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Xian Zhao, Pantea Keikhosrokiani^*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3855-3874, 2022, DOI:10.32604/cmc.2022.019750
Abstract The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for… More >

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D. Kothandaraman¹, A. Balasundaram^2,*, R. Dhanalakshmi³, Arun Kumar Sivaraman⁴, S. Ashokkumar⁵, Rajiv Vincent⁴, M. Rajesh⁴
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3875-3890, 2022, DOI:10.32604/cmc.2022.020744
（This article belongs to this Special Issue: Innovative Technologies in Pervasive Computing)
Abstract Internet of Things (IoT) is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links. Particularly, some of the nodes in IoT are mobile and dynamic in nature. Hence maintaining the link among the nodes, efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing. In such networks, many routing-problems arise due to difficulties in energy and bandwidth based quality of service. Due to the mobility and finite nature of the… More >

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Waqar Hafeez¹, Nazrina Aziz^1,2,*, Zakiyah Zain^1,2, Nur Azulia Kamarudin¹
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3891-3902, 2022, DOI:10.32604/cmc.2022.019695
Abstract Acceptance sampling is a statistical quality control technique that consists of procedures for sentencing one or more incoming lots of finished products. Acceptance or rejection is based on the inspection of sampled products drawn randomly from the lot. The theory of previous acceptance sampling was built upon the assumption that the process from which the lots are produced is stable and the process fraction nonconforming is a constant. Process variability is inevitable due to random fluctuations, which may inadvertently lead to quality variation. As an alternative to traditional sampling plans, Bayesian approach can be used by considering prior information of… More >

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951

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Junaid Tariq^1,*, Ayman Alfalou², Amir Ijaz¹, Hashim Ali³, Imran Ashraf¹, Hameedur Rahman¹, Ammar Armghan⁴, Inzamam Mashood¹, Saad Rehman¹
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3903-3918, 2022, DOI:10.32604/cmc.2022.019541
（This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Comprehension algorithms like High Efficiency Video Coding (HEVC) facilitates fast and efficient handling of multimedia contents. Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality. However, the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content. Therefore, a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block. Normally, the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost… More >

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940

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Disha Deotale¹, Madhushi Verma², P. Suresh³, Sunil Kumar Jangir⁴, Manjit Kaur², Sahar Ahmed Idris⁵, Hammam Alshazly^6,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3919-3938, 2022, DOI:10.32604/cmc.2022.020655
（This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
Abstract Nowadays, the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data. The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos. Smarter monitoring is a historical necessity in which commonly occurring, regular, and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology. In a long video, human activity may be present anywhere in the video. There can be a single or multiple human activities present… More >

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ARTICLE

Ghanshyam Raghuwanshi¹, Yogesh Gupta², Deepak Sinwar¹, Dilbag Singh³, Usman Tariq⁴, Muhammad Attique⁵, Kuntha Pin⁶, Yunyoung Nam^7,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3939-3954, 2022, DOI:10.32604/cmc.2022.018423
（This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
Abstract In the recent decade, the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities. Image segmentation is a key step in digitalization. Segmentation plays a key role in almost all areas of image processing, and various approaches have been proposed for image segmentation. In this paper, a novel approach is proposed for image segmentation using a nonuniform adaptive strategy. Region-based image segmentation along with a directional binary pattern generated a better segmented image. An adaptive mask of 8 × 8 was circulated over the pixels whose bit value was 1 in the… More >

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1087

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Meteb M. Altaf^1,*, Ahmed Samir Roshdy², Hatoon S. AlSagri³
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3955-3967, 2022, DOI:10.32604/cmc.2022.019448
（This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
Abstract The overall healthcare system has been prioritized within development top lists worldwide. Since many national populations are aging, combined with the availability of sophisticated medical treatments, healthcare expenditures are rapidly growing. Blood banks are a major component of any healthcare system, which store and provide the blood products needed for organ transplants, emergency medical treatments, and routine surgeries. Timely delivery of blood products is vital, especially in emergency settings. Hence, blood delivery process parameters such as safety and speed have received attention in the literature, as well as other parameters such as delivery cost. In this paper, delivery time and… More >

