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Murtaza Ahmed Siddiqi, Wooguil Pak^*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3921-3949, 2022, DOI:10.32604/cmc.2022.029541
Abstract The network infrastructure has evolved rapidly due to the ever-increasing volume of users and data. The massive number of online devices and users has forced the network to transform and facilitate the operational necessities of consumers. Among these necessities, network security is of prime significance. Network intrusion detection systems (NIDS) are among the most suitable approaches to detect anomalies and assaults on a network. However, keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the intruders. This paper presents an effective and prevalent framework for NIDS by merging image processing… More >

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665

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Moheb R. Girgis, Rofida M. Gamal, Enas Elgeldawi^*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3951-3967, 2022, DOI:10.32604/cmc.2022.030934
Abstract Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine, biotechnology and more. Protein secondary structure prediction (PSSP) has a significant role in the prediction of protein tertiary structure, as it bridges the gap between the protein primary sequences and tertiary structure prediction. Protein secondary structures are classified into two categories: 3-state category and 8-state category. Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems, respectively. The 8 classes of… More >

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739

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Manar M. Aldaseen¹, Khaled M. Matrouk¹, Laiali H. Almazaydeh^2,*, Khaled M. Elleithy³
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3969-3981, 2022, DOI:10.32604/cmc.2022.028520
Abstract The Wireless Sensor Networks (WSNs) are characterized by their widespread deployment due to low cost, but the WSNs are vulnerable to various types of attacks. To defend against the attacks, an effective security solution is required. However, the limits of these networks’ battery-based energy to the sensor are the most critical impediments to selecting cryptographic techniques. Consequently, finding a suitable algorithm that achieves the least energy consumption in data encryption and decryption and providing a highly protected system for data remains the fundamental problem. In this research, the main objective is to obtain data security during transmission by proposing a… More >

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574

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Abdullah A. Asiri¹, Tariq Ali², Ahmad Shaf², Muhammad Aamir², Muhammad Shoaib³, Muhammad Irfan⁴, Hassan A. Alshamrani^1,*, Fawaz F. Alqahtani¹, Osama M. Alshehri⁵
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3983-4002, 2022, DOI:10.32604/cmc.2022.030923
Abstract Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death. Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue. Radiologists checked the affected tissue in the slice-by-slice manner, which was time-consuming and hectic task. Therefore, auto segmentation of the affected part is needed to facilitate radiologists. Therefore, we have considered a hybrid model that inherits the convolutional neural network (CNN) properties to the support vector machine (SVM) for the auto-segmented brain tumor region. The CNN model is initially used to detect brain tumors, while SVM is integrated to… More >

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607

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Le Thi Diem My¹, Chia-Nan Wang¹, Nguyen Van Thanh^2,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4003-4015, 2022, DOI:10.32604/cmc.2022.030209
Abstract Fertilizer industry in Vietnam and globally have entered the saturation phase. With the growth rate slowing down, this poses challenges for the development impetus of the fertilizer industry in the next period. In fact, over the past few decades, Vietnam’s crop industry has abused excessive investment in chemical fertilizers, with organic fertilizers are rarely used or not at all, limiting crop productivity, increasing pests and diseases. To develop sustainable agriculture, Vietnam’s crop industry must limit the use of chemical fertilizers, increase the use of environmentally friendly organic and natural mineral fertilizers to produce clean agricultural products which is safe. Therefore,… More >

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783

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A. Chinnappa^*, C. Vijayakumaran
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4017-4031, 2022, DOI:10.32604/cmc.2022.027858
Abstract Internet of Things (IoT) allows several low resources and controlled devices to interconnect, calculate processes and make decisions in the communication network. In the heterogeneous environment for IoT devices, several challenging issues such as energy, storage, efficiency, and security. The design of encryption techniques enables the transmission of the data in the IoT environment in a secured way. The proper selection of optimal keys helps to boost the encryption performance. With this motivation, the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission (SCQCKH-SDT) in IoT environment. The proposed SCQCKH-SDT technique aims to effectively encrypts… More >

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628

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Ming Wan¹, Quanliang Li¹, Jiangyuan Yao^2,*, Yan Song³, Yang Liu⁴, Yuxin Wan⁵
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4033-4049, 2022, DOI:10.32604/cmc.2022.030895
Abstract Anomaly detection is becoming increasingly significant in industrial cyber security, and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks. However, different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples. As a sequence, after developing one feature generation approach, the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm. Based on process control features generated by directed function transition diagrams, this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their… More >

