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  • Open Access

    REVIEW

    Embracing the Future: AI and ML Transforming Urban Environments in Smart Cities

    Gagan Deep*, Jyoti Verma

    Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329

    Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information on the integration of AI… More >

  • Open Access

    ARTICLE

    Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

    Yongsoo Lee, Yeeun Lee, Eungyu Lee, Taejin Lee*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1701-1719, 2023, DOI:10.32604/cmc.2023.040235

    Abstract Cybersecurity increasingly relies on machine learning (ML) models to respond to and detect attacks. However, the rapidly changing data environment makes model life-cycle management after deployment essential. Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models. However, detecting drift in unsupervised environments can be challenging. This study introduces a novel approach leveraging Shapley additive explanations (SHAP), a widely recognized explainability technique in ML, to address drift detection in unsupervised settings. The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers… More >

  • Open Access

    ARTICLE

    A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

    Mahmood Hussain Mir1,*, Sanjay Jamwal1, Ummer Iqbal2, Abolfazl Mehbodniya3, Julian Webber3, Umar Hafiz Khan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2529-2565, 2023, DOI:10.32604/cmes.2023.027173

    Abstract The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID- 19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework is based on edge computing… More >

  • Open Access

    ARTICLE

    An Optimized Feature Selection and Hyperparameter Tuning Framework for Automated Heart Disease Diagnosis

    Saleh Ateeq Almutairi*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

    Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then, the optimal combination of the… More >

  • Open Access

    ARTICLE

    Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

    P. Venkatesh1,*, N. Visali2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1001-1012, 2023, DOI:10.32604/iasc.2023.036268

    Abstract In the conventional technique, in the evaluation of the severity index, clustering and loading suffer from more iteration leading to more computational delay. Hence this research article identifies, a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects. The polynomial load modelling or ZIP (constant impedances (Z), Constant Current (I) and Constant active power (P)) is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security. The process of finding the severity of the line using a Hybrid Line Stability Ranking Index (HLSRI) is used for… More >

  • Open Access

    ARTICLE

    Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

    Hala Ahmed1, Hassan Soliman1, Shaker El-Sappagh2,3,4, Tamer Abuhmed4,*, Mohammed Elmogy1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2773-2795, 2023, DOI:10.32604/csse.2023.036371

    Abstract The precise diagnosis of Alzheimer’s disease is critical for patient treatment, especially at the early stage, because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs. It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities, known as image fusion. In this paper, the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images. First, the preprocessing was performed on the data. Then, the data augmentation techniques are used to handle overfitting. Also, the skull is removed to lead… More >

  • Open Access

    ARTICLE

    Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

    Sarah Alzahrani1, Joud Alderaan1, Dalya Alatawi1, Bandar Alotaibi1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 651-667, 2023, DOI:10.32604/cmc.2023.035173

    Abstract Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices’ security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric… More >

  • Open Access

    ARTICLE

    Social Engineering Attack Classifications on Social Media Using Deep Learning

    Yichiet Aun1,*, Ming-Lee Gan1, Nur Haliza Binti Abdul Wahab2, Goh Hock Guan1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4917-4931, 2023, DOI:10.32604/cmc.2023.032373

    Abstract In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom dataset crawled from hundreds of… More >

  • Open Access

    ARTICLE

    Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam

    Huong Thi Thanh Ngo1, Nguyen Duc Dam1, Quynh-Anh Thi Bui1, Nadhir Al-Ansari2,*, Romulus Costache3,4,*, Hang Ha5, Quynh Duy Bui5, Sy Hung Mai6, Indra Prakash7, Binh Thai Pham1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.022566

    Abstract Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were… More >

  • Open Access

    ARTICLE

    COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

    Tahir Sher1, Abdul Rehman2, Dongsun Kim2,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1561-1574, 2023, DOI:10.32604/cmc.2023.032020

    Abstract COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well. Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world. However, with the advancement of technology, the Internet of Things (IoT) and social IoT (SIoT), the versatile data produced by smart devices helped a lot in overcoming this lethal disease. Data mining is a technique that could be used for extracting useful information from massive data. In this study, we used five… More >

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