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• Distributed Trusted Computing for Blockchain-Based Crowdsourcing
• Yihuai Liang, Yan Li, Byeong-Seok Shin^*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2825-2842, 2021, DOI:10.32604/cmc.2021.016682
• （This article belongs to this Special Issue: Advances of AI and Blockchain technologies for Future Smart City)
• Abstract A centralized trusted execution environment (TEE) has been extensively studied to provide secure and trusted computing. However, a TEE might become a throughput bottleneck if it is used to evaluate data quality when collecting large-scale data in a crowdsourcing system. It may also have security problems compromised by attackers. Here, we propose a scheme, named dTEE, for building a platform for providing distributed trusted computing by leveraging TEEs. The platform is used as an infrastructure of trusted computations for blockchain-based crowdsourcing systems, especially to securely evaluate data quality and manage remuneration: these operations are handled by a TEE group. First,… More
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• An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data
• Romany F. Mansour^1,*, Shaha Al-Otaibi², Amal Al-Rasheed², Hanan Aljuaid³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2843-2858, 2021, DOI:10.32604/cmc.2021.016626
• Abstract Big data streams started becoming ubiquitous in recent years, thanks to rapid generation of massive volumes of data by different applications. It is challenging to apply existing data mining tools and techniques directly in these big data streams. At the same time, streaming data from several applications results in two major problems such as class imbalance and concept drift. The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection (MOMBD-CDD) method on High-Dimensional Streaming Data. The presented MOMBD-CDD model has different operational stages such as pre-processing, CDD, and classification. MOMBD-CDD model overcomes class… More
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• Bayesian Analysis in Partially Accelerated Life Tests for Weighted Lomax Distribution
• Rashad Bantan¹, Amal S. Hassan², Ehab Almetwally³, M. Elgarhy⁴, Farrukh Jamal⁵, Christophe Chesneau⁶, Mahmoud Elsehetry^7,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2859-2875, 2021, DOI:10.32604/cmc.2021.015422
• Abstract Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress, such as pressure, temperature, vibration, voltage, or load to induce early failures. In this paper, a step stress partially accelerated life test (SS-PALT) is regarded under the progressive type-II censored data with random removals. The removals from the test are considered to have the binomial distribution. The life times of the testing items are assumed to follow length-biased weighted Lomax distribution. The maximum likelihood method… More
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• A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification
• D. Venugopal¹, T. Jayasankar², Mohamed Yacin Sikkandar³, Mohamed Ibrahim Waly³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar^6,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2877-2893, 2021, DOI:10.32604/cmc.2021.015480
• Abstract Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In… More
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• Impact Assessment of COVID-19 Pandemic Through Machine Learning Models
• Fawaz Jaber Alsolami¹, Abdullah Saad Al-Malaise ALGhamdi², Asif Irshad Khan^1,*, Yoosef B. Abushark¹, Abdulmohsen Almalawi¹, Farrukh Saleem², Alka Agrawal³, Rajeev Kumar^3,4, Raees Ahmad Khan³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2895-2912, 2021, DOI:10.32604/cmc.2021.017469
• （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
• Abstract Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor… More
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• Minimizing Warpage for Macro-Size Fused Deposition Modeling Parts
• Thanh Thuong Huynh¹, Tien V. T. Nguyen^2,3, Quoc Manh Nguyen⁴, Trieu Khoa Nguyen^2,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2913-2923, 2021, DOI:10.32604/cmc.2021.016064
• （This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
• Abstract In this study, we investigated warpage and corner lifting minimization for three-dimensional printed parts generated by macro-size fused deposition modeling (FDM). First, the reasons for warpage were theoretically elucidated. This approach revealed that the thermal deformation and differential volumetric shrinkage of the extruded molten plastic resulted in warpage of FDM parts. In addition, low adhesion between the deposited model and the heated or non-heated printing bed may intensify warpage further. As a next step, initial small-size and medium-size models were used to identify parameters to manage and minimize warpage in a way that would reduce material consumption and running time.… More
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• An Optimized SW/HW AVMF Design Based on High-Level Synthesis Flow for Color Images
• Turki M. Alanazi¹, Ahmed Ben Atitallah^1,2,*, Imen Abid²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2925-2943, 2021, DOI:10.32604/cmc.2021.017575
• Abstract In this paper, a software/hardware High-level Synthesis (HLS) design is proposed to compute the Adaptive Vector Median Filter (AVMF) in real-time. In fact, this filter is known by its excellent impulsive noise suppression and chromaticity conservation. The software (SW) study of this filter demonstrates that its implementation is too complex. The purpose of this work is to study the impact of using an HLS tool to design ideal floating-point and optimized fixed-point hardware (HW) architectures for the AVMF filter using square root function (ideal HW) and ROM memory (optimized HW), respectively, to select the best HLS architectures and to design… More
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• Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting
• Jaeuk Moon, Seungwon Jung, Sungwoo Park, Eenjun Hwang^*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2945-2959, 2021, DOI:10.32604/cmc.2021.017435
• （This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
• Abstract One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection… More
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• An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations
• Mohamed Abdel-Basset¹, Reda Mohamed¹, Mohamed Abouhawwash^2,3, Ripon K. Chakrabortty⁴, Michael J. Ryan⁴, Yunyoung Nam^5,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2961-2977, 2021, DOI:10.32604/cmc.2021.016956
• Abstract Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel… More
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• A Secure Rotation Invariant LBP Feature Computation in Cloud Environment
• Shiqi Wang¹, Mingfang Jiang^2,*, Jiaohua Qin¹, Hengfu Yang², Zhichen Gao³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2979-2993, 2021, DOI:10.32604/cmc.2021.017094
• Abstract In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called PPRILBP). To protect image content,… More
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• Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN
• M. S. Maharajan¹, T. Abirami², Irina V. Pustokhina³, Denis A. Pustokhin⁴, K. Shankar^5,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2995-3013, 2021, DOI:10.32604/cmc.2021.016139
• Abstract Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP (Non-deterministic Polynomial-time) hard problem, Swarm… More
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• Frequency Reconfigurable Antenna for Portable Wireless Applications
• Shakir Ullah¹, Sadiq Ullah¹, Inzamam Ahmad¹, Wasi Ur Rehman Khan¹, Toufeeq Ahmad¹, Usman Habib², Mahmoud A. Albreem³, Mohammed H. Alsharif⁴, Peerapong Uthansakul^5,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3015-3027, 2021, DOI:10.32604/cmc.2021.015549
