Human Action Recognition (HAR) is a branch of computer vision that deals with the identification of human actions at various levels including low level, action level, and interaction level. Previously, a number of HAR algorithms have been proposed based on handcrafted methods for action recognition. However, the handcrafted techniques are inefficient in case of recognizing interaction level actions as they involve complex scenarios. Meanwhile, the traditional deep learning-based approaches take the entire image as an input and later extract volumes of features, which greatly increase the complexity of the systems; hence, resulting in significantly higher computational time and utilization of resources. Therefore, this research focuses on the development of an efficient multi-view interaction level action recognition system using 2D skeleton data with higher accuracy while reducing the computation complexity based on deep learning architecture. The proposed system extracts 2D skeleton data from the dataset using the OpenPose technique. Later, the extracted 2D skeleton features are given as an input directly to the Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) architecture for action recognition. To reduce the complexity, instead of passing the whole image, only extracted features are given to the CNN-LSTM architecture, thus eliminating the need for feature extraction. The proposed method was compared with other existing methods, and the outcomes confirm the potential of the proposed technique. The proposed OpenPose-CNNLSTM achieved an accuracy of 94.4% for MCAD (Multi-camera action dataset) and 91.67% for IXMAS (INRIA Xmas Motion Acquisition Sequences). Our proposed method also significantly decreases the computational complexity by reducing the number of inputs features to 50.

Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.

The adoption of the Internet of Things (IoT) in smart household energy systems offers new opportunities for efficiency and automation, while also posing substantial security challenges. These systems utilize diverse standards and protocols to autonomously access, collect, and share energy-related data over distributed networks. However, this interconnectivity increases their vulnerability to cyber threats, making the system vulnerable to cyber threats. The literature reveals numerous cases of cyberattacks on IoT-based energy infrastructures, primarily involving unauthorized access, data breaches, and device exploitation. Therefore, designing a robust ecosystem with secure and efficient access control (AC), while safeguarding user functionality and privacy, is essential. This paper proposes a dynamic attribute-based access control (ABAC) model that leverages a hybrid blockchain architecture to enhance security and trust in smart household energy systems. The proposed architecture integrates Hyperledger Fabric for managing user, resource, and device attributes using smart contracts, while Hyperledger Besu enforces decentralized access policies. Additionally, a trust recalibration mechanism dynamically adjusts access permissions based on behavioral analysis, mitigating unauthorized access risks and improving energy system adaptability. Experimental results demonstrate the model’s effectiveness in securing IoT smart home energy, while ensuring seamless device onboarding and efficient access control.

Wind and solar energy are increasingly vital for meeting clean renewable energy needs, with Brushless Doubly Fed Induction Generators gaining popularity due to their cost efficiency and reliability. A key challenge in integrating wind energy into the grid is ensuring low-voltage ride-through capability during faults and mitigating voltage fluctuations at the point of common coupling. Existing techniques, such as analytical models and evolutionary algorithms, aim to optimize reactive current compensation but suffer from low accuracy and high response times, respectively. This paper introduces a novel reinforcement learning-based current compensation technique for brushless doubly fed induction generators to address these limitations. The proposed reinforcement learning agent dynamically adjusts the reactive power to minimize voltage dips and stabilize the voltage profile during transient- and low-voltage ride-through faults, leveraging a reward function that penalizes deviations in voltage magnitude and increases in total harmonic distortion beyond 3%. By integrating reinforcement learning with traditional methods, the approach achieves faster and more adaptive compensation. Simulation results show that the reinforcement learning-based technique improves voltage recovery time by up to 50%, reduces total harmonic distortion by up to 44%, and minimizes current overshoot by up to 90% compared to state-of-the-art methods, enhancing the reliability and efficiency of wind energy systems.

The increasing demand for sustainable and energy-aware agricultural practices due to climate change, limited natural resources, and increasing food requirements has accelerated the adoption of Internet of Things (IoT) and machine learning (ML) technologies in smart farming. This study proposes a solar-powered IoT and ML-based framework for energy-aware crop suitability prediction in precision agriculture. The system employs low-power IoT sensors to continuously monitor important environmental and soil parameters, including temperature, humidity, soil moisture, soil temperature, heat index, nitrogen (N), phosphorus (P), and potassium (K) levels. To reduce dependence on conventional energy sources, the framework is powered by solar energy, making it suitable for deployment in remote and resource-constrained agricultural environments. The collected sensor data are transmitted to a cloud-based platform for storage and real-time monitoring. Different ML models, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and Neural Network (NN), are implemented for crop suitability classification while evaluating their predictive performance and computational overhead. A real-world dataset containing 11,644 initial sensor records was collected from agricultural regions of Sindh, Pakistan, covering four major crops: cotton, rice, sugarcane, and wheat. After preprocessing, 11,643 valid records were retained for model training and evaluation. Since the original dataset was imbalanced, particularly for the Sugarcane class, tabular augmentation was applied exclusively to the training set, while the test set remained unchanged to ensure an unbiased evaluation. Experimental results demonstrate that the proposed framework provides reliable crop suitability prediction and supports data-driven agricultural decision-making under resource-constrained field conditions. Among the evaluated models, RF achieved the highest held-out test performance, with an accuracy of 94.3331%, precision of 94.5985%, recall of 94.3331%, and F1-score of 94.3727%. By integrating solar-powered IoT infrastructure with ML-based analytics, class-imbalance handling, and computational-efficiency evaluation, the proposed framework offers a practical and scalable solution for energy-aware suitability assessment in resource-constrained farming environments.