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1170

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Nataliya Shakhovska¹, Nataliia Melnykova^1,*, Valentyna Chopiyak², Michal Gregus ml³
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3969-3984, 2022, DOI:10.32604/cmc.2022.019882
（This article belongs to this Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)
Abstract The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. The open dataset is used for data analysis methods development. The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’ health. Machine learning is associated with many expectations, but its quality is determined by choosing a good algorithm and the proper steps to plan, develop, and implement the model. The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy. Pierson coefficient and Boruta algorithm are used… More >

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Muhammad Junaid^1,*, Adnan Sohail¹, Monagi H. Alkinani², Adeel Ahmed³, Mehmood Ahmed³, Faisal Rehman⁴
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3985-4007, 2022, DOI:10.32604/cmc.2022.019962
Abstract Metaheuristic approaches in cloud computing have shown significant results due to their multi-objective advantages. These approaches are now considering hybrid metaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors. The critical factors such as execution time, throughput time, response time, energy consumption, SLA violations, communication overhead, makespan, and migration time need careful attention while designing such dynamic algorithms. To improve such factors, an optimized multi-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization (CSO) with machine learning classifiers such as Support Vector Machine (SVM).… More >

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Phimmarin Keerin¹, Tossapon Boongoen^2,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4009-4025, 2022, DOI:10.32604/cmc.2022.020261
（This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
Abstract The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is… More >

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1448

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Palli Padmini¹, C. Paramasivam¹, G. Jyothish Lal², Sadeen Alharbi^3,*, Kaustav Bhowmick⁴
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4027-4051, 2022, DOI:10.32604/cmc.2022.020065
Abstract The present work presents a statistical method to translate human voices across age groups, based on commonalities in voices of blood relations. The age-translated voices have been naturalized extracting the blood relation features e.g., pitch, duration, energy, using Mel Frequency Cepstrum Coefficients (MFCC), for social compatibility of the voice-impaired. The system has been demonstrated using standard English and an Indian language. The voice samples for resynthesis were derived from 12 families, with member ages ranging from 8–80 years. The voice-age translation, performed using the Pitch synchronous overlap and add (PSOLA) approach, by modulation of extracted voice features, was validated by… More >

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843

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Mohamed Abdel-Basset¹, Abduallah Gamal¹, Rehab Mohamed¹, Mohamed Abouhawwash^2,3,*, Abdulwahab Almutairi⁴, Osama M. ELkomy¹
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4053-4073, 2022, DOI:10.32604/cmc.2022.020048
Abstract There are several challenges that hospitals are facing according to the emergency department (ED). The main two issues are department capacity and lead time. However, the lack of consensus on performance criteria to evaluate ED increases the complication of this process. Thus, this study aims to evaluate the efficiency of the emergency department in 20 Egyptian hospitals (12 private and 8 general hospitals) based on 13 performance metrics. This research suggests an integrated evaluation model assess ED under a framework of plithogenic theory. The proposed framework addressed uncertainty and ambiguity in information with an efficient manner via presenting the evaluation… More >

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924

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Ahmed Abdullah Alqarni¹, Nizar Alsharif¹, Nayeem Ahmad Khan^1,*, Lilia Georgieva², Eric Pardade³, Mohammed Y. Alzahrani¹
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4075-4085, 2022, DOI:10.32604/cmc.2022.020389
（This article belongs to this Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)
Abstract The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing. A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic. In recent years, researchers have used different machine learning methods to detect network attacks without relying on those signatures. The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product. In this study, we propose and implement a new method that relies on a modular deep neural network for reducing the… More >

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2002

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Venkatesan Rajinikanth¹, Shabnam Mohamed Aslam², Seifedine Kadry³, Orawit Thinnukool^4,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4087-4105, 2022, DOI:10.32604/cmc.2022.019786
Abstract The incident rate of the Gastrointestinal-Disease (GD) in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image (EI/CI) supported evaluation of the GD is an approved practice. Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity. The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp (GP) with better accuracy. The proposed GP detection system consist; (i) Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy (Fuzzy/Shannon/Kapur) and between-class-variance (Otsu) technique, (ii) Automated (Watershed/Markov-Random-Field) and semi-automated (Chan-Vese/Level-Set/Active-Contour) segmentation… More >