• （This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
• Abstract In this paper, the design and experimental evaluation of a hexagonal-shaped coplanar waveguide (CPW)-feed frequency reconfigurable antenna is presented using flame retardant (FR)-4 substrate with size of mm³. The antenna is made tunable to three different modes through the status of two pin diodes to operate in four distinct frequency bands, i.e., 2.45 GHz wireless fidelity (Wi-Fi) in MODE 1, 3.3 GHz (5G sub-6 GHz band) in MODE 2, 2.1 GHz (3G Long Term Evolution (LTE)-advanced) and 3.50 GHz Worldwide Interoperability for Microwave Access (WiMAX) in MODE 3. The optimization through simulation modeling shows that the proposed antenna can provide… More
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• An Optimized Algorithm for D2D-MIMO 5G Wireless Networks
• Shahid Bashir¹, Imran Khan², Fahd N. Al-Wesabi³, Nadhem Nemri³, Ammar Zahary⁴, Quang Ngoc Nguyen^5,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3029-3044, 2021, DOI:10.32604/cmc.2021.017015
• Abstract The device-to-device (D2D) networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network, forming a heterogeneous 5G and beyond (B5G) communication network. D2D communication in a cellular cell will boost the efficiency of the spectrum, increase the ability of the device, and reduce the communication burden of base stations through the sharing of approved cell resources, causing serious interference as well. The device-to-device (D2D) networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network, forming a heterogeneous 5G communication network. D2D communication in a… More
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• Gait Recognition via Cross Walking Condition Constraint
• Runsheng Wang¹, Hefei Ling^1,*, Ping Li¹, Yuxuan Shi¹, Lei Wu¹, Jialie Shen²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3045-3060, 2021, DOI:10.32604/cmc.2021.017275
• Abstract Gait recognition is a biometric technique that captures human walking pattern using gait silhouettes as input and can be used for long-term recognition. Recently proposed video-based methods achieve high performance. However, gait covariates or walking conditions, i.e., bag carrying and clothing, make the recognition of intra-class gait samples hard. Advanced methods simply use triplet loss for metric learning, which does not take the gait covariates into account. For alleviating the adverse influence of gait covariates, we propose cross walking condition constraint to explicitly consider the gait covariates. Specifically, this approach designs center-based and pair-wise loss functions to decrease discrepancy of… More
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• Modelling Intelligent Driving Behaviour Using Machine Learning
• Qura-Tul-Ain Khan¹, Sagheer Abbas¹, Muhammad Adnan Khan^2,*, Areej Fatima³, Saad Alanazi⁴, Nouh Sabri Elmitwally^4,5
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3061-3077, 2021, DOI:10.32604/cmc.2021.015441
• Abstract In vehicular systems, driving is considered to be the most complex task, involving many aspects of external sensory skills as well as cognitive intelligence. External skills include the estimation of distance and speed, time perception, visual and auditory perception, attention, the capability to drive safely and action-reaction time. Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments. The parameters for understanding intelligent behaviour are knowledge, reasoning, decision making, habit and cognitive skill. Modelling intelligent behaviour reveals that many of these parameters… More
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• Development of Social Media Analytics System for Emergency Event Detection and Crisis Management
• Shaheen Khatoon^1,*, Majed A. Alshamari¹, Amna Asif¹, Md Maruf Hasan¹, Sherif Abdou², Khaled Mostafa Elsayed³, Mohsen Rashwan⁴
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3079-3100, 2021, DOI:10.32604/cmc.2021.017371
• （This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
• Abstract Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze… More
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• Prediction of Parkinson’s Disease Using Improved Radial Basis Function Neural Network
• Rajalakshmi Shenbaga Moorthy^1,*, P. Pabitha²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3101-3119, 2021, DOI:10.32604/cmc.2021.016489
• Abstract Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression. This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network (IRBFNN). Particle swarm optimization (PSO) with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN. The performance of RBFNN is seriously affected by the centers of hidden neurons. Conventionally K-means was used to find the centers of hidden neurons. The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.… More
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• Phase Error Compensation of Three-Dimensional Reconstruction Combined with Hilbert Transform
• Tao Zhang^1,*, Jie Shen¹, Shaoen Wu²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3121-3131, 2021, DOI:10.32604/cmc.2021.016362
• Abstract Nonlinear response is an important factor affecting the accuracy of three-dimensional image measurement based on the fringe structured light method. A phase compensation algorithm combined with a Hilbert transform is proposed to reduce the phase error caused by the nonlinear response of a digital projector in the three-dimensional measurement system of fringe structured light. According to the analysis of the influence of Gamma distortion on the phase calculation, the algorithm establishes the relationship model between phase error and harmonic coefficient, introduces phase shift to the signal, and keeps the signal amplitude constant while filtering out the DC component. The phase… More
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• Ergodic Capacity Evaluation of Multi-Hop Decode-and-Forward MIMO-OFDM Relaying Network
• Latif Jan¹, Mohammad Haseeb Zafar¹, Abdul Waheed², Mahdi Zareei³, Faisal Alanazi^4,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3133-3145, 2021, DOI:10.32604/cmc.2021.014857
• Abstract Spatial diversity plays a significant role in wireless communication systems, including the Fourth Generation (4G) and Fifth Generation (5G) systems, and it is expected to be a fundamental part of the future wireless communication systems as well. The Multiple-Input Multiple-Output (MIMO) technology, which is included in the IEEE 802.16j standard, still holds the most crucial position in the 4G spectrum as it promises to improve the throughput, capacity, spectral, and energy efficiency of wireless communication systems in the 2020s. This makes MIMO a viable technology for delay constrained medical and health care facilities. This paper presents an approximate closed-form expression… More
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• Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks
• Muhammad Reazul Haque¹, Saw Chin Tan¹, Zulfadzli Yusoff^2,*, Kashif Nisar^3,5,6, Lee Ching Kwang^2,7, Rizaludin Kaspin⁴, Bhawani Shankar Chowdhry⁵, Rajkumar Buyya⁸, Satya Prasad Majumder⁹, Manoj Gupta¹⁰, Shuaib Memon¹¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3147-3165, 2021, DOI:10.32604/cmc.2021.016591
• （This article belongs to this Special Issue: Advanced 5G Communication System for Transforming Health Care)
• Abstract In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical… More
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• Web Attack Detection Using the Input Validation Method: DPDA Theory
• Osamah Ibrahim Khalaf¹, Munsif Sokiyna^2,*, Youseef Alotaibi³, Abdulmajeed Alsufyani⁴, Saleh Alghamdi⁵
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3167-3184, 2021, DOI:10.32604/cmc.2021.016099
