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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Computer Science
Pathipati, Harikrishna
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Economics, Econometrics & Finance

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Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks Arunachalam, Sripriya; Vijaya Kumar, Ashok Kumar; Reddy, Desidi Narsimha; Pathipati, Harikrishna; Priyadarsini, Nethala Indira; Babu Ramisetti, Lova Naga
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp221-230

Abstract

For smart environments in the digital age, wireless sensor networks (WSNs) are needed. Node localization (NL) in WSNs is complicated for recent researchers. WSN localization focuses on finding sensor nodes (SNs) in two dimensions. WSN NL provides decision-making information in packets sent to base stations. This article describes modeling of chimp optimization algorithm node localization system in wireless sensor networks (MCOANL-WSN). The MCOANL-WSN approach uses metaheuristic optimization to locate unknown network nodes. To simulate chimpanzees' cooperative hunting behavior, the MCOANL-WSN approach includes chimp optimization algorithm (COA) into the NL process. The system uses mathematical modeling to represent node collaboration to improve placements. COA-based localization is being proposed for dynamically responding to resource-constrained and dynamic WSNs. Wide-ranging simulations may assess the MCOANL-WSN system's scalability, energy efficiency, and localization accuracy. The findings demonstrate the superiority of the new modeling method over current NL schemes in improving WSN reliability and efficiency in various applications.
Utilizing metaheuristic optimization with transfer learning for efficient colorectal carcinoma detection in biomedical imaging Babu Ramisetti, Lova Naga; Reddy, Desidi Narsimha; Pathipati, Harikrishna; Srividya, Yenumula; Pesaru, Swetha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1693-1703

Abstract

Colorectal cancer (CRC) is the third most popular cancer across the world. Its morbidity and death are reduced by early screening and detection. The screening outcomes are enhanced by computer-aided detection (CAD) and artificial intelligence (AI) in screening models. Contemporary imaging technologies such as near-infrared (NIR) fluorescence and optical coherence tomography (OCT) are implemented to identify the early-phase CRC of the gastrointestinal tract (GI tract) via the identification of morphological and microvasculature changes. Most recently, deep learning (DL)-based approaches have been used directly on raw data. Nevertheless, they are hampered by biomedical data deficiency. These studies can enhance metaheuristic optimization using the transfer learning to detect colorectal cancer successfully (MHOTL-ECRCD). The MHOTL-ECRCD method concentrates on biomedical imaging of CRC categorization and detection. MHOTL-ECRCD minimizes noise through the process of adaptive bilateral filtering (ABF). In MHOTL-ECRCD methodology, Inception-ResNet-V2 is adopted to learn the inherent and complicated image preprocessing features thus used during feature extraction. To classify CRC and detect it, the gated recurrent unit (GRU) approach is applied. Lastly, parameters of the GRU model are optimized with a human evolutionary algorithm. Good classification results of MHOTL-ECRCD are demonstrated by a number of benchmark dataset trials. MHOTL-ECRCD technology superseded the recent techniques as large volumes of comparison were made.
Co-Authors Arunachalam, Sripriya Babu Ramisetti, Lova Naga Pesaru, Swetha Priyadarsini, Nethala Indira Reddy, Desidi Narsimha Srividya, Yenumula Vijaya Kumar, Ashok Kumar