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992

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ARTICLE

S. Zulaikha Beevi, Abdullah Alabdulatif^*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4107-4122, 2022, DOI:10.32604/cmc.2022.020292
（This article belongs to this Special Issue: Next - Generation Secure Solutions for Wireless Communications, IoT and SDNs)
Abstract The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it many industries developed smart task for different purposes. Sensor nodes interact with each other and this interaction technique are handled by different routing protocol. Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained. So the overall performance of WSN are degraded by this limitation. To resolve this unreliable low power link, many researches have provided various routing protocols to make the network as dependable and sustainable as possible. While speeding up the data… More >

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1087

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Shahab Ul Islam¹, Saifullah Jan², Abdul Waheed^3,4,*, Gulzar Mehmood⁵, Mahdi Zareei⁶, Faisal Alanazi⁷
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4123-4145, 2022, DOI:10.32604/cmc.2022.019226
Abstract Spatial and temporal information on urban infrastructure is essential and requires various land-cover/land-use planning and management applications. Besides, a change in infrastructure has a direct impact on other land-cover and climatic conditions. This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature (LST) during the years 1996 and 2019. For this purpose, firstly, satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper) plus and OLI (Operational Land Imager) of 30 m resolution were taken. Secondly, for classification and image processing, remote sensing (RS) applications ENVI (Environment for Visualising Images)… More >

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1408

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Aref Hassan Kurd Ali^1,*, Halikul Lenando¹, Slim Chaoui^2,3, Mohamad Alrfaay^1,4, Medhat A. Tawfeek^5,6
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4147-4167, 2022, DOI:10.32604/cmc.2022.020659
Abstract Recently, Opportunistic Networks (OppNets) are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent devices. OppNets are characterized by a rough and dynamic topology as well as unpredictable contacts and contact times. Data is forwarded and stored in intermediate nodes until the next opportunity occurs. Therefore, achieving a high delivery ratio in OppNets is a challenging issue. It is imperative that any routing protocol use network resources, as far as they are available, in order to achieve higher network performance. In this article, we introduce the… More >

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796

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Jinsu Kim¹, Sungwook Ryu², Namje Park^1,3,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4169-4184, 2022, DOI:10.32604/cmc.2022.019277
（This article belongs to this Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)
Abstract A significant number of cloud storage environments are already implementing deduplication technology. Due to the nature of the cloud environment, a storage server capable of accommodating large-capacity storage is required. As storage capacity increases, additional storage solutions are required. By leveraging deduplication, you can fundamentally solve the cost problem. However, deduplication poses privacy concerns due to the structure itself. In this paper, we point out the privacy infringement problem and propose a new deduplication technique to solve it. In the proposed technique, since the user’s map structure and files are not stored on the server, the file uploader list cannot… More >

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Waqar Hafeez¹, Nazrina Aziz^1,2,*, Zakiyah Zain^1,2, Nur Azulia Kamarudin¹
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4185-4198, 2022, DOI:10.32604/cmc.2022.018146
Abstract Acceptance sampling is a well-established statistical technique in quality assurance. Acceptance sampling is used to decide, acceptance or rejection of a lot based on the inspection of its random sample. Experts concur that the Bayesian approach is the best approach to make a correct decision, when historical knowledge is available. This paper suggests a Bayesian new group chain sampling plan (BNGChSP) to estimate average probability of acceptance. Binomial distribution function is used to differentiate between defective and non-defective products. Beta distribution is considered as a suitable prior distribution. Derivation is completed for the estimation of the average proportion of defectives.… More >

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Nebojsa Bacanin¹, Khaled Alhazmi^2,*, Miodrag Zivkovic¹, K. Venkatachalam³, Timea Bezdan¹, Jamel Nebhen⁴
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4199-4215, 2022, DOI:10.32604/cmc.2022.020449
（This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
Abstract In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing… More >

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