Enhancement of the dispatching capacity and efficiency of grid management requires knowledge of photovoltaic power generation beforehand. Intrinsically, photovoltaic power generation is highly volatile and irregular, which impedes its prediction accuracy. In order to handle these constraints, this paper proposes deep learning-based approaches and a pre-processing algorithm. The proposed scheme employs Pearson’s Correlation Coefficient to find the similarity between atmospheric variables and PV power generation. Based on high PCC values, top atmospheric variables, and PV power generated time series data are passed through the Empirical Mode Decomposition (EMD) to simplify the complex data streams into Intrinsic Mode Functions (IMFs). Further, to streamline the prediction process, the proposed correlation-based signal synthesis (CBSS) algorithm finds combinations of these IMFs, which have a high correlation value between atmospheric variables and PV power data. Deep learning models of algorithms Long Short Term Memory (LSTM) and Nonlinear Autoregressive Network with Exogenous Inputs (NARX) network with the configurations of three networks, a single network, and the direct approach employed for the prediction of IMFs combinations. LSTM network was analyzed under the Adaptive moment estimation (ADAM), Stochastic Gradient Descent with Momentum (SGDM), and Root Mean Square Propagation (RMSP) optimization. Extensive experimentation was evaluated using atmospheric data collected at the Climate, Energy, and Water Research Institute (CEWRI), NARC, Islamabad, Pakistan. RMSE, MAE, MAPE, and R ² performance measures show promising prediction results for the LSTM under the configuration of three networks and ADAM optimization.

In IoT based systems, authentication of users and devices is a major challenge where the traditional authentication mechanisms such as login-password are no longer supportable. It requires continuous authentication methods to ensure authenticity of the users and devices specifically for long sessions. The authentication through the IoT-Sensors based data has already gained the attention of a considerable number of researchers as it does not require direct involvement of the users and provide an extra layer of security and user privacy. Due to the instability of IoT-Sensor data, the authentication techniques need to extract a large number of features to produce high-accurate results. Moreover, the limited capabilities of computation, communication, storage, and the small battery power of IoT devices further makes its implementation hard. In this paper, we have introduced a user authentication framework for remote IoT users based on unique walking patterns to extract gait-related features with minimum data samples and cycle length. For an in-depth analysis of the distinctive features of the sensor-based gait profiles, we have applied an ECG signal processing technique. The proposed approach applies to a diverse range of IoT devices such as cellphones, smartwatches, and wearable sensors. The main contribution of the proposed approach is the deeper gait analysis with the least number of features and data for authentication purposes. We have introduced an authentication algorithm for feature comparison. The machine learning models have been applied to the gait based profiles for validation of the proposed approach. The detailed experimental analysis of different data sets has achieved an accuracy of 94% and an equal error rate (EER) of 6% higher than the existing approaches.

The revolution of IoT highly impacts on different applications such as remote sensing, smart cities, and remote digital healthcare. People use IoT devices for performing business transactions, daily tasks, and healthcare monitoring. IoT devices generate huge amounts of data assets that have potential applications. Biometrics is a potential application of sensors data. The traditional biometric methods such as PINs, passwords are exposed to numerous attacks such as replication, repeated passwords, etc. Sensors’ data-based continuous authentication methods are suitable for maintaining users’ privacy and security in mobile IoT systems. Most of the existing authentication methods have applied motion-based sensors for building users’ identification profiles. The proposed method uses motion sensors and biomedical sensors for reliable and multi-factor user authentication. In this article, we have introduced an IoT sensors data analytics framework to construct user authentication models. We apply the fiducial points-based feature extraction method data for extracting discriminative features. These features act as unique user profiles for authentication purposes. We have performed a detailed analysis of the proposed approach using the publically available datasets. The experiments elaborate on the effectiveness of IoTauth for improved authentication results.

Background/Objectives: Medical vision–language learning integrates radiographic images and textual information for chest X-ray (CXR) classification. However, in long-tailed multi-label CXR classification, modality imbalance can cause models to rely disproportionately on radiology reports while underutilizing visual information. This study investigates text dominance and proposes a modality-balanced framework to strengthen visual representation while preserving multimodal performance. Methods: Experiments used MIMIC-CXR with patient-level training, validation, and test splits of 32,942, 4024, and 3955 paired samples, respectively. Swin-Tiny and PubMedBERT served as visual and textual encoders. The proposed framework combines adaptive gated fusion, Asymmetric Loss (ASL), and training-time modality dropout. It was compared with Binary Cross-Entropy (BCE) concatenation, ASL concatenation, gated fusion, and cross-attention fusion. Results: The BCE baseline achieved a Macro AUROC of 0.9957 under Image + Text evaluation and 0.5804 under Image-Only evaluation, while Text-Only evaluation yielded 0.9955, indicating substantial modality disparity. Other alternatives achieved Image-Only Macro AUROC values of 0.5610–0.5720. The proposed framework improved Image-Only Macro AUROC to 0.7697 and Macro PR-AUC from 0.1814 to 0.3428 while maintaining an Image + Text Macro AUROC of 0.9958. The modality-dominance gap decreased from 0.4151 to 0.2260, a 45.6% reduction in performance disparity. Grad-CAM and Integrated Gradients provided insights into visual evidence and textual attribution, revealing reliance on explicit disease-related terminology in reports. Conclusions: These findings suggest that combining adaptive gated fusion, ASL, and modality dropout can improve Image-Only performance while preserving multimodal discrimination in long-tailed multi-label CXR classification.