Fog which present in the atmosphere due to temperature variance reduces the clear vision of driver or driving applications. Restricted visibility due to fog will affect the accuracy of visualization and it may leads t...
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This research presents an innovative method for education group recommendation systems by leveraging Knowledge-Aware Attentive Embedding Learning (KA-AEL), aimed at enhancing the collaborative learning experience. The...
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Energy preservation in wireless sensor network (WSN) is vital for prolonging the network lifetime as sensors have stringent energy constraints. Reducing energy consumption is an uphill task, and process of clustering ...
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One of the devices that police officers often come into contact with when conducting investigations and obtaining evidence is the smartphones. Mobile phones are now widely available and due to the increasing intellige...
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QKD, ECC, PQC, SMPC, and biometric-based authentication are examined in this work as essential information-theoretic security approaches. Each solution improves information-theoretic security by improving communicatio...
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People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,incl...
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People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,including products,news,blogs,*** user reviews and tweets,sentiment analysis is used to discover opinions and *** polarity is a term used to describe how sentiment is ***,neutral and negative are all examples of *** area is still in its infancy and needs several critical *** and hidden emotions can detract from the accuracy of traditional *** methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative *** existing strategies are *** proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion ***,classification was performed using *** proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and *** results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.
Identification of vehicles is an important area of research. It has several applications namely toll gate fee management systems, automatic parking applications, theft prevention, traffic monitoring and ticket(fine) i...
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In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonsee...
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Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approache...
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Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approaches. In cybersecurity, strong adversaries often complicate their strategies by constantly developing emerging attacks. Meanwhile, security practitioners desire to grasp the reasons for inference results. However, existing deep learning approaches lack efficient adaptation for incremental traffic types and often have less interpretability. In this paper, we propose I $^{2}$ RNN, an Incremental and Interpretable Recurrent Neural Network for encrypted traffic classification. The I $^{2}$ RNN proposes a novel propagation process to extract the sequence fingerprints from sessions with local robustness. Meanwhile, this proposal provides interpretability including time-series feature attribution and inter-class similarity portrait. Moreover, we design I $^{2}$ RNN in an incremental manner to adapt to emerging traffic types. The I $^{2}$ RNN only needs to train an additional set of parameters for the newly added traffic type rather than retraining the whole model with the entire dataset. Extensive experimental results show that our I $^{2}$ RNN can achieve remarkable performance in traffic classification, incremental learning, and model interpretability. Compared with other local interpretability methods, our I $^{2}$ RNN exhibits excellent stability, robustness, and effectiveness in the interpretation of network traffic data. IEEE
In recent years, AI has made many advances in multiple fronts. One such recent advance is the development of Large Language Models(LLMs), a deep learning model trained with a large set of data on billions of parameter...
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