Muhammad Ali, Minhaj Ahmad Khan^*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.043010

Abstract The Extensible Markup Language (XML) files, widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications. With the existing Document Object Model (DOM) based parsing, the performance degrades due to sequential processing and large memory requirements, thereby requiring an efficient XML parser to mitigate these issues. In this paper, we propose a Parallel XML Tree Generator (PXTG) algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework (RXPF) that analyzes and predicts performance through profiling, regression, and code generation for efficient parsing. The PXTG algorithm… More >

Amr Ismail¹, Walid Hamdy^1,2, Aya M. Al-Zoghby³, Wael A. Awad³, Ahmed Ismail Ebada³, Yunyoung Nam⁴, Byeong-Gwon Kang^4,*, Mohamed Abouhawwash^5,6

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.038192

Abstract Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has… More >

Nesreen M. Alharbi^*, Ahmed Hamza Osman, Arwa A. Mashat, Hasan J. Alyamani

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.041044

Abstract Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision, which calls for the adoption of anomaly detection techniques to enhance Optical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection using MLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced… More >

Sanxiu Jiao¹, Lecai Cai^2,*, Jintao Meng³, Yue Zhao³, Kui Cheng²

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.040194

Abstract Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while protecting the parameter information uploaded… More >

Xiaodan Wang^*, Peng Wang, Yafei Song, Qian Xiang, Jingtai Li

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.039846

Abstract High-resolution range profile (HRRP) automatic recognition has been widely applied to military and civilian domains. Present HRRP recognition methods have difficulty extracting deep and global information about the HRRP sequence, which performs poorly in real scenes due to the ambient noise, variant targets, and limited data. Moreover, most existing methods improve the recognition performance by stacking a large number of modules, but ignore the lightweight of methods, resulting in over-parameterization and complex computational effort, which will be challenging to meet the deployment and application on edge devices. To tackle the above problems, this paper proposes an HRRP sequence recognition method… More >

Ahmad Almadhor¹, Chitapong Wechtaisong^2,*, Usman Tariq³, Natalia Kryvinska^4,*, Abdullah Al Hejaili⁵, Uzma Ghulam Mohammad⁶, Mohana Alanazi⁷

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.039546

Abstract Thermal comfort is an essential component of smart cities that helps to upgrade, analyze, and realize intelligent buildings. It strongly affects human psychological and physiological levels. Residents of buildings suffer stress because of poor thermal comfort. Buildings frequently use Heating, Ventilation, and Air Conditioning (HVAC) systems for temperature control. Better thermal states directly impact people’s productivity and health. This study revealed a human thermal comfort model that makes better predictions of thermal sensation by identifying essential features and employing a tuned Extra Tree classifier, MultiLayer Perceptron (MLP) and Naive Bayes (NB) models. The study employs the ASHRAE RP-884 standard dataset… More >

Noman Khan^1,2, Samee Ullah Khan^1,2, Sung Wook Baik^1,2,*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.039407

Abstract Energy management systems for residential and commercial buildings must use an appropriate and efficient model to predict energy consumption accurately. To deal with the challenges in power management, the short-term Power Consumption (PC) prediction for household appliances plays a vital role in improving domestic and commercial energy efficiency. Big data applications and analytics have shown that data-driven load forecasting approaches can forecast PC in commercial and residential sectors and recognize patterns of electric usage in complex conditions. However, traditional Machine Learning (ML) algorithms and their features engineering procedure emphasize the practice of inefficient and ineffective techniques resulting in poor generalization.… More >

Jameel Almalki^*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.038085

Abstract The recent global pandemic has resulted in growth in the medical and healthcare sectors. Applications used in these domains have become more advanced and digitally integrated. Sensor-based Internet of Things (IoT) devices are increasing in healthcare and medical units. The emerging trend with the use of IoT devices in medical healthcare is termed as Internet of Medical Things (IoMT). The instruments used in these healthcare units comprise various sensors that can record patient body observations. These recorded observations are streamed across Internetbased channels to be stored and analyzed in centralized servers. Patient diagnostics are performed based on the information retrieved… More >

A. Vikram¹, J. Akshya², Sultan Ahmad^3,4, L. Jerlin Rubini⁵, Seifedine Kadry^6,7,8, Jungeun Kim^9,*

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.037928

Abstract Traffic monitoring through remote sensing images (RSI) is considered an important research area in Intelligent Transportation Systems (ITSs). Vehicle counting systems must be simple enough to be implemented in realtime. With the fast expansion of road traffic, real-time vehicle counting becomes essential in constructing ITS. Compared with conventional technologies, the remote sensing-related technique for vehicle counting exhibits greater significance and considerable advantages in its flexibility, low cost, and high efficiency. But several techniques need help in balancing complexity and accuracy technique. Therefore, this article presents a deep learning-based vehicle detection and counting system for ITS (DLVDCS-ITS) in remote sensing images.… More >

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Najm Alotaibi³, Mohamed K. Nour⁴, Mahmoud Othman⁵, Gouse Pasha Mohammed¹, Mohammed Rizwanullah¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.034520

Abstract Computational linguistics is the scientific and engineering discipline related to comprehending written and spoken language from a computational perspective and building artefacts that effectively process and produce language, either in bulk or in a dialogue setting. This paper develops a Chaotic Bird Swarm Optimization with deep ensemble learning based Arabic poem classification and dictarization (CBSOEDL-APCD) technique. The presented CBSOEDL-APCD technique involves the classification and dictarization of Arabic text into Arabic poetries and prose. Primarily, the CBSOEDL-APCD technique carries out data pre-processing to convert it into a useful format. Besides, the ensemble deep learning (EDL) model comprising deep belief network (DBN),… More >