Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.

Smart farming helps to increase yield by smartly deciding the steps that should be practised in the season. A few components of precision farming are recommending the crops for cultivation, predicting the weather conditions, examining the soil; determining the pesticides, and fertilizers that have to be used. Smart Farming utilizes advanced technologies namely data mining (DM), machine learning (ML), the Internet of Things (IoT), and data analytics for collecting the data, predicting the outcomes and training the system. One of the most significant parameters is proper soil prediction which decides the proper crop and is manually executed by the agriculturalists. Hence, the farmer’s efficacy can be improved by producing automated tools for soil type classification. This study presents a Chaotic Jaya Optimization Algorithm with Computer Vision based Soil Type Classification (CJOCV-STC) for smart farming. The presented CJOCV-STC technique applies CV with metaheuristic algorithms for the automated soil classification process, which identifies the soil into distinct types. To accomplish this, the presented CJOCV-STC technique uses the SqueezeNet model for producing a set of feature vectors. To improve the performance of the SqueezeNet model, the CJO algorithm is used for the hyperparameter tuning process. Moreover, the Elman neural network (ENN) technique is applied for soil type classification and the parameters related to it can be adjusted by the chicken swarm algorithm (CSA). The soil classification performance of the CJOCV-STC method can be studied on the Kaggle dataset and the outcomes stated the better performance of the CJOCV-STC algorithm over other recent approaches with increased accuracy of 98.47%.

Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician probability function. A Rician K-factor is used to describe the fading severity in a Rician fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the Rician K-factor is important in wireless communications research and development. Traditionally, a Rician K-factor equation, the statistics of the instantaneous frequency of the received signal with a lookup table, or the James-Stein estimator with the maximum likelihood estimation is used for the K-factor estimation. In this paper, we explore the use of deep learning for K-factor estimation. Specifically, we use the convolutional neural network (CNN) to estimate the Rician K-factor from a waveform signal in a Rician channel. Numerical results demonstrate its good performance in estimating the K-factor of the Rician channel.

The Federated learning (FL) technique resolves the issue of training machine learning (ML) techniques on distributed networks, including the huge volume of modern smart devices. FL clients frequently use Wi-Fi and have to interact in unstable network surroundings. However, as the present FL aggregation approaches receive and send a large number of weights, accuracy can be decreased considerably in unstable network surroundings. Therefore, this study presents a Quantum with Metaheuristics Algorithm Based Minimization of Communication Costs in Federated Learning (QMAMCC-FL) technique. The presented QMAMCC-FL technique is designed a federated hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model with a quantum Aquila optimization (QAO) algorithm. The QMAMCC-FL technique upgrades the global model via weight collection of the learned model, which is commonly used in FL. The proposed model can be employed to increase the performance of network communication and reduce the size of data transmitted from clients to servers such as smartphones and tablets. The experimental analysis of the QMAMCC-FL approach is tested, and the outcomes show better performance over other existing models.

Unmanned aerial vehicles (UAVs) are assumed to be a promising model of automatic emergency tasks in dynamic marine ecosystems. But, the real-time communication efficacy betwixt UAVs and base platforms is developing a serious challenge. The compact-sized powerful flying robots can be wirelessly controlled and accomplish end tasks with and without human involvement. UAVs still face severe challenges that limit the dream of completely autonomous unmanned flying machines. The main difficulties contain path planning and hindrance avoidance of such unmanned flying robots, which are mandatory but carry out the application-specific functionality in either indoor or outdoor environments. This study introduces a new Dispersal Foraging Strategy with Cuckoo Search Optimization based Path Planning (DFSCSO-PP) technique for UAV networks. In the presented DFSCSO-PP technique, the identification of optimal paths for data transmission is performed in the UAV network. In addition, the presented DFSCSO-PP technique involves the optimal allocation of resources while finding the optimal paths in the network. Moreover, the DFSCSO technique can be designed by integrating the DFS concept into the CSO method to avoid local optima problems. A widespread simulation analysis is performed to exhibit the enhanced outcome of the DFSCSO-PP approach. A detailed set of comparative studies assured the improved performance of the DFSCSO-PP technique over other approaches.