Maria Gul¹, Imran Ali Khan¹, Gohar Zaman², Atta Rahman^3,*, Jamaluddin Mir², Sardar Asad Ali Biabani^4,5, May Issa Aldossary⁶, Mustafa Youldash⁷, Ashraf Saadeldeen⁸, Maqsood Mahmud⁹, Asiya Abdus Salam⁶, Dania Alkhulaifi³, Abdullah AlTurkey³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1631-1657, 2024, DOI:10.32604/cmc.2024.048289

Abstract Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, many researchers proposed game theoretic models to avoid and remove obstacles for crowd evacuation. Game theoretical models aim to study and analyze the strategic behaviors of individuals within a crowd and their interactions during the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. These models consider a group of individuals as homogeneous objects with the same goals, involve complex mathematical formulation, and cannot model real-world scenarios such as panic, environmental information, crowds that move dynamically, etc. The proposed work presents… More >

Wael M. S. Yafooz^*, Abdullah Alsaeedi

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 735-759, 2024, DOI:10.32604/cmc.2024.048270

Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions, Umrah and Hajj. However, pilgrims… More >

Kai Wei¹, Song Yu², Qingxian Pan^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 607-622, 2024, DOI:10.32604/cmc.2024.048240

Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value on the public dataset. In… More >

Hala AlShamlan^*, Halah AlMazrua^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 675-694, 2024, DOI:10.32604/cmc.2024.048146

Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray… More >

Chunming Wu¹, Wukai Liu^2,*, Xin Ma³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1441-1461, 2024, DOI:10.32604/cmc.2024.048136

Abstract A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvement module. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the… More >

Ankita Gangwar¹, Vijaypal Singh Dhaka¹, Geeta Rani^2,*, Shrey Khandelwal¹, Ester Zumpano^3,4, Eugenio Vocaturo^3,4

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 117-142, 2024, DOI:10.32604/cmc.2024.048119

Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes… More >

Qiang Fu¹, Jun Wang^1,*, Changfu Si¹, Jiawei Liu²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 329-349, 2024, DOI:10.32604/cmc.2024.048117

Abstract As industrialization and informatization become more deeply intertwined, industrial control networks have entered an era of intelligence. The connection between industrial control networks and the external internet is becoming increasingly close, which leads to frequent security accidents. This paper proposes a model for the industrial control network. It includes a malware containment strategy that integrates intrusion detection, quarantine, and monitoring. Based on this model, the role of key nodes in the spread of malware is studied, a comparison experiment is conducted to validate the impact of the containment strategy. In addition, the dynamic behavior of the model is analyzed, the… More >

Tianshu Wei¹, Jinjie Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1599-1612, 2024, DOI:10.32604/cmc.2024.048098

Abstract Zero-shot learning enables the recognition of new class samples by migrating models learned from semantic features and existing sample features to things that have never been seen before. The problems of consistency of different types of features and domain shift problems are two of the critical issues in zero-shot learning. To address both of these issues, this paper proposes a new modeling structure. The traditional approach mapped semantic features and visual features into the same feature space; based on this, a dual discriminator approach is used in the proposed model. This dual discriminator approach can further enhance the consistency between… More >

Ameni Ellouze¹, Nesrine Kadri², Alaa Alaerjan^3,*, Mohamed Ksantini¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 351-372, 2024, DOI:10.32604/cmc.2024.048061

Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection… More >

Sapiah Sakri¹, Shakila Basheer¹, Zuhaira Muhammad Zain¹, Nurul Halimatul Asmak Ismail^2,*, Dua’ Abdellatef Nassar¹, Manal Abdullah Alohali¹, Mais Ayman Alharaki¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1157-1185, 2024, DOI:10.32604/cmc.2024.048051

Abstract Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction method proposed here utilizes… More >