Sixian Chan^1,2, Binghui Wu¹, Guodao Zhang³, Yuan Yao⁴, Hongqiang Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485

Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection methods have the potential… More > Graphic Abstract

Xiaoli Li, Tongtong Jiao^#, Jinfeng Ma, Dongxing Duan, Shengbin Liang^#,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 595-617, 2024, DOI:10.32604/cmes.2023.029367

Abstract In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles… More > Graphic Abstract

Huafeng Chen¹, Junxing Xue², Hanyun Wen², Yurong Hu¹, Yudong Zhang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 301-320, 2024, DOI:10.32604/cmes.2023.028738

Abstract Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters. Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection. To solve this problem, we present a hybrid ship detection framework which is named EfficientShip in this paper. The core parts of the EfficientShip are DLA-backboned object location (DBOL) and CascadeRCNN-guided object classification (CROC). The DBOL is responsible for finding potential ship objects, and the CROC is used to categorize the potential ship objects. We… More >

Oliva Debnath¹, Saptarshi Debnath¹, Sreyashi Karmakar², MD Tausif Mallick³, Himadri Nath Saha^4,*

Journal on Internet of Things, Vol.5, pp. 13-43, 2023, DOI:10.32604/jiot.2023.039391

Abstract The Internet of Things (IoT) will significantly impact our social and economic lives in the near future. Many Internet of Things (IoT) applications aim to automate multiple tasks so inactive physical objects can behave independently of others. IoT devices, however, are also vulnerable, mostly because they lack the essential built-in security to thwart attackers. It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment. As a result, the IoT designs that are now in use do not completely support all of the advancements that have been… More >

Denis Benka^*, Sabína Vašová, Michal Kebísek, Maximilián Strémy

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2519-2535, 2023, DOI:10.32604/iasc.2023.040799

Abstract Pattern recognition algorithms are commonly utilized to discover certain patterns, particularly in image-based data. Our study focuses on quasiperiodic oscillations (QPO) in celestial objects referred to as cataclysmic variables (CV). We are dealing with interestingly indistinct QPO signals, which we analyze using a power density spectrum (PDS). The confidence in detecting the latter using certain statistical approaches may come out with less significance than the truth. We work with real and simulated QPO data of a CV called MV Lyrae. Our primary statistical tool for determining confidence levels is sigma intervals. The aforementioned CV has scientifically proven QPO existence, but… More >

Ouyang Liu, Kun Li^*, Ziwei Yin, Deyun Gao, Huachun Zhou

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2955-2977, 2023, DOI:10.32604/iasc.2023.039995

Abstract Due to the many types of distributed denial-of-service attacks (DDoS) attacks and the large amount of data generated, it becomes a challenge to manage and apply the malicious behavior knowledge generated by DDoS attacks. We propose a malicious behavior knowledge base framework for DDoS attacks, which completes the construction and application of a multi-domain malicious behavior knowledge base. First, we collected malicious behavior traffic generated by five mainstream DDoS attacks. At the same time, we completed the knowledge collection mechanism through data pre-processing and dataset design. Then, we designed a malicious behavior category graph and malicious behavior structure graph for… More >

Seungmin Lee, Hyunghoon Kim, Haehyun Cho, Hyo Jin Jo^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2941-2954, 2023, DOI:10.32604/iasc.2023.039992

Abstract Modern vehicles are equipped with multiple Electronic Control Units (ECUs) that support various convenient driving functions, such as the Advanced Driver Assistance System (ADAS). To enable communication between these ECUs, the Controller Area Network (CAN) protocol is widely used. However, since CAN lacks any security technologies, it is vulnerable to cyber attacks. To address this, researchers have conducted studies on machine learning-based intrusion detection systems (IDSs) for CAN. However, most existing IDSs still have non-negligible detection errors. In this paper, we propose a new filtering-based intrusion detection system (FIDS) to minimize the detection errors of machine learning-based IDSs. FIDS uses… More >

Sun-Woo Yun¹, Na-Eun Park¹, Il-Gu Lee^1,2,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2897-2917, 2023, DOI:10.32604/iasc.2023.039940

Abstract As time and space constraints decrease due to the development of wireless communication network technology, the scale and scope of cyberattacks targeting the Internet of Things (IoT) are increasing. However, it is difficult to apply high-performance security modules to the IoT owing to the limited battery, memory capacity, and data transmission performance depending on the size of the device. Conventional research has mainly reduced power consumption by lightening encryption algorithms. However, it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security performance. In this study, we propose wake-up… More >

Jie Zhang, Jianxun Zhang^*, Bowen Li, Jie Cao, Yifan Guo

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2883-2895, 2023, DOI:10.32604/iasc.2023.039805

Abstract With the improvement of image editing technology, the threshold of image tampering technology decreases, which leads to a decrease in the authenticity of image content. This has also driven research on image forgery detection techniques. In this paper, a U-Net with multiple sensory field feature extraction (MSCU-Net) for image forgery detection is proposed. The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing. MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the… More >

Ahmed S. Almasoud^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2849-2863, 2023, DOI:10.32604/iasc.2023.039718

Abstract The Internet of Things (IoT) is considered the next-gen connection network and is ubiquitous since it is based on the Internet. Intrusion Detection System (IDS) determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour. In this background, the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification (EMML-CADC) model in an IoT environment. The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency. To attain this, the EMML-CADC model primarily employs a data… More >