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  • Open Access

    ARTICLE

    A Drone-Based Blood Donation Approach Using an Ant Colony Optimization Algorithm

    Sana Abbas1, Faraha Ashraf1, Fahd Jarad2,3,*, 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 - 06 February 2023

    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 >

  • Open Access

    ARTICLE

    Multi-Objective Optimization of External Louvers in Buildings

    Tzu-Chia Chen1, Ngakan Ketut Acwin Dwijendra2, I. Wayan Parwata3, Agata Iwan Candra4, Elsayed M. Tag El Din5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1305-1316, 2023, DOI:10.32604/cmc.2023.033274 - 06 February 2023

    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… More >

  • Open Access

    ARTICLE

    A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

    ZEKUN XIN1,#, YUDAN MA2,#, WEIQIANG SONG3, HAO GAO3, LIJUN DONG3, BAO ZHANG1,*, ZHILONG REN3,*

    BIOCELL, Vol.47, No.3, pp. 555-567, 2023, DOI:10.32604/biocell.2023.026254 - 03 January 2023

    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… More >

  • Open Access

    ARTICLE

    A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

    Chuan Wang1,*, 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 - 27 October 2022

    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… More >

  • Open Access

    ARTICLE

    Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

    S. Vanitha1,*, P. Balasubramanie2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 849-864, 2023, DOI:10.32604/iasc.2023.032324 - 29 September 2022

    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… More >

  • Open Access

    ARTICLE

    Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm

    C. Nandagopal1,*, 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 - 29 September 2022

    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… More >

  • Open Access

    ARTICLE

    Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine

    S. Keerthi1,*, P. Santhi2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1173-1188, 2023, DOI:10.32604/iasc.2023.029959 - 29 September 2022

    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… More >

  • Open Access

    ARTICLE

    Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

    Harish Gunigari1,*, S. Chitra2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3557-3571, 2023, DOI:10.32604/iasc.2023.029155 - 17 August 2022

    Abstract Real-time applications based on Wireless Sensor Network (WSN) technologies quickly lead to the growth of an intelligent environment. Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks. The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things (IoT) network is widely recognized. Sensor nodes are low-power devices with low power devices, storage, and quantitative processing capabilities. The existing system uses the Artificial Immune System-Particle Swarm Optimization method to minimize the energy and improve the network’s lifespan.… More >

  • Open Access

    ARTICLE

    An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization

    Lina M. K. Al-Ebbini*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1971-1985, 2023, DOI:10.32604/iasc.2023.030100 - 19 July 2022

    Abstract A relationship between lung transplant success and many features of recipients’/donors has long been studied. However, modeling a robust model of a potential impact on organ transplant success has proved challenging. In this study, a hybrid feature selection model was developed based on ant colony optimization (ACO) and k-nearest neighbor (kNN) classifier to investigate the relationship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing (UNOS). The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’ functional status (i.e.,… More >

  • Open Access

    ARTICLE

    Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

    Dalel Ben Ismail1, Hela Elmannai2,*, Souham Meshoul2, Mohamed Saber Naceur1

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2219-2236, 2023, DOI:10.32604/iasc.2023.028944 - 19 July 2022

    Abstract Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity… More >

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