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Nyayadhish, Renuka
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Fake News Detection in Model Integral: A Hybrid CNN-BiLSTM Model Nyayadhish, Renuka; Jadhav, Chaya; Bhupati, Ch; Mabel Rose, R.A.; Prabhu, M
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1058

Abstract

The act of recognizing news that intentionally spreads false information via social media or traditional news sources is known as fake news detection. The characteristics of fake news make it difficult to identify. The spread of fake news and misleading information has increased dramatically due to social media's role as a communication tool and the quick advancement of technology. There is an urgent need for automated and intelligent systems that can differentiate between authentic and fraudulent information due to the fast dissemination of unverified content. The proposed hybrid model efficiently captures regional and worldwide relationships in textual details to address this by combining multiscale residual CNN and BiLSTM layers. The BiLSTM layers manage contextual representations and sequential dependencies, while the CNN layers concentrate on extracting deep local features. The model's capacity to recognize patterns of deception in textual content and comprehend semantic flow is enhanced by this dual architecture. The Edge-IIoT set data and the IoT-23 information from Aposemat were utilized in this study to assess the suggested framework empirically. A concept based on information transfer and sophisticated adaptive systems, we provide an understanding of outliers management paradigm of "generation–spread–identification–refutation" for identifying false information during emergencies. Findings from experiments clearly illustrate the superiority of the BiLSTM approach, demonstrating not only its state-of-the-art efficacy in identifying fake news but also its significant edge over traditional machine learning algorithms. This highlights the BiLSTM approach's critical role in protecting our information ecosystems from the ubiquitous threat of misinformation.
Swarm Intelligence-Based Performance Optimization for Wireless Sensor Networks for Hole Detection Padmapriya, T; Jadhav, Chaya; Nyayadhish, Renuka; Kumar, Neeraj; Kaliappan, P
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1127

Abstract

Extensive research into maintaining coverage over time has been spurred by the growing need for wireless sensor networks to monitor certain regions.  Coverage gaps brought on either haphazard node placement or failures pose the biggest threat to this objective.  In order to identify and fix coverage gaps, this study suggests an algorithm based on swarm intelligence.  Using both local and relative information, the swarm of agents navigates a potential field toward the nearest hole and activates in reaction to holes found.  In order to spread out effectively and speed up healing, the agents quantize their perceptions and approach holes from various angles. The need for wireless sensor networks to monitor certain areas has grown, leading to many studies on maintaining coverage over time. Random node deployment or failures create coverage gaps, which pose the biggest threat to this objective.  A swarm intelligence-based approach is proposed in this paper to identify and fix coverage deficiencies. Even with Their encouraging performance and operational quality, WSNs are susceptible to various security threats. The security of WSNs is seriously threatened by sinkhole attacks, one of these. In this research, a detection strategy against sinkhole attacks is proposed and developed using the Swarm Intelligence (SI) optimization algorithm. MATLAB has been used to implement the proposed work, and comprehensive Models have been run to assess its effectiveness in terms of energy consumption, packet overhead, convergence speed, detection accuracy, and detection time. The findings demonstrate that the mechanism we have suggested is effective and reliable in identifying sinkhole attacks with a high rate of detection accuracy.
A CNN-Driven Image Analysis Approach for Accurate Detection of Plant Leaf Diseases Jha, Suresh Kumar; Misra, Yogesh; Nyayadhish, Renuka; Rawat, Manoj Kumar; K, Kiran Kumar; Neelam, Nagaveni
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1299

Abstract

Plant leaf diseases are a key concern for agriculture and result in significant loss of crop yield and economic losses globally. It is vital to efficiently and accurately detect plant diseases to properly manage crops and control their diseases. This paper demonstrates a CNN-based image analysis model to automatically identify and classify plant leaf diseases from digital images. Deep learning is used in the proposed method to spontaneously learn hierarchical features from original image data, without the use of feature engineering. The model was trained and evaluated on a collection of high-resolution healthy and diseased leaf images collected from different plant species. Preprocessing (normalisation, noise filtering, and contrast increment) and data augmentation (rotations, scale changes, and flips) were also performed on the pre-processed images, and it was expected to achieve good generalisation and reduce overfitting. The CNN architecture was optimised using transfer learning in combination with hyperparameter tuning. Evaluation experiments showed that the framework attained a classification “accuracy of 96.2%, 95.8% precision, 96.5% recall, and 96.1% F1-score”. The model proved to be robust under varying light conditions and complex background settings, demonstrating its real-world applicability. In addition, the model’s lightweight architecture supports mobile and edge computing implementation, enabling real-time and on-site diagnostic capabilities. This method provides an automated, scalable system for plant disease detection, thus enabling early intervention, reducing chemical treatment reliance, and fostering sustainable agricultural practices, fostering environmentally friendly approaches. The results demonstrated the capability of CNN systems towards transforming the plant health monitoring practices in precision agriculture.
Co-Authors Bhupati, Ch Jadhav, Chaya Jha, Suresh Kumar K, Kiran Kumar Kaliappan, P Kumar, Neeraj Mabel Rose, R.A. Misra, Yogesh Neelam, Nagaveni Padmapriya, T Prabhu, M Rawat, Manoj Kumar