Agriculture has played a significant role in the lifespan of human beings for their survival and the better economic growth of the country. Due to its intensified growth, smart agriculture has been a popular domain recently. This aggregates the benefits of any computing technologies, like wireless sensor networks (WSN), drones, Internet of Things (IoT), remote sensing, etc. The IoT system places sensors on agricultural areas to accumulate critical information for the crops and fields to increase the overall rate of productivity. While broadcasting the sensed information in the domains to the target, there is a chance of the existence of cyber-threats, which intruders design to attain access to the content being transferred. Intrusion detection in IoT-based smart farming using deep learning (DL) leverages the power of deep neural networks (DNN) to safeguard agricultural systems from cyber threats. DL algorithms can autonomously detect unauthorized and anomalies activities by analyzing data streams from sensors, IoT devices, and farm management systems. With this motivation, this study designs an Enhanced Black Widow Optimization with Hybrid Deep Learning Enabled Intrusion Detection (EBWO-HDLID) technique in the IoT-based Smart Farming environment. The proposed EBWO-HDLID technique captures complex patterns and detects significant intrusions, assuring the security and reliability of smart farming. In the presented EBWO-HDLID approach, the bald eagle search (BES) algorithm can be used for the feature selection process. For the intrusion detection and classification process, the EBWO-HDLID technique applies the HDL classification model. Finally, the EBWO algorithm can be used for the parameter tuning of the HDL technique. The experimental validation process demonstrates the satisfactory performance of the EBWO-HDLID model on two benchmark datasets: ToN-IoT, and Edge-IIoTset datasets.

Predicting ship trajectories can effectively forecast navigation trends and enable the orderly management of ships, which holds immense significance for maritime traffic safety. This paper introduces a novel ship trajectory prediction method utilizing Convolutional Neural Network (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Our research comprises two main parts: the first involves preprocessing the large raw AIS dataset to extract features, and the second focuses on trajectory prediction. We emphasize a specialized preprocessing approach tailored for AIS data, including advanced filtering techniques to remove outliers and erroneous data points, and the incorporation of contextual information such as environmental conditions and ship-specific characteristics. Our deep learning models utilize trajectory data sourced from the Automatic Identification System (AIS) to train and learn regular patterns within ship trajectory data, enabling them to predict trajectories for the next hour. Experimental results reveal that CNN has substantially reduced the Mean Absolute Error (MAE) and Mean Square Error (MSE) of ship trajectory prediction, showcasing superior performance compared to other deep learning algorithms. Additionally, a comparative analysis with other models—Recurrent Neural Network (RNN), GRU, LSTM, and DBS-LSTM—using metrics such as Average Displacement Error (ADE), Final Displacement Error (FDE), and Non-Linear ADE (NL-ADE), demonstrates our method’s robustness and accuracy. Our approach not only cleans the data but also enriches it, providing a robust foundation for subsequent deep learning applications in ship trajectory prediction. This improvement effectively enhances the accuracy of trajectory prediction, promising advancements in maritime traffic safety.

In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely recognized for their tracking and prediction capabilities. However, their performance is often limited by static parameters that cannot adapt to changing conditions. Addressing this limitation, this paper introduces innovative neural network-based prediction models that enhance the adaptability and accuracy of these conventional filters. Our approach involves the integration of neural networks within the filtering algorithms, enabling the dynamic augmentation of parameters in response to performance feedback. We present two modified filters: a neural network-based Kalman filter and an alpha-beta filter, each augmented to incorporate neural network-driven parameter tuning. The alpha-beta filter is enhanced with neural network outputs for its α and β parameters, while the Kalman filter employs a neural network to optimize its internal parameter R and noise factor F. We assess these advanced models using the root mean square error (RMSE) metric, where our neural network-based alpha-beta filter demonstrates a significant 38.2% improvement in prediction accuracy, and the neural network-based Kalman filter achieves a 53.4% enhancement. Hence, our novel approach of integrating neural networks into filtering algorithms stands out as an effective strategy to significantly enhance their performance in dynamic environments.

Traffic congestion has become a critical issue worldwide, significantly impacting the quality of life and causing economic losses in urban areas. To address this, the concept of smart cities has emerged, utilizing technologies like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) to optimize transportation systems. One of the main challenges in smart city traffic management is accurately predicting traffic congestion. Existing approaches often struggle with providing reliable predictions, especially in cloud-based environments, which can lead to inaccurate traffic estimates. This article, propose a novel data-driven approach to predict traffic congestion in smart cities using a hybrid model that combines bi-directional long short-term memory (Bi-LSTM), convolutional neural networks (CNN), and an attention network. This model leverages factors such as traffic conditions, road types, and weather data to improve prediction accuracy. The model’s performance is evaluated using key metrics including root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE), across four junctions (J1, J2, J3, J4). The proposed model consistently outperforms existing methods, including gated recurrent unit (GRU), long short-term memory (LSTM), CNN, and multi-layer perceptron (MLP). In Junction 1, the proposed model achieved the best results with RMSE = 0.252, MSE = 0.063, and MAE = 0.178. In Junction 2, the proposed model again led with RMSE = 0.561, while other models, like GRU and LSTM, showed higher error rates. These results confirm that the hybrid model is more effective at predicting traffic congestion, particularly in dynamic urban environments. The proposed approach offers a promising solution for improving traffic flow and optimizing smart city infrastructure.

The sixth most frequent cancer and one with the highest death rate is esophageal cancer (EC). Initial diagnosis might increase the survival level of people with esophageal abnormalities. Esophagitis to non-dysplasia is the progression of EC in that order. Convolutional neural networks (CNN) equipped with effective feature extractors improve the prognosis $\text{of Barrett's}$ esophagus, esophagitis as well as precancerous stage conditions. More relevant characteristics may be extracted for the mechanized Barrett's esophagus classification as well as esophagitis using transfer learning methods with CNN. This research reports on a study on the employment of Enhanced Convolution Neural Networks (ECNN) for the classification of esophagitis. The evaluation $\text{will}$ be done with the K vasir open-access dataset, which delivers considered esophagitis along with Barrett's esophagus images. Several performance metrics, including Accuracy (acc), precision (prec), Recall (rec), and flscore, are used to assess the outcomes. To demonstrate the effectiveness of the proposed strategy, comparison studies are conducted with numerous different existing methodologies.

Researchers are always looking to find ways to use energy more efficiently while keeping people comfortable at home. They've tried different methods like fuzzy logic and genetic algorithms to do this. In this paper, we introduce a novel model that combines different techniques like data smoothing, optimization, machine learning, and control algorithms to reach this goal in smart homes. The proposed model is made up of three main parts: smoothing, optimization, and control. The smoothing part uses the Kalman filter to get rid of any unnecessary noise and make sure the data used to predict energy use is clean and smooth. The optimization part uses both genetic with firefly algorithms and artificial neural networks to minimize the gap between the actual environmental conditions and the conditions users find comfortable. It adjusts the comfort settings in real-time using machine learning. The control part uses Mamdani fuzzy logic to make sure energy is delivered in the best way possible to the systems that control temperature, lighting, etc., making the home comfortable and saving energy at the same time. Comparative analyses demonstrate the superiority of the proposed model over existing algorithms, including the Particle Swarm Optimization (PSO) algorithm. Additionally, the paper discusses the importance of adaptive controllers in addressing issues associated with incorrect PID controller selection and highlights the benefits of using the FA-GA in energy optimization and comfort improvement. Overall, the proposed model offers an effective solution for efficient energy consumption optimization and enhanced user comfort in diverse settings through the integration of advanced algorithms and optimization modules.