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All Journal International Journal of Electrical and Computer Engineering International Journal of Engineering, Science and Information Technology
Jadhav, Chaya
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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A comprehensive analysis of different models: skin cancer detection Thorat, Amruta; Jadhav, Chaya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2404-2415

Abstract

Due to fast-growing worldwide air pollution and ozone layer destruction, an alarming number of people are found to have skin cancer, more than any other kind of cancer combined. It is known to be one of the deadliest malignancies; if not identified and cured in its early stages, it is likely to spread to other body parts. Early detection is critical and helps prevent cancer from spreading. This allows for early decisions on diagnostic and treatment options. Early diagnosis and discovery, combined with the right treatment, can save lives. In this paper, we have done a detailed survey on various techniques and models developed for skin cancer detection and also discussed different security-related issues. This work thoroughly explores the several types of models utilized to identify cancer in the skin.
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.
Co-Authors Bhupati, Ch Mabel Rose, R.A. Nyayadhish, Renuka Prabhu, M Thorat, Amruta