Special Issues
Table of Content

Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data

Submission Deadline: 31 May 2025 (closed) View: 1844 Submit to Special Issue

Guest Editors

Dr. Hamed Jelodar

E-mail: h.jelodar@unb.ca

Affiliation: Computer Science Deparetment. University of Brunswick, Fredericton, E3B 5A3, Canada

Homepage: https://www.unb.ca/cic/membership/researchers.html

Research Interests: Natural Language Processing; Medical Digital Mining; Machine/Deep Learning, Recommendation Systems

Hamed-Jelodar (1).jpg

 

Dr. Sajjad Bagheri Baba Ahmadi

E-mail: Sajjad.bagheri@uws.ac.uk

Affiliation: School of Computing, Engineering & Physical Sciences, University of the West of Scotland, Paisley, PA1 2BE, UK

Homepage:

Research Interests: Cybersecurity; Ownership Protection, Privacy & Digital Multimedia Forensics; Machine Learning, Deep Learning

Sajjad Bagheri Baba Ahmadi.jpg


Summary

The rapid evolution of Artificial Intelligence (AI), data analysis techniques, and big data has significantly transformed various fields. AI technologies have shown tremendous potential in enhancing various applications, predicting trends, and automating complex tasks. Natural Language Processing (NLP) has also advanced, enabling better understanding and generation of human language. However, these advancements come with challenges such as data privacy concerns, algorithmic biases, and the constant need for adaptation to emerging trends. This special issue aims to explore both the advancements and challenges associated with AI, data analysis, and big data, highlighting their impact on various domains and ensuring robust implementations.

 

Aim:

This special issue seeks to provide a comprehensive overview of the latest advancements in AI, data analysis, and big data technologies and their applications across different fields. It aims to present innovative solutions, evaluate their effectiveness, and discuss the challenges faced in implementing these technologies in real-world scenarios

 

Scope:

The scope includes but is not limited to :

·  AI-driven prediction and automation systems

·  Advanced data analysis techniques

·  Big data processing and Social Media analysis

·  Natural Language Processing (NLP) advancements and applications

·  Ethical and privacy considerations in AI and data analysis

·  Case studies and real-world applications

·  Challenges in integrating AI with existing systems

·  Future trends and emerging technologies in the field


Suggested Themes:

·  AI and Machine Learning for Predictive Analytics

·  Behavioral Analytics an big data

·  Automated Systems and Process Optimization

·  Big Data Analytics and Processing Techniques

·  Natural Language Processing (NLP) for Various Applications

·  Privacy and Ethical Issues in AI Solutions

·  Data Analysis Techniques for Predictive Modelling

·  Challenges in AI-Enhanced Systems


Keywords

AI, Machine Learning, Data Analysis, Big Data, Natural Language Processing, Real-world Applications, Trend Prediction

Published Papers


  • Open Access

    ARTICLE

    Chinese DeepSeek: Performance of Various Oversampling Techniques on Public Perceptions Using Natural Language Processing

    Anees Ara, Muhammad Mujahid, Amal Al-Rasheed, Shaha Al-Otaibi, Tanzila Saba
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.065566
    (This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
    Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… More >

  • Open Access

    ARTICLE

    AI-Driven Sentiment-Enhanced Secure IoT Communication Model Using Resilience Behavior Analysis

    Menwa Alshammeri, Mamoona Humayun, Khalid Haseeb, Ghadah Naif Alwakid
    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 433-446, 2025, DOI:10.32604/cmc.2025.065660
    (This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
    Abstract Wireless technologies and the Internet of Things (IoT) are being extensively utilized for advanced development in traditional communication systems. This evolution lowers the cost of the extensive use of sensors, changing the way devices interact and communicate in dynamic and uncertain situations. Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system. Therefore, it leads to the design of effective and trusted routing to support sustainable smart cities. This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model, which combines big data analytics and analysis rules to evaluate… More >

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