The paper gives a Hybrid Evolutionary Optimization and Neural Network Model of climate adaptive renewable energy forecasting. The need to increase renewable energy has led to the creation of more precise and flexible forecasting tools. This paper integrates evolutionary optimization techniques, including Genetic algorithms (GA) and Particle Swarm optimization (PSO), with deep learning neural networks, specifically Long short-term memory (LSTM), to predict renewable energy production using solar and wind energy. The methodology will consist of gathering up-to-date environmental measurements (temperature, wind speed, and solar radiation), as well as the previous data on energy generation by using renewable resources. The hyperparameters of the deep learning model are optimized using evo0.lutionary optimization algorithms to make accurate predictions of the model under different climatic conditions. The proposed hybrid model was compared to the conventional models, such as ARIMA and SVM, and the outcomes indicate that it has a better performance with respect to the accuracy of the prediction, the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE), and the R2 value. The hybrid model also saves a lot of time when it comes to predicting failure, thus it is more effective in proactive energy management. Also, the model can be adjusted to changing conditions of the environment and offers real-time predictions and useful information concerning the optimization of energy production and its integration into the power grid. The results indicate that this mixed method has the potential to maximize the accuracy and effectiveness of renewable energy prediction, which will further result in improved energy grid management and low operation costs.
Nidhi Mishra, Aakansha Soy· 2026 International Conferenc...· 0 citations
The challenge of real-time object detection in dynamic environments is complicated by issues like noise, occlusion, changes in illumination, and ambiguity of object boundaries. Non-Bayesian models of deep learning tend to be confident about their predictions. Such approach is dangerous since it renders these models inappropriate for use in safety-critical applications such as self-driving vehicles, surveillance and robotics. In this paper, an uncertainty-aware deep learning method is suggested which can be applied to real-time object detection in dynamic computational vision environments. This method combines a lightweight detector based on YOLO architecture, Monte Carlo dropout and uncertainty estimation via entropy measure to account for both aleatoric and epistemic uncertainties. The approach is expected to increase robustness to challenges including occlusion, motion blur and illumination variation. Experimental results obtained on COCO and KITTI data sets show that the proposed model reaches mAP of 88.9%, that is, 6.8% better compared to baseline YOLO and CNNs models. False positives are reduced by 12.3% and ECE score is increased by 9.5%. The model runs at 38 FPS which ensures its real-time operation. The results confirm that uncertainty-aware reasoning significantly enhances prediction reliability and interpretability in object detection systems.
Nidhi Mishra, Aakansha Soy· 2026 6th International Confe...· 0 citations
Advent of Internet of Things (IoT) sensor networks of scale, the amount and speed of data produced has become a major problem in real-time data processing and analytics. Conventional computing models cannot provide real-time insights because are constrained by scalability, resource management, and computational capabilities. The paper discusses how parallel computing frameworks, including Apache Spark, Apache Flink, and CUDA, can be used to improve the efficiency of processing and analyzing massive sensor data in real time. These models allow distributed data processing across multiple nodes, significantly reducing latency and enhancing throughput. The load balancing, fault tolerance, and data partitioning problems are some of the challenges that proposed approach will address using these frameworks to ensure significant performance gains in large-scale IoT settings. Data processing is found to be significantly faster, and the analytics latency is reduced, which is shown by experimental results, thereby showing the potential of parallel computing in real-time IoT analytics. The contributions made in this work are the design and implementation of an optimized framework of IoT sensor networks and the performance evaluation of a comprehensive framework across different real-world conditions.
Dr.Nidhi Mishra, Aakansha Soy· International Conference Com...· 0 citations
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