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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Puliyanjalil, Ezudheen
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Golden jackal optimization-based clustering scheme for energy-aware vehicular ad-hoc networks Baladhandapani, Mahalakshmi; Kamal, Shoaib; Kumar, Chevella Anil; Balakrishnan, Jegajothi; Praveena, Segu; Puliyanjalil, Ezudheen
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp942-951

Abstract

Clustering in vehicular ad-hoc networks (VANETs) plays a pivotal role in enhancing the reliability and efficiency of transmission among vehicles. VANET is a dynamic and highly mobile network where vehicles form clusters to enable effective data exchange, resource allocation, and cooperative actions. Clustering algorithm, helps vehicles self-organize into clusters based on connectivity and proximity, thus improving scalability and reducing transmission overhead. This cluster enables critical applications such as traffic management, collision avoidance, and data dissemination in VANET, which contribute to more efficient and safer transportation systems. Effective clustering strategy remains an active area of research to address the unique challenges posed by the diverse and rapidly changing environments of VANET. Therefore, this article presents a golden jackal optimization-based energy aware clustering scheme (GJO-EACS) approach for VANET. The presented GJO-EACS technique uses a dynamic clustering approach which adapts to the varying network topologies and traffic conditions, intending to extend the network lifetime and improve energy utilization. The results highlight the potential of the GJO-EACS technique to contribute to the sustainable operation of VANETs, making it a valuable contribution to the field of vehicular networking and smart transportation systems.
Hybrid deep learning with pelican optimization algorithm for M2M communication on UAV image classification Sharmili, Kasturi Chandrahaasan; Kumar, Chevella Anil; Subbaiyan, Arunmurugan; Beena Bethel, Gundemadugula Nelson; Puliyanjalil, Ezudheen; Sapkale, Pallavi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1526-1534

Abstract

Machine-to-machine (M2M) communication for unmanned aerial vehicle (UAVs) and image classification is essential to current remote sensing and data processing. UAVs and ground stations or other linked devices exchange information seamlessly using M2M communication. M2M connectivity helps UAVs with cameras and sensors communicate aerial pictures in real time or post-mission for image categorization and analysis. During flight, UAVs acquire massive volumes of picture data. Image classification, commonly using deep learning (DL) methods like convolutional neural network (CNN), automatically categorizes and annotates photos based on predetermined classes or attributes. This work uses UAV photos to produce hybrid deep learning with pelican optimization algorithm for M2M communication (HDLPOA-M2MC). HDLPOA-M2MC automates UAV picture class identification. GhostNet model is used to derive features in HDLPOA-M2MC. The HDLPOA-M2MC approach leverages pelican optimization algorithm (POA) for hyperparameter adjustment in this investigation. Finally, autoencoder-deep belief network (AE-DBN) model can classify. The HDLPOA-M2MC method’s enhanced outcomes were shown by several studies. The complete results showed that HDLPOA M2MC performed better across measures.
Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis Manoharan, Thiyagarajan; Velvizhi, Ramalingam; Juluru, Tarun Kumar; Kamal, Shoaib; Mallick, Shrabani; Puliyanjalil, Ezudheen
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1670-1679

Abstract

Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL.
Co-Authors Baladhandapani, Mahalakshmi Balakrishnan, Jegajothi Beena Bethel, Gundemadugula Nelson Juluru, Tarun Kumar Kamal, Shoaib Kumar, Chevella Anil Mallick, Shrabani Manoharan, Thiyagarajan Praveena, Segu Sapkale, Pallavi Sharmili, Kasturi Chandrahaasan Subbaiyan, Arunmurugan Velvizhi, Ramalingam