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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Reddy, Desidi Narsimha
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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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.
HybridPPI: A Hybrid Machine Learning Framework for Protein-Protein Interaction Prediction Reddy, Desidi Narsimha; Venkateswararao, Pinagadi; Vani, M. Sree; Pranathi, Vodapelli; Patil, Anitha
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6278

Abstract

Protein-protein interactions (PPIs) are key to cellular functions and disease mechanisms and are crucial for drug discovery and systems biology. Though experimental approaches, including yeast two-hybrid systems, provide informative discoveries, they are time-consuming, costly, and frequently yield significant false-positive rates. Newer computational tools, including DeepPPI and PIPR, have demonstrated their potential, but their reliance on single-modal features or specific machine-learning models limits their generalization and robustness. These limitations highlight the need for an enhanced framework that assimilates different types of features while integrating a diverse array of machine learning models to exploit the strengths offered by each model class. In this paper, we present a hybrid machine learning framework, HybridPPI, to effectively incorporate the power of sequence-based, structure-based, and network-based features based on wellknown ensemble learning techniques to predict PPIs. Our proposed algorithm is a stacking ensemble of multiple models (Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM)), with Gradient Boosting as the metamodel. Results show that HybridPPI (94.5% accuracy, 95.2% precision, and Area Under Curve of 0.97) outperforms the most advanced methods, indicating its robustness for PPI prediction. This scalable and generalizable framework can accommodate various biological applications. HybridPPI overcomes significant shortcomings of current methodologies and contributes to biological discovery. 
DCDNet: A Deep Learning Framework for Automated Detection and Localization of Dental Caries Using Oral Imagery Reddy, Desidi Narsimha; Venkateswararao, Pinagadi; Patil, Anitha; Srikanth, Geedikanti; Chinnareddy, Varalakshmi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6245

Abstract

Dental caries is a common oral health condition that requires early diagnosis and identification for effective intervention. Existing deep models, such as Faster R-CNN, YOLOv3, SSD, or RetinaNet, exhibit great effectiveness in generic medical imaging; however, they struggle to precisely and explicitly handle localization in complex dental radiographs. In this paper, we propose DCDNet, a convolutional neural network architecture specifically designed for the detection and segmentation of dental caries in oral X-ray images. However, such deep learning methods currently lack strong generalization due to imbalanced training data, limited lesion-localization ability, and noninterpretable features, which hamper their utility for large-scale clinical evaluation. In addition, most models overlook the severity distinction between classes, which is less ideal for the entire diagnosis and treatment planning process. DCDNet was trained and tested on the UFBA UESC Dental Image Dataset, which comprises over 1,500 labeled grayscale dental radiographic images. The proposed network incorporates multiscale feature extraction, residual connections, and non-maximum suppression (NMS) for more accurate classification and bounding box prediction. Data augmentation techniques were used to increase generalization. The model was evaluated based on accuracy, precision, recall, and F1-score, and compared with ResNet50, VGG16, AlexNet, Faster R-CNN, YOLOv3, SSD, and RetinaNet in terms of accuracy. DCDNet achieved excellent performance in all its performance indices, with precision at 97.23%, recall at 97.02%, F1-score at 97.12%, and overall accuracy at 97.61%. Experiments demonstrate that the proposed DCDNet surpasses all the baselines and state-of-the-art methods by a significant margin. Ablation experiments validated the importance of residual connections, NMS, and data augmentation for performance improvement. DCDNet represents a significant step toward automatic dental diagnosis, having successfully detected and localized carious lesions in X-ray images. Its design overcomes the drawbacks of previous models and is a ready option for integration into clinical routine.
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 Chinnareddy, Varalakshmi Pathipati, Harikrishna Patil, Anitha Pesaru, Swetha Pranathi, Vodapelli Priyadarsini, Nethala Indira Srikanth, Geedikanti Srividya, Yenumula Vani, M. Sree Venkateswararao, Pinagadi Vijaya Kumar, Ashok Kumar