Boundaries and Future Trends of ChatGPT Based on AI and Security Perspectives

In decades, technology and artificial intelligence have significantly impacted aspects of life. One noteworthy development is ChatGPT, an AI-based model that has created a revolution and attracted attention from researchers, academia, and organizations in a short period of time. Experts predict that ChatGPT will continue advancing, bringing about a leap in artificial intelligence. It is believed that this technology holds the potential to address cybersecurity concerns, protect against threats and attacks, and overcome challenges associated with our increasing reliance on technology and the internet. This technology may change our lives in productive and helpful ways, from the interaction with other AI technologies to the potential for enhanced personalization and customization to the continuing improvement of language model performance. While these new developments have the potential to enhance our lives, it is our responsibility as a society to thoroughly examine and confront the ethical and societal impacts. This research delves into the state of ChatGPT and its developments in the fields of artificial intelligence and security. It also explores the challenges faced by ChatGPT regarding privacy, data security, and potential misuse. Furthermore, it highlights emerging trends that could influence the direction of ChatGPT's progress. This paper also offers insights into the implications of using ChatGPT in security contexts. Provides recommendations for addressing these issues. The goal is to leverage the capabilities of AI-powered conversational systems while mitigating any risks.   Doi: 10.28991/HIJ-2024-05-01-010 Full Text: PD

A Hybrid CNN-LSTM Random Forest Model for Dysgraphia Classification from Hand-Written Characters with Uniform/Normal Distribution

Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model’s accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model’s accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy

SSM: Stylometric and semantic similarity oriented multimodal fake news detection

Over the years, there has been a rise in the number of fabricated and fake news stories that utilize both textual and visual information formats. This coincides with the increased likelihood that users will acquire their news from websites and social media platforms. While there has been various research into the detection of fake news in text using machine learning techniques, less attention has been paid to the problem of multimedia data fabrication. In this paper, we propose a Stylometric, and Semantic similarity oriented for Multimodal Fake News Detection (SSM). There are five distinct modules that make up our methodology: Firstly, we used a Hyperbolic Hierarchical Attention Network (Hype-HAN) for extracting stylometric textual features. Secondly, we generated the news content summary and computed the similarity between Headline and summary. Thirdly, semantic similarity is computed between visual and textual features. Fourthly, images are analyzed for forgery. Lastly, the extracted features are fused for final classification. We have tested SSM framework on three standard fake news datasets. The results indicated that our suggested model has outperformed the base line and state-of-the-art methods and is more likely to detect fake news in complex environments