• Abstract A major issue while building web applications is proper input validation and sanitization. Attackers can quickly exploit errors and vulnerabilities that lead to malicious behavior in web application validation operations. Attackers are rapidly improving their capabilities and technologies and now focus on exploiting vulnerabilities in web applications and compromising confidentiality. Cross-site scripting (XSS) and SQL injection attack (SQLIA) are attacks in which a hacker sends malicious inputs (cheat codes) to confuse a web application, to access or disable the application’s back-end without user awareness. In this paper, we explore the problem of detecting and removing bugs from both client-side and… More
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• The Investigation of the Fractional-View Dynamics of Helmholtz Equations Within Caputo Operator
• Rashid Jan¹, Hassan Khan^2,3, Poom Kumam^4,5,*, Fairouz Tchier⁶, Rasool Shah², Haifa Bin Jebreen⁶
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3185-3201, 2021, DOI:10.32604/cmc.2021.015252
• （This article belongs to this Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)
• Abstract It is eminent that partial differential equations are extensively meaningful in physics, mathematics and engineering. Natural phenomena are formulated with partial differential equations and are solved analytically or numerically to interrogate the system’s dynamical behavior. In the present research, mathematical modeling is extended and the modeling solutions Helmholtz equations are discussed in the fractional view of derivatives. First, the Helmholtz equations are presented in Caputo’s fractional derivative. Then Natural transformation, along with the decomposition method, is used to attain the series form solutions of the suggested problems. For justification of the proposed technique, it is applied to several numerical examples.… More
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• Tibetan Question Generation Based on Sequence to Sequence Model
• Yuan Sun^1,2,*, Chaofan Chen^1,2, Andong Chen³, Xiaobing Zhao^1,2
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3203-3213, 2021, DOI:10.32604/cmc.2021.016517
• Abstract As the dual task of question answering, question generation (QG) is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph. The QG task is of great significance to question answering systems, conversational systems, and machine reading comprehension systems. Recent sequence to sequence neural models have achieved outstanding performance in English and Chinese QG tasks. However, the task of Tibetan QG is rarely mentioned. The key factor impeding its development is the lack of a public Tibetan QG dataset. Faced with this challenge, the present paper first collects 425 articles from the Tibetan… More
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• Bitcoin Candlestick Prediction with Deep Neural Networks Based on Real Time Data
• Reem K. Alkhodhairi¹, Shahad R. Aljalhami¹, Norah K. Rusayni¹, Jowharah F. Alshobaili¹, Amal A. Al-Shargabi^1,*, Abdulatif Alabdulatif²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3215-3233, 2021, DOI:10.32604/cmc.2021.016881
• （This article belongs to this Special Issue: Artificial Intelligence and Big Data in Entrepreneurship)
• Abstract Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better prediction model, the present study… More
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• Secure Multifactor Remote Access User Authentication Framework for IoT Networks
• Mohammed Mujib Alshahrani^*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3235-3254, 2021, DOI:10.32604/cmc.2021.015310
• （This article belongs to this Special Issue: Security Issues in Industrial Internet of Things)
• Abstract The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require… More
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• Hybrid Nanofluid Flow with Homogeneous-Heterogeneous Reactions
• Iskandar Waini^1,2, Anuar Ishak^2,*, Ioan Pop³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3255-3269, 2021, DOI:10.32604/cmc.2021.017643
• Abstract This study examines the stagnation point flow over a stretching/shrinking sheet in a hybrid nanofluid with homogeneous-heterogeneous reactions. The hybrid nanofluid consists of copper (Cu) and alumina (Al₂O₃) nanoparticles which are added into water to form Cu-Al₂O₃/water hybrid nanofluid. The similarity equations are obtained using a similarity transformation. Then, the function bvp4c in MATLAB is utilised to obtain the numerical results. The dual solutions are found for limited values of the stretching/shrinking parameter. Also, the turning point arises in the shrinking region . Besides, the presence of hybrid nanoparticles enhances the heat transfer rate, skin friction coefficient, and the concentration… More
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• Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System
• Thavavel Vaiyapuri^*, Adel Binbusayyis
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3271-3288, 2021, DOI:10.32604/cmc.2021.017665
• （This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
• Abstract In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the… More
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• Unknown Attack Detection: Combining Relabeling and Hybrid Intrusion Detection
• Gun-Yoon Shin¹, Dong-Wook Kim¹, Sang-Soo Kim², Myung-Mook Han^3,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3289-3303, 2021, DOI:10.32604/cmc.2021.017502
• Abstract Detection of unknown attacks like a zero-day attack is a research field that has long been studied. Recently, advances in Machine Learning (ML) and Artificial Intelligence (AI) have led to the emergence of many kinds of attack-generation tools developed using these technologies to evade detection skillfully. Anomaly detection and misuse detection are the most commonly used techniques for detecting intrusion by unknown attacks. Although anomaly detection is adequate for detecting unknown attacks, its disadvantage is the possibility of high false alarms. Misuse detection has low false alarms; its limitation is that it can detect only known attacks. To overcome such… More
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• CARM: Context Based Association Rule Mining for Conventional Data
• Muhammad Shaheen^1,*, Umair Abdullah²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3305-3322, 2021, DOI:10.32604/cmc.2021.016766
• Abstract This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations. Many association rules related… More
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• Suggestion Mining from Opinionated Text of Big Social Media Data
• Youseef Alotaibi^1,*, Muhammad Noman Malik², Huma Hayat Khan³, Anab Batool², Saif ul Islam⁴, Abdulmajeed Alsufyani⁵, Saleh Alghamdi⁶
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3323-3338, 2021, DOI:10.32604/cmc.2021.016727
• Abstract Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The… More
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• Numerical Solution of a Problem of Thermal Stresses of a Magnetothermoelastic Cylinder with Rotation by Finite-Difference Method
• F. S. Bayones¹, A. M. Abd-Alla², A. M. Farhan^3,4,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3339-3352, 2021, DOI:10.32604/cmc.2021.016021
• Abstract The present article deals with the investigation thermal stress of a magnetothermoelastic cylinder subjected to rotation, open or closed circuit, thermal and mechanical boundary conditions. The outer and inner surfaces of the cylinder are subjected to both mechanical and thermal boundary conditions. A The transient coupled thermoelasticity in an infinite cylinder with its base abruptly exposed to a heat flux of a decaying exponential function of time is devised solve by the finite-difference method. The fundamental equations’ system is solved by utilizing an implicit finite-difference method. This current method is a second-order accurate in time and space; it is also… More
