Fisher-Yates Chaotic Shuffling Based Image Encryption

In Present era, information security is of utmost concern and encryption is one of the alternatives to ensure security. Chaos based cryptography has brought a secure and efficient way to meet the challenges of secure multimedia transmission over the networks. In this paper, we have proposed a secure Grayscale image encryption methodology in wavelet domain. The proposed algorithm performs shuffling followed by encryption using states of chaotic map in a secure manner. Firstly, the image is transformed from spatial domain to wavelet domain by the Haar wavelet. Subsequently, Fisher Yates chaotic shuffling technique is employed to shuffle the image in wavelet domain to confuse the relationship between plain image and cipher image. A key dependent piece-wise linear chaotic map is used to generate chaos for the chaotic shuffling. Further, the resultant shuffled approximate coefficients are chaotically modulated. To enhance the statistical characteristics from cryptographic point of view, the shuffled image is self keyed diffused and mixing operation is carried out using keystream extracted from one-dimensional chaotic map and the plain-image. The proposed algorithm is tested over some standard image dataset. The results of several experimental, statistical and sensitivity analyses proved that the algorithm provides an efficient and secure method to achieve trusted gray scale image encryption

A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification

Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of all methods is that they require a considerable amount of training data, which poses a challenge for classifying medical images as limited datasets are available. The problem can be tackled through transfer learning, in which a model pre-trained on a huge dataset is utilized and fine-tuned as per the problem domain. This paper proposes a new Convolution neural network architecture to classify skin lesions into two classes: benign and malignant. The Google Xception model is used as a base model on top of which new layers are added and then fine-tuned. The model is optimized using various optimizers to achieve the maximum possible performance gain for the classifier output. The results on ISIC archive data for the model achieved the highest training accuracy of 99.78% using Adam and LazyAdam optimizers, validation and test accuracy of 97.94% and 96.8% using RMSProp, and on the HAM10000 dataset utilizing the RMSProp optimizer, the model achieved the highest training and prediction accuracy of 98.81% and 91.54% respectively, when compared to other models

Application of Event Detection to Improve Waste Management Services in Developing Countries

This study illustrates a proof-of-concept model to improve solid waste management (SWM) services by analyzing people’s behavior towards waste. A deep neural network model is implemented to detect and identify the specific types of events/activities in the proximity of the waste bin. This model consists of a three-dimensional convolutional neural network (3D CNN) and a long short-term memory (LSTM)-based recurrent neural network. The model was trained and tested over a handcrafted data set and achieved an average precision of 0.944–0.986. This precision is promising to support the implementation of the model on a large scale in the actual environment. The performance measures of all individual events indicate that the model successfully detected the individual events and has high precision for classifying them. The study also designed and built an experimental setup to record the data set, which comprises 3200 video files duration between 150–1200 s. Methodologically, the research is supported through a case study based on the recorded data set. In this case study, the frequencies of identified events/activities at a bin are plotted and thoroughly analyzed to determine people’s behavior toward waste. This frequency analysis is used to determine the locations where one of the following actions is required to improve the SWM service: (i) people need to be educated about the consequences of waste scattering; (ii) bin capacity or waste collection schedules are required to change; (iii) both actions are required simultaneously; (iv) none of the actions are needed