Robust detection of LLM-generated text through transfer learning with pre-trained Distilled BERT model (Published)
Detecting text generated by large language models (LLMs) is a growing challenge as these models produce outputs nearly indistinguishable from human writing. This study explores multiple detection approaches, including a Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, a Transformer block, and a fine-tuned distilled BERT model. Leveraging BERT’s contextual understanding, we train the model on diverse datasets containing authentic and synthetic texts, focusing on features like sentence structure, token distribution, and semantic coherence. The fine-tuned BERT outperforms baseline models, achieving high accuracy and robustness across domains, with superior AUC scores and efficient computation times. By incorporating domain-specific training and adversarial techniques, the model adapts to sophisticated LLM outputs, improving detection precision. These findings underscore the efficacy of pretrained transformer models for ensuring authenticity in digital communication, with potential applications in mitigating misinformation, safeguarding academic integrity, and promoting ethical AI usage.
Keywords: Classifier, GenAI, detection, fine tuning, large language models, machine learning, natural language processing, pretraining
A Survey on Techniques of Wireless Capsule Endoscopy for Image Enhancement and Disease Detection (Published)
Wireless capsule endoscopy (WCE) is the gold standard for diagnosing small bowel disorders and is considered the future of effective diagnostic gastrointestinal (GI) endoscopy. Patients find it comfortable and more likely to adopt it than traditional colonoscopy and gastroscopy, making it a viable option for detecting cancer or ulcerations. WCE can obtain images of the GI tract from the inside, but pinpointing the disease’s location remains a challenge. This paper reviews studies on endoscopy capsule development and discusses techniques and solutions for higher efficiency. Research has demonstrated that artificial intelligence (AI) enhances the accuracy of disease detection and minimizes errors resulting from physicians’ fatigue or lack of attention. When it comes to WCE, deep learning has shown remarkable success in detecting a wide variety of disorders.
Keywords: CNN, Location, bowel, detection, wireless capsule endoscopy
A Model for Malicious Website Detection Using Feed Forward Neural Network (Published)
Malicious websites are the most unsafe criminal exercises in cyberspace. Since a large number of users go online to access the services offered by the government and financial establishments, there has been a notable increase in malicious websites attacks for the past few years. This paper presents a model for Malicious website URL detection using Feed Forward Neural Network. The design methodology used here is Object-Oriented Analysis and Design. The model uses a Malicious website URLs dataset, which comprises 48,006 legitimate website URLs and 48,006 Malicious website URLs making 98,012 website URLs. The dataset was pre-processed by removing all duplicate and Nan values, therefore making it fit for better training performance. The dataset was segmented into X_train and y_train, X_test and y_test which holds 60% training data and 40% testing data. The X_train contains the dataset of malicious and benign websites, while the y_train holds the label which indicates if the dataset is malicious or not. For the testing dataset the X_test contains both the malicious and non-malicious websites, while the y_test holds label which indicates if the dataset is malicious or not. The model was trained using Feed Forward Neural Network, which had an optimal accuracy 97%.
Keywords: Model, Neural network, detection, feed, malicious website