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  • Open Access

    ARTICLE

    Comparison among Classical, Probabilistic and Quantum Algorithms for Hamiltonian Cycle Problem

    Giuseppe Corrente1,2,*, Carlo Vincenzo Stanzione3,4, Vittoria Stanzione5

    Journal of Quantum Computing, Vol.5, pp. 55-70, 2023, DOI:10.32604/jqc.2023.044786

    Abstract The Hamiltonian cycle problem (HCP), which is an NP-complete problem, consists of having a graph G with nodes and m edges and finding the path that connects each node exactly once. In this paper we compare some algorithms to solve a Hamiltonian cycle problem, using different models of computations and especially the probabilistic and quantum ones. Starting from the classical probabilistic approach of random walks, we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine (QTM), which can be a useful conceptual project tool for quantum algorithms. Introducing several constraints to the graphs,… More >

  • Open Access

    ARTICLE

    Effects of T-Factor on Quantum Annealing Algorithms for Integer Factoring Problem

    Zhiqi Liu1, Shihui Zheng1, Xingyu Yan1, Ping Pan1,2, Licheng Wang1,3,*

    Journal of Quantum Computing, Vol.5, pp. 41-54, 2023, DOI:10.32604/jqc.2023.045572

    Abstract The hardness of the integer factoring problem (IFP) plays a core role in the security of RSA-like cryptosystems that are widely used today. Besides Shor’s quantum algorithm that can solve IFP within polynomial time, quantum annealing algorithms (QAA) also manifest certain advantages in factoring integers. In experimental aspects, the reported integers that were successfully factored by using the D-wave QAA platform are much larger than those being factored by using Shor-like quantum algorithms. In this paper, we report some interesting observations about the effects of QAA for solving IFP. More specifically, we introduce a metric, called T-factor that measures the… More >

  • Open Access

    ARTICLE

    Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

    Arsal Javaid1, Areeb Abbas1, Jehangir Arshad1, Mohammad Khalid Imam Rahmani2,*, Sohaib Tahir Chauhdary3, Mujtaba Hussain Jaffery1, Abdulbasid S. Banga2,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1795-1814, 2023, DOI:10.32604/cmc.2023.044140

    Abstract To detect the improper sitting posture of a person sitting on a chair, a posture detection system using machine learning classification has been proposed in this work. The addressed problem correlates to the third Sustainable Development Goal (SDG), ensuring healthy lives and promoting well-being for all ages, as specified by the World Health Organization (WHO). An improper sitting position can be fatal if one sits for a long time in the wrong position, and it can be dangerous for ulcers and lower spine discomfort. This novel study includes a practical implementation of a cushion consisting of a grid of 3… More >

  • Open Access

    ARTICLE

    Electroencephalography (EEG) Based Neonatal Sleep Staging and Detection Using Various Classification Algorithms

    Hafza Ayesha Siddiqa1, Muhammad Irfan1, Saadullah Farooq Abbasi2,*, Wei Chen1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970

    Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual age 37 ± 05 weeks.… More >

  • Open Access

    ARTICLE

    An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

    Yuting Liu1, Hongwei Ding1,*, Zongshan Wang1,*, Gaurav Dhiman2,3,4, Zhijun Yang1, Peng Hu5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2385-2406, 2023, DOI:10.32604/cmc.2023.039883

    Abstract The equilibrium optimizer (EO) represents a new, physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium. Despite its innovative foundation, the EO exhibits certain limitations, including imbalances between exploration and exploitation, the tendency to local optima, and the susceptibility to loss of population diversity. To alleviate these drawbacks, this paper introduces an improved EO that adopts three strategies: adaptive inertia weight, Cauchy mutation, and adaptive sine cosine mechanism, called SCEO. Firstly, a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance… More >

  • Open Access

    ARTICLE

    Fake News Classification: Past, Current, and Future

    Muhammad Usman Ghani Khan1, Abid Mehmood2, Mourad Elhadef2, Shehzad Ashraf Chaudhry2,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2225-2249, 2023, DOI:10.32604/cmc.2023.038303

    Abstract The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network Model for Fire Detection in Real-Time Environment

    Abdul Rehman, Dongsun Kim*, Anand Paul

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435

    Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm. The proffered ISSA empowers smart… More >

  • Open Access

    ARTICLE

    Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm

    R. Ani1,*, O. S. Deepa2, B. R. Manju1

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3033-3048, 2023, DOI:10.32604/csse.2023.033807

    Abstract The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein. The use of virtual screening in pharmaceutical research is growing in popularity. During the early phases of medication research and development, it is crucial. Chemical compound searches are now more narrowly targeted. Because the databases contain more and more ligands, this method needs to be quick and exact. Neural network fingerprints were created more effectively than the well-known… More >

  • Open Access

    ARTICLE

    Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

    Jianguo Wang1, Kuan Zhang1,*, Yuesheng Zhao2,*, Xiaoling Wang2, Muhammad Shamrooz Aslam2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 79-96, 2023, DOI:10.32604/cmc.2023.040506

    Abstract The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm,… More >

  • Open Access

    ARTICLE

    An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

    Nourmeen Lotfy1, Abdulaziz Shehab1,2,*, Mohammed Elhoseny1,3, Ahmed Abu-Elfetouh1

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1227-1249, 2023, DOI:10.32604/cmc.2023.039185

    Abstract Despite the extensive effort to improve intelligent educational tools for smart learning environments, automatic Arabic essay scoring remains a big research challenge. The nature of the writing style of the Arabic language makes the problem even more complicated. This study designs, implements, and evaluates an automatic Arabic essay scoring system. The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks. Then, it comprises two main components: the grading engine and the adaptive fusion engine. The grading engine employs string-based and corpus-based similarity algorithms separately. After that, the adaptive fusion… More >

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