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• Hybrid Trainable System for Writer Identification of Arabic Handwriting
• Saleem Ibraheem Saleem^*, Adnan Mohsin Abdulazeez
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3353-3372, 2021, DOI:10.32604/cmc.2021.016342
• （This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
• Abstract Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles. Previous studies have indicated that a word’s attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient (HOG) features are modified to… More
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• Unsupervised Domain Adaptation Based on Discriminative Subspace Learning for Cross-Project Defect Prediction
• Ying Sun¹, Yanfei Sun^1,2,*, Jin Qi¹, Fei Wu¹, Xiao-Yuan Jing^1,3, Yu Xue⁴, Zixin Shen⁵
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3373-3389, 2021, DOI:10.32604/cmc.2021.016539
• Abstract Cross-project defect prediction (CPDP) aims to predict the defects on target project by using a prediction model built on source projects. The main problem in CPDP is the huge distribution gap between the source project and the target project, which prevents the prediction model from performing well. Most existing methods overlook the class discrimination of the learned features. Seeking an effective transferable model from the source project to the target project for CPDP is challenging. In this paper, we propose an unsupervised domain adaptation based on the discriminative subspace learning (DSL) approach for CPDP. DSL treats the data from two… More
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• Kernel Search-Framework for Dynamic Controller Placement in Software-Defined Network
• Ali Abdi Seyedkolaei¹, Seyed Amin Hosseini Seno^1,*, Rahmat Budiarto²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3391-3410, 2021, DOI:10.32604/cmc.2021.017313
• （This article belongs to this Special Issue: Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks)
• Abstract In software-defined networking (SDN) networks, unlike traditional networks, the control plane is located separately in a device or program. One of the most critical problems in these networks is a controller placement problem, which has a significant impact on the network’s overall performance. This paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load balancing. The researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement problem. It demonstrates through careful computational studies that the formulations can design… More
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• L-Moments Based Calibrated Variance Estimators Using Double Stratified Sampling
• Usman Shahzad^1,2,*, Ishfaq Ahmad¹, Ibrahim Mufrah Almanjahie^3,4, Nadia H.Al –Noor⁵
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3411-3430, 2021, DOI:10.32604/cmc.2021.017046
• Abstract Variance is one of the most vital measures of dispersion widely employed in practical aspects. A commonly used approach for variance estimation is the traditional method of moments that is strongly influenced by the presence of extreme values, and thus its results cannot be relied on. Finding momentum from Koyuncu’s recent work, the present paper focuses first on proposing two classes of variance estimators based on linear moments (L-moments), and then employing them with auxiliary data under double stratified sampling to introduce a new class of calibration variance estimators using important properties of L-moments (L-location, L-cv, L-variance). Three populations are… More
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• Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network
• Zhenzhou Wang¹, Yinuo Ma¹, Pingping Yu^1,*, Ning Cao², Heiner Dintera³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3431-3446, 2021, DOI:10.32604/cmc.2021.017729
• Abstract Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase… More
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• UFC-Net with Fully-Connected Layers and Hadamard Identity Skip Connection for Image Inpainting
• Chung-Il Kim¹, Jehyeok Rew², Yongjang Cho², Eenjun Hwang^2,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3447-3463, 2021, DOI:10.32604/cmc.2021.017633
• （This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
• Abstract Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel reconstruction failure and unintended filling.… More
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• Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance
• Ghulfam Zahra¹, Muhammad Imran¹, Abdulrahman M. Qahtani^2,*, Abdulmajeed Alsufyani², Omar Almutiry³, Awais Mahmood³, Fayez Eid Alazemi⁴
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3465-3481, 2021, DOI:10.32604/cmc.2021.017454
• （This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
• Abstract In recent years, video surveillance application played a significant role in our daily lives. Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility. The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery, object detection, target killing, and surveillance. To remove fog and enhance visibility, a number of visibility enhancement algorithms and methods have been proposed in the past. However, these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer… More
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• Race Classification Using Deep Learning
• Khalil Khan¹, Rehan Ullah Khan², Jehad Ali³, Irfan Uddin⁴, Sahib Khan⁵, Byeong-hee Roh^3,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3483-3498, 2021, DOI:10.32604/cmc.2021.016535
• Abstract Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a race-classification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used… More
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• Novel Unilateral Dental Expander Appliance (UDEX): A Compound Innovative Materials
• Hasan Sabah Hasan^1,*, Abdallah A. Abdallah², Imran Khan³, Hala Sadek Alosman⁴, Ayshan Kolemen⁵, Bilal Alhayani⁶
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3499-3511, 2021, DOI:10.32604/cmc.2021.015968
• （This article belongs to this Special Issue: Wireless Sensors Networks Application in Healthcare and Medical Internet of Things (Miot) in Bio-Medical Sensors Networks)
• Abstract True unilateral posterior crossbite in adults is a challenging malocclusion to treat, especially when we need to correct cross-arch segments with unwanted effects on non-cross segments. Conventional expansion methods are expected to have some shortcomings; the Unilateral dental expander appliance used to restore unilateral cross bite dental arch is an uncommon appliance; for this, a designed new device is needed. This paper aimed to invite a new unilateral dental expander appliance (UDEX) to treat unilateral dental posterior crossbite in adults using available dental material, easy to use and handle, well tolerated by the patient, and biocompatible with oral structure. It… More
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• An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease
• Pushpendra Kumar^1,2,*, Ramjeevan Singh Thakur³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.016957
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this paper, a model has been… More
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• A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds
• Mohammad Aldossary^*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3531-3562, 2021, DOI:10.32604/cmc.2021.017477
