The rapid dissemination of real-time updates in interconnected networks often encounters the challenge of misinformation spreading alongside accurate information. This paper examines the interplay between timeliness a...
详细信息
The rapid dissemination of real-time updates in interconnected networks often encounters the challenge of misinformation spreading alongside accurate information. This paper examines the interplay between timeliness and accuracy of updates in fully-connected gossip networks, where probabilistic mutations during transmission can convert truth into misinformation. We consider a network of n user nodes that receives updates from a source and employs an age-based gossip protocol for faster dissemination of version updates to all nodes. When a node forwards its packet to another node, the packet information gets mutated with probability p during transmission, creating misinformation. The receiver node does not know whether an incoming packet contains correct information or misinformation. The receiver runs a gossip protocol that looks only at the version age of the incoming packet and accepts it if it is fresher than the packet in its possession. For the case when the incoming packet has the same version age as the receiver’s own packet, we consider two system models: In the first model, we assume that truth prevails over misinformation, and therefore, when a receiver encounters both accurate information and misinformation corresponding to the same version, the accurate information gets chosen for storage at the node. In the second model, we assume the opposite scenario, where misinformation prevails over truth. For both models, we study the expected fraction of nodes with correct information in the network and the version age at the nodes using the stochastic hybrid systems (SHS) method. We observe that when truth prevails over misinformation, very high or very low gossiping rates help curb misinformation, and misinformation spread is higher with moderate gossiping rates. However, when misinformation prevails, misinformation rises with increased inter-node gossiping. We support our theoretical findings with simulation results which shed further light on the behavior of
The financial markets are inherently volatile, and investors constantly seek ways to mitigate risks while maximizing returns. This work aims to develop a stop-loss hedging strategy for a portfolio of stocks listed on ...
详细信息
Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through ...
详细信息
Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color Histogram) and modified deep-learning architectures. We propose two models for this purpose: PestNet-EF and PestNet-LF. PestNet-EF uses an early fusion technique to integrate handcrafted and deep learning features, followed by adaptive feature selection methods such as CFS and Recursive Feature Elimination (RFE). PestNet-LF utilizes a late fusion technique, incorporating three additional layers (fully connected, softmax, and classification) to enhance performance. These models were evaluated across 15 classes of pests, including five classes each for rice, corn, and wheat. The performance of our suggested algorithms was tested against the IP102 dataset. Simulation demonstrates that the Pestnet-EF model achieved an accuracy of 96%, and the PestNet-LF model with majority voting achieved the highest accuracy of 94%, while PestNet-LF with the average model attained an accuracy of 92%. Also, the proposed approach was compared with existing methods that rely on hand-crafted and transfer learning techniques, showcasing the effectiveness of our approach in real-time pest detection for improved agricultural yield.
Road marking detection area avails research with the application of computer vision and machine learning in the identification and analysis of various types of road markings such as lane markers, crosswalks, and road ...
详细信息
Road marking detection area avails research with the application of computer vision and machine learning in the identification and analysis of various types of road markings such as lane markers, crosswalks, and road signs. It is very important to enhance safety on the roads by making these markings visible to drivers, cyclists, and pedestrians. Therefore, this study was designed to further enhance the efficiency and performance of the road marking detection models by incorporating the advanced AdamW optimizer and proposing a novel architecture known as Multi-Stage Cross-Convolutional Bottleneck Network-MSCCBN further-efficient in describing complex patterns and shapes from road markings. The testing was performed on a newly developed benchmark dataset containing 2,887 high-resolution images divided into training and test sets, with a total of 11 distinct road marking categories. Applying the Ceymo dataset and YOLOv7 detection framework, the result of the proposed model was an mAP50 score of 0.889, which means very high accuracy in object detection. This model indeed had a high precision of 0.886 and recall of 0.83, hence effectively minimizing both false positives and false negatives.
This paper introduces a novel offline approach to power control in wireless networks using a multi-agent reinforcement learning (MARL) framework. We develop a multi-agent decision transformer method to optimize perfor...
详细信息
ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
This paper introduces a novel offline approach to power control in wireless networks using a multi-agent reinforcement learning (MARL) framework. We develop a multi-agent decision transformer method to optimize performance metrics including sum-rate or packet delay. In this distributed method, each agent controls an individual link and determines its power level based on its own measurements and information exchange with a few agents within a limited *** results demonstrate that the proposed method achieves quality of service performance comparable to centralized methods using global information, for both sum-rate maximization and traffic-driven packet delay minimization problems. As an offline learning solution, it can efficiently leverage knowledge from existing mature techniques and offers significant advantages in the safety, stability, and convergence rate over existing online methods. This work provides a promising alternative for learning-based resource management in wireless networks.
Sarcasm is a type of sentiment employed by humans for comedic relief. The widespread use of sarcasm is a significant reason why native Bangla speakers often misunderstand humor-based comments. The increasing use of sa...
详细信息
ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Sarcasm is a type of sentiment employed by humans for comedic relief. The widespread use of sarcasm is a significant reason why native Bangla speakers often misunderstand humor-based comments. The increasing use of sarcasm in the Bangla language requires further natural language processing-based study, as Bangla sarcasm is particularly challenging to detect. We present BanSarc3, a ternary-class dataset (7,984 Facebook comments: sarcastic, non-sarcastic, neutral) addressing humor misinterpretation that fuels digital conflict. A hybrid RNN-BiLSTM model, leveraging bidirectional context for morphologically rich syntax, achieves state-of-the-art 89.6% accuracy (5.12–16.82% gain over prior work). Ternary classification reduced ambiguity-driven errors by 18% versus binary frameworks. Error analysis reveals generational lexical gaps and cultural hyperbole as key challenges. This work enables safer social media ecosystems for Bangla speakers and offers a blueprint for low-resource languages through open data/model release, advocating dialect adaptation and multimodal integration for equitable NLP.
It is extremely difficult to address the intricacies of traffic sign recognition in driver assistance systems and self-driving cars. The creation of trustworthy and extremely accurate algorithms are essential for the ...
详细信息
This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare...
详细信息
Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently identify anomalous, out-of-distribution...
In modern wireless communication systems, the efficient allocation of power among multiple users is a critical challenge, particularly in interference-limited environments. This research introduces an adaptive power c...
详细信息
ISBN:
(数字)9798331523657
ISBN:
(纸本)9798331523664
In modern wireless communication systems, the efficient allocation of power among multiple users is a critical challenge, particularly in interference-limited environments. This research introduces an adaptive power control scheme using the Marine Predators Algorithm (MPA), a metaheuristic inspired by the foraging strategies of marine predators. The problem is modeled as a constrained non-linear optimization task that maximizes the system's sum-rate while adhering to power budget and interference constraints. We incorporate practical considerations, including path loss, shadow fading, small-scale fading, and external interference, to reflect real-world conditions. The MPA's exploration and exploitation capabilities are tailored to handle the high-dimensional solution space, ensuring rapid convergence to high-quality power allocations. Simulation results from ten distinct experimental configurations demonstrate the robustness of MPA, showcasing its ability to achieve significant through-put gains, efficiently utilize available power, and adapt to varying interference levels.
暂无评论