Mohamed Karray^1,*, Nesrine Triki^2,*, Mohamed Ksantini²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562

Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… More >

Fuxi Zhu¹, Jin Xie^2,*, Mohammed Alshahrani³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 949-971, 2024, DOI:10.32604/cmc.2024.051046

Abstract User representation learning is crucial for capturing different user preferences, but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data, and thus cannot be measured directly. Text-based data models can learn user representations by mining latent semantics, which is beneficial to enhancing the semantic function of user representations. However, these technologies only extract common features in historical records and cannot represent changes in user intentions. However, sequential feature can express the user’s interests and intentions that change time by time. But the sequential recommendation… More >

Ahmad Ababneh¹, Yousef Sanjalawe², Salam Fraihat^3,*, Salam Al-E’mari⁴, Hamzah Alqudah⁵

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1347-1379, 2024, DOI:10.32604/cmc.2024.050600

Abstract This study introduces the Orbit Weighting Scheme (OWS), a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval (IR) models, which have traditionally relied on weighting schemes like tf-idf and BM25. These conventional methods often struggle with accurately capturing document relevance, leading to inefficiencies in both retrieval performance and index size management. OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space, emphasizing term relationships and distribution patterns overlooked by existing models. Our research focuses on evaluating OWS’s impact… More >

Amel Ali Alhussan¹, S. K. Towfek^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1179-1201, 2024, DOI:10.32604/cmc.2024.049874

Abstract In the contemporary world of highly efficient technological development, fifth-generation technology (5G) is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second (Gbps). As far as the current implementations are concerned, they are at the level of slightly below 1 Gbps, but this allowed a great leap forward from fourth generation technology (4G), as well as enabling significantly reduced latency, making 5G an absolute necessity for applications such as gaming, virtual conferencing, and other interactive electronic processes. Prospects of this change are not limited to connectivity… More >

Dhiaa Musleh¹, Atta Rahman^1,*, Mohammed Abbas Alkherallah¹, Menhal Kamel Al-Bohassan¹, Mustafa Mohammed Alawami¹, Hayder Ali Alsebaa¹, Jawad Ali Alnemer¹, Ghazi Fayez Al-Mutairi¹, May Issa Aldossary², Dalal A. Aldowaihi¹, Fahd Alhaidari³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1033-1054, 2024, DOI:10.32604/cmc.2024.048003

Abstract With the rapid growth of internet usage, a new situation has been created that enables practicing bullying. Cyberbullying has increased over the past decade, and it has the same adverse effects as face-to-face bullying, like anger, sadness, anxiety, and fear. With the anonymity people get on the internet, they tend to be more aggressive and express their emotions freely without considering the effects, which can be a reason for the increase in cyberbullying and it is the main motive behind the current study. This study presents a thorough background of cyberbullying and the techniques used… More >

Abdel Aziz Osséni¹, Gbodja Houéhanou François Gbesso^1,*, Sedami Igor Armand Yevide², Etienne Romaric Adéwalé Godonou¹

Revue Internationale de Géomatique, Vol.33, pp. 201-220, 2024, DOI:10.32604/rig.2024.053500

Abstract Urban green areas play a vital role in enhancing the social balance, resilience, and environmental sustainability of urban settings. In Benin, while the landscaping sector is expanding, finding appropriate locations for creating green spaces remains a challenge. The purpose of this study was to identify areas conducive to the incorporation of green landscapes into urban planning within the Sèmè-Podji District. The approach used involved a multi-criteria analysis leveraging a combined GIS and Analytic Hierarchy Process (AHP) framework. Six key factors were considered: land use, elevation, slope, distance to major roads, proximity to urban hubs, and… More > Graphic Abstract

Asad Raza^1,*, Shahzad Memon¹, Muhammad Ali Nizamani¹, Mahmood Hussain Shah²

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 545-566, 2024, DOI:10.32604/iasc.2024.051779

Abstract Smart Industrial environments use the Industrial Internet of Things (IIoT) for their routine operations and transform their industrial operations with intelligent and driven approaches. However, IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet. Traditional signature-based IDS are effective in detecting known attacks, but they are unable to detect unknown emerging attacks. Therefore, there is the need for an IDS which can learn from data and detect new threats. Ensemble Machine Learning (ML) and individual Deep Learning (DL) based IDS have been developed, and these individual models achieved… More >

Weijian Song^1,, Xi Li^1,, Peng Chen^1,*, Juan Chen¹, Jianhua Ren², Yunni Xia^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563

Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >

Cinthia T. Lucero¹, María de los Á. Ruíz², Fabiola Pagliero¹, Carolina Castaño¹, Mariela L. Ambrosino¹, Graciela S. Lorda^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1081-1099, 2024, DOI:10.32604/phyton.2024.050762

Abstract Beneficial microbes can improve soil health by promoting soil structure, nutrient cycling, and disease suppression. In addition, a wide array of rhizospheric microbes are responsible for producing metabolically active compounds including various types of plant growth regulators. So, microbial biodiversity studies could contribute to the improvement of agricultural practices in deprived areas, such as the Pampean semiarid region. The vast majority of studies conducted on endophytic microorganisms have focused on intensive crop legume species. In contrast, little attention has been paid to microorganisms of native legumes, whose ecology is not directly affected by human action.… More >

Zhenyu Qian¹, Yizhang Jiang¹, Zhou Hong¹, Lijun Huang², Fengda Li³, KhinWee Lai⁶, Kaijian Xia^4,5,6,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920

Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More > Graphic Abstract