• Abstract With the striking rise in penetration of Cloud Computing, energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’ infrastructures. Subsequently, recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources, where the energy consumption and the operational costs are minimized. However, to make better cost decisions in these strategies, the performance and energy awareness should be supported at both Physical Machine (PM) and Virtual Machine (VM) levels. Therefore, in this paper, a novel hybrid approach is proposed, which jointly considered the prediction… More
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• DTLM-DBP: Deep Transfer Learning Models for DNA Binding Proteins Identification
• Sara Saber¹, Uswah Khairuddin^2,*, Rubiyah Yusof², Ahmed Madani¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3563-3576, 2021, DOI:10.32604/cmc.2021.017769
• Abstract The identification of DNA binding proteins (DNABPs) is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g., in the study of the biological, biophysical, and biochemical effects of antibiotics, drugs, and steroids on DNA. This paper presents an efficient approach for DNABPs identification based on deep transfer learning, named “DTLM-DBP.” Two transfer learning methods are used in the identification process. The first is based on the pre-trained deep learning model as a feature’s extractor and classifier. Two different pre-trained Convolutional Neural Networks (CNN), AlexNet 8 and VGG… More
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• Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks
• Ajla Kulaglic^1,*, Burak Berk Ustundag²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3577-3593, 2021, DOI:10.32604/cmc.2021.014768
• Abstract Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples… More
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• Energy Efficient Clustering Protocol to Enhance Network Lifetime in Wireless Sensor Networks
• S. Nanthini^1,*, S. Nithya Kalyani², Sudhakar Sengan³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3595-3614, 2021, DOI:10.32604/cmc.2021.015038
• Abstract In this paper, the energy conservation in the ununiform clustered network field is proposed. The fundamental reason behind the methodology is that in the process of CH election, nodes Competition Radius (CR) task is based on not just the space between nodes and their Residual Energy (RE), which is utilized in Energy-Aware Distributed Unequal Clustering (EADUC) protocol but also a third-degree factor, i.e., the nearby multi-hop node count. In contrast, a third-factor nearby nodes count is also used. This surrounding data is taken into account in the clustering feature to increase the network’s life span. The proposed method, known as… More
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• Describe the Mathematical Model for Exchanging Waves Between Bacterial and Cellular DNA
• Mohamed S. Mohamed^1,*, Sayed K. Elagan¹, Saad J. Almalki¹, Muteb R. Alharthi¹, Mohamed F. El-Badawy², Amr M. S. Mahdy¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3615-3628, 2021, DOI:10.32604/cmc.2021.017208
• （This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
• Abstract In this article, we have shown that bacterial DNA could act like some coils which interact with coil-like DNA of host cells. By decreasing the separating distance between two bacterial cellular DNA, the interaction potential, entropy, and the number of microstates of the system grow. Moreover, the system gives its energy to the medium and the temperature of the host body grows. This could be seen as fever in diseases. By emitting some special waves and changing the temperature of the medium, the effects of bacterial waves could be reduced and bacterial diseases could be controlled. Many investigators have shown… More
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• Enhancements of SDR-Based FPGA System for V2X-VLC Communications
• Lukas Danys¹, Radek Martinek¹, Rene Jaros^1,*, Jan Baros¹, Petr Simonik², Vaclav Snasel³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3629-3652, 2021, DOI:10.32604/cmc.2021.017333
• Abstract This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication (SDR-V2X-VLC). The presented system is based on a novel adaptive optimization of the feed-forward software defined equalization (FFSDE) methods of the least mean squares (LMS), normalized LMS (NLMS) and QR decomposition-based recursive least squares (QR-RLS) algorithms. Individual parameters of adaptive equalizations are adjusted in real-time to reach the best possible results. Experiments were carried out on a conventional LED Octavia III taillight drafted directly from production line and universal software radio peripherals (USRP) from National Instruments. The transmitting/receiving… More
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• Bit Rate Reduction in Cloud Gaming Using Object Detection Technique
• Daniyal Baig¹, Tahir Alyas¹, Muhammad Hamid², Muhammad Saleem³, Saadia Malik⁴, Nadia Tabassum^5,*, Natash Ali Mian⁶
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3653-3669, 2021, DOI:10.32604/cmc.2021.017948
• Abstract The past two decades witnessed a broad-increase in web technology and on-line gaming. Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology. The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers. As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience. In cloud-based video gaming, game engines are hosted in cloud gaming data centers, and compressed gaming scenes are rendered to the players over the… More
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• Q-Learning Based Routing Protocol for Congestion Avoidance
• Daniel Godfrey¹, Beom-Su Kim¹, Haoran Miao¹, Babar Shah², Bashir Hayat³, Imran Khan⁴, Tae-Eung Sung⁵, Ki-Il Kim^1,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3671-3692, 2021, DOI:10.32604/cmc.2021.017475
• （This article belongs to this Special Issue: Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks)
• Abstract The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol,… More
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• Optimal Sprint Length Determination for Agile-Based Software Development
• Adarsh Anand¹, Jasmine Kaur¹, Ompal Singh¹, Omar H. Alhazmi^2,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3693-3712, 2021, DOI:10.32604/cmc.2021.017461
• Abstract A carefully planned software development process helps in maintaining the quality of the software. In today’s scenario the primitive software development models have been replaced by the Agile based models like SCRUM, KANBAN, LEAN, etc. Although, every framework has its own boon, the reason for widespread acceptance of the agile-based approach is its evolutionary nature that permits change in the path of software development. The development process occurs in iterative and incremental cycles called sprints. In SCRUM, which is one of the most widely used agile-based software development modeling framework; the sprint length is fixed throughout the process wherein; it… More
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• Real-Time Dense Reconstruction of Indoor Scene
• Jinxing Niu^1,*, Qingsheng Hu¹, Yi Niu¹, Tao Zhang¹, Sunil Kumar Jha²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3713-3724, 2021, DOI:10.32604/cmc.2021.017418
• Abstract Real-time dense reconstruction of indoor scenes is of great research value for the application and development of service robots, augmented reality, cultural relics conservation and other fields. ORB-SLAM2 method is one of the excellent open source algorithms in visual SLAM system, which is often used in indoor scene reconstruction. However, it is time-consuming and can only build sparse scene map by using ORB features to solve camera pose. In view of the shortcomings of ORB-SLAM2 method, this article proposes an improved ORB-SLAM2 solution, which uses a direct method based on light intensity to solve the camera pose. It can greatly… More
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• Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering
