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Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users. It hinders the economic growth of utility companies, poses electrical risks, and impacts the high energy costs borne by consumers. The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data, including information on client consumption, which may be used to identify electricity theft using machine learning and deep learning techniques. Moreover, there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware. Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse, class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance. In this research, a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity. The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance, feature extraction and final theft detection. Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue, the near miss for class imbalance, and transfer learning for classification. The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters. A significant number of simulations demonstrate that, when compared to its competitors, the RoGRUT achieves the best classification results. The performance evaluation of the proposed model revealed exemplary results across variousmetrics. The accuracy achieved was 88%, with precision at an impressive 86% and recall reaching 84%. The F1-Score, a measure of overall performance, stood at 85%. Furthermore, themodel exhibited a noteworthyMatthew correlation coefficient of 78% and excelled with an area under the curve of 91%. [ABSTRACT FROM AUTHOR]

Ambient gas measurement is essential in diverse fields and applications, from preventing accidents, to air pollution warnings, and granting the correct gas mixture to patients in hospitals. Gas leakage can reach large proportions, affecting neighborhoods or even cities, causing enormous environmental impacts. The integration of gas sensors through the Internet of things (IoT) paradigm should ease the information collecting and sharing processes, granting better experiences to users, and avoiding major losses and expenses. With this integration, final users must be capable of operating these gadgets without previous technological knowledge, thus, the proposal of plug‐and‐play (PnP) sensors and actuators should be considered. This article proposes an IoT‐based solution for sensing environmental gases preceding a PnP approach, combining the hardware and software integrations for a better user experience. In this proposal, the PnP interface is defined, using the universal asynchronous receiver/transmitter protocol. The wireless communication is made through the IEEE 802.15.4 and IEEE 802.11 (Wi‐Fi) standards. The proposed sensor is capable of detecting smoke, oxygen, hydrogen, carbon monoxide, alcohol vapor, liquefied petroleum gas, benzene, ammonia, methane, and propane. The solution is integrated with a middleware for IoT, called In.IoT, a software that receives the sensed data, stores, and forward them to other smart devices on the same context, allowing an Android‐based mobile application to warn users regarding the environmental gases concentrations. The proposed solution is experimented, demonstrated, validated, and it is ready for use. [ABSTRACT FROM AUTHOR]