Fall is one of the biggest challenge in elderly people, pregnant and small children’s, who stays alone in home. Sometimes this fall leads to severe injuries and even to death. Detecting the fall is very much important for elderly people. Convolutional Neural Network (CNN) is an deep learning algorithm used for image processing. In this paper, we present a video-based fall detection using CNN, this CNN will perform background subtraction and captures only foreground objects to detect the human movements and detect if fall happens. Firstly, camera will be capturing all the movements of the person. Our proposed model will detect the fall and finally an alarm is raised and email is sent to a given particular caretaker and family member. Our experimental results show the best performance of the proposed model.
According to NHTSA (National Highway Traffic Safety Administration), every year on an average 1,578 deaths is due to two-wheelers and four-wheelers crashes near traffic signals and road intersections. Drivers who neglected the red signals are the main reasons behind these accidents. Around 51% of major crashes are because of this negligence by the drivers. 30% of these accidents are caused by the drivers who are having age above 65 years. To avoid such fatal crashes near the road intersections and traffic signals a Vehicle Information Retrieval System (VIRS) is introduced in this article. This Vehicle Information Retrieval System (VIRS) collects the details like owner of vehicle, Registration State & City, Engine details, Age of Vehicle from the day that user has purchased, Vehicle Pollution Certification dates, Vehicle Insurance and other necessary information is gathered by using the cameras near the signals. The system is trained by using a Convolutional Neural Network (CNN) machine learning algorithm to identify the number plates of the vehicles. If the vehicle violates the traffic rules near the signals and intersection points the vehicle is detected through the cameras and the vehicle details is sent to the RTO office and the officers present there will generate fines to those vehicles.
Tag based image search plays an vital role to find images shared by clients in social networks. However, how to make the top ranked result relevant and with diversity. In previous papers these systems search image by using tags as query. However search results are weakly relevant tags and noisy tags. In this work we are using secure and efficient image re-ranking for TBIR. It overcomes the drawbacks that are present in the image re-ranking based on topic diversity.1) Tag mismatch 2) Query Ambiguity The main advantage of AES algorithm is to protect image data from unauthorized users. To detect and find unauthorized users for tag based image retrieval. AES is a type of symmetric key block cipher based on 10 rounds and key length is 128 bits .during encryption each round performs four transformations .sub bytes, shift rows, mix columns and add round key. AES is essential to secure the image data .
An important and crucial aspect of image processing is effective identification of lung cancer at an initial stage. One of the state of the art methods in lung cancer detection is machine learning, namely ANNs (Artificial Neural Networks) and Fuzzy Logic. These researches mainly focus upon image quality and accuracy. ANN has proved to be efficient due to their ability to learn and generalize from data. To detect lung cancer based on fuzzy logic to classify the normal and abnormal images, in the abnormal result, use other symptoms as input to fuzzy logic system to find case of the patient (cancerous or noncancerous) depending on the membership function of inputs. Expanding rough approximations into fuzzy environment which help to obtain solutions for various real time problems. Patterns are conferred to the network via the input layer which communicates to one or more hidden layers where the actual processing is done via a system of weighted connections. The hidden layers then bond to an output layer. The objective of the proposal is to materialize a means to fasten the process as well as the accuracy of detecting the cancer cells to a valuable extent it helps in saving human lives.
The necessary and critical step is to evaluate the development of lung cancer and nodule segmentation. Immobile challenge in the field of segmentation and classification of pulmonary lung nodule is particularly used to identify the small size nodule. To improve and sustain the diagnosis analysis, this paper puts forward and widens a new approach to segmentation and classification method for lung nodule size less than 3mm. In this paper, we examined and proposed a new method based on transition region based P-Tile thresholding and followed by Watershed processing for segmentation. First we reap the ROI from the input CT image and enhance the region of nodule by median filtering algorithm. Second Object contours are obtained by transition region based analysis. Third to extract multiple objects ROI from object contours employ M-Type morphological operation. Fourth prepare the images for segmentation by reducing noise and smoothing operations like weighted average filter. Kuwahara filter is used to smoothen the images and to preserve the edge position. Then we make use of crack code analysis to renovate lung boundaries. Finally the result is obtained by overlap the extracted image with the restored lung mask. To evaluate the novel segmentation method examines 90 lung nodules with 3mm to 9mm diameter from LIDC database. The presented novel approach attain ground truth rate of 86.93% ±0.09 with false positive rate of 15.09% ±0.06.After evaluation and investigation the results of segmentation our proposed method outperformed compared to other literature. .
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