Qiqi Zhang¹, Shaojin Geng², Xingjuan Cai^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1863-1900, 2023, DOI:10.32604/cmes.2023.022287

Abstract Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. This paper reviews the concepts,… More > Graphic Abstract

Reem Almutiri^*, Sarah Alhabeeb, Sarah Alhumud, Rehan Ullah Khan

Journal on Big Data, Vol.4, No.2, pp. 97-111, 2022, DOI:10.32604/jbd.2022.028363

Abstract Today’s world is a data-driven one, with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives. New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms. Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it. This study conducts a literature review on the application of machine learning techniques in big data processing. It provides a general overview of machine learning… More >

Junxiang Yin^*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4601-4629, 2023, DOI:10.32604/cmc.2023.034190

Abstract The objective of crime prediction, one of the most important technologies in social computing, is to extract useful information from many existing criminal records to predict the next process-related crime. It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas. With the rapid growth of big data, the Internet of Things, and other technologies, as well as the increasing use of artificial intelligence in forecasting models, crime prediction models based on deep learning techniques are accelerating. Therefore, it is necessary to classify the existing crime prediction algorithms and compare in depth… More >

Jieren Cheng^1,3, Hua Li^2,*, Dengbo Li³, Shuai Hua², Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1941-1957, 2023, DOI:10.32604/cmc.2023.032757

Abstract Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation… More >

Theerapat Sangpetch¹, Tossapon Boongoen^1,*, Natthakan Iam-On²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1641-1655, 2023, DOI:10.32604/cmc.2023.026739

Abstract Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined… More >

Zheng Zhang, Juan Chen^*, Qing Guo

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1529-1563, 2023, DOI:10.32604/cmes.2022.021451

Abstract Automated Guided Vehicles (AGVs) have been introduced into various applications, such as automated warehouse systems, flexible manufacturing systems, and container terminal systems. However, few publications have outlined problems in need of attention in AGV applications comprehensively. In this paper, several key issues and essential models are presented. First, the advantages and disadvantages of centralized and decentralized AGVs systems were compared; second, warehouse layout and operation optimization were introduced, including some omitted areas, such as AGVs fleet size and electrical energy management; third, AGVs scheduling algorithms in chessboardlike environments were analyzed; fourth, the classical route-planning algorithms for single AGV and multiple… More > Graphic Abstract

Belayneh Yitayew^1,*, Wondwossen Bogale²

Energy Engineering, Vol.119, No.6, pp. 2513-2532, 2022, DOI:10.32604/ee.2022.018961

Abstract Energy is one of the most important factors in socio-economic development. The rapid increase in energy demand and air pollution has increased the number of ways to generate energy in the power sector. Currently, wind energy capacity in Ethiopia is estimated at 10,000 MW. Of these, however, only eight percent of its capacity has been used in recent years. One of the reasons for the low use of wind energy is the lack of accurate wind atlases in the country. Therefore, the purpose of this study is to develop an accurate wind atlas and review the wind resources using Wind… More >

Shuna Peng¹, Xiaohong Luo¹, Shiyu Liang¹, Fengning Deng¹, Yuning Liu², Hong Zeng^1,*, Xuesong Yang^3,*

International Journal of Mental Health Promotion, Vol.24, No.5, pp. 711-724, 2022, DOI:10.32604/ijmhp.2022.020556

Abstract Background: The coronavirus (COVID-19) outbreak in 2019 triggered psychological and emotional responses. This research investigates the psychological status and emotional problems of those who sought psychological assistance during the epidemic period by calling a mental health hotline. Methods: This study aims to combine qualitative and quantitative research. Descriptive analysis was used for undertaking qualitative research. We analyzed the data from group 1 (n = 706), in which the people used the mental health hotline from 25 January 2020 to 23 June 2020. A self-designed questionnaire was developed in accordance with the classification and summarized items from group 1’s psychological problems… More >

Gabriela Nazar^1,2,*, Carlos-María Alcover³, Yeny Concha-Cisternas^4,5, Igor Cigarroa⁵, Ximena Díaz-Martínez⁶, Mariela Gatica-Saavedra⁷, Fabián Lanuza^8,9, Ana María Leiva-Ordónez¹⁰, María Adela Martínez-Sanguinetti¹¹, Miquel Martorell^2,12, Fanny Petermann-Rocha^13,14, Claudia Troncoso-Pantoja¹⁵, Carlos Celis-Morales¹⁶

International Journal of Mental Health Promotion, Vol.24, No.5, pp. 679-697, 2022, DOI:10.32604/ijmhp.2022.020105

Abstract Depressive disorders are recognized as one of the most common mental health conditions across different age groups. However, the risk factors associated with depression among older people from low-and middle-income countries remains unclear. This study aims to identify socio-demographic, health and psychosocial-related factors associated with depression in Chilean older adults. A cross-sectional study was carried out in a representative sample of 1,765 adults aged ≥60 years participants from the Chilean National Health Survey 2016–2017. Depression was assessed with the Composite International Diagnostic Interview (CIDI-SF). Associations between the exposure variables and depression were investigated using Poisson regression analyses. The main findings… More >

Haitao Zhang^*

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 41-51, 2022, DOI:10.32604/jihpp.2022.031707

Abstract Deep learning related technologies, especially generative adversarial network, are widely used in the fields of face image tampering and forgery. Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery, especially face images, but there is still a lack of overview of anti-forensic methods at this stage. Therefore, this paper will systematically discuss the anti-forensic methods for face image tampering and forgery. Firstly, this paper expounds the relevant background, including the relevant tampering and forgery methods and forensic schemes of face images. The former mainly includes four aspects: conventional processing, fake face… More >