The COVID-19 pandemic has already ravaged the world for two years and infected more than 600 million people, having an irreparable impact on the health, economic, and political dimensions of human society. There have ...
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The telegrapher’s equations constitute a set of linear partial differential equations that establish a mathematical correspondence between the electrical current and voltage within transmission lines, taking into acc...
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The most serious and hazardous for an electrical provider these days are non-technical losses caused due to electricity theft. The economy as a whole is impacted by fraudulent electricity usage, which lowers supply qu...
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The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification *** to its importance,numerous studies have been conducted in various...
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The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification *** to its importance,numerous studies have been conducted in various *** have established several learning methods for writer identification including supervised and unsupervised ***,supervised methods require a large amount of annotation data,which is impossible in most *** the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be *** paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text ***,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual *** proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification *** addition,traditional evaluation metrics are used in the proposed ***,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.
This study examines the use of experimental designs, specifically full and fractional factorial designs, for predicting Alzheimer’s disease with fewer variables. The full factorial design systematically investigates ...
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Automatic emotion identification from speech is a difficult problem that significantly depends on the accuracy of the speech characteristics employed for categorization. The display of emotions seen in human speech is...
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Automatic emotion identification from speech is a difficult problem that significantly depends on the accuracy of the speech characteristics employed for categorization. The display of emotions seen in human speech is inherently integrated with hidden representations of several dimensions and the fundamentals of human behaviour. This illustrates the significance of using auditory data gathered from discussions between people to determine people's emotions. In order to engage with people more closely, next-generation artificial intelligence will need to be able to recognize and express emotional states. Even though recovery of emotions from verbal descriptions of human interactions has shown promising outcomes, the accuracy of auditory feature-based emotion recognition from speech is still lacking. This paper suggests a unique method for Speech-based Emotion Recognition (SER) that makes use of Improved and a Faster Region-based Convolutional Neural Network (IFR-CNN). IFR-CNN employs Improved Intersection over Unification (IIOU) in the positioning stage with better loss function for improving Regions of Interest (RoI). With the help of a Recurrent Neural Network (RNN)-based model that considers both the dialogue structure and the unique emotional states;modern categorical emotion forecasts may be created quickly. In particular, IFR-CNN was developed to learn and store affective states, as well as track and recover speech properties. The effectiveness of the proposed method is evaluated with the help of real-time prediction capabilities, empirical evaluation, and benchmark datasets. From the speech dataset, we have extracted the Mel frequency cepstral coefficients (MFCC), as well as spectral characteristics and temporal features. Emotion recognition using retrieved information is the goal of the IFR-development. Quantitative analysis on two datasets, the Berlin Database of Emotional Speech (EMODB) and the Serbian Emotional Speech Database (GEES), revealed encouraging r
In this paper, an evaluation strategy is proposed for evaluation of optimization algorithms, called the Complex Preference Analysis, that assesses the efficiency of different evolutionary algorithms by considering mul...
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Power load forecasting is essential for optimizing power generation and distribution efficiency. This paper proposes a novel method for daily average load forecasting, referred to as LARSI-TPE-XGB, which integrates th...
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Text steganography, the science of hiding secret messages in innocent-looking text documents ensures the secrecy of the embedded secret. Cryptography, on the other hand, encrypts and converts the secret message into a...
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Sentiment classification using emojis on social media has become increasingly crucial in recent years. Social media commonly uses emojis to convey feelings, emotions, and moods. Hence, in this article, a Jellyfish Alg...
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