Muhammad Asad

The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries.

Background Positive feelings about one's work are referred to as job satisfaction. On the other hand, a bad attitude regarding one's employment is job discontent.

The frequent occurrence of localized and seasonal droughts has caused severe economic losses in maize production in South China. To promote sustainable maize production, selecting and breeding drought-tolerant varieties is vital for addressing water scarcity. Drought stress affects all aspects of crop morphological performance. In this study, the morphological performance of 285 maize inbred lines under drought stress was investigated using D-value analysis, correlation analysis, principal component analysis, cluster analysis and stepwise regression analysis. All indicators were significantly different in the regular treatment compared to the drought treatment. Specifically, survival rate, root fresh weight, root dry weight, plant dry weight, root/crown ratio, and plant fresh weight were used as indicators for drought-tolerance evaluation. Furthermore, the drought-tolerant inbred line CML323 and the drought-sensitive inbred line CB2-49-1 were screened by comprehensively evaluating D values. The drought-tolerant inbred line CML323 exhibits higher leaf relative water content, chlorophyll content, proline content, and ascorbate peroxidase and peroxidase activity while having lower malondialdehyde content, consequently demonstrating excellent drought tolerance. This study provides valuable insights into drought-tolerance indicators and reference materials for breeding maize varieties.

Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images.

Abstract Mountain landscapes can be fragmented due to various human activities such as tourism, road construction, urbanization, and agriculture.

BACKGROUND Dicer1 plays a crucial role in regulating the development and reproduction of insects.

School of Agriculture and Food Sustainability, The University of Queensland, Gatton Campus, Brisbane, QLD 4343, Australia

Correction to: Scientific Reports https://doi.org/10.1038/s41598-024-71689-5 , published online 05 September 2024

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets.

Abstract With the expansion of the manufacturing sector, it has become crucial to incorporate sustainable production methods in order to remain competitive in the market.