• Shinjin Kang¹, Soo Kyun Kim^2,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3725-3736, 2021, DOI:10.32604/cmc.2021.016828
• Abstract In this study, we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment. We focus on the users’ app usage to analyze unusual behavior, especially in indoor spaces. This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased. Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied for effective singular movement analysis. To analyze high-level… More
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• Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach
• Mohamed. A. H. Sabry^1,*, Ehab M. Almetwally², Hisham M. Almongy³, Gamal M. Ibrahim⁴
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3737-3753, 2021, DOI:10.32604/cmc.2021.017510
• Abstract In this paper, a joint analysis consisting of goodness-of-fit tests and Markov chain Monte Carlo simulations are used to assess the performance of some ranked set sampling designs. The Markov chain Monte Carlo simulations are conducted when Bayesian methods with Jeffery’s priors of the unknown parameters of Weibull distribution are used, while the goodness of fit analysis is conducted when the likelihood estimators are used and the corresponding empirical distributions are obtained. The ranked set sampling designs considered in this research are the usual ranked set sampling, extreme ranked set sampling, median ranked set sampling, and neoteric ranked set sampling… More
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• Prediction Flashover Voltage on Polluted Porcelain Insulator Using ANN
• Ali Salem¹, Rahisham Abd-Rahman¹, Waheed Ghanem^2,*, Samir Al-Gailani^3,4, Salem Al-Ameri¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3755-3771, 2021, DOI:10.32604/cmc.2021.016988
• Abstract

  This paper aims to assess the effect of dry band location of contaminated porcelain insulators under various flashover voltages due to humidity. Four locations of dry bands are proposed to be tested under different severity of contamination artificially produce using salt deposit density (SDD) sprayed on an insulator. Laboratory tests of polluted insulators under proposed scenarios have been conducted. The flashover voltage of clean insulators has been identified as a reference value to analyze the effect of contamination distribution and its severity. The dry band dimension has been taken into consideration in experimental tests. The flashover voltage has been predicted… More

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• Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network
• Sajid Habib Gill¹, Noor Ahmed Sheikh¹, Samina Rajpar¹, Zain ul Abidin², N. Z. Jhanjhi^3,*, Muneer Ahmad⁴, Mirza Abdur Razzaq¹, Sultan S. Alshamrani⁵, Yasir Malik⁶, Fehmi Jaafar⁷
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3773-3787, 2021, DOI:10.32604/cmc.2021.016001
• Abstract Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients’ medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a… More
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• Arabic Feature-Based Text Watermarking Technique for Sensitive Detecting Tampering Attack
• Fahd N. Al-Wesabi^1,2,*, Huda G. Iskandar^2,3, Saleh Alzahrani⁴, Abdelzahir Abdelmaboud⁴, Mohammed Abdul⁴, Nadhem Nemri⁴, Mohammad Medani⁴, Mohammed Y. Alghamdi⁵
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3789-3806, 2021, DOI:10.32604/cmc.2021.017674
• Abstract In this article, a high-sensitive approach for detecting tampering attacks on transmitted Arabic-text over the Internet (HFDATAI) is proposed by integrating digital watermarking and hidden Markov model as a strategy for soft computing. The HFDATAI solution technically integrates and senses the watermark without modifying the original text. The alphanumeric mechanism order in the first stage focused on the Markov model key secret is incorporated into an automated, null-watermarking approach to enhance the proposed approach’s efficiency, accuracy, and intensity. The first-level order and alphanumeric Markov model technique have been used as a strategy for soft computing to analyze the text of… More
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• Reinforcement Learning-Based Optimization for Drone Mobility in 5G and Beyond Ultra-Dense Networks
• Jawad Tanveer¹, Amir Haider², Rashid Ali², Ajung Kim^1,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3807-3823, 2021, DOI:10.32604/cmc.2021.016087
• Abstract Drone applications in 5th generation (5G) networks mainly focus on services and use cases such as providing connectivity during crowded events, human-instigated disasters, unmanned aerial vehicle traffic management, internet of things in the sky, and situation awareness. 4G and 5G cellular networks face various challenges to ensure dynamic control and safe mobility of the drone when it is tasked with delivering these services. The drone can fly in three-dimensional space. The drone connectivity can suffer from increased handover cost due to several reasons, including variations in the received signal strength indicator, co-channel interference offered to the drone by neighboring cells,… More
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• Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI
• Ayesha Sarwar¹, Kashif Javed¹, Muhammad Jawad Khan¹, Saddaf Rubab¹, Oh-Young Song^2,*, Usman Tariq³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3825-3840, 2021, DOI:10.32604/cmc.2021.016893
• （This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
• Abstract Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give… More
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• Video Analytics Framework for Human Action Recognition
• Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Fayadh Alenezi³, Usman Tariq⁴, Yunyoung Nam^5,*, Tallha Akram⁶
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3841-3859, 2021, DOI:10.32604/cmc.2021.016864
• （This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
• Abstract Human action recognition (HAR) is an essential but challenging task for observing human movements. This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms. This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation, features reduction and selection framework. A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted. An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion. A custom made genetic algorithm is implemented on… More
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• Research on Face Anti-Spoofing Algorithm Based on Image Fusion
• Pingping Yu¹, Jiayu Wang¹, Ning Cao^2,*, Heiner Dintera³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3861-3876, 2021, DOI:10.32604/cmc.2021.017527
• Abstract Along with the rapid development of biometric authentication technology, face recognition has been commercially used in many industries in recent years. However, it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs, videos or forged 3D masks. In order to solve this problem, this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images. The normal temperature distribution of the human face is stable and characteristic, and the important physiological information of the human body can be observed by the infrared thermal… More
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• Hydrodynamics and Sensitivity Analysis of a Williamson Fluid in Porous-Walled Wavy Channel
• A. Shahzad¹, W. A. Khan^2,*, R. Gul¹, B. Dayyan¹, M. Zubair¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3877-3893, 2021, DOI:10.32604/cmc.2021.012524
• Abstract In this work, a steady, incompressible Williamson fluid model is investigated in a porous wavy channel. This situation arises in the reabsorption of useful substances from the glomerular filtrate in the kidney. After 80% reabsorption, urine is left, which behaves like a thinning fluid. The laws of conservation of mass and momentum are used to model the physical problem. The analytical solution of the problem in terms of stream function is obtained by a regular perturbation expansion method. The asymptotic integration method for small wave amplitudes and the RK-Fehlberg method for pressure distribution has been used inside the channel. It… More
