Speech emotion recognition (SER) plays a vital role in enhancing human–computer interaction (HCI) and can be applied in affective computing, virtual support, and healthcare. This research presents a high-performance SER framework based on a lightweight 1D Convolutional Neural Network (1D-CNN) and a multi-feature fusion technique. Rather than employing spectrograms as image-based input, frame-level characteristics (Mel-Frequency Cepstral Coefficients, Mel-Spectrograms, and Chroma vectors) are calculated throughout the sequences to preserve temporal information and reduce the computing expense. The model attained classification accuracies of 94.0% on MELD (multi-party talks) and 91.9% on RAVDESS (acted speech). Ablation experiments demonstrate that the integration of complimentary features significantly outperforms the utilisation of a singular feature as a baseline. Data augmentation techniques, including Gaussian noise and time shifting, enhance model generalisation. The proposed method demonstrates significant potential for real-time emotion recognition using audio only in embedded or resource-constrained devices.

The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and practitioners interested in developing and implementing deep learning-based face mask detection systems. Although current deep learning models have made significant strides and tremendous advances in many applications, including security, access control, and identity verification, development efforts continue. This paper also discusses the importance of the datasets used to train and evaluate these models, emphasising the need for diverse data such as mask types, facial occlusions, lighting conditions, and high-quality data to enhance model performance. In addition to the challenges posed by real-world conditions that can affect detection accuracy, face detection and face mask recognition methods were compared to deep learning models, where the accuracy of the Multi-Task ArcFace model reached 99.78, which is the highest accuracy among other detection methods.

Interest in facial recognition and estimating a person’s age through photographs has increased in recent years. Age estimation apps have become popular to preve

Abstract— Clustering is a major exploratory data mining activity, and a popular statistical data analysis technique used in many fields. Cluster analysis generally speaking isn't just an automated function, but rather reiterated information exploration procedure or multipurpose dynamic optimisation Comprising trial and error. Parameters for pre-processing and modeling data frequently need to be modified until the output hits the desired properties. -Data points in fuzzy clustering may probably belong to several clusters. Each Data Point is assigned membership grades. Such grades of membership reflect the degree to which data points belong to each cluster. The Fuzzy C-means clustering (FCM) algorithm is among the most widely used fuzzy clustering algorithms. In this paper We use this method to find typological analysis for dynamic Ad Hoc network nodes movement and demonstrate that we can achieve good performance of fuzziness on a simulated data set of dynamic ad hoc network nodes (DANET) and How to use this principle to formulate node clustering as a partitioning problem. Cluster analysis aims at grouping a collection of nodes into clusters in such a way that nodes seeing a high degree of correlation within the same cluster, whereas nodes members of various clusters are extremely dissimilar in nature. The FCM algorithm is used for implementation and evaluation the simulated data set using NS2 simulator with optimized AODV protocol. The results from the algorithm 's application show the technique achieved the maximum values of stability for both cluster centers and nodes (98.41 %, 99.99 %) respectively.

Iris-biometrics are an alternative way of authenticating and identifying a person because biometric identifiers are unique to people. This paper introduces a method aims to efficient human identification by enhanced iris detection method within acceptable time. After preparing various type of images, then perform a series of pre-processing steps and standardize them, after that use Uni-Net learning, so identify the human by Navie-Bays method is the last step based on the output of Uni-Net which is role as feature extractor for the iris part and another sub-net for non-iris part that may involve identification-outcome. The outcome of this method looked good compared to some high-level methods, so, was accuracy-rate 9855, 99.25, and 99.81 for CASIA-v4, ITT-Delhi, and MMU-database respectively. Also, this paper introduces a method of iris recognition using CNN model which is improved the preprocessed patterns that together from dataset applied some procedures to develop them based on techniques of equalization and acclimate contrast ones. After that characteristic extracted and classified using CNN that comprises of 10 layers with back-propagation schema and adjusted moment evaluation Adam-optimizer for modernize weights. The overall accuracy was 95.31% with utilization time 17.58 (mints) for training-model.