ABSTRACT The proposed MotionBlend GAN model marks a significant step forward in video synthesis by blending the motion from a source video with the appearance of a target person's image. As training progresses, the model improves video creation by enhancing the smoothness and natural flow of motion, resulting in more coherent and lifelike videos. Using advanced techniques like MoBConv blocks of EfficientNet‐B7, OpenPose for precise pose detection, ResNet blocks for feature integration, and a 3D CNN discriminator, the model produces high‐quality videos that maintain both spatial and temporal consistency. After 200 epochs, the model achieved an adversarial loss of 0.2265, with metrics like PSNR at 20.246, SSIM at 0.867, and LPIPS at 0.178. The high PSNR and SSIM values, along with the low LPIPS, show that the generated frames are well aligned and preserve important details. These results highlight the model's strong performance over time, consistently generating visually convincing videos of human activities using a reference image and source video. The model effectively transfers motion from video to image, creating realistic videos of human activity in comparison to existing models.

Summary Recently, researchers have placed a great deal of emphasis on modeling activity patterns to better understand human behavior. Several approaches have been researched so far to develop automatic human activity recognition systems that infer detailed semantics from visual images, aiming to understand real human behavior patterns. However, there is still a need for a cost effective solution to distinguish human actions in the real‐world environment. With this encouragement, a novel approach is proposed to recognize shoplifting acts by examining the posture evidence of the human being. This approach begins by obtaining the two‐dimensional pose reflecting human's body joints as a skeleton from the recorded frames. Subsequently, a preprocessing step is used to preprocess skeleton data, which can handle the occlusion too. Postural feature generation is then used to extract pertinent features from such preprocessed skeletons. Finally, feature deduction is performed to downsize the derived features to a smaller dimension, and activity classification is performed on such reduced features to identify shoplifting behaviors in real time. A synthetic shoplifting dataset and real store recorded videos are used to conduct the experiments, the findings of which appear more promising than those obtained using other cutting‐edge methods, with an accuracy of 97.36% and 91.66% for synthesized and real store recorded inputs.

Abstract Face image generation plays an important role in generating innovative and unique multimedia content using the GAN model. With these qualities of the GAN model, they have numerous challenges in the human face image generation. The problems encountered in the generation of facial images are like blurriness in images, incomplete details in the generated facial images, high computational power requirements, and so forth. In this manuscript, we proposed a GAN model that utilizes the composite strength of VGG‐16 and ResNet‐50's models to overcome those difficulties. It uses VGG‐16 to build a discriminator model to discriminate between real and fake images. The generator model utilizes a combination of components from the ResNet‐50 and VGG‐16 models to enhance the image generation process at each iteration, resulting in the creation of realistic face images. The proposed DRFI GAN (Diversified and Realistic Face Image Generation GAN) model's generator achieves an impressive low FID score of 20.50, which is less than existing state‐of‐the‐art approaches. Furthermore, our findings indicate that the images generated by the DRFI GAN model exhibit 10%–15% greater efficiency and realism with reduced training time compared to existing state‐of‐the‐art methods with lower FID scores.

Abstract Urbanization is expected to hold about 50% of the world population by 2050 and there will be stress on available resources including food and freshwater. Further, inefficient utilization of irrigation water possesses additional challenges and it is predicted that by 2050, India will face shortage of freshwater resources. Urban agriculture and Precision Agriculture are the possible solutions in smart cities for the predicted challenges providing food security and safety, sustainable life in cities. Internet of Things (IoT) and Machine Learning (ML) are the possible solutions to smart cities' farming. Agricultural activities such as irrigation of plants, rain and drought conditions monitoring supported by IoT and ML succour farmers to righteous decision on agriculture activities. A detailed study is carried out on the role, opportunities, and different aspects of smart cities, urban farming, communication technologies, IoT and ML with respect to agriculture. The article presents the design of intelligent irrigation system based on soil and weather conditions. The soil and weather parameters are selected through various research articles in Agriculture 4.0 and ML. The article also juxtapositions the designed weather station with the various patents developed. The system developed in this article provides a cost‐effective and state‐of‐the‐art solution to local weather monitoring.