B. Keerthi Samhitha^*, R. Subhashini

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3125-3142, 2023, DOI:10.32604/iasc.2023.040643

Abstract Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management decisions about recirculating the aquaculture system while decreasing labor. The classic detection approach involves placing sensors on the skin or body of the fish, which may interfere with typical behavior and welfare. The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy. This study develops an intelligent fish behavior classification using… More >

K. Naveen Durai^*, R. Subha, Anandakumar Haldorai

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 467-483, 2022, DOI:10.32604/iasc.2022.026020

Abstract In cloud computing, the processes of load balancing and task scheduling are major concerns as they are the primary mechanisms responsible for executing tasks by allocating and utilizing the resources of Virtual Machines (VMs) in a more optimal way. This problem of balancing loads and scheduling tasks in the cloud computing scenario can be categorized as an NP-hard problem. This problem of load balancing needs to be efficiently allocated tasks to VMs and sustain the trade-off among the complete set of VMs. It also needs to maintain equilibrium among VMs with the objective of maximizing throughput with a minimized time… More >

Eslam Mohammed Abdelkader^1,2,*, Osama Moselhi³, Mohamed Marzouk⁴, Tarek Zayed⁵

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 643-661, 2020, DOI:10.32604/iasc.2020.010100

Abstract Image segmentation is one of the fundamental stages in computer vision applications. Several meta-heuristics have been applied to solve the segmentation problems by extending the Otsu and entropy functions. However, no single-objective function can optimally handle the diversity of information in images besides the multimodality issues of gray-level images. This paper presents a self-adaptive multi-objective optimization-based method for the detection of crack images in reinforced concrete bridges. The proposed method combines the flexibility of information theory functions in addition to the invasive weed optimization algorithm for bi-level thresholding. The capabilities of the proposed method are demonstrated through comparisons with singleobjective… More >