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This survey explores electricity theft detection in smart grids, where traditional power systems meet modern technology. Smart grids, designed for efficient energy management and continuous integration of renewables, face a pressing challenge electricity theft, costing utility companies over $96 billion annually. The survey traces the evolution from conventional to smart grids, emphasizing their core components. It underscores the economic impact of theft, driving researchers to explore Artificial Intelligence (AI) and Deep Learning (DL) techniques for detection. A comprehensive literature review reveals various approaches, with a focus on DL's growing influence. Public datasets are explored as invaluable resources, and methods for theft detection, including advanced AI and DL, are dissected. Performance metrics like accuracy and precision are discussed, and challenges, including imbalanced data and privacy concerns, are highlighted. In conclusion, the survey emphasizes the need for diverse AI and DL approaches, data sources, and features to create robust theft detection systems for smart grids, ensuring their secure and efficient operation.

In recent years, Generative Adversarial Networks (GANs) appeared as a prevailing solution for combating data scarcity in various domains. This study delves into utilizing WaveGAN, a specialized GAN architecture, to address the inherent challenges stemming from the limited availability of audio datasets. Our primary objective is to tackle the issue of constrained audio data resources by utilizing the potential of WaveGAN. Our research is driven by the overarching goal of investigating the capacity of CNN to gather significant insights from an extensive corpus of human speech data. A key focus of our work is to demonstrate the effectiveness of WaveGAN in generating synthetic audio data, thereby expanding the breadth of our audio dataset and bolstering the resilience of our classification models. Our study aims to yield improved classification results, providing crucial insights into the viability of this approach in alleviating data scarcity challenges of audio analysis.

Fire detection is a significant attempt for preserving public safety in complex surveillance environments. Although advances in deep learning for fire detection, the task remains challenging due to the natural irregularity in fire images, including differences in lighting conditions, occlusions, and background complexity. To address these challenges, we present a novel framework for fire detection named fire channel attention network (FCAN), which is capable of differentiating challenging fire scenes. Our approach is motivated by the need to enhance the accuracy of fire detection by selectively emphasizing the most informative channels of the input image through a channel attention (CA). Furthermore, our model captures the salient features from the input image and suppresses the irrelevant ones, thereby overcoming the aforementioned challenges of fire detection. The FCAN is evaluated on two benchmark datasets and surpassed existing methods in terms of accuracy and F1 score. The proposed model demonstrates the effectiveness of fire detection, highlighting its potential for practical applications in fire safety and prevention.

Accurate detection of small targets in aerial images is crucial but challenging due to the limited computational resources of UAVs. This paper presents an efficient approach based on YOLO-V5S for detecting and classifying distant vehicles in aerial scenes. Extensive ablation study is conducted to find the optimal YOLO architecture. The proposed method is efficient and effective, making it applicable for real-time deployment. A dataset of 1000 annotated images are developed to validate the proposed method's effectiveness. The proposed network outperforms existing state-of-the-art methods in accuracy, speed, and resource efficiency, making it a promising solution for aerial vision-based applications.

Animal detection and classification are crucial for effective wildlife management (WM) and reducing risks associated with animals related road accidents and attacks. Previous attempts trained the models using imbalanced data with fewer representative features and baseline models without improvement. This paper presents a new dataset of five animal classes captured in various poses, lighting conditions, and intraclass variations. The standard coupled detection head of the YoloV4 algorithm faces limitations when performing simultaneous classification and localization due to shared parameters and inputs. To address this issue, we propose a decoupled detection head (DDH) that handles these tasks separately, improving performance. We conducted extensive experiments using the proposed dataset. We found that the optimal backbone features marginally improve the performance of the modified network compared to state-of-the-art (SOTA) works in the subject domain. Our work contributes by addressing the limitations of the standard YoloV4 algorithm and proposing a new dataset for researchers to use in future studies.

Nowadays, renewable energy resources such as Photovoltaic (PV) is one of the convenient ways to integrate it into the distributed grid to fulfill the huge energy demands without burning costly and pollutant fossil fuels. Researchers have been contributing from various aspects to develop accurate PV-power forecasting methods however further improvements are needed for an effective power management system. Therefore, in this work, we propose an attention-based deep learning (DL) model (PV-ANet) for short-term PV-power forecasting. The proposed system mainly consists of three modules. First, data from an actual PV power plant is acquired and preprocessed to remove outliers and normalized for efficient processing. Next, the PV-ANet model is developed, which is consisting of an encoder and decoder modules. The encoder encodes the input attributes via stack conventional and attention layer. While the decoder part contains the normalization and series of the dense layers to expends the encoded features into optimal features and generate one hour ahead forecast. Finally, the proposed model is evaluated via standard error metrics including MSE, MAE, and RMSE and achieved the lowest errors rates compared to state-of-the-art methods.

An observational study was conducted at the Section of Clinical Chemistry, Department of Pathology and Laboratory Medicine, to assess the iodine status using the World Health Organization (WHO), United Nations International Children's Emergency Fund (UNICEF), and the International Council for Control of Iodine Deficiency Disorders (ICCIDD) consensus criteria, which state that >3% prevalence of serum thyroid stimulating hormone (TSH) ≥10 mIU/L in the population is an indicator of iodine deficiency. Serum neonatal TSH was analyzed from January to December 2013. In a period of one year, a total of 11,597 neonates with the mean (25 percentile, 75 percentile value) age of 2.0 days (0.5?3.5) were tested for serum TSH. The overall mean TSH level was 3.38 mIU/L (5.63?1.96), with optimal levels (1?39 mIU/L) in 93%, <1 mIU/L in 6.3%, and ≥40 mIU/L in 0.3% neonates. Of all the neonates, 7.9% (N=916) showed TSH ≥10 mIU/L which is higher than the recommended WHO/UNICEF/ICCIDD criteria for mild endemicity for iodine deficiency in the population. These results suggest that iodine deficiency is still prevalent in our population, indicating a need for effective intervention programs and increasing awareness regarding the use of iodized salt and supplementation in all reproductive-aged women to prevent iodine deficiency in neonates.