Jean Aime Mono^1,2,*, Apollinaire Bouba³, Jean Daniel Ngoh⁴, Olivier Ulrich Igor Owono Amougou⁵, Françoise Martine Enyegue A Nyam⁶, Théophile Ndougsa Mbarga⁷

Revue Internationale de Géomatique, Vol.33, pp. 135-154, 2024, DOI:10.32604/rig.2024.049966

Abstract This study focuses on the mapping of lineaments using remote sensing techniques and Geographic Information Systems. The aim is to carry out a statistical analysis of the lineaments in order to better understand the organization of fracturing in the Batie district, and to identify areas of high fracturing density and their relationship with the hydrographic network. The methodology implemented to achieve these objectives is based on the processing and analysis of Landsat 9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite imagery and Shuttle Radar Topography Mission (SRTM) data covering the study area. After essential pre-processing of data, the following processing… 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 their discriminative power. To enhance… More >

Sohaib Latif^1,*, M. Saad Bin Ilyas¹, Azhar Imran², Hamad Ali Abosaq³, Abdulaziz Alzubaidi⁴, Vincent Karovič Jr.⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080

Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were also unable to detect denial-of-service… More >

Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 337-352, 2024, DOI:10.32604/iasc.2024.047017

Abstract Multi-Agent Reinforcement Learning (MARL) has proven to be successful in cooperative assignments. MARL is used to investigate how autonomous agents with the same interests can connect and act in one team. MARL cooperation scenarios are explored in recreational cooperative augmented reality environments, as well as real-world scenarios in robotics. In this paper, we explore the realm of MARL and its potential applications in cooperative assignments. Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory with minimal damage. To accomplish this, we utilize the StarCraft Multi-Agent Challenge (SMAC) environment and… More >

Do Nang Toan^1,*, Lam Thanh Hien², Ha Manh Toan¹, Nguyen Trong Vinh², Pham Trung Hieu¹

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925

Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem. We… 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 and gender, including Composite Hypercube… 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, such as an automatic tuberculosis… More >

Nguyen Huu Loc Khuu^1,2,3, Thuy Duy Truong^1,2,3, Quoc Dien Le^1,2,3, Tran Thanh Cong Vu^1,2,3, Hoa Binh Tran^1,2,3, Tuong Quan Vo^1,2,3,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 255-279, 2024, DOI:10.32604/iasc.2024.043854

Abstract Optimizing Flow Path Design (FPD) is a popular research area in transportation system design, but its application to Overhead Transportation Systems (OTSs) has been limited. This study focuses on optimizing a double-spine flow path design for OTSs with 10 stations by minimizing the total travel distance for both loaded and empty flows. We employ transportation methods, specifically the North-West Corner and Stepping-Stone methods, to determine empty vehicle travel flows. Additionally, the Tabu Search (TS) algorithm is applied to branch the 10 stations into two main layout branches. The results obtained from our proposed method demonstrate a reduction in the objective… More >

Chao-Lung Yang^1,*, Atinkut Atinafu Yilma^1,2, Bereket Haile Woldegiorgis², Hendrik Tampubolon^3,4, Hendri Sutrisno⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 233-254, 2024, DOI:10.32604/iasc.2024.043091

Abstract This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations. Since real-time production process monitoring is critical in today’s smart manufacturing. The more robust the monitoring model, the more reliable a process is to be under control. In the past, many researchers have developed real-time monitoring methods to detect process shifts early. However, these methods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties. In this paper, a robust monitoring model combining Gated Recurrent Unit (GRU) and Random Forest (RF) with Real-Time Contrast… More >

Mozamel M. Saeed^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 213-231, 2024, DOI:10.32604/iasc.2024.043020

Abstract The study aims to recognize how efficiently Educational Data Mining (EDM) integrates into Artificial Intelligence (AI) to develop skills for predicting students’ performance. The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University. The first step’s initial population placements were created using Particle Swarm Optimization (PSO). Then, using adaptive feature space search, Educational Grey Wolf Optimization (EGWO) was employed to choose the optimal attribute combination. The second stage uses the SVM classifier to forecast classification accuracy. Different classifiers were utilized to evaluate the performance of students. According to the results, it was revealed… More >