Ahmed A. Alsheikhy¹, Ahmad S. Azzahrani¹, A. Khuzaim Alzahrani², Tawfeeq Shawly³

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2024.051630

Abstract This article has no abstract. More >

Leila Safari-Monjeghtapeh¹, Mansour Esmaeilpour^2,*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2024.044892

Abstract Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree “MST”, and meta-heuristic algorithms, including Genetic Algorithm “GA” and Particle Swarm Optimization “PSO”. The Fast Path-based Clustering “FPC” algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm… More >

Nidhika Chauhan¹, Navneet Kaur², Kamaljit Singh Saini², Sahil Verma³, Abdulatif Alabdulatif⁴, Ruba Abu Khurma^5,7, Maribel Garcia-Arenas⁶, Pedro A. Castillo^6,*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2024.042690

Abstract As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation… More >

Xindi Huang¹, Liwei Liang¹, Sakirin Tam², Hao Liang³, Xiong Cai⁴, Changsong Ding^1,5,*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2022.029970

Abstract Chinese Medicine (CM) has been widely used as an important avenue for disease prevention and treatment in China especially in the form of CM prescriptions combining sets of herbs to address patients’ symptoms and syndromes. However, the selection and compatibility of herbs are complex and abstract due to intrinsic relationships between herbal properties and their overall functions. Network analysis is applied to demonstrate the complex relationships between individual herbal efficacy and the overall function of CM prescriptions. To illustrate their connections and correlations, prescription function (PF), prescription herb (PH), and herbal efficacy (HE) intra-networks are proposed based on CM theory… More >

Pakorn Santakij¹, Samai Srisuay^2,*, Pongporn Punpeng¹

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2024.045066

Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models. An assessment of coherence metrics… More >

Jili Chen^1,2, Hailan Wang², Xiaolan Xie^1,2,*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.037957

Abstract Fuzzy C-Means (FCM) is an effective and widely used clustering algorithm, but there are still some problems. considering the number of clusters must be determined manually, the local optimal solutions is easily influenced by the random selection of initial cluster centers, and the performance of Euclid distance in complex high-dimensional data is poor. To solve the above problems, the improved FCM clustering algorithm based on density Canopy and Manifold learning (DM-FCM) is proposed. First, a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers, which improves the self-adaptability… More >

Hae Sung Park¹, Yong Suk Choi^2,*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.043245

Abstract Much like humans focus solely on object movement to understand actions, directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension. In the recent study, Video Masked Auto-Encoder (VideoMAE) employs a pre-training approach with a high ratio of tube masking and reconstruction, effectively mitigating spatial bias due to temporal redundancy in full video frames. This steers the model’s focus toward detailed temporal contexts. However, as the VideoMAE still relies on full video frames during the action recognition stage, it may exhibit a progressive shift in attention towards spatial contexts, deteriorating its ability… More >

Maha Farouk Sabir¹, Mahmoud Ragab^2,3,*, Adil O. Khadidos², Khaled H. Alyoubi¹, Alaa O. Khadidos^1,4

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.041551

Abstract Big data and information and communication technologies can be important to the effectiveness of smart cities. Based on the maximal attention on smart city sustainability, developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems. Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions. Relating to air pollution occurs a main environmental problem in smart city environments. The effect of the deep learning (DL) approach quickly increased and penetrated almost every domain, comprising air pollution forecast. Therefore, this article develops a new Coot Optimization Algorithm… More >

Bijon Mallik¹, Md Abdur Rahim¹, Abu Saleh Musa Miah², Keun Soo Yun^3,*, Jungpil Shin²

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.045981

Abstract In the digital age, non-touch communication technologies are reshaping human-device interactions and raising security concerns. A major challenge in current technology is the misinterpretation of gestures by sensors and cameras, often caused by environmental factors. This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions. Our study presents a novel virtual keyboard allowing character input via distinct hand gestures, focusing on two key aspects: hand gesture recognition and character input mechanisms. We developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture recognition.… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.042080

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >