Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news det...
详细信息
ISBN:
(数字)9798331517878
ISBN:
(纸本)9798331517885
Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods.
Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the ...
详细信息
Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.
Brain tumor is a leading cause of death globally and Magnetic Resonance Imaging (MRI) is a powerful tool for its diagnosis. However, the ability to extract more representative features for efficient characterization o...
Brain tumor is a leading cause of death globally and Magnetic Resonance Imaging (MRI) is a powerful tool for its diagnosis. However, the ability to extract more representative features for efficient characterization of brain tumors is still a highly targeted topic. This paper aims to study the power of different deep learning approaches such as traditional Convolutional Neural Network (CNN), transfer learning via three well-known models (VGG16, InceptionV3, and ResNet50), and convolutional auto-encoder in extracting deep representative features for efficient characterization of brain tumors. The performance evaluation was done using two public datasets of MRI images and various benchmarking evaluation metrics. Both traditional CNN and convolutional auto-encoder achieved 100% accuracy, with the auto-encoder producing the lowest loss and highly productive with balanced datasets. Transfer learning models achieved an overall performance of 99% in different evaluation metrics and were found to be preferred with unbalanced datasets.
Increasing bus frequency to fulfill the performance requirements of dependable applications may increase the susceptibility of the system to transient faults. This paper describes an integration of protection against ...
Increasing bus frequency to fulfill the performance requirements of dependable applications may increase the susceptibility of the system to transient faults. This paper describes an integration of protection against transient bus faults into the interface of the Hardisc RISC-V core. The protection is based on information redundancy with spatial redundancy features. It enables uninterrupted execution in the presence of transient faults and provides a hardware-software interface for its reporting. The benchmarking results indicate that most of the applications will be impacted minimally. The protection has a negligible impact on the maximal frequency and 8% area and power consumption overhead.
This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to e...
详细信息
Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable...
详细信息
Researchers have presented new ideas to effectively handle the traffic jams on the roads and junctions throughout the world, which are expected to increase manifold in the upcoming decades. They have come up with the ...
详细信息
The optoacoustic effect is triggered by directing an optical signal in the air (using laser) to the surface of water, leading to the generation of a corresponding acoustic signal inside the water. Careful modulation o...
详细信息
The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the ...
详细信息
ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.
E-learning is used as one of the innovations in online learning, but in its implementation e-learning has limitations, especially in terms of flexibility, so that new innovations emerge, namely learning using m-learni...
详细信息
暂无评论