Fatimah Mudhhi Alanazi^*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

Computer Systems Science and Engineering, Vol.48, No.3, pp. 835-851, 2024, DOI:10.32604/csse.2024.042401

Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the… More >

Xiaonan Li^1,2, Qiang Wang¹, Conglai Fan^2,3, Wei Zhan¹, Mingliang Zhang^4,*

Computer Systems Science and Engineering, Vol.48, No.3, pp. 773-792, 2024, DOI:10.32604/csse.2024.039849

Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the… More >

Fadl Dahan^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921

Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… More >

Aiman¹, Muhammad Arshad¹, Bilal Khan¹, Khalil Khan², Ali Mustafa Qamar^3,*, Rehan Ullah Khan⁴

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 301-317, 2024, DOI:10.32604/iasc.2024.045402

Abstract This study explores the area of Author Profiling (AP) and its importance in several industries, including forensics, security, marketing, and education. A key component of AP is the extraction of useful information from text, with an emphasis on the writers’ ages and genders. To improve the accuracy of AP tasks, the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1 (ABM1) and Random Forest (RF). The work uses an extensive technique that involves text message dataset pretreatment, model training, and assessment. To evaluate the effectiveness of several machine learning (ML) algorithms in classifying age… More >

Nguyen Trong Vinh¹, Lam Thanh Hien¹, Ha Manh Toan², Ngo Duc Vinh³, Do Nang Toan^2,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 281-299, 2024, DOI:10.32604/iasc.2024.045297

Abstract Tuberculosis is a dangerous disease to human life, and we need a lot of attempts to stop and reverse it. Significantly, in the COVID-19 pandemic, access to medical services for tuberculosis has become very difficult. The late detection of tuberculosis could lead to danger to patient health, even death. Vietnam is one of the countries heavily affected by the COVID-19 pandemic, and many residential areas as well as hospitals have to be isolated for a long time. Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessing medical services,… More >

Halil Ibrahim Okur^1,2,*, Kadir Tohma¹, Ahmet Sertbas²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2209-2228, 2024, DOI:10.32604/cmc.2024.050585

Abstract Text classification, by automatically categorizing texts, is one of the foundational elements of natural language processing applications. This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata (Wikipedia database) database and BERT-based pre-trained Named Entity Recognition (NER) models. Focusing on a significant challenge in the field of natural language processing (NLP), the research evaluates the potential of using entity and relational information to extract deeper meaning from texts. The adopted methodology encompasses a comprehensive approach that includes text preprocessing, entity detection, and the integration of… More >

Kamlesh Kumar Soothar^1,2, Yuanxiang Chen^1,2,*, Arif Hussain Magsi³, Cong Hu¹, Hussain Shah¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

Zewen Zhang¹, Sheng Zhou¹, Chunzheng Cao^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2465-2480, 2024, DOI:10.32604/cmc.2024.049605

Abstract The classification of functional data has drawn much attention in recent years. The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy. In this paper, we propose a mean-variance-based (MV) feature weighting method for classifying functional data or functional curves. In the feature extraction stage, each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis. After that, a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the… More >

Sujithra Sankar^1,*, S. Sathyalakshmi²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3111-3138, 2024, DOI:10.32604/cmc.2024.048408

Abstract In the era of advanced machine learning techniques, the development of accurate predictive models for complex medical conditions, such as thyroid cancer, has shown remarkable progress. Accurate predictive models for thyroid cancer enhance early detection, improve resource allocation, and reduce overtreatment. However, the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency. This paper proposes a novel association-rule based feature-integrated machine learning model which shows better classification and prediction accuracy than present state-of-the-art models. Our study also focuses on the application of SHapley Additive exPlanations (SHAP) values as… More >

K. Umapathi^1,*, S. Shobana¹, Anand Nayyar², Judith Justin³, R. Vanithamani³, Miguel Villagómez Galindo⁴, Mushtaq Ahmad Ansari⁵, Hitesh Panchal^6,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1875-1901, 2024, DOI:10.32604/cmc.2024.047961

Abstract Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach… More >