In recent years, connected vehicle technologies emerged to provide a safer and more connected environment for transportation systems, which has further evolved into cellular vehicle-to-everything (C-V2X) technology. The current study contributes in the direction of future research of smart India @2047 in connected vehicles. The study presents the diverse aspects of 5G V2X as a comprehensive overview of Vehicle-to-Everything (V2X) technology that covers the significant scope of V2X technology, including its development, components, functionality, use cases, and future potential. It is observed that few surveys are available that cover limited aspects of 5G V2X communication technologies, with one or two preferred architectures, and only few provides the definitions of 3GPP use cases. Emergency vehicles (EMV) are identified as an important part of an emergency service, and the main goal of EMV is to reduce the response time. The study discusses the research challenges in the field of V2X and presents future directions to reduce the probability of untoward incidents and enhance the EMV response time. The work leverages by synthesizing the latest research in 5G V2X technological advancements like Multiple Input Multiple Output, Beamforming, and Integrated Sensing and Communication, highlighting the roles of 5G architectures and providing a deeper understanding of the 5GAA V2X use cases from the perspective of societal benefit. The study provides a comprehensive future research directions to the researchers on 5G V2X technologies and current research trends for smart EMV applications.

Grapes are widely cultivated fruit crops, essential for fresh consumption, winemaking and dried product production. However, their yield and quality are significantly impacted by various fungal diseases. This paper provides a large dataset of 2,726 high-quality grape leaf disease images collected from grapes farm of Nashik, India in two years of span 2023 to 2025. The dataset is precisely annotated under the guidance and observation of agriculture domain expert and organized in a well-defined folder structure. The dataset captures the two major categories healthy leaves and unhealthy leaves, during cultivation period. A primary directory containing two main classes Heathy Leaf Images and Unhealthy Leaf images. Further unhealthy class is divided into three subfolders for disease class, namely Downy Mildew, Powdery Mildew and Bacterial Leaf Spot. These are the major fungal disease observed on grape crop causes substantially crop losses and ultimately impact on the yield production. Timely identification of these diseases can significantly reduce the risk of crop loss and help to improve quality of fruit with maximum yield production. This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 %. These results validate the dataset’s quality and its suitability for deep learning-based grape disease detection. Overall, this open-access resource provides a valuable foundation for computer vision, machine learning, and agricultural technology researchers aims to enhance disease management practices in grape production. thus, this is an effective source of data for future studies and real-world applications in sustainable grape production.

Autonomous face emotion recognition (FER) with landmarks has become an important field of research for human–computer interaction. A significant achievement has been achieved through deep learning algorithms in recent times. Recognizing faces can be done using an end-to-end approach with deep learning techniques, which learns a mapping from raw pixels to the target label. In the field of emotional classification, the research community has extensively utilized 98 and 68 facial landmarks. In particular, pre-trained convolutional neural networks such as the residual network 50-layer network with the random sampler, Visual Geometry Group 16-layer network, and MobileNet including their ensemble versions of deep learning models are popular among researchers due to their ability to handle complex data. Researchers have mostly evaluated the model on a single dataset. A single dataset poses a challenge in developing a generalized model capable of capturing the full versatility of emotions. The key challenge in the dataset is that a single emotion is represented in multiple facial expressions with low-resolution images. This research study uses a combined dataset (CK+, KDEF, and FER-2013), which is more challenging than a single dataset. This research study offers a comprehensive analysis involving 68 and 98 landmarks with different FER deep models, examining how landmarking and different network architectures contribute to emotion recognition accuracy. This research study also considers the overfitting and class imbalance of the proposed ensemble model, which improves its performance by batch-wise feature extraction. Results show 78% accuracy with 98 landmarks and 75% with 68 landmarks. Overall, the model significantly reduces the gap between training and testing accuracy for both single and combined datasets. Received: 28 September 2024 | Revised: 13 January 2025 | Accepted: 13 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The CK+ data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/shuvoalok/ck-dataset. The KDEF data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/tom99763/testtt. The FER2013 data that support the findings of this study is openly available in Kaggle at https://www.kaggle.com/datasets/msambare/fer2013. Author Contribution Statement Nandhini Krishnasamy: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Supervision, Funding acquisition. Nilima Zade: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Supervision, Project administration. Dhruvi Khambholia: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Rabinder Henry: Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Aditya Gupte: Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, Supervision, Project administration.

The research presented here is the investigation of Particle filter (PF) performance in a state estimation of point mass type non-cooperative moving target in object tracking wireless senor network (OTWSN). Motion dynamics of the target are modeled as a stochastic state space model by simulating different time varying parameters of Jump Markov Linear System (JMLS) in a finite way considering these parameters are sufficiently large to meet the practical needs. Performance is evaluated on the basis of lower bound of posterior Cramer-Rao Bound (PCRB) as Root Mean Square Error (RMSE). It has been found that the performance is subject to dynamics of the target motion and sensor observation process under consideration. The present research focuses on multiple motion dynamics keeping in mind the real time non-cooperative moving target tracking in OTWSN. Results indicate a positive correlation between RMSE and motion models. Correlation increases as the motion dynamics change but still PF gives suboptimal response to motion dynamics.

Prediction of droughts has recently become imperative as frequency and intensity are increasing mostly due to climatic variation. Indeed, drought is a highly significant disaster that results in widespread damage to all kinds of ecosystems, agricultural production systems, and water resources systems. Accurate techniques of forecasting are necessary for the purpose. Conventional methods lack the intricate time-space correlation in meteorological data. The research proposes an ensemble of Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), and Tabular Network (TabNet) for a higher accuracy in drought forecasting. With the large meteorological dataset that involves temperature, precipitation, humidity, and wind speed as features, the model integrates: • The tree capabilities of XGBoost perform feature selection very effectively. • Temporal Pattern Analysis using LSTM. • Insight obtained from the attention mechanism-based TabNet. Empirical results demonstrate that the proposed ensemble outperforms individual models, achieving the lowest Root Mean Square Error (RMSE: 0.6582) and Mean Absolute Error (MAE: 0.5377), and the highest Coefficient of Determination (R²: 0.5069). Furthermore, it yields the best Nash–Sutcliffe Efficiency (NSE: 0.5107) and Kling–Gupta Efficiency (KGE: 0.6039), confirming its superiority in drought severity forecasting. The ensemble outperforms traditional models, aiding early drought warnings and water conservation planning.