Abdulaziz Attaallah¹, Khalil al-Sulbi², Areej Alasiry³, Mehrez Marzougui³, Mohd Waris Khan^4,*, Mohd Faizan⁴, Alka Agrawal⁵, Dhirendra Pandey⁵

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3165-3195, 2023, DOI:10.32604/csse.2023.040259

Abstract Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using the Average Percentage of Fault… More >

Abdul Rauf¹, Muhammad Asif Khan^1,*, Hamid Hussain Awan², Waseem Shahzad³, Najeeb Ul Husaan⁴

Journal of Cyber Security, Vol.4, No.4, pp. 201-222, 2022, DOI:10.32604/jcs.2022.038791

Abstract In the modern world, law enforcement authorities are facing challenges due to the advanced technology used by criminals to commit crimes. Criminals follow specific patterns to carry out their crimes, which can be identified using machine learning and swarm intelligence approaches. This article proposes the use of the Ant Colony Optimization algorithm to create an associative classification of crime data, which can reveal potential relationships between different features and crime types. The experiments conducted in this research show that this approach can discover various associations among the features of crime data and the specific patterns that major crime types depend… More >

Abida Sharif¹, Imran Sharif¹, Muhammad Asim Saleem², Muhammad Attique Khan³, Majed Alhaisoni⁴, Marriam Nawaz^5,6, Abdullah Alqahtani⁷, Ye Jin Kim⁸, Byoungchol Chang^9,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5379-5393, 2023, DOI:10.32604/cmc.2023.034413

Abstract The Internet of Vehicles (IoV) is a networking paradigm related to the intercommunication of vehicles using a network. In a dynamic network, one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion. Therefore, optimal path selection to route traffic between the origin and destination is vital. This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access. Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO) algorithm and formulated the path planning optimization problem as an… More >

Sana Abbas¹, Faraha Ashraf¹, Fahd Jarad^2,3,*, Muhammad Shoaib Sardar¹, Imran Siddique⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1917-1930, 2023, DOI:10.32604/cmes.2023.024700

Abstract This article presents an optimized approach of mathematical techniques in the medical domain by manoeuvring the phenomenon of ant colony optimization algorithm (also known as ACO). A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem (often TSP). The wide use promises to accelerate and offers the opportunity to cultivate health care, particularly in remote or unmerited environments by shrinking lab testing reversal times, empowering just-in-time lifesaving medical supply. More >

Tzu-Chia Chen¹, Ngakan Ketut Acwin Dwijendra², I. Wayan Parwata³, Agata Iwan Candra⁴, Elsayed M. Tag El Din^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1305-1316, 2023, DOI:10.32604/cmc.2023.033274

Abstract Because solar energy is among the renewable energies, it has traditionally been used to provide lighting in buildings. When solar energy is effectively utilized during the day, the environment is not only more comfortable for users, but it also utilizes energy more efficiently for both heating and cooling purposes. Because of this, increasing the building’s energy efficiency requires first controlling the amount of light that enters the space. Considering that the only parts of the building that come into direct contact with the sun are the windows, it is essential to make use of louvers in order to regulate the… More >

ZEKUN XIN^1,#, YUDAN MA^2,#, WEIQIANG SONG³, HAO GAO³, LIJUN DONG³, BAO ZHANG^1,*, ZHILONG REN^3,*

BIOCELL, Vol.47, No.3, pp. 555-567, 2023, DOI:10.32604/biocell.2023.026254

Abstract Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence. Methods: To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO,… More >

Chuan Wang^1,*, Ruoyu Zhu², Yi Jiang³, Weili Liu⁴, Sang-Woon Jeon⁵, Lin Sun², Hua Wang⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1209-1228, 2023, DOI:10.32604/cmes.2022.022807

Abstract The dynamic traveling salesman problem (DTSP) is significant in logistics distribution in real-world applications in smart cities, but it is uncertain and difficult to solve. This paper proposes a scheme library-based ant colony optimization (ACO) with a two-optimization (2-opt) strategy to solve the DTSP efficiently. The work is novel and contributes to three aspects: problem model, optimization framework, and algorithm design. Firstly, in the problem model, traditional DTSP models often consider the change of travel distance between two nodes over time, while this paper focuses on a special DTSP model in that the node locations change dynamically over time. Secondly,… More >

S. Vanitha^1,*, P. Balasubramanie²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 849-864, 2023, DOI:10.32604/iasc.2023.032324

Abstract Internet of things (IOT) possess cultural, commercial and social effect in life in the future. The nodes which are participating in IOT network are basically attracted by the cyber-attack targets. Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain. Machine Learning Based Ensemble Intrusion Detection (MLEID) method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport (MQTT) and Hyper-Text Transfer Protocol (HTTP) protocols. The proposed work has two significant contributions which are a selection of features and detection of attacks. New… More >

C. Nandagopal^1,*, P. Siva Kumar², R. Rajalakshmi³, S. Anandamurugan⁴

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 113-126, 2023, DOI:10.32604/iasc.2023.031103

Abstract Vehicle Ad hoc Networks (VANETs) have high mobility and a randomized connection structure, resulting in extremely dynamic behavior. Several challenges, such as frequent connection failures, sustainability, multi-hop data transfer, and data loss, affect the effectiveness of Transmission Control Protocols (TCP) on such wireless ad hoc networks. To avoid the problem, in this paper, mobility-aware zone-based routing in VANET is proposed. To achieve this concept, in this paper hybrid optimization algorithm is presented. The hybrid algorithm is a combination of Ant colony optimization (ACO) and artificial bee colony optimization (ABC). The proposed hybrid algorithm is designed for the routing process which… More >

S. Keerthi^1,*, P. Santhi²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1173-1188, 2023, DOI:10.32604/iasc.2023.029959

Abstract The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors. The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant. Most tumors are misdiagnosed due to the variability and complexity of lesions, which reduces the survival rate in patients. Diagnosis of brain tumors via computer vision algorithms is a challenging task. Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures. Traditional brain tumor identification techniques require manual segmentation or handcrafted… More >