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Deep One-Directional Neural Semantic Siamese Network for High-Accuracy Fact Verification

by Muchammad Naseer, Jauzak Hussaini Windiatmaja, Muhamad Asvial and Riri Fitri Sari

Big Data Cogn. Comput. 2025, 9(7), 172; https://doi.org/10.3390/bdcc9070172 - 30 Jun 2025

Viewed by 1647

Fake news has eroded trust in credible news sources, driving the need for tools to verify the accuracy of circulating information. Fact verification addresses this issue by classifying claims as Supports (S), Refutes (R), or Not Enough Info (NEI) based on evidence. Neural Semantic Matching Networks (NSMN) is an algorithm designed for this purpose, but its reliance on BiLSTM has shown limitations, particularly overfitting. This study aims to enhance NSMN for fact verification through a structured framework comprising encoding, alignment, matching, and output layers. The proposed approach employed Siamese MaLSTM in the matching layer and introduced the Manhattan Fact Relatedness Score (MFRS) in the output layer, culminating in a novel algorithm called Deep One-Directional Neural Semantic Siamese Network (DOD–NSSN). Performance evaluation compared DOD–NSSN with NSMN and transformer-based algorithms (BERT, RoBERTa, XLM, XL-Net). Results demonstrated that DOD–NSSN achieved 91.86% accuracy and consistently outperformed other models, achieving over 95% accuracy across diverse topics, including sports, government, politics, health, and industry. The findings highlight the DOD–NSSN model’s capability to generalize effectively across various domains, providing a robust tool for automated fact verification. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)

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17 pages, 4881 KB

Open AccessArticle

Intelligent Packet Priority Module for a Network of Unmanned Aerial Vehicles Using Manhattan Long Short-Term Memory

by Dino Budi Prakoso, Jauzak Hussaini Windiatmaja, Agus Mulyanto, Riri Fitri Sari and Rosdiadee Nordin

Drones 2024, 8(5), 183; https://doi.org/10.3390/drones8050183 - 7 May 2024

Cited by 2 | Viewed by 2688

Abstract

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances. Full article

(This article belongs to the Special Issue UAV-Assisted Mobile Wireless Networks and Applications)

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