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Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability. SER concentrates on the automatic identification of the emotional state of a human being using speech signals. It mainly depends upon the in-depth analysis of the speech signal, extracts features containing emotional details from the speech signal, and utilises pattern recognition techniques for emotional state identification. The major problem in automatic SER is to extract discriminate, powerful, and emotional salient features from the acoustical content of speech signals. The proposed model aims to detect and classify three emotional states of speech such as happy, neutral, and sad. The presented model makes use of Convolution neural network – Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set of feature vectors. A comprehensive simulation takes place using the Berlin German Database and SJTU Chinese Database which comprises numerous audio files under a collection of different emotion labels.

The rainfall prediction is important for metrological department as it closely associated with our environment and human life. An accuracy of rainfall prediction has great important for countries like India whose economy is dependent on agriculture. Because of dynamic nature of atmosphere, statistical techniques fail to predict rainfall information. The process of support vector machine (SVM) is to find an optimal boundary also known as hyper plane in which separates the samples (examples in a dataset) of different classes by a maximum margin. The proposed model uses the dynamic integrated model for exploring and learning large amount of data set. Balanced communication-avoiding support vector machine (CA-SVM) prediction model is proposed to achieve better performance and accuracy with limited number of iteration without any error. The rain fall dataset is used for performance evaluation. The proposed model starts with independent sample to the integrated samples without any collision in prediction. The proposed algorithm achieves 89% of accuracy when compared to the existing algorithms. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm Balanced CA-SVM has much better accuracy than the local learning model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the Balanced CA-SVM model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables.

Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence-based forest fire warning system to prevent major disasters. This work aims to present an overview of vision-based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception-v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision-makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN-RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no-fire photos, the proposed RBFN-RAISR fire detection approach achieves 97.55% accuracy, 93.33% F-Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one-of-a-kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.

Green energy management has become a critical economic solution for efficient energy consumption; however, the available literature is deficient in emphasising the importance of edge intelligence in a controllable Internet of Things (IoT). Green energy aware IoT networks are well-suited for a variety of real-time applications such as smart cities, smart homes, smart grids, and industries. Due to the fact that IoT nodes are energy constrained and function on a small internal battery, the development of energy-efficient solutions becomes important. Simultaneously, it is necessary to forecast energy consumption in energy aware IoT networks in order to meet upcoming load. With this in mind, this research proposes a novel green energy-aware cluster communication and future load prediction technique for IoT networks called GEQCC-FLP. The proposed GEQCC-FLP technique’s objective is to determine the effective set of cluster heads (CHs) and forecast the network’s incoming load. The GEQCC-FLP technique consists of two primary stages: clustering using a satin bowerbird optimizer (SBO) and load prediction using a deep random vector functional link network (DRVFLN). Additionally, the constructs a fitness function from three parameters: energy, distance, and delay. Additionally, the Adam optimizer can be used to optimise the DRVFLN model’s hyperparameters, hence improving the prediction outcomes. A DRVFLN-based Adam optimizer with deep random vector functional link networks is used to forecast the network’s incoming load (DRVFLN). This clustering technique is based on the SBO algorithm, which generates three fitness function parameters: energy input, distance travelled, and delay. To demonstrate the enhanced outcome of the GEQCC-FLP technique, a variety of simulations are run and the results examined from a variety of angles. The GEQCC-FLP methodology outperformed the prior methods by 0.563 and 0.687, respectively, in terms of root mean square error (RMSE) and mean absolute percent error (MAPE). The extensive results and discussion support the conclusion that the GEQCC-FLP strategy is an extremely effective method for regulating the usage of renewable energy in Internet of Things networks. The experimental results indicated that the GEQCC-FLP technique outperformed the other techniques.

The applications of composite materials based on Carbon Fiber Reinforced Plastic (CFRP) have been increasing considerably in aerospace industries, because of their good physical and mechanical characteristics. It is necessary to develop new manufacturing process and production techniques to ensure good surface quality of the components. As a result of this manufacturing scenario, it is necessary to study the machining process. This study reports practical experiments in turning, to study the performance of Poly Crystalline Diamond (PCD) inserts of 1500 grade. The results show that diamond tools are suitable for use in finish turning. The tests were carried out with PCD insert at various cutting speeds on a medium duty lathe of spindle power 2KW. In the optimization method, turning was done by grade (PCD 1500) for 30 seconds duration for each trial at different cutting conditions by using design of experiments. Parameters such as power consumed by main spindle, surface roughness were measured. Graphs were drawn to analyze the performance of1500 grade on various parameters.