Amir Yasseen Mahdi^1,2,*, Siti Sophiayati Yuhaniz¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1375-1392, 2023, DOI:10.32604/cmc.2023.032671

Abstract The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text… More >

Zubair Nabi¹, Ramzan Talib^1,*, Muhammad Kashif Hanif¹, Muhammad Awais²

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1357-1374, 2022, DOI:10.32604/csse.2022.025712

Abstract Digitalization has changed the way of information processing, and new techniques of legal data processing are evolving. Text mining helps to analyze and search different court cases available in the form of digital text documents to extract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reduced time. The rapid development of judicial ontologies seems to deliver interesting problem solving to legal knowledge formalization. Mining context information through ontologies from corpora is a challenging and interesting field. This research paper presents a three tier contextual text… More >

Kamal ElDahshan¹, Eman Selim², Ahmed Ismail Ebada², Mohamed Abouhawwash^3,4, Yunyoung Nam^5,*, Gamal Behery²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5149-5164, 2022, DOI:10.32604/cmc.2022.028326

Abstract Currently, relational database management systems (RDBMSs) face different challenges in application development due to the massive growth of unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, which do not adhere to the relational model. The migration from relational databases to NoSQL databases is challenging due to the data complexity. This study aims to enhance the storage performance of RDBMSs in handling a variety of data. The paper presents two approaches. The first approach proposes a convenient representation of unstructured data storage. Several extensive experiments were implemented to assess the efficiency… More >

S. Sreedhar Kumar¹, Syed Thouheed Ahmed^2,*, Qin Xin³, S. Sandeep⁴, M. Madheswaran⁵, Syed Muzamil Basha²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 281-299, 2022, DOI:10.32604/cmc.2022.023693

Abstract This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and… More >

Arash Mehraban¹, Henry Tufo¹, Stein Sture², Richard Regueiro^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1283-1303, 2021, DOI:10.32604/cmes.2021.017476

Abstract Higher-order displacement-based finite element methods are useful for simulating bending problems and potentially addressing mesh-locking associated with nearly-incompressible elasticity, yet are computationally expensive. To address the computational expense, the paper presents a matrix-free, displacement-based, higher-order, hexahedral finite element implementation of compressible and nearly-compressible (ν → 0.5) linear isotropic elasticity at small strain with p-multigrid preconditioning. The cost, solve time, and scalability of the implementation with respect to strain energy error are investigated for polynomial order p = 1, 2, 3, 4 for compressible elasticity, and p = 2, 3, 4 for nearly-incompressible elasticity, on different number of CPU cores for… More >

Mohammad Shoab, Abdullah Shawan Alotaibi^*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5765-5781, 2022, DOI:10.32604/cmc.2022.021941

Abstract Deep Reinforcement Learning (DRL) is a class of Machine Learning (ML) that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently. DRL has been used in many application fields, including games, robots, networks, etc. for creating autonomous systems that improve themselves with experience. It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially. Therefore, a novel query routing approach called Deep Reinforcement Learning based Route Selection… More >

Ajay Arunachalam^1,*, Vinayakumar Ravi², Moez Krichen³, Roobaea Alroobaea⁴, Saeed Rubaiee⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1807-1829, 2021, DOI:10.32604/cmc.2021.016692

Abstract Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node’s neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many… More >

Nany Katamesh, Osama Abu-Elnasr^*, Samir Elmougy

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 589-606, 2021, DOI:10.32604/cmc.2021.015761

Abstract Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by any of the above mentioned… More >

Ajay Arunachalam^1,*, Vinayakumar Ravi², Moez Krichen³, Roobaea Alroobaea⁴, Jehad Saad Alqurni⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 983-1001, 2021, DOI:10.32604/cmc.2021.015371

Abstract One of the key challenges in ad-hoc networks is the resource discovery problem. How efficiently & quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question? Broadcasting is a basic technique in the Mobile Ad-hoc Networks (MANETs), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique floods the network with query messages, while the random walk scheme operates by contacting subsets of each… More >

Uuganbayar Gankhuyag, Ji-Hyeong Han^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 133-152, 2021, DOI:10.32604/iasc.2021.015227

Abstract The automated reconstruction of building information modeling (BIM) objects from unstructured point cloud data for indoor as-built modeling is still a challenging task and the subject of much ongoing research. The most important part of the process is to detect the wall geometry clearly. A popular method is first to segment and classify point clouds, after which the identified segments should be clustered according to their corresponding objects, such as walls and clutter. To perform this process, a major problem is low-quality point clouds that are noisy, cluttered and that contain missing parts in the data. Moreover, the size of… More >