Power dissipation has been the prime concern for CMOS circuits. Approximate computing is a potential solution for addressing this concern as it reduces power consumption resulting in improved performance in terms of power–delay product (PDP). Decrease of power consumption in approximate computing is achieved by approximating the demand of accuracy as per the error tolerance of the system. This paper presents a new approach for designing approximate adder by introducing inexactness in the existing logic gate(s). Approximated logic gates provide flexibility in designing low power error-resilient systems depending on the error tolerance of the applications such as image processing and data mining. The proposed approximate adder (PAA) has higher accuracy than existing approximate adders with normalized mean error distance of 0.123 and 0.1256 for 16-bit and 32-bit adder, respectively, and lower PDP of 1.924E[Formula: see text]18[Formula: see text]J for 16-bit adder and 5.808E[Formula: see text]18[Formula: see text]J for 32-bit adder. The PAA also performs better than some of the recent approximate adders reported in literature in terms of layout area and delay. Performance of PAA has also been evaluated with an image processing application.
Skin cancer is very important notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasible challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized by dividing all the RGB values by 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, by using the kernel filter matrix. MaxPool reduce the computational cost by down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less number of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.
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