Mitra Madanchian^1,*, Hamed Taherdoost^1,2,3,4

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2133-2159, 2024, DOI:10.32604/cmc.2024.057094 - 18 November 2024

Abstract This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the… More >

Raed Alotaibi¹, Omar Reyad^2,3, Mohamed Esmail Karar^4,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1133-1147, 2024, DOI:10.32604/csse.2024.053358 - 13 September 2024

Abstract E-learning behavior data indicates several students’ activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures. This article proposes a new analytics system to support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments. The proposed e-learning analytics system includes a new deep forest model. It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks. The developed forest model can analyze each student’s activities during the use of an e-learning… More >

Deema Mohammed Alsekait^1,*, Asif Nawaz², Ayman Nabil³, Mehwish Bukhari², Diaa Salama AbdElminaam^3,4,5,6,*

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1031-1054, 2024, DOI:10.32604/csse.2024.051847 - 17 July 2024

Abstract The article describes a novel method for sentiment analysis and requirement elicitation from social media feedback, leveraging advanced machine learning techniques. This innovative approach automates the extraction and classification of user requirements by analyzing sentiment in data gathered from social media platforms such as Twitter and Facebook. Utilizing APIs (Application Programming Interface) for data collection and Graph-based Neural Networks (GNN) for feature extraction, the proposed model efficiently processes and analyzes large volumes of unstructured user-generated content. The preprocessing pipeline includes data cleaning, normalization, and tokenization, ensuring high-quality input for the sentiment analysis model. By classifying… More >

Qinyue Wu, Hui Xu^*, Mengran Liu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4091-4107, 2024, DOI:10.32604/cmc.2024.048461 - 26 March 2024

Abstract Network traffic identification is critical for maintaining network security and further meeting various demands of network applications. However, network traffic data typically possesses high dimensionality and complexity, leading to practical problems in traffic identification data analytics. Since the original Dung Beetle Optimizer (DBO) algorithm, Grey Wolf Optimization (GWO) algorithm, Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution, an Improved Dung Beetle Optimizer (IDBO) algorithm is proposed for network traffic identification. Firstly, the Sobol sequence is utilized to initialize the… More >

Amira M. Idrees^1,*, Abdul Lateef Marzouq Al-Solami²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1115-1133, 2024, DOI:10.32604/cmc.2023.046457 - 30 January 2024

Abstract The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular… More >

Gagan Deep^*, Jyoti Verma

Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329 - 22 September 2023

Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information… More >

Saeed M. Alshahrani¹, Jameel Almalki², Waleed Alshehri², Rashid Mehmood³, Marwan Albahar², Najlaa Jannah², Nayyar Ahmed Khan^1,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1793-1817, 2023, DOI:10.32604/csse.2023.039648 - 28 July 2023

Abstract Artificial Intelligence (AI) has gained popularity for the containment of COVID-19 pandemic applications. Several AI techniques provide efficient mechanisms for handling pandemic situations. AI methods, protocols, data sets, and various validation mechanisms empower the users towards proper decision-making and procedures to handle the situation. Despite so many tools, there still exist conditions in which AI must go a long way. To increase the adaptability and potential of these techniques, a combination of AI and Bigdata is currently gaining popularity. This paper surveys and analyzes the methods within the various computational paradigms used by different researchers More >

A. Manju¹, A. Revathi², M. Arivukarasi¹, S. Hariharan³, V. Umarani⁴, Shih-Yu Chen^5,*, Jin Wang⁶

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 905-920, 2023, DOI:10.32604/iasc.2023.038444 - 29 April 2023

Abstract With the proliferation of the internet, big data continues to grow exponentially, and video has become the largest source. Video big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their semantic association. Two evaluation methods including clustering and… More >

I. Mettildha Mary^1,*, K. Karuppasamy²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2667-2685, 2023, DOI:10.32604/csse.2023.031114 - 03 April 2023

Abstract CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations More >

Muhammad Hammad Waseem¹, Malik Sajjad Ahmed Nadeem^1,*, Ishtiaq Rasool Khan², Seong-O-Shim³, Wajid Aziz¹, Usman Habib⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710 - 31 March 2023

Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the… More >