Israa K. Salman Al-Tameemi^1,3, Mohammad-Reza Feizi-Derakhshi^1,*, Saeed Pashazadeh², Mohammad Asadpour²

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2145-2177, 2023, DOI:10.32604/cmc.2023.040997

Abstract Multimodal Sentiment Analysis (SA) is gaining popularity due to its broad application potential. The existing studies have focused on the SA of single modalities, such as texts or photos, posing challenges in effectively handling social media data with multiple modalities. Moreover, most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations, leading to unsatisfactory sentiment classification results. Motivated by this, we propose a new visual-textual sentiment classification model named Multi-Model Fusion (MMF), which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual… More >

Hyeon Park¹, SeoYeon Kim², Seok Min Ko¹, TaeGuen Kim^2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1891-1909, 2023, DOI:10.32604/cmc.2023.039464

Abstract The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. Our experimental results revealed that… More >

Umar Abdulkadir Isa^*, Anil Fernando^*

Journal on Artificial Intelligence, Vol.5, pp. 31-42, 2023, DOI:10.32604/jai.2023.040213

Abstract Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%–98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating a model-independent, locally accurate explanation… More >

Saleh Ateeq Almutairi^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then, the optimal combination of the… More >

Jintao Fu¹, Yong Zhou^1,*, Qian Qiu², Guangwei Xu³, Neng Wan³

Journal of Quantum Computing, Vol.4, No.3, pp. 147-163, 2022, DOI:10.32604/jqc.2022.038358

Abstract In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression… More >

Zhiwei Ye¹, Ziqian Fang¹, Zhina Song^1,*, Haigang Sui², Chunyan Yan¹, Wen Zhou¹, Mingwei Wang¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2019-2035, 2023, DOI:10.32604/iasc.2023.039949

Abstract Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of easy implementation, less parameters and… More >

Jing Geng^1,*, Huali Yang², Shengze Hu³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1311-1324, 2023, DOI:10.32604/iasc.2023.038481

Abstract Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students, which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress. To this end, we propose a noise-filtering enhanced deep cognitive diagnosis method to improve the fitting ability of traditional models and obtain students’ skill mastery status by mining the interaction between students and problems nonlinearly through neural networks. First, modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of… More >

Sharad Saxena¹, Shailendra Mishra^2,*, Mohammed Baljon^2,*, Shamiksha Mishra³, Sunil Kumar Sharma², Prakhar Goel¹, Shubham Gupta¹, Vinay Kishore¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1027-1045, 2023, DOI:10.32604/cmc.2023.039677

Abstract In recent years, the growth of female employees in the commercial market and industries has increased. As a result, some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety. The exponential increase in assaults and attacks on women, on the other hand, is posing a threat to women’s growth, development, and security. At the time of the attack, it appears the women were immobilized and needed immediate support. Only self-defense isn’t sufficient against abuse; a new technological solution is desired and can be used as quickly as hitting a switch or button.… More >

Niharika Gupta¹, Baijnath Kaushik¹, Mohammad Khalid Imam Rahmani^2,*, Saima Anwar Lashari^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 347-366, 2023, DOI:10.32604/cmc.2023.038864

Abstract Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN),… More >

Gang Xiang^1,2, Xiaoyu Cheng³, Wei He^3,4,*, Peng Han³

Computer Systems Science and Engineering, Vol.47, No.1, pp. 273-298, 2023, DOI:10.32604/csse.2023.037892

Abstract A liquid launch vehicle is an important carrier in aviation, and its regular operation is essential to maintain space security. In the safety assessment of fluid launch vehicle body structure, it is necessary to ensure that the assessment model can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process. Therefore, a belief rule base with interpretability (BRB-i) assessment method of liquid launch vehicle structure safety status combines data and knowledge. Moreover, an innovative whale optimization algorithm with interpretable constraints is proposed. The experiments are carried out based on the liquid launch… More >