things Cloud Computing is a new way of improving the data processing of multiple sensors obtained and transmitted by the low-cost Raspberry pi ARM minicomputer.

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In this paper, substrate integrated waveguide based filtenna operating at X band is proposed. The model is designed on a low-loss dielectric substrate having a thickness of 1.6 mm and comprises shorting vias along two edges of the substrate walls. To realize a bandpass filter, secondary shorting vias are placed close to primary shorting vias. The dimension and position of the vias are carefully analyzed for X band frequencies. The model is fabricated on Roger RT/duroid 5880 and the performance characteristics are measured. The proposed model achieves significant impedance characteristics with wider bandwidth in the X band. The model also achieves a maximum gain of 7.46 dBi in the operating band, thus making it suitable for X band applications.

-We are developing an embedded product that helps in prevention of accidents. Our product has concentrated on two aspects to prevent accidents. First, Drowsiness of drivers. Second, Narrow curves on roads of hills. In order to prevent accidents because of these two aspects our product has been proposed. For preventing accidents because of drowsiness we have implemented image processing based on Eye Aspect Ratio (EAR) [1] , it monitors driver and alert them. For preventing at curves we have implemented camera that traps the radius of curve and the vehicle coming opposite. This system also comprises MEMS sensor, GPS, GSM to send message in case of emergency