Global Journal on Innovation, Opportunities and Challenges in Applied Artificial Intelligence and Machine Learning [ISSN: 2581-5156 (online)]

Leveraging Bidirectional LSTM for Enhanced Sentiment Analysis: A Deep Learning Approach

Khushi Anil Teli

In this work, we use LSTM for this model to show that it is effective for NL processing tasks such as sentiment analysis as discussed herein. It was thus the purpose of this paper to leverage LSTM in capturing long-term dependencies of sequential data for improved identifications of sentiments in a big Twitter data set. The rationale for doing so is that the given set of data provided a huge number of textual inputs for training and testing of the proposed model as all the additional tweets were marked as positive, negative, or neutral emotion. Hyper parameters were also adjusted to set the best scenario since we_sess2rnted on believes that such probing areas like several LSTM units, batch size, learning rate, and dropout rates among others could best deliver. As predicted the better-proposed model and the newly proposed improved model got 84% of accuracy this confirms the efficiency of LSTM in sentiment analysis. This method brought out the seriousness of choosing the right model parameter and it also helped proclaim the flexibility of the deep learning model in handling Real-World noisier Social Media data for sentiment analysis.

Ethical Considerations of Using ChatGPT: Promoting Creative and Responsible AI Utilization

Rujittika Mungmunpuntipantip, Viroj Wiwanitkit

As AI technology such as ChatGPT becomes increasingly integrated into various sectors, it is imperative to address the ethical considerations surrounding its usage. This article delves into the importance of responsible information sourcing when using AI for research, the potential risks associated with misinformation, and the implications of delegating tasks to AI on human skill development. Understanding these ethical dimensions is crucial for promoting the creative and responsible use of AI.

Unlocking the Power of AI: Microsoft’s Enhanced Bing with GPT-4

Rujittika Mungmunpuntipantip, Viroj Wiwanitkit

Microsoft has unveiled an upgraded version of Bing, leveraging the advanced capabilities of GPT-4 to create a smart chatbot that can respond to user inquiries across various topics. This article explores how to access and utilize the new Bing, its features that enhance data protection, and important guidelines governing its usage.

Enhancing Programming Efficiency with ChatGPT: The Pros and Cons of AI-Assisted Coding

Rujittika Mungmunpuntipantip, Viroj Wiwanitkit

The integration of artificial intelligence (AI) into software development has revolutionized the way programmers approach coding tasks. Among these AI tools, ChatGPT has emerged as a significant resource for developers, assisting in code generation, debugging, and documentation. This article explores the advantages and disadvantages of using ChatGPT in programming, discusses effective collaboration strategies with AI, and evaluates the potential impacts of AI on the software development landscape.

The Impact of Artificial Intelligence on Cybersecurity: Opportunities and Threats

Introduction: This study delves into the profound impact of Artificial Intelligence (AI) on cybersecurity. It specifically focuses on the comprehensive evaluation of AI's opportunities and threats, including enhanced threat detection, automated responses, the evolving nature of cyberattacks, and the challenges posed by AI-driven malicious activities.

Objectives: This research explores both the benefits and risks that Artificial Intelligence (AI) presents to cybersecurity. The primary focus is on quantifying and analysing the opportunities AI provides in improving security measures, such as real-time threat detection and predictive analytics, as well as the threats it introduces, including AI-powered cyberattacks and the weaponisation of AI The study offers comprehensive insights into the evolving landscape of cybersecurity in the AI era. It aims to provide valuable input for policy recommendations and augment understanding of the broader implications of AI in cybersecurity.

Methods and Materials: This quantitative study uses a structured questionnaire to investigate the impact of AI on cybersecurity among professionals in the field. Developed with 27 questions, the survey gathered primary data from 303 participants through snowball and convenience sampling. It includes closed-ended questions, employing a 5-point Likert scale for the final eight questions, and open-ended questions focusing on demographics, AI adoption, and cybersecurity challenges. Data collection was facilitated via Google Forms for accessibility, with analysis employing correlational techniques to explore relationships among variables such as the effectiveness of AI in threat detection, the occurrence of AI-driven cyberattacks, and other cybersecurity impacts, shedding light on the complex role of AI in modern cybersecurity practices.

Findings: The study reveals significant effects of AI on cybersecurity, highlighting both opportunities and threats. On average, respondents noted a 45% increase in threat detection efficiency due to AI implementations. However, 32% of participants reported encountering AI-driven cyberattacks, underscoring the dual-edged nature of AI in this field. The study also found that AI adoption led to a 40% reduction in response times to incidents, but it also revealed a 28% increase in sophisticated, AI-powered phishing attacks. These findings underscore the critical need to balance the advantages of AI with the emerging risks it presents in cybersecurity.

Conclusion: Artificial Intelligence (AI) significantly influences cybersecurity, presenting remarkable opportunities and serious threats. Recognising these impacts is crucial for shaping policies that leverage AIto enhance security while mitigating risks. This recognition necessitates the development of a comprehensive strategy that includes public education on AI's role in cybersecurity. Such education is vital for enhancing understanding and preparedness alongside evidence-based guidelines to manage the dual-edged nature of AI technologies. The urgency of this need cannot be overstated. Improving the development of AI governance frameworks is also vital for minimising risks and maximising the benefits of AI in cybersecurity. By integrating these measures, the cybersecurity industry can foster a safer digital environment and strengthen resilience against the growing threats posed by AI