Amira Hamed Abo-Elghit^1,*, Taher Hamza¹, Aya Al-Zoghby²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1967-1994, 2022, DOI:10.32604/cmc.2022.025353

Abstract Nowadays, we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task. In this work, we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking, essay grading, and question answering systems. We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset. The used schemes include lexical-based similarity features, frequency-based features, and pre-trained model-based features. Also, we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers (AraBERT). We… More >

Mohammad Alkhedher^*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1901-1920, 2022, DOI:10.32604/cmc.2022.025334

Abstract Modern fighters are designed to fly at high angle of attacks reaching 90 deg as part of their routine maneuvers. These maneuvers generate complex nonlinear and unsteady aerodynamic loading. In this study, different aerodynamic prediction tools are investigated to achieve a model which is highly accurate, less computational, and provides a stable prediction of associated unsteady aerodynamics that results from high angle of attack maneuvers. These prediction tools include Artificial Neural Networks (ANN) model, Adaptive Neuro Fuzzy Logic Inference System (ANFIS), Fourier model, and Polynomial Classifier Networks (PCN). The main aim of the prediction model is to estimate the pitch… More >

Mudita Uppal¹, Deepali Gupta¹, Divya Anand², Fahd S. Alharithi³, Jasem Almotiri³, Arturo Mansilla^4,5, Dinesh Singh⁶, Nitin Goyal^1,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1799-1814, 2022, DOI:10.32604/cmc.2022.025330

Abstract The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.… More >

Jae Myung Kim^1,2, Gyu Ho Choi², Min-Gu Kim², Sung Bum Pan^1,2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1213-1227, 2022, DOI:10.32604/cmc.2022.025213

Abstract Recently, user recognition methods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things (IoT) services through fifth-generation technology (5G) based mobile devices. The EMG signals generated inside the body with unique individual characteristics are being studied as a part of next-generation user recognition methods. However, there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time. Hence, it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over… More >

Imran Arshad Choudhry^*, Adnan N. Qureshi

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1445-1463, 2022, DOI:10.32604/cmc.2022.025208

Abstract The well-established mortality rates due to lung cancers, scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second opinion. To this end, we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health (NIH) chest X-Ray dataset comprised of 30,805 unique patients. The performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom layer. For classification with both approaches, Support Vectors Machines (SVM) are used.… More >

Jianbing Ma^1,*, Xianghao Cui¹, Nan Jiang²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1939-1949, 2022, DOI:10.32604/cmc.2022.025206

Abstract Sudden precipitations may bring troubles or even huge harm to people's daily lives. Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life. Traditionally, the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor (Z) and rainfall rate (R), which is typically estimated by location-dependent experiential formula and arguably uncertain. Therefore, in this paper, we propose a deep learning-based method to model the ZR relation. To evaluate, we conducted our experiment with the Shenzhen precipitation dataset. We proposed a combined method of deep learning and the… More >

Purnachand Kollapudi¹, Saleh Alghamdi², Neenavath Veeraiah^3,*, Youseef Alotaibi⁴, Sushma Thotakura⁵, Abdulmajeed Alsufyani⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1339-1355, 2022, DOI:10.32604/cmc.2022.025118

Abstract The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas. In recent years, a lot of interest has been generated in researching remote sensing image scene classification. Remote sensing image scene retrieval, and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding (RSISU) research. In the last several years, the number of deep learning (DL) methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs, providing new research and development possibilities for RS… More >

Mouazma Batool¹, Yazeed Yasin Ghadi², Suliman A. Alsuhibany³, Tamara al Shloul⁴, Ahmad Jalal¹, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1747-1764, 2022, DOI:10.32604/cmc.2022.025112

Abstract Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.… More >

Thien-Phuc Doan, Souhwan Jung^*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1699-1711, 2022, DOI:10.32604/cmc.2022.025096

Abstract Container technology plays an essential role in many Information and Communications Technology (ICT) systems. However, containers face a diversity of threats caused by vulnerable packages within container images. Previous vulnerability scanning solutions for container images are inadequate. These solutions entirely depend on the information extracted from package managers. As a result, packages installed directly from the source code compilation, or packages downloaded from the repository, etc., are ignored. We introduce DAVS–A Dockerfile analysis-based vulnerability scanning framework for OCI-based container images to deal with the limitations of existing solutions. DAVS performs static analysis using file extraction based on Dockerfile information to… More >

Mutasem K. Alsmadi^1,*, Ghaith M. Jaradat², Malek Alzaqebah³, Ibrahim ALmarashdeh¹, Fahad A. Alghamdi¹, Rami Mustafa A. Mohammad⁴, Nahier Aldhafferi⁴, Abdullah Alqahtani⁴

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1995-2014, 2022, DOI:10.32604/cmc.2022.025077

Abstract Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity, capability, and capacity. Such tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or decision-making. Swarm intelligence techniques including Particle Swarm Optimization (PSO) have proved to be effective examples. Different recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling, machine scheduling, etc. However, having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is guaranteed. This research paper seeks… More >