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  • Open Access

    ARTICLE

    Robust Real-Time Analysis of Cow Behaviors Using Accelerometer Sensors and Decision Trees with Short Data Windows and Misalignment Compensation

    Duc-Nghia Tran1, Viet-Manh Do1,2, Manh-Tuyen Vi3,*, Duc-Tan Tran3,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2525-2553, 2025, DOI:10.32604/cmc.2025.062590 - 16 April 2025

    Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >

  • Open Access

    ARTICLE

    Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions

    Faisal Ghazi Beshaw1, Thamir Hassan Atyia2, Mohd Fadzli Mohd Salleh1, Mohamad Khairi Ishak3, Abdul Sattar Din1,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3553-3583, 2025, DOI:10.32604/cmc.2025.061400 - 16 April 2025

    Abstract The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was… More >

  • Open Access

    ARTICLE

    BIG-ABAC: Leveraging Big Data for Adaptive, Scalable, and Context-Aware Access Control

    Sondes Baccouri1,2,#,*, Takoua Abdellatif 3,#

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1071-1093, 2025, DOI:10.32604/cmes.2025.062902 - 11 April 2025

    Abstract Managing sensitive data in dynamic and high-stakes environments, such as healthcare, requires access control frameworks that offer real-time adaptability, scalability, and regulatory compliance. BIG-ABAC introduces a transformative approach to Attribute-Based Access Control (ABAC) by integrating real-time policy evaluation and contextual adaptation. Unlike traditional ABAC systems that rely on static policies, BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes, ensuring precise and efficient access control. Leveraging decision trees evaluated in real-time, BIG-ABAC overcomes the limitations of conventional access control models, enabling seamless adaptation to complex, high-demand scenarios. The framework adheres to the… More >

  • Open Access

    ARTICLE

    Overfitting in Machine Learning: A Comparative Analysis of Decision Trees and Random Forests

    Erblin Halabaku, Eliot Bytyçi*

    Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 987-1006, 2024, DOI:10.32604/iasc.2024.059429 - 30 December 2024

    Abstract Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data. This paper presents a comprehensive analysis of machine learning algorithms, focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models. It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP (ACV-CCP). This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter, leveraging cross-validation within the pruning process to achieve More >

  • Open Access

    ARTICLE

    A Boosted Tree-Based Predictive Model for Business Analytics

    Mohammad Al-Omari1, Fadi Qutaishat1, Majdi Rawashdeh1, Samah H. Alajmani2, Mehedi Masud3,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 515-527, 2023, DOI:10.32604/iasc.2023.030374 - 29 September 2022

    Abstract Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations’ competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested More >

  • Open Access

    ARTICLE

    DDoS Detection in SDN using Machine Learning Techniques

    Muhammad Waqas Nadeem, Hock Guan Goh*, Vasaki Ponnusamy, Yichiet Aun

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 771-789, 2022, DOI:10.32604/cmc.2022.021669 - 03 November 2021

    Abstract Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of… More >

  • Open Access

    ARTICLE

    A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

    José Escorcia-Gutierrez1,4,*, Jordina Torrents-Barrena4, Margarita Gamarra2, Natasha Madera1, Pedro Romero-Aroca3, Aida Valls4, Domenec Puig4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2971-2989, 2022, DOI:10.32604/cmc.2022.020074 - 27 September 2021

    Abstract Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a… More >

  • Open Access

    ARTICLE

    Research on College English Teaching Model Based on Decision Trees

    Hao Wu1,*, B. Nagaraj2

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 81-95, 2021, DOI:10.32604/iasc.2021.017654 - 26 July 2021

    Abstract English teaching has always attracted much attention. However, the processes of its transmission and acquirement is often divided into two separate parts, which seriously hinders the effective implementation of its objectives. Teachers attach particular importance to the choice of the curriculum structure and teaching material. Students are busy comprehending the assignments their teachers deem important. Under such a scenario, the effective acquisition of knowledge and the development of sustainable comprehensive abilities are ignored. The random forest algorithm in machine learning applications could play important role improving on the current English teaching system. A random forest… More >

  • Open Access

    ARTICLE

    Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

    Shabib Aftab1,2, Saad Alanazi3, Munir Ahmad1, Muhammad Adnan Khan4,*, Areej Fatima5, Nouh Sabri Elmitwally3,6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1341-1357, 2021, DOI:10.32604/cmc.2021.016814 - 22 March 2021

    Abstract Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It is a metabolic disease characterized by insulin deficiency, or glucose (blood sugar) levels that exceed 200 mg/dL (11.1 ml/L) for prolonged periods, and may lead to death if left uncontrolled by medication or insulin injections. Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above “normal,” defined as 140 mg/dL. Diabetes is triggered by malfunction of the pancreas, which releases insulin, a natural hormone responsible for controlling glucose levels in blood cells. Diagnosis… More >

  • Open Access

    ARTICLE

    Implementation of Decision Trees as an Alternative for the Support in the Decisionmaking within an Intelligent System in Order to Automatize the Regulation of the Vocs in Non-Industrial Inside Environments

    Liliana Aguilar, Salvador W. Nava-Díaz, Gabriel Chavira

    Computer Systems Science and Engineering, Vol.34, No.5, pp. 297-303, 2019, DOI:10.32604/csse.2019.34.297

    Abstract Natural ventilation is a component that provides a positive impact in the quality of air conditions in indoor environments, especially in non-industrial buildings. The maintenance of a continuous entrance of outside air through windows provides to the indoor a feasible and affordable manner to regulate and sustain low standards in theVOC (Volatile Organic Compounds). The technology and the Human Computer-Interaction have contributed to the creation of Intelligent Environments (EI) that provides to humans being a positive and non-intrusive responsiveness of the environment to improve their quality of life in daily activities. The Decision Trees for… More >

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