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616

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ARTICLE

Hassen Mestiri^1,2,3,*, Imen Barraj^1,4,5, Mohsen Machhout³
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4051-4066, 2022, DOI:10.32604/cmc.2022.029794
Abstract Robustness of the electronic cryptographic devices against fault injection attacks is a great concern to ensure security. Due to significant resource constraints, these devices are limited in their capabilities. The increasing complexity of cryptographic devices necessitates the development of a fast simulation environment capable of performing security tests against fault injection attacks. SystemC is a good choice for Electronic System Level (ESL) modeling since it enables models to run at a faster rate. To enable fault injection and detection inside a SystemC cryptographic model, however, the model’s source code must be updated. Without altering the source code, Aspect-Oriented Programming (AOP)… More >

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697

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Ahmed M. Ayoup^1,*, Ashraf A. M. Khalaf¹, Fathi E. Abd El-Samie², Fahad Alraddady³, Salwa M. Serag Eldin³
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4067-4085, 2022, DOI:10.32604/cmc.2022.025767
Abstract In this paper, a proposed cancellable biometric scheme is based on multiple biometric image identifiers, Arnold’s cat map and double random phase encoding (DRPE) to obtain cancellable biometric templates. The proposed segmentation scheme that is used to select the region of interest for generating cancelable templates is based on chaos entropy low correlation statistical metrics. The objective of segmentation is to reduce the computational cost and reliability of template creation. The left and right biometric (iris, fingerprint, palm print and face) are divided into non-overlapping blocks of the same dimensions. To define the region of interest (ROI), we select the… More >

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Chaebeen Nam¹, Sa Math¹, Prohim Tam¹, Seokhoon Kim^1,2,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4087-4102, 2022, DOI:10.32604/cmc.2022.030575
Abstract Heterogeneous Internet of Things (IoT) applications generate a diversity of novelty applications and services in next-generation networks (NGN), which is essential to guarantee end-to-end (E2E) communication resources for both control plane (CP) and data plane (DP). Likewise, the heterogeneous 5th generation (5G) communication applications, including Mobile Broadband Communications (MBBC), massive Machine-Type Commutation (mMTC), and ultra-reliable low latency communications (URLLC), obligate to perform intelligent Quality-of-Service (QoS) Class Identifier (QCI), while the CP entities will be suffered from the complicated massive HIOT applications. Moreover, the existing management and orchestration (MANO) models are inappropriate for resource utilization and allocation in large-scale and complicated… More >

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987

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562

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Yassir Edrees Almalki¹, Maida Khalid², Sharifa Khalid Alduraibi³, Qudsia Yousaf², Maryam Zaffar², Shoayea Mohessen Almutiri⁴, Muhammad Irfan⁵, Mohammad Abd Alkhalik Basha⁶, Alaa Khalid Alduraibi³, Abdulrahman Manaa Alamri⁷, Khalaf Alshamrani⁸, Hassan A. Alshamrani^8,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4103-4121, 2022, DOI:10.32604/cmc.2022.029039
Abstract Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of… More >

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694

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Areej A. Malibari¹, Marwa Obayya², Mohamed K. Nour³, Amal S. Mehanna⁴, Manar Ahmed Hamza^5,*, Abu Sarwar Zamani⁵, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4123-4138, 2022, DOI:10.32604/cmc.2022.030492
Abstract With the rapid increase of new cases with an increased mortality rate, cancer is considered the second and most deadly disease globally. Breast cancer is the most widely affected cancer worldwide, with an increased death rate percentage. Due to radiologists’ processing of mammogram images, many computer-aided diagnoses have been developed to detect breast cancer. Early detection of breast cancer will reduce the death rate worldwide. The early diagnosis of breast cancer using the developed computer-aided diagnosis (CAD) systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with… More >

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711

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ARTICLE

Mehrdad Ahmadi Kamarposhti^1,*, Hassan Shokouhandeh², Ilhami Colak³, Kei Eguchi⁴
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4139-4156, 2022, DOI:10.32604/cmc.2022.029011
Abstract One method for eliminating oscillations in power systems is using stabilizers. By applying an appropriate control signal in the excitation system of a generator, a power system stabilizer improves the dynamic stability of power systems. However, the issue that is of high importance is the correct design of these stabilizers. These stabilizers must be designed to have proper performance when operating conditions change. When designed incorrectly, not only they do not improve the stability margin, but also increase the oscillations. In this paper, the robust design of power system stabilizers on a four-machine power system has been performed. For this… More >

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540

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Ahmed ElShafee¹, Walid El-Shafai², Abdulaziz Alarifi^3,*, Mohammed Amoon³, Aman Singh⁴, Moustafa H. Aly⁵
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4157-4177, 2022, DOI:10.32604/cmc.2022.028847
Abstract Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine. Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable, consistent, and timely, successfully lowering mortality rates, particularly during endemics and pandemics. To prevent this pandemic’s rapid and widespread, it is vital to quickly identify, confine, and treat affected individuals. The need for auxiliary computer-aided diagnostic (CAD) systems has grown. Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus. Utilizing advanced convolutional neural network (CNN) architectures in conjunction with radiological… More >