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• Data-Fusion for Epidemiological Analysis of Covid-19 Variants in UAE
• Anoud Bani-Hani^1,*, Anaïs Lavorel², Newel Bessadet³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3895-3913, 2021, DOI:10.32604/cmc.2021.015355
• （This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
• Abstract Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain… More
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• A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked Autoencoder
• Nojood O. Aljehane^*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3915-3929, 2021, DOI:10.32604/cmc.2021.017905
• （This article belongs to this Special Issue: Advanced IoT Industrial Solutions and Cyber Security Threats in Communication Networks)
• Abstract Cyber physical systems (CPSs) are a networked system of cyber (computation, communication) and physical (sensors, actuators) elements that interact in a feedback loop with the assistance of human interference. Generally, CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart devices. Increased utilization of CPSs, however, poses many threats, which may be of major significance for users. Such security issues in CPSs represent a global issue; therefore, developing a robust, secure, and effective CPS is currently a hot research topic. To resolve this issue, an intrusion… More
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• BitmapAligner: Bit-Parallelism String Matching with MapReduce and Hadoop
• Mary Aksa¹, Junaid Rashid^2,*, Muhammad Wasif Nisar¹, Toqeer Mahmood³, Hyuk-Yoon Kwon⁴, Amir Hussain⁵
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3931-3946, 2021, DOI:10.32604/cmc.2021.016081
• Abstract Advancements in next-generation sequencer (NGS) platforms have improved NGS sequence data production and reduced the cost involved, which has resulted in the production of a large amount of genome data. The downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of bioinformatics. The traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream analysis. This study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences… More
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• Intelligent Framework for Secure Transportation Systems Using Software-Defined-Internet of Vehicles
• Mohana Priya Pitchai¹, Manikandan Ramachandran^1,*, Fadi Al-Turjman², Leonardo Mostarda³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3947-3966, 2021, DOI:10.32604/cmc.2021.015568
• （This article belongs to this Special Issue: Management of Security, Privacy and Trust of Multimedia Data in Mobile devices communication)
• Abstract The Internet of Things plays a predominant role in automating all real-time applications. One such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic rules. As vehicles are connected to the internet through wireless communication technologies, the Internet of Vehicles network infrastructure is susceptible to flooding attacks. Reconfiguring the network infrastructure is difficult as network customization is not possible. As Software Defined Network provide a flexible programming environment for network customization, detecting flooding attacks on the Internet of Vehicles is integrated on top of it. The basic methodology used is crypto-fuzzy rules, in which cryptographic… More
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• Energy-Efficient Deployment of Water Quality Sensor Networks
• Qian Sun^1,2, Zhiping Shen^1,2, Jinglin Liang^1,2, Xiaoyi Wang^1,2,*, Jiping Xu^1,2, Li Wang^1,2, Huiyan Zhang^1,2, Jiabin Yu^1,2, Ning Cao³, Ruichao Wang⁴
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3967-3977, 2021, DOI:10.32604/cmc.2021.017252
• Abstract Water quality sensor networks are promising tools for the exploration of oceans. Some key areas need to be monitored effectively. Water quality sensors are deployed randomly or uniformly, however, and understanding how to deploy sensor nodes reasonably and realize effective monitoring of key areas on the basis of monitoring the whole area is an urgent problem to be solved. Additionally, energy is limited in water quality sensor networks. When moving sensor nodes, we should extend the life cycle of the sensor networks as much as possible. In this study, sensor nodes in non-key monitored areas are moved to key areas.… More
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• Digital Forensics for Skulls Classification in Physical Anthropology Collection Management
• Imam Yuadi^1,*, Myrtati D. Artaria², Sakina³, A. Taufiq Asyhari⁴
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3979-3995, 2021, DOI:10.32604/cmc.2021.015417
• Abstract The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet transforms, and combinations of features.… More
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• A New Hybrid Feature Selection Method Using T-test and Fitness Function
• Husam Ali Abdulmohsin^1,*, Hala Bahjat Abdul Wahab², Abdul Mohssen Jaber Abdul Hossen³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3997-4016, 2021, DOI:10.32604/cmc.2021.014840
• （This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
• Abstract

  Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes the well-designed fitness function… More

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• Data Matching of Solar Images Super-Resolution Based on Deep Learning
• Liu Xiangchun¹, Chen Zhan¹, Song Wei^1,2,3,*, Li Fenglei¹, Yang Yanxing⁴
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4017-4029, 2021, DOI:10.32604/cmc.2021.017086
• Abstract The images captured by different observation station have different resolutions. The Helioseismic and Magnetic Imager (HMI: a part of the NASA Solar Dynamics Observatory (SDO) has low-precision but wide coverage. And the Goode Solar Telescope (GST, formerly known as the New Solar Telescope) at Big Bear Solar Observatory (BBSO) solar images has high precision but small coverage. The super-resolution can make the captured images become clearer, so it is wildly used in solar image processing. The traditional super-resolution methods, such as interpolation, often use single image’s feature to improve the image’s quality. The methods based on deep learning-based super-resolution image… More
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• Mobile Memory Management System Based on User’s Application Usage Patterns
• Jaehwan Lee, Sangoh Park^*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4031-4050, 2021, DOI:10.32604/cmc.2021.017872
• Abstract Currently, the number of functions to improve user convenience in smartphone applications is increasing. In addition, more mobile applications are being loaded into mobile operating system memory for faster launches, thus increasing the memory requirements for smartphones. The memory used by applications in mobile operating systems is managed using software; allocated memory is freed up by either considering the usage state of the application or terminating the least recently used (LRU) application. As LRU-based memory management schemes do not consider the application launch frequency in a low memory situation, currently used mobile operating systems can lead to the termination of… More
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• Image-to-Image Style Transfer Based on the Ghost Module
• Yan Jiang¹, Xinrui Jia¹, Liguo Zhang^1,2,*, Ye Yuan¹, Lei Chen³, Guisheng Yin¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4051-4067, 2021, DOI:10.32604/cmc.2021.016481
