Quy Thy Truong¹ , Guillaume Touya², Cyril de Runz³

Revue Internationale de Géomatique, Vol.29, No.1, pp. 31-56, 2019, DOI:10.3166/rig.2019.00073

Abstract Since vandalism is a serious matter for the quality of Volunteered Geographic Information, this paper aims at exploring machine learning techniques that enable its detection. First, a focus on the various definitions of vandalism highlights the complexity of this concept. This focus comprises a case study on proven vandals in OpenStreetMap (OSM). Second, we present an experimental vandalism detection on OSM data using a clustering-based outlier detection. The analysis of initial results leads to a discussion about the construction of an OSM vandalism corpus that would be useful in a supervised learning context.

RÉSUMÉ
Dans un contexte où le vandalisme de… More >

Chamseddine Zaki¹ , Mohamed Ayet^1,2, Allah Bilel Soussi²

Revue Internationale de Géomatique, Vol.29, No.3, pp. 255-262, 2019, DOI:10.3166/rig.2019.00086

Abstract A conceptual spatiotemporal data model must be able to offer users a semantic richness of expression to meet their diverse needs concerning the modeling of spatio-temporal data. The conceptual spatiotemporal data model must be able to represent the objects, relationships and events that can occur in a field of study, track data history, support the multirepresentation of these data, and represent temporal and spatial data with two and three dimensions features. The model must also allow the assignment of different types of constraints to relations and provide a complete orthogonality between dimensions and concepts. The MADS model meets several requirements… More >

Mireille Fargette¹ , Maud Loireau², Najet Raouani³ , Thérèse Libourel⁴

Revue Internationale de Géomatique, Vol.31, No.1, pp. 135-197, 2022, DOI:10.3166/RIG.31.135-197

Abstract This work is interested in observation, in scientific knowledge acquired from what is perceived (Link making Sense) from a complex systemic world. The approach leads to proposing the concept of imprint within the interdisciplinary framework “System – Reality – World as perceived – Model” and testing it against data, then to proposing systemic ontology as an approach. This makes it possible to deploy the Link making Shape from the systemic domain to the world as perceived, to analyze and describe the relevant part in the data and to show how the whole of this mostly symbolic work can contribute, with… More >

Mingjie Zhang^a , Kai Yang^a, Le Qin^b, Xiaole Yao^b, Qian Liu^b, Xing Ju^b,*

Frontiers in Heat and Mass Transfer, Vol.19, No.1, pp. 1-10, 2022, DOI:10.5098/hmt.19.13

Abstract Lithium-ion batteries used for energy storage systems will release amount of heat during operation. It will cause serious consequences of thermal runaway if not dissipate in time. In this study, a self-forming air-cooled battery rack of the energy storage system is established based on the normal battery rack for energy storage and the shape of the energy storage battery itself. The frames of the battery rack acts as air ducts, which greatly reduce the system complexity. In this paper, the heat generation model is established based on the experiment, and the four battery rack forms are studied by CFD simulation.… More >

Tianyu Xu, Zhouming Su, Yanru Su, Zonglei Li, Quanjie Chen, Shuteng Zhi, Ennan Zheng^*, Kaili Meng

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.8, pp. 2147-2160, 2023, DOI:10.32604/fdmp.2023.025556

Abstract The leaf-vein drip irrigation emitter is a new type of drip emitter based on a bionic structure able to support shunting, sharp turns, and increased dissipation. In the present work, the results of twenty-five tests executed in the framework of an orthogonal design strategy are presented in order to clarify the influence of the geometrical parameters of the flow channel on the hydraulic characteristics of such emitter. The corresponding flow index and head loss coefficient are determined through numerical simulations and model testing. The results show that the flow index of the flow channel is 0.4970∼0.5461, which corresponds to good… More >

Nur Laila Ab Ghani^1,2,*, Izzatdin Abdul Aziz^1,2, Said Jadid AbdulKadir^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4649-4668, 2023, DOI:10.32604/cmc.2023.035987

Abstract Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature… More >

Emad Alsuwat^1,*, Suhare Solaiman¹, Hatim Alsuwat²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3743-3759, 2023, DOI:10.32604/cmc.2023.035126

Abstract Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network.… More >

JUAN C. STOCKERT^1,2,*, SILVINA A. ROMERO¹, MARCELO N. FELIX-POZZI³, ALFONSO BLÁZQUEZ-CASTRO⁴

BIOCELL, Vol.47, No.4, pp. 919-928, 2023, DOI:10.32604/biocell.2023.026631

Abstract

The ¹⁰boron neutron-capture therapy (BNCT) is an emerging antitumoral method that shows increasing biomedical interest. BNCT is based on the selective accumulation of the ¹⁰boron isotope within the tumor, which is then irradiated with low-energy thermal neutrons, generating nuclear fission that produces ⁷lithium, ⁴helium, and γ rays. Simple catechol-borate esters have been rather overlooked as precursors of melanin biosynthesis, and therefore, a proof-of-concept approach for using dopamine-borate (DABO) as a suitable boron-containing candidate for potential BNCT is presented here. DABO can spontaneously oxidize and autopolymerize in vitro, giving a soluble, eumelanin-like brown-black poly-DABO product. Melanotic melanoma cell cultures treated with… More >

Abdul Sattar Palli^1,6,*, Jafreezal Jaafar^1,2, Manzoor Ahmed Hashmani^1,3, Heitor Murilo Gomes^4,5, Aeshah Alsughayyir⁷, Abdul Rehman Gilal¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1827-1845, 2023, DOI:10.32604/cmc.2023.033934

Abstract Every application in a smart city environment like the smart grid, health monitoring, security, and surveillance generates non-stationary data streams. Due to such nature, the statistical properties of data changes over time, leading to class imbalance and concept drift issues. Both these issues cause model performance degradation. Most of the current work has been focused on developing an ensemble strategy by training a new classifier on the latest data to resolve the issue. These techniques suffer while training the new classifier if the data is imbalanced. Also, the class imbalance ratio may change greatly from one input stream to another,… More >

Idris Rabiu^1,3,*, Naomie Salim², Maged Nasser^1,4, Aminu Da’u¹, Taiseer Abdalla Elfadil Eisa⁵, Mhassen Elnour Elneel Dalam⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6001-6017, 2023, DOI:10.32604/cmc.2023.035221

Abstract Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents an adaptive unsupervised learning technique,… More >