Human activity recognition (HAR) is a process of identifying the daily living activities of an individual using a set of sensors and appropriate learning algorithms. Most of the works on HAR are done using a mix of sensor data that is collected in a simulated environment, and due to that, the real-time recognition suffers. This article proposes an efficient adaptive batch size-based-CNN-LSTM model for recognizing different human activities in an uncontrolled environment. It uses adaptive batch sizes from 128 to 1024 for iterative model training and validation. The proposed model can handle imbalanced classes and un-normalized data efficiently. A state-of-art HAR dataset is also generated in an open environment to get the activity data of an uncontrolled scenario. With minimal data preprocessing and data augmentation, the model is tested, and the proposed model managed to get the highest accuracy of 99.29% with an average loss of 0.08 $\pm$ 0.136%. The presented model is also tested with two public datasets named- mHealth and MotionSense and achieved an accuracy of 99.5% and 99.8%, respectively. The proposed model outperforms all the previous benchmarks and approaches by a good margin.

With the advancement of image editing tools in today's world, the manipulation of images like cropping, cloning, resizing, etc., becomes an easy proposition and on the other end, checking or determining whether an image has been manipulated or not, becomes a great challenge. Copy‐move forgery in images is the most popular tampering method in which a portion of an image is copied and pasted in some other location of the same image. The detection of copy‐move forgery has become a prominent research area. This study presents a detailed review and critical discussions with pros and cons of each of copy‐move forgery detection techniques from 2007 to 2017. This study also addresses the variation in databases, issues, challenges, future directions and references in this domain.

Human Activity Recognition is the process of identifying daily living activities of a person using sensor attributes and intelligent learning algorithms. Identifying complex human activities is tedious, as capturing long-term dependencies and extracting efficient features from the raw sensor data is challenging. This paper proposes an efficient and lightweight hybrid deep learning model for recognizing complex human activities using physiological EMG sensors and enhanced feature learning. The proposed CNN-LSTM incorporates multiple 1-dimensional convolution layers for spatial feature extraction and then feeds the generated feature maps to the recurrent layers to identify long-term temporal dependencies. Incorporating physiological sensor-based raw EMG dataset and minimal pre-processing, we trained and tested our proposed model and achieved the highest accuracy of 84.12% and an average accuracy of 83%. The proposed model outperformed the benchmark models with optimal performance margins and generalized the patterns in significantly less computational time than other deep learning models.