• Abstract The technology for image-to-image style transfer (a prevalent image processing task) has developed rapidly. The purpose of style transfer is to extract a texture from the source image domain and transfer it to the target image domain using a deep neural network. However, the existing methods typically have a large computational cost. To achieve efficient style transfer, we introduce a novel Ghost module into the GANILLA architecture to produce more feature maps from cheap operations. Then we utilize an attention mechanism to transform images with various styles. We optimize the original generative adversarial network (GAN) by using more efficient calculation… More
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• Steganography-Based Transmission of Medical Images Over Unsecure Network for Telemedicine Applications
• Romany F. Mansour^1,*, Moheb R. Girgis²
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4069-4085, 2021, DOI:10.32604/cmc.2021.017064
• （This article belongs to this Special Issue: Advanced IoT Industrial Solutions and Cyber Security Threats in Communication Networks)
• Abstract Steganography is one of the best techniques to hide secret data. Several steganography methods are available that use an image as a cover object, which is called image steganography. In image steganography, the major features are the cover object quality and hiding data capacity. Due to poor image quality, attackers could easily hack the secret data. Therefore, the hidden data quantity should be improved, while keeping stego-image quality high. The main aim of this study is combining several steganography techniques, for secure transmission of data without leakage and unauthorized access. In this paper, a technique, which combines various steganography-based techniques,… More
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• Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN
• Sehar Shahzad Farooq¹, Mustansar Fiaz¹, Irfan Mehmood², Ali Kashif Bashir³, Raheel Nawaz⁴, KyungJoong Kim⁵, Soon Ki Jung^1,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4087-4108, 2021, DOI:10.32604/cmc.2021.015612
• （This article belongs to this Special Issue: Machine Learning for Data Analytics)
• Abstract Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In… More
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• Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction
• Kathiravan Srinivasan¹, Lalit Garg^2,*, Debajit Datta³, Abdulellah A. Alaboudi⁴, N. Z. Jhanjhi⁵, Rishav Agarwal³, Anmol George Thomas¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4109-4124, 2021, DOI:10.32604/cmc.2021.016736
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semi-automated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research… More
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• Context and Machine Learning Based Trust Management Framework for Internet of Vehicles
• Abdul Rehman^1,*, Mohd Fadzil Hassan¹, Yew Kwang Hooi¹, Muhammad Aasim Qureshi², Tran Duc Chung³, Rehan Akbar⁴, Sohail Safdar⁵
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4125-4142, 2021, DOI:10.32604/CMC.2021.017620
• （This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
• Abstract Trust is one of the core components of any ad hoc network security system. Trust management (TM) has always been a challenging issue in a vehicular network. One such developing network is the Internet of vehicles (IoV), which is expected to be an essential part of smart cities. IoV originated from the merger of Vehicular ad hoc networks (VANET) and the Internet of things (IoT). Security is one of the main barriers in the on-road IoV implementation. Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements. Trust plays a vital role in ensuring security,… More
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• Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment
• Jaber Almutairi¹, Mohammad Aldossary^2,*,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4143-4160, 2021, DOI:10.32604/cmc.2021.018145
• Abstract Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. However, different service architecture and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network. Also, it introduces the… More
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• Joint Event Extraction Based on Global Event-Type Guidance and Attention Enhancement
• Daojian Zeng¹, Jian Tian², Ruoyao Peng¹, Jianhua Dai^1,*, Hui Gao³, Peng Peng⁴
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4161-4173, 2021, DOI:10.32604/cmc.2021.017028
• Abstract Event extraction is one of the most challenging tasks in information extraction. It is a common phenomenon where multiple events exist in the same sentence. However, extracting multiple events is more difficult than extracting a single event. Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long. In addition, the current argument extraction relies on the results of syntactic dependency analysis, which is complicated and prone to error transmission. In order to solve the above problems, a joint event extraction method based on global event-type guidance and attention enhancement was… More
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• Surveillance Video Key Frame Extraction Based on Center Offset
• Yunzuo Zhang^1,*, Shasha Zhang¹, Yi Li¹, Jiayu Zhang¹, Zhaoquan Cai², Shui Lam³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4175-4190, 2021, DOI:10.32604/cmc.2021.017011
• Abstract With the explosive growth of surveillance video data, browsing videos quickly and effectively has become an urgent problem. Video key frame extraction has received widespread attention as an effective solution. However, accurately capturing the local motion state changes of moving objects in the video is still challenging in key frame extraction. The target center offset can reflect the change of its motion state. This observation proposed a novel key frame extraction method based on moving objects center offset in this paper. The proposed method utilizes the center offset to obtain the global and local motion state information of moving objects,… More
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• Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm
• J. Jayanthi^1,*, M. Kavitha², T. Jayasankar³, A. Sagai Francis Britto⁴, N. B. Prakash⁵, Mohamed Yacin Sikkandar⁶, C. Bharathiraja⁷
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4191-4203, 2021, DOI:10.32604/cmc.2021.012252
• Abstract Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era. Among various cancers identified so far, glioma, a type of brain tumor, is one of the deadliest cancers, and it remains challenging to the medicinal world. The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease. Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time. From the captured image, the… More
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• Virtual Reality-Based Random Dot Kinematogram
• Jun Ma¹, Hyo-Jung Kim², Ji-Soo Kim^3,4, Eek-Sung Lee⁵, Min Hong^6,*
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4205-4213, 2021, DOI:10.32604/cmc.2021.018080
• （This article belongs to this Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)
• Abstract This research implements a random dot kinematogram (RDK) using virtual reality (VR) and analyzes the results based on normal subjects. Visual motion perception is one of visual functions localized to a specific cortical area, the human motion perception area (human analogue for the middle temporal/middle superior temporal area) located in the parieto–occipito–temporal junction of the human brain. The RDK measures visual motion perception capabilities. The stimuli in conventional RDK methods are presented using a monitor screen, so these devices require a spacious dark room for installation and use. Recently, VR technology has been implemented in different medical domains. The test… More
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