Zhonghao Guo^*, Xinyue Xu, Xiangxian Chen

Computer Systems Science and Engineering, Vol.48, No.2, pp. 479-509, 2024, DOI:10.32604/csse.2023.043932

Abstract In software testing, the quality of test cases is crucial, but manual generation is time-consuming. Various automatic test case generation methods exist, requiring careful selection based on program features. Current evaluation methods compare a limited set of metrics, which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment. To address this, we propose an evaluation tool, the Test Case Generation Evaluator (TCGE), based on the learning to rank (L2R) algorithm. Unlike previous approaches, our method comprehensively evaluates algorithms by considering multiple metrics, resulting in a more reasoned assessment. The… More >

Amal Hkiri^1,*, Mouna Karmani¹, Omar Ben Bahri², Ahmed Mohammed Murayr², Fawaz Hassan Alasmari², Mohsen Machhout¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 227-247, 2024, DOI:10.32604/cmc.2023.047087

Abstract The RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem. Despite its significance, RPL’s susceptibility to attacks remains a concern. This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static and mobile network environments. We employ the Random Direction Mobility Model (RDM) for mobile scenarios within the Cooja simulator. Our systematic evaluation focuses on critical performance metrics, including Packet Delivery Ratio (PDR), Average End to End Delay (AE2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption… More >

Liufeng Du^1,*, Shaoru Shang¹, Linghua Zhang², Chong Li¹, Jianing Yang³, Xiyan Tian¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1749-1767, 2024, DOI:10.32604/cmes.2023.030144

Abstract Due to the fine-grained communication scenarios characterization and stability, Wi-Fi channel state information (CSI) has been increasingly applied to indoor sensing tasks recently. Although spatial variations are explicitly reflected in CSI measurements, the representation differences caused by small contextual changes are easily submerged in the fluctuations of multipath effects, especially in device-free Wi-Fi sensing. Most existing data solutions cannot fully exploit the temporal, spatial, and frequency information carried by CSI, which results in insufficient sensing resolution for indoor scenario changes. As a result, the well-liked machine learning (ML)-based CSI sensing models still struggling with stable performance. This paper formulates a… More >

Jebran Khan¹, Kashif Ahmad², Kyung-Ah Sohn^1,3,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159

Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme is to generate adversarial examples… More >

T. E. Boukelia^1,2,*, A. Bourouis¹, M. E. Abdesselem³, M. S. Mecibah³

Energy Engineering, Vol.120, No.11, pp. 2449-2467, 2023, DOI:10.32604/ee.2023.030183

Abstract Concentrating Solar Power (CSP) plants offer a promising way to generate low-emission energy. However, these plants face challenges such as reduced sunlight during winter and cloudy days, despite being located in high solar radiation areas. Furthermore, their dispatch capacities and yields can be affected by high electricity consumption, particularly at night. The present work aims to develop an off-design model that evaluates the hourly and annual performances of a parabolic trough power plant (PTPP) equipped with a waste heat recovery system. The study aims to compare the performances of this new layout with those of the conventional Andasol 1 plant,… More >

Shun Ma¹, Na Yu¹, Xiuna Wang², Shiyan Mei¹, Mingrui Zhao^2,*, Xiaoyu Han²

Energy Engineering, Vol.120, No.10, pp. 2369-2392, 2023, DOI:10.32604/ee.2023.028727

Abstract Using the improved prospect theory with the linear transformations of rewarding good and punishing bad (RGPBIT), a new investment ranking model for power grid construction projects (PGCPs) is proposed. Given the uncertainty of each index value under the market environment, fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones. Taking into account decision-maker’s subjective risk attitudes, a multi-criteria decision-making (MCDM) method based on improved prospect theory is proposed. First, the [−1, 1] RGPBIT operator is proposed to normalize the original data, to obtain the best and worst schemes of PGCPs. Furthermore, the… More >

Seshu Bhavani Mallampati¹, Seetha Hari^2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1721-1744, 2023, DOI:10.32604/cmc.2023.040567

Abstract Expanding internet-connected services has increased cyberattacks, many of which have grave and disastrous repercussions. An Intrusion Detection System (IDS) plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks. Although extensive research was reported in IDS, detecting novel intrusions with optimal features and reducing false alarm rates are still challenging. Therefore, we developed a novel fusion-based feature importance method to reduce the high dimensional feature space, which helps to identify attacks accurately with less false alarm rate. Initially, to improve training data quality, various preprocessing techniques are utilized. The Adaptive Synthetic oversampling… More >

Popuri Srinivasarao, Aravapalli Rama Satish^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1527-1547, 2023, DOI:10.32604/csse.2023.038951

Abstract Responding to complex analytical queries in the data warehouse (DW) is one of the most challenging tasks that require prompt attention. The problem of materialized view (MV) selection relies on selecting the most optimal views that can respond to more queries simultaneously. This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs. The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique (ECHT). The constraints such as self-adaptive penalty, epsilon (ε)-parameter and stochastic… More >

Muhammad Hameed Siddiqi^1,*, Asfandyar Khan², Muhammad Bilal Khan², Abdullah Khan², Madallah Alruwaili¹, Saad Alanazi¹

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2219-2239, 2023, DOI:10.32604/csse.2023.040212

Abstract The use of the internet is increasing all over the world on a daily basis in the last two decades. The increase in the internet causes many sexual crimes, such as sexual misuse, domestic violence, and child pornography. Various research has been done for pornographic image detection and classification. Most of the used models used machine learning techniques and deep learning models which show less accuracy, while the deep learning model ware used for classification and detection performed better as compared to machine learning. Therefore, this research evaluates the performance analysis of intelligent neural-based deep learning models which are based… More >

Liqing Yan¹, Jiang Liu^1,2,*, Guangwei Ying³, Ning Zhang⁴

Energy Engineering, Vol.120, No.8, pp. 1919-1938, 2023, DOI:10.32604/ee.2023.027546

Abstract Recovery of waste heat from boiler flue gas is an effective way to improve energy utilization efficiency. Taking a heating station heating project as an example, the existing heating system of this heating station was analyzed for its underutilized flue gas waste heat and low energy utilization rate. Rankine cycle is an effective waste heat recovery method, and a steam boiler organic Rankine cycle (ORC) cogeneration waste heat utilization method is proposed. The system model simulation is constructed and verified. First, a thermodynamic model was constructed in MATLAB and five suitable work gases were selected to analyze the effects of… More > Graphic Abstract