Detection and Recognition of on-shelf and off-shelf retail products is challenging in computer vision because of an extremely high number of different products. Some of the existing approaches extracted text information to recognize the retail products. However, these approaches work effectively only if the text information, such as product name, price, and expiration date, is explicitly visible in the image. In this paper, a novel image processing framework is implemented to detect and recognize retail products using shape and colour features. This framework comprises of three modules: illumination-invariant representation, colour-based object identification, and shape-based object identification. The images acquired from retail stores may contain reflective effects such as shadows and illuminations because they are captured in an uncontrolled environment that can be eliminated using a three-step process defined in the first module. In the second module, the salient region detection and mean shift segmentation algorithms are utilized to capture the location of the product region based on colour features from a rectified image. After removing backgrounds, the segmented region of interest containing retail products is given as input to the final module. In the final module, a Contour Line Inflection Arc Extraction (CLIAE) algorithm is proposed to detect and recognize retail products using shape features. Finally, the Speeded Up Robust Features (SURF) algorithm is employed to perform the object matching between the known object and the target object. The performance and robustness of the proposed framework are evaluated using two benchmark datasets, GroZi-3.2k and Grocery Products (GP). However, the proposed framework outperforms both combined features-based object recognition algorithms and state-of-the-art retail product recognition algorithms by achieving a recognition accuracy of 95.5% and 95.8% on GroZi-3.2k and GP datasets, respectively. The usefulness of each module in the framework is also demonstrated by conducting a variety of experiments on benchmark datasets.

Malware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex data sets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison to traditional deep learning and standard machine learning models. In comparison to state-of-the-art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, f1 measure of 99.42%, and a quicker detection rate of 5.14 ms by utilizing the transfer learning strategy.