Ayaz Hussain¹, Asad Hayat², Muhammad Hasnain^1,*

Journal on Artificial Intelligence, Vol.7, pp. 485-498, 2025, DOI:10.32604/jai.2025.071933 - 17 November 2025

Abstract Violent hate speech and scapegoating people against one another have emerged as a rising worldwide issue. But identifying and combating such content is crucial to create safer and more inclusive societies. The current study conducted research using Machine Learning models to classify hate speech and overcome the limitations posed in the existing detection techniques. Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbour (KNN) and Decision Tree were used on top of a publicly available hate speech dataset. The data was preprocessed by cleaning the text and tokenization and using normalization techniques to efficiently train the… More >

Lihao Zhang^1,2, Lin Cao^1,2, Lige Huang^1,2, Jie Wang^1,2, Jiabing Li^2,3,*

Canadian Journal of Urology, Vol.32, No.5, pp. 457-468, 2025, DOI:10.32604/cju.2025.067364 - 30 October 2025

Abstract Background: Studies have indicated an association between inflammatory factors (IFs) in the blood and the development of bladder cancer (BC). This study aimed to explore the correlation and clinical significance of IFs with the pathological grading of BC. Methods: A retrospective analysis was conducted on the preoperative blood routine results, postoperative pathological findings, and baseline information of 163 patients. Patients were divided into high-grade and low-grade groups based on pathological grading. Group comparisons and logistic regression analyses were performed using R software version 4.1.3 to explore the relationships between IFs and BC pathological grading. Results: The… More >

Nurul A. Asram¹, Eran S. S. Md Sadek^2,*

Revue Internationale de Géomatique, Vol.34, pp. 619-637, 2025, DOI:10.32604/rig.2025.065667 - 06 August 2025

Abstract Landslides are a frequent geomorphological hazard in tropical regions, particularly where steep terrain and high precipitation coincide. This study evaluates landslide susceptibility in the Jelapang area of Perak, Malaysia, using Shannon Entropy-weighted bivariate models (i.e., Frequency Ratio, Information Value, and Weight of Evidence), in comparison with Logistic Regression. Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity: slope gradient, slope aspect, curvature, vegetation cover, lineament density, terrain ruggedness index, and flow accumulation. Each model generated susceptibility maps, which were validated using Receiver Operating Characteristic curves and Area Under the Curve… More >

Penghui Liu^1,2,*, Yaning Zhang¹, Yuxing Dai², Yanzhou Sun^1,3

Energy Engineering, Vol.121, No.7, pp. 1883-1901, 2024, DOI:10.32604/ee.2024.049318 - 11 June 2024

Abstract Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product,… More >

Seungwoo Kang¹, Seyha Ros¹, Inseok Song¹, Prohim Tam¹, Sa Math², Seokhoon Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020 - 29 November 2023

Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model… More >

Shabana R. Ziyad^1,*, Liyakathunisa², Eman Aljohani², I. A. Saeed³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1515-1534, 2023, DOI:10.32604/cmc.2023.040874 - 29 November 2023

Abstract Autism spectrum disorder (ASD), classified as a developmental disability, is now more common in children than ever. A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children. Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years. This research study aims to develop an automated tool for diagnosing autism in children. The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition, feature selection, and classification phases.… More >

Yousef Alhwaiti^1,*, Muhammad Hameed Siddiqi¹, Irshad Ahmad²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110 - 09 November 2023

Abstract Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments.… More >

J. R. Arunkumar^1,*, S. Velmurugan², Balarengadurai Chinnaiah³, G. Charulatha⁴, M. Ramkumar Prabhu⁴, A. Prabhu Chakkaravarthy⁵

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2635-2645, 2023, DOI:10.32604/csse.2023.031605 - 21 December 2022

Abstract Nowadays, Wireless Sensor Network (WSN) is a modern technology with a wide range of applications and greatly attractive benefits, for example, self-governing, low expenditure on execution and data communication, long-term function, and unsupervised access to the network. The Internet of Things (IoT) is an attractive, exciting paradigm. By applying communication technologies in sensors and supervising features, WSNs have initiated communication between the IoT devices. Though IoT offers access to the highest amount of information collected through WSNs, it leads to privacy management problems. Hence, this paper provides a Logistic Regression machine learning with the Elliptical More >

Mohemmed Sha^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 857-868, 2023, DOI:10.32604/csse.2023.031321 - 16 August 2022

Abstract The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean More >

M. Valavan, S. Rita^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 231-245, 2023, DOI:10.32604/csse.2023.026508 - 16 August 2022

Abstract Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such More >