Mustafa Mohammed Alhassow^1,*, Oguz Ata², Dogu Cagdas Atilla¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 749-771, 2024, DOI:10.32604/cmc.2024.055592 - 15 October 2024

Abstract This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict-Based Search (CBS) approach, introducing a unique hierarchical search mechanism for planning paths for multiple robots.… More >

Anitha Ramachandran, Sendhil Kumar K.S.^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 827-842, 2024, DOI:10.32604/cmc.2024.054768 - 15 October 2024

Abstract Research on panicle detection is one of the most important aspects of paddy phenotypic analysis. A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based methods. Nevertheless, it entails many other challenges, including different illuminations, panicle sizes, shape distortions, partial occlusions, and complex backgrounds. Object detection algorithms are directly affected by these factors. This work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation (BSKD). It is designed to prioritize the preservation of knowledge in border areas through the use of feature distillation. Our feature-based knowledge distillation method More >

Muhammad Naeem Ul Hassan^1,2, Zhengtao Yu^1,2,*, Jian Wang^1,2, Ying Li^1,2, Shengxiang Gao^1,2, Shuwan Yang^1,2, Cunli Mao^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 951-969, 2024, DOI:10.32604/cmc.2024.054673 - 15 October 2024

Abstract Thanks to the strong representation capability of pre-trained language models, supervised machine translation models have achieved outstanding performance. However, the performances of these models drop sharply when the scale of the parallel training corpus is limited. Considering the pre-trained language model has a strong ability for monolingual representation, it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models. To alleviate the dependence on the parallel corpus, we propose a Linguistics Knowledge-Driven Multi-Task (LKMT) approach to… More >

Mohamed Rabik Mohamed Ismail¹, Muthuramalingam Thangaraj^1,*, Khaja Moiduddin^2,*, Zeyad Almutairi^2,3, Mustufa Haider Abidi²

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4263-4279, 2024, DOI:10.32604/cmc.2024.055271 - 12 September 2024

Abstract The measurement and mapping of objects in the outer environment have traditionally been conducted using ground-based monitoring systems, as well as satellites. More recently, unmanned aerial vehicles have also been employed for this purpose. The accurate detection and mapping of a target such as buildings, trees, and terrains are of utmost importance in various applications of unmanned aerial vehicles (UAVs), including search and rescue operations, object transportation, object detection, inspection tasks, and mapping activities. However, the rapid measurement and mapping of the object are not currently achievable due to factors such as the object’s size,… More >

Xianghui Hu¹, Yiming Tang^2,3, Witold Pedrycz^3,4, Jiuchuan Jiang^5,*, Yichuan Jiang^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4917-4945, 2024, DOI:10.32604/cmc.2024.054775 - 12 September 2024

Abstract Traditional Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms are data-driven, and their objective function minimization process is based on the available numeric data. Recently, knowledge hints have been introduced to form knowledge-driven clustering algorithms, which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints. However, these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself; they require the assistance of evaluation indices. Moreover, knowledge hints are usually used as part of the data structure (directly replacing some clustering centers),… More >

Yu Song, Pengcheng Wu, Dongming Dai, Mingyu Gui, Kunli Zhang^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4767-4790, 2024, DOI:10.32604/cmc.2024.053506 - 12 September 2024

Abstract The growing prevalence of knowledge reasoning using knowledge graphs (KGs) has substantially improved the accuracy and efficiency of intelligent medical diagnosis. However, current models primarily integrate electronic medical records (EMRs) and KGs into the knowledge reasoning process, ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text. To better integrate EMR text information, we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning (GATiT), which comprises text representation, subgraph construction, knowledge reasoning, and diagnostic classification. In the… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.9, pp. 1529-1529, 2024, DOI:10.32604/or.2024.056121 - 23 August 2024

Abstract This article has no abstract. More >

Lirong Yin¹, Lei Wang¹, Zhuohang Cai², Siyu Lu^2,*, Ruiyang Wang², Ahmed AlSanad³, Salman A. AlQahtani³, Xiaobing Chen⁴, Zhengtong Yin⁵, Xiaolu Li⁶, Wenfeng Zheng^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 771-786, 2024, DOI:10.32604/cmes.2024.052622 - 20 August 2024

Abstract Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset More >

Bo Cao^1,*, Qinghua Xing², Longyue Li², Huaixi Xing¹, Zhanfu Song¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1251-1275, 2024, DOI:10.32604/cmc.2024.052842 - 18 July 2024

Abstract As a core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of insufficient feature extraction and misclassification in intention recognition, this paper designs an air target intention recognition method (KGTLIR) based on Knowledge Graph and Deep Learning. Firstly, the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism. Meanwhile, the accuracy, recall, and F1-score after iteration are introduced More >

Feng Zhao¹, Hongtao Ding², Chunhai Li^1,*, Zhaoyu Su², Guoling Liang², Changsong Yang³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 567-585, 2024, DOI:10.32604/cmc.2024.052233 - 18 July 2024

Abstract The Internet of Vehicles (IoV) is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the network. However, due to the open and variable nature of its network topology, vehicles frequently engage in cross-domain interactions. During such processes, directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers, thus compromising the security of the cross-domain authentication process. Additionally, IoV imposes high real-time requirements, and existing cross-domain authentication schemes for IoV often encounter efficiency issues. To mitigate More >