The resilience and safety of the modern smart grids are threatened as there is a higher sophistication of cyberattacks on cyber-physical power systems (CPSS). The low accuracy of the detection and high-level false-opportunities are caused by the fact that conventional machine learning-based intrusion detection systems often fail to simultaneously elicit temporal relations and geographical associations with attack information. To address these limitations, this paper proposes a hybrid deep learning model, which is a mixture of Long Short-Term Memory (LSTM) network as a temporal sequence modeller and Convolutional Neural Networks (CNN) as a spatial feature extractor. The experiments of a simulated dataset of CPPS demonstrate that the proposed hybrid model is superior to the existing methods, which have an accuracy of 81 to 91, with an accuracy of 94. These findings prove that the LSTM-CNN algorithm provides a reliable and effective security system against adaptable cyberattacks in smart CPPS.
Akkala Yugandhara Reddy, Tenali Ramya, Maredi Yamini et al.· 2026 7th International Confe...· 0 citations
Short-term temperature prediction plays a vital role in agriculture, water resource management, energy planning and climate change monitoring. Nevertheless, traditional statistical methods do not account for the nonlinearities in weather patterns, whereas deep learning models may not consider the linear time series characteristics. To solve this problem, the present study proposes a hybrid Auto-Regressive Integrated Moving Average-Long Short Term Memory (ARIMA-LSTM) model for short-term temperature prediction. The ARIMA model will be used in the proposed framework to identify the linear and seasonal components of temperature patterns. The errors produced by the ARIMA model will be further analyzed by means of an LSTM neural network that would model the non-linear time series behavior. The model is tested on the Tamil Nadu and Puducherry Weather Dataset (2022), consisting of roughly 1.8 million weather data sets, including variables like temperature of the air, humidity level, wind speed, and pressure. The evaluation criteria include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The experimental findings reveal that the developed model performs better in terms of accuracy than traditional deep learning models, thus providing an effective and reliable tool for predicting temperature in short intervals.
Akkala Yugandhara Reddy, D.Sravani, M.Vaishnavi et al.· 2026 4th International Confe...· 0 citations
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