Journal Menu

Special Issues

Table of Content

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

Submission Deadline: 31 May 2025 (closed) View: 5219 Submit to Journal

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

Show export options

Open Access

ARTICLE

Day-Ahead Electricity Price Forecasting Using the XGBoost Algorithm: An Application to the Turkish Electricity Market
Yağmur Yılan, Ahad Beykent
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.068440
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters More >

View
416

Download
125
Open Access

ARTICLE

Robust Multi-Label Cartoon Character Classification on the Novel Kral Sakir Dataset Using Deep Learning Techniques
Candan Tumer, Erdal Guvenoglu, Volkan Tunali
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5135-5158, 2025, DOI:10.32604/cmc.2025.067840
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract Automated cartoon character recognition is crucial for applications in content indexing, filtering, and copyright protection, yet it faces a significant challenge in animated media due to high intra-class visual variability, where characters frequently alter their appearance. To address this problem, we introduce the novel Kral Sakir dataset, a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions. This paper conducts a comprehensive benchmark study, evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks (CNNs), including DenseNet, ResNet, and VGG, against a custom baseline model trained More >

View
640

Download
167
Open Access

ARTICLE

Leveraging Machine Learning to Predict Hospital Porter Task Completion Time
You-Jyun Yeh, Edward T.-H. Chu, Chia-Rong Lee, Jiun Hsu, Hui-Mei Wu
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3369-3391, 2025, DOI:10.32604/cmc.2025.065336
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract Porters play a crucial role in hospitals because they ensure the efficient transportation of patients, medical equipment, and vital documents. Despite its importance, there is a lack of research addressing the prediction of completion times for porter tasks. To address this gap, we utilized real-world porter delivery data from National Taiwan University Hospital, Yunlin Branch, Taiwan. We first identified key features that can influence the duration of porter tasks. We then employed three widely-used machine learning algorithms: decision tree, random forest, and gradient boosting. To leverage the strengths of each algorithm, we finally adopted an… More >

View
855

Download
550
Open Access

REVIEW

Homomorphic Encryption for Machine Learning Applications with CKKS Algorithms: A Survey of Developments and Applications
Lingling Wu, Xu An Wang, Jiasen Liu, Yunxuan Su, Zheng Tu, Wenhao Liu, Haibo Lei, Dianhua Tang, Yunfei Cao, Jianping Zhang
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 89-119, 2025, DOI:10.32604/cmc.2025.064346
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract Due to the rapid advancement of information technology, data has emerged as the core resource driving decision-making and innovation across all industries. As the foundation of artificial intelligence, machine learning(ML) has expanded its applications into intelligent recommendation systems, autonomous driving, medical diagnosis, and financial risk assessment. However, it relies on massive datasets, which contain sensitive personal information. Consequently, Privacy-Preserving Machine Learning (PPML) has become a critical research direction. To address the challenges of efficiency and accuracy in encrypted data computation within PPML, Homomorphic Encryption (HE) technology is a crucial solution, owing to its capability to… More >

View
4907

Download
3118
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, Vol.84, No.2, pp. 2717-2731, 2025, 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 >

View
1015

Download
373
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 >

View
1133

Download
366

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

Guest Editors

Summary

Keywords

Published Papers

View

416

Download

125

View

640

Download

167

View

855

Download

550

View

4907

Download

3118

View

1015

Download

373

View

1133

Download

366

Further Information

Guidelines

Follow Us

Join Us

Contact Us

WhatsApp:

Share Link