The Odia language is one of the many regional languages spoken in India. It is the official language of Odisha, a State in eastern India. The Odia language carries a 1500-year-old history and worldwide is spoken by mo...
The Odia language is one of the many regional languages spoken in India. It is the official language of Odisha, a State in eastern India. The Odia language carries a 1500-year-old history and worldwide is spoken by more than 50 million people. The Odia digits are complex due to the presence of many curves in each character. Handwritten scripts are even more complex due to free-style writing. However, the development of an innovative machine learning model is essential because Odia scripts consist of a huge number of historical documents of more than 1000 years old. A robust automation method will help in converting historical documents into digital form and will help to preserve the documents. This will solve a big problem in society. This work experiments with handwritten Odia numerals by implementing two different classifiers. The first one is the implementation of a Convolutional Neural Network (CNN) and the second experiment implements a Support Vector Machine (SVM). Finally, results from both experiments have been compared. The dataset has been generated through software by writing the digits on MS Paint. Both CNN and SVM models have been implemented through Python programming to recognize the inputs into a particular class. Both training and testing of the models have been done using this dataset. The accuracy from the CNN Model is obtained to be 94.999% which is ≈95% and for SVM, the model accuracy is 86%. Comparing both results, it is concluded that the CNN model is comparatively better than the SVM classifier in the case of the proposed work.
Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating rese...
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Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware energy grid management systems with anomaly detection networks and distributed learning mechanisms are proposed. The anomaly detection network consists of a server and a client learning network, which collaboratively learn patterns without sharing data, and periodically train and exchange knowledge. We also develop learning mechanisms with federated, distributed, and split learning to improve privacy and use Q-learning for decision-making to facilitate interpretability. To demonstrate the effectiveness and robustness of the proposed schemes, extensive simulations are conducted in different energy grid environments with different target distributions, ORRs, and attack scenarios. The experimental results show that the proposed schemes not only improve management performance but also enhance privacy and security levels. We also compare the management performance and privacy level of the different learning machines and provide usage recommendations.
Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
Human perception heavily relies on two primary senses: vision and hearing, which are closely inter-connected and capable of complementing each other. Consequently, various multimodal learning tasks have emerged, with ...
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Human perception heavily relies on two primary senses: vision and hearing, which are closely inter-connected and capable of complementing each other. Consequently, various multimodal learning tasks have emerged, with audio-visual event localization (AVEL) being a prominent example. AVEL is a popular task within the realm of multimodal learning, with the primary objective of identifying the presence of events within each video segment and predicting their respective categories. This task holds significant utility in domains such as healthcare monitoring and surveillance, among others. Generally speaking, audio-visual co-learning offers a more comprehensive information landscape compared to single-modal learning, as it allows for a more holistic perception of ambient information, aligning with real-world applications. Nevertheless, the inherent heterogeneity of audio and visual data can introduce challenges related to event semantics inconsistency, potentially leading to incorrect predictions. To track these challenges, we propose a multi-task hybrid attention network (MHAN) to acquire high-quality representation for multimodal data. Specifically, our network incorporates hybrid attention of uni- and parallel cross-modal (HAUC) modules, which consists of a uni-modal attention block and a parallel cross-modal attention block, leveraging multimodal complementary and hidden information for better representation. Furthermore, we advocate for the use of a uni-modal visual task as auxiliary supervision to enhance the performance of multimodal tasks employing a multi-task learning strategy. Our proposed model has been proven to outperform the state-of-the-art results based on extensive experiments conducted on the AVE dataset.
WSC2008Chair’s Welcome Message Dear Colleague, The World Soft computing (WSC) conference is an annual international online conference on applied and theoretical soft computingtechnology. This WSC 2008 is the thirtee...
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ISBN:
(数字)9783540896197
ISBN:
(纸本)9783540896180
WSC2008Chair’s Welcome Message Dear Colleague, The World Soft computing (WSC) conference is an annual international online conference on applied and theoretical soft computingtechnology. This WSC 2008 is the thirteenth conference in this series and it has been a great success. We received a lot of excellent paper submissions which were peer-reviewed by an international team of experts. Only60 papers out of111 submissions were selected for online publication. This assured a high quality standard for this online conference. The corresponding online statistics are a proof of the great world-wide interest in the WSC 2008 conference. The conference website had a total of33,367di?erent human user accessesfrom43 countries with around100 visitors every day,151 people signed up to WSC to discuss their scienti?c disciplines in our chat rooms and the forum. Also audio and slide presentations allowed a detailed discussion of the papers. The submissions and discussions showed that there is a wide range of soft computing applications to date. The topics covered by the conference range from applied to theoretical aspects of fuzzy, neuro-fuzzy and rough sets over to neural networks to single and multi-objective optimisation. Contributions aboutparticleswarmoptimisation,geneexpressionprogramming,clustering, classi?cation,supportvectormachines,quantumevolutionandagentsystems have also been received. One whole session was devoted to soft computing techniques in computer graphics, imaging, vision and signal processing.
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