In this paper, physics informed neural networks are used for numerical approximation of partial differential equations. The data which is used in the process is generated by Latin Hypercube sampling which has been dis...
In this paper, physics informed neural networks are used for numerical approximation of partial differential equations. The data which is used in the process is generated by Latin Hypercube sampling which has been discussed. Adam optimization technique has been implemented to minimize the loss for the discussed partial differential equation. The above proposed methodology has been applied for Burger’s equation and the obtained results have been discussed in section 5. Loss function graphs have also been provided to showcase the efficiency of the proposed methodologies.
The automotive industry has been focused on developing autonomous driving technology, which has been a subject of research for many years. Recent advancements in Convolutional Neural Networks (CNN) have shown remarkab...
The automotive industry has been focused on developing autonomous driving technology, which has been a subject of research for many years. Recent advancements in Convolutional Neural Networks (CNN) have shown remarkable performance in computer vision tasks, including autonomous driving systems. Behavioral cloning is a widely adopted technique in autonomous vehicle systems, that involves training a model to replicate the driving behavior of human drivers. This paper presents research on implementing behavioral cloning using a CNN architecture for an autonomous vehicle system in the Udacity self-driving car simulator environment. Our approach employs a custom-designed CNN Model to learn driving behavior from a dataset of images captured from the simulator, and we have applied various data augmentation techniques to the dataset to enhance the model’s performance. This model has a total of 27 layers, including convolutional layers, normalization layers, activation layers, dropout layers, flatten layers, and dense layers. We evaluate the model’s performance on a metric, called mean square loss error and demonstrate that our approach outperforms previous works in this domain. The results of this research underscore the effectiveness of using behavioral cloning and data augmentation techniques for autonomous vehicle systems.
A three-party authenticated key exchange protocol enables two entities to agree on a session key with the help of a dedicated server through an insecure channel. Lattice based cryptography plays a very important role ...
A three-party authenticated key exchange protocol enables two entities to agree on a session key with the help of a dedicated server through an insecure channel. Lattice based cryptography plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. However, it is not easy to design quantum resistant password based three-party protocol due to the high demand for security requirements and the limited resources nature of mobile devices. In this article, we have proposed a new post quantum three party key exchange based on a variant of lattice assumption, the ring learning errors. The protocol ensures security against impersonation attack, stolen smartcard attack, password guessing attack, and other existing attacks. In authentication phase, the protocol have used lattice based cryptography that plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. The proposed protocol ensures both securities against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications.
Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification...
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Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification, evolutionary-based feature selection methods play a vital role in effective treatment. Previous studies have shown that existing evolutionary methods are complicated in correctly differentiating BC disease subtypes with high consistency, which seriously affects the performance of classification methods. To prevent diagnostic errors with hostile implications for patient health, in this study, we develop a new evolutionary method called SeQTLBOGA that incorporates the learner quantization before the search capability of the feature space to prevent premature falls into the local optima. In the SeQTLBOGA algorithm, quantum theory and a self-adaptive mechanism are employed to update the Teaching Learning-based Optimization (TLBO) rule to enhance convergence search capabilities. Most importantly, a self-adaptive genetic algorithm (GA) is also incorporated into TLBO to tradeoff between exploration and exploitation to handle slow convergence and exploitation competence, and simultaneously optimizing parameters of support vector machines (SVM) and the best features subset is our primary objective. Comparative results based on optimal computing time and performance are also offered to empirically analyze the traditional algorithms. Therefore, this paper aims to evaluate the most recent quantum-inspired metaheuristic algorithms in WBCD and WDBC databases, emphasizing their advantages and disadvantages.
The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to ...
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The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to detect harmful speech such as hate speech, cyberbullying, and abuse. Recently, another type of speech referred to as ‘Hope Speech’ has gained attention from the research community. Hope speech consists of positive affirmations or words of reassurance, encouragement, consolation or motivation offered to the affected individual/ community during the lean periods of life. However, there has been relatively less research focused on the detection of hope speech, more particularly in low-resource languages. This paper, therefore, attempts to develop an ensemble model for detecting hope speech in some low-resource languages. Data for four different languages, namely English, Kannada, Malayalam and Tamil are obtained and experimented with different deep learning-based models. An ensemble model is proposed to combine the advantages of the better performing models. Experimental results demonstrate the superior performance of the proposed Ensemble (LSTM, mBERT, XLM-RoBERTa) model compared to individual models based on data from all four languages (weighted average F1-score for English is 0.93; for Kannada is 0.74; for Malayalam is 0.82; and for Tamil is 0.60). Thus, the proposed ensemble model proves to be a suitable approach for hope speech detection in the given low resource languages.
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