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465

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R. Uma Mageswari¹, Sara A. Althubiti², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gyanendra Prasad Joshi⁵, Woong Cho^6,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4179-4192, 2022, DOI:10.32604/cmc.2022.030806
Abstract Traditional Wireless Sensor Networks (WSNs) comprise of cost-effective sensors that can send physical parameters of the target environment to an intended user. With the evolution of technology, multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target domain. The existence of multimedia sensors, integrated with effective signal processing and multimedia source coding approaches, has led to the increased application of Wireless Multimedia Sensor Network (WMSN). This sort of network has the potential to capture, transmit, and receive multimedia content. Since energy is a major source in WMSN, novel clustering… More >

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591

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ARTICLE

Nagwan Abdel Samee¹, El-Sayed M. El-Kenawy^2,3, Ghada Atteia^1,*, Mona M. Jamjoom⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, Noha E. El-Attar⁸, Tarek Gaber^9,10, Adam Slowik¹¹, Mahmoud Y. Shams¹²
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4193-4210, 2022, DOI:10.32604/cmc.2022.031147
Abstract As corona virus disease (COVID-19) is still an ongoing global outbreak, countries around the world continue to take precautions and measures to control the spread of the pandemic. Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals, a rapid, reliable, and automatic detection of COVID-19 is in extreme need to curb the number of infections. By analyzing the COVID-19 chest X-ray images, a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers. The lung region was segmented from the original chest X-ray images and augmented using various… More >

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2100

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815

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Phi-Hung Nguyen^*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4211-4229, 2022, DOI:10.32604/cmc.2022.030115
Abstract The outbreak of the COVID-19 pandemic has impacted the development of the global economy. As most developing and third world countries are heavily dependent on agriculture and agricultural imports, the agricultural supply chains (ASC) in all these countries are exposed to unprecedented risks following COVID-19. Therefore, it is vital to investigate the impact of risks and create resilient ASC organizations. In this study, critical risks associated with ASC were assessed using a novel Analytical Hierarchy Process based on spherical fuzzy sets (SF-AHP). The findings indicated that depending on the scope and scale of the organization, supply risks, demand risks, financial… More >

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Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4231-4244, 2022, DOI:10.32604/cmc.2022.030090
Abstract In recent research, deep learning algorithms have presented effective representation learning models for natural languages. The deep learning-based models create better data representation than classical models. They are capable of automated extraction of distributed representation of texts. In this research, we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module, and also addresses memory issues that were not addresses before. The proposed model employs a tree structured mechanism to generate the phrase and text embedding. The proposed architecture mimics the tree configuration of the text-texts and provide better… More >

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656

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Sang-Hyun Lee^*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4245-4260, 2022, DOI:10.32604/cmc.2022.026012
Abstract X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by the lack of data to… More >

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854

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580

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Pingping Yu¹, Hongda Wang¹, Xiaodong Zhao^1,*, Guangchen Ruan²
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4261-4276, 2022, DOI:10.32604/cmc.2022.029239
Abstract Aiming at the problems of low target image resolution, insufficient target feature extraction, low detection accuracy and poor real time in remote engineering vehicle detection, an improved CenterNet target detection model is proposed in this paper. Firstly, EfficientNet-B0 with Efficient Channel Attention (ECA) module is used as the basic network, which increases the quality and speed of feature extraction and reduces the number of model parameters. Then, the proposed Adaptive Fusion Bidirectional Feature Pyramid Network (AF-BiFPN) module is applied to fuse the features of different feature layers. Furthermore, the feature information of engineering vehicle targets is added by making full… More >

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583

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Areej A. Malibari¹, Reem M. Alshehri², Fahd N. Al-Wesabi³, Noha Negm³, Mesfer Al Duhayyim⁴, Anwer Mustafa Hilal^5,*, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4277-4290, 2022, DOI:10.32604/cmc.2022.027030
Abstract In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based feature selection technique is used… More >

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589

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Kais Tissaoui^1,2,*, Sahbi Boubaker^3,4, Waleed Saud Alghassab¹, Taha Zaghdoudi^1,5, Jamel Azibi⁶
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4291-4309, 2022, DOI:10.32604/cmc.2022.028830
Abstract The application of optimization methods to prediction issues is a continually exploring field. In line with this, this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective. The complex characteristics of implied volatility risk index such as non-linearity structure, time-varying and non-stationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters. We use the Hybrid Particle Swarm Optimization (HPSO) tool to identify the model parameters of nonlinear polynomial Hammerstein model. Findings indicate that, following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input… More >

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566

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Abdallah Abdallah¹, Mohammed Dauwed², Ayman A. Aly³, Bassem F. Felemban³, Imran Khan⁴, Bong Jun Choi^5,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4311-4327, 2022, DOI:10.32604/cmc.2022.031514
Abstract From raw material storage through final product distribution, a cold supply chain is a technique in which all activities are managed by temperature. The expansion in the number of imported meat and other comparable commodities, as well as exported seafood has boosted the performance of cold chain logistics service providers. On the basis of the standard basic-pursuit (BP) neural network, a rough BP particle swarm optimization (PSO) neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers. To reduce duplicate… More >

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938

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Wajdi Elhamzi^1,2,*, Malika Jallouli³, Yassine Bouteraa^1,4
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4329-4347, 2022, DOI:10.32604/cmc.2022.030954
Abstract Since 3D mesh security has become intellectual property, 3D watermarking algorithms have continued to appear to secure 3D meshes shared by remote users and saved in distant multimedia databases. The novelty of our approach is that it uses a new Clifford-multiwavelet transform to insert copyright data in a multiresolution domain, allowing us to greatly expand the size of the watermark. After that, our method does two rounds of insertion, each applying a different type of Clifford-wavelet transform. Before being placed into the Clifford-multiwavelet coefficients, the watermark, which is a mixture of the mesh description, source mesh signature (produced using SHA512),… More >

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693

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Sana Yasin¹, Umar Draz^2,3,*, Tariq Ali⁴, Kashaf Shahid¹, Amna Abid¹, Rukhsana Bibi¹, Muhammad Irfan⁵, Mohammed A. Huneif⁶, Sultan A. Almedhesh⁶, Seham M. Alqahtani⁶, Alqahtani Abdulwahab⁶, Mohammed Jamaan Alzahrani⁶, Dhafer Batti Alshehri⁶, Alshehri Ali Abdullah⁷, Saifur Rahman⁵
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4349-4367, 2022, DOI:10.32604/cmc.2022.028251
Abstract Diagnosing a baby’s feelings poses a challenge for both doctors and parents because babies cannot explain their feelings through expression or speech. Understanding the emotions of babies and their associated expressions during different sensations such as hunger, pain, etc., is a complicated task. In infancy, all communication and feelings are propagated through cry-speech, which is a natural phenomenon. Several clinical methods can be used to diagnose a baby’s diseases, but nonclinical methods of diagnosing a baby’s feelings are lacking. As such, in this study, we aimed to identify babies’ feelings and emotions through their cry using a nonclinical method. Changes… More >

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553

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Irfan Ahmed Usmani^1,*, Muhammad Tahir Qadri¹, Razia Zia¹, Asif Aziz², Farheen Saeed³
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4369-4392, 2022, DOI:10.32604/cmc.2022.030698
Abstract Knowledge-based transfer learning techniques have shown good performance for brain tumor classification, especially with small datasets. However, to obtain an optimized model for targeted brain tumor classification, it is challenging to select a pre-trained deep learning (DL) model, optimal values of hyperparameters, and optimization algorithm (solver). This paper first presents a brief review of recent literature related to brain tumor classification. Secondly, a robust framework for implementing the transfer learning technique is proposed. In the proposed framework, a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters: batch size and learning rate. An extensive… More >

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842

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Vasumathi Devi Majety¹, N. Sharmili², Chinmaya Ranjan Pattanaik³, E. Laxmi Lydia⁴, Subhi R. M. Zeebaree⁵, Sarmad Nozad Mahmood⁶, Ali S. Abosinnee⁷, Ahmed Alkhayyat^8,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4393-4406, 2022, DOI:10.32604/cmc.2022.031109
Abstract Histopathology is the investigation of tissues to identify the symptom of abnormality. The histopathological procedure comprises gathering samples of cells/tissues, setting them on the microscopic slides, and staining them. The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge. At the same time, deep learning (DL) techniques are able to derive features, extract data, and learn advanced abstract data representation. With this view, this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification (EHCDL-HIC) model. The proposed EHCDL-HIC technique initially performs Weiner filtering based noise removal technique. Once the… More >

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524

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Muhammad Abid Jamil^1,*, Deafallah Alsadie¹, Mohamed K. Nour¹, Normi Sham Awang Abu Bakar²
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4407-4422, 2022, DOI:10.32604/cmc.2022.029096
Abstract To improve the maintenance and quality of software product lines, efficient configurations techniques have been proposed. Nevertheless, due to the complexity of derived and configured products in a product line, the configuration process of the software product line (SPL) becomes time-consuming and costly. Each product line consists of a various number of feature models that need to be tested. The different approaches have been presented by Search-based software engineering (SBSE) to resolve the software engineering issues into computational solutions using some metaheuristic approach. Hence, multiobjective evolutionary algorithms help to optimize the configuration process of SPL. In this paper, different multi-objective… More >

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Muhammad Shahid Amin¹, Jamal Hussain Shah¹, Mussarat Yasmin¹, Ghulam Jillani Ansari², Muhamamd Attique Khan³, Usman Tariq⁴, