In this paper, we describe the design, development and preliminary evaluation of a novel animation tool for smartphones and tablets, with the goal of improving creativity and problem-solving ability among novice users...
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z As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have...
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This work introduces an emotional speech dataset for Speaker Recognition (SR) task. The proposed dataset is recorded in the Kannada language from the people of Karnataka state of India. The speech dataset is collected...
This work introduces an emotional speech dataset for Speaker Recognition (SR) task. The proposed dataset is recorded in the Kannada language from the people of Karnataka state of India. The speech dataset is collected by simulating five different emotions, such as Fear, Sad, Anger, Happy, and Neutral. The dataset is named as National Institute of technology Karnataka, India- Kannada Language Emotional Speech Corpus (NITK-KLESC). The proposed dataset will be useful for SR tasks in various emotions. The proposed emotional speech dataset will be useful for emotion recognition, analysis of emotional speech, speech recognition, gender identification, and age identification of the age group 20 to 50 years. The proposed work describes the development, processing, analysis, acquisition, and evaluation of the proposed emotional speech dataset (NITK-KLESC). The analysis of emotional speech was done by considering various basic speech parameters like Pitch, Tempo, Intensity, and Zero Crossing Rate (ZCR). The characteristics of the dataset are reported using MFCC feature extraction and considered the CNN model as a classifier, compared with the existing EmoDB dataset. The average accuracy of the Emotional Speech Speaker Recognition (ESSR) task was measured at 84.44% with the EmoDB dataset and 95.2% with the proposed NITK-KLESC dataset.
Power systems are critical infrastructures that require robust monitoring and control mechanisms to ensure reliability, stability, and efficiency. It utilizes the data from Phasor Measurement Units (PMUs) and other mo...
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Image classification is one of the computer vision problems. It is a supervised learning technique, in this, images are classified according to their different characteristics (features). There are various facial imag...
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Image classification is one of the computer vision problems. It is a supervised learning technique, in this, images are classified according to their different characteristics (features). There are various facial image datasets of different persons with different facial expressions. Classification based on different facial expressions is always a challenging task for researchers because a person can have different facial expressions like smiling, sad, normal, with or without goggles, etc., depending upon the mood of the person. Deep learning technique like Convolutional neural networks (CNN) is an indemand technique to classify images based on their features. This paper demonstrates the performance of CNN using a different number of images with different numbers of expressions per person and keeping all the other parameters the same. For performance measurement of CNN, the faces94 dataset of is used. Based on the evaluated results, some important points are highlighted.
One of the most crucial decisions a company makes is its pricing strategy. When it comes to pricing, a company must consider the present, as well as the future and the past pricing. It enables a company to make sound ...
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Diseases such as diabetes, high blood pressure, high cholesterol, etc. have grown significantly in importance in recent years. In order to preserve optimum health, the blood pressure measurements, medications, and die...
Diseases such as diabetes, high blood pressure, high cholesterol, etc. have grown significantly in importance in recent years. In order to preserve optimum health, the blood pressure measurements, medications, and diet of the patients diagnosed with these diseases must be effectively monitored and controlled. However, frequent monitoring requires a person to go to a healthcare center, which is not feasible due to various real-time interfaces. To address this issue, a mobile application that monitors the factors that influence a patient's blood work, and assists patients in making decisions about their diet, treatment, and medication adjustments based on the data gathered is presented. It also gives alerts and suggestions based on a graph-generated report. In addition, the app features a communication channel that connects the patient and doctor, facilitating easy and efficient monitoring and management of diseases.
In the realm of Decentralized Finance (DeFi), this manuscript introduces a Hybrid Cross-Chain Model. As DeFi architectures grapple with the complexities of monolithic single-chain platforms, our proposed model orchest...
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ISBN:
(数字)9798350391343
ISBN:
(纸本)9798350391350
In the realm of Decentralized Finance (DeFi), this manuscript introduces a Hybrid Cross-Chain Model. As DeFi architectures grapple with the complexities of monolithic single-chain platforms, our proposed model orchestrates a symphony of multiple chains to facilitate seamless cross-chain communication, offering a poised solution to scalability and transaction speed challenges. Incorporating modeling effects and simulations, our rigorous performance evaluation underscores the model's excellence and includes an in-dept. analysis of its performance, particularly focusing on robust security measures. The model is positioned as a cornerstone in an interconnected DeFi landscape by emphasizing stringent measures to ensure data integrity and uphold consensus mechanisms. User-centric enhancements promise swift transaction confirmations and reduced fees, improving the overall experience. The abstract culminates with a comparative analysis, positioning the Hybrid Cross-Chain Model as an innovative solution with profound implications for the future of DeFi. This manuscript advocates for ongoing research and development, heralding a new era of sophistication and resilience in decentralized finance.
Locomotion mechanics of legged robots are suitable when pacing through difficult terrains. Recognising terrains for such robots are important to fully yoke the versatility of their movements. Consequently, robotic ter...
Locomotion mechanics of legged robots are suitable when pacing through difficult terrains. Recognising terrains for such robots are important to fully yoke the versatility of their movements. Consequently, robotic terrain classification becomes significant to classify terrains in real time with high accuracy. The conventional classifiers suffer from overfitting problem, low accuracy problem, high variance problem, and not suitable for live dataset. On the other hand, classifying a growing dataset is difficult for convolution based terrain classification. Supervised recurrent models are also not practical for this classification. Further, the existing recurrent architectures are still evolving to improve accuracy of terrain classification based on live variable-length sensory data collected from legged robots. This paper proposes a new semi-supervised method for terrain classification of legged robots, avoiding preprocessing of long variable-length dataset. The proposed method has a stacked Long Short-Term Memory architecture, including a new loss regularization. The proposed method solves the existing problems and improves accuracy. Comparison with the existing architectures show the improvements.
This work introduced a method to identify English spam comments on YouTube, a platform that has experienced a significant increase in spam activity. Despite YouTube's efforts to implement its own spam filtering sy...
This work introduced a method to identify English spam comments on YouTube, a platform that has experienced a significant increase in spam activity. Despite YouTube's efforts to implement its own spam filtering system, it still struggles to effectively block spam comments. To address such an issue, we analyzed existing research on YouTube spam comment detection and performed classification experiments using an ensemble model that combines various techniques. A stacked ensemble model combines the predictions of multiple individual models to improve the accuracy and robustness of spam detection in social media applications. It leverages the strengths of different models and captures a broader range of features, leading to a more accurate classification of spam and non-spam content. It has then implemented a web extension to hide the spam comments from the page by modifying the CSS (Cascading Style Sheets). The comment data utilized in experiments was sourced from popular music videos by artists such as Psy, Katy Perry, LMFAO, Eminem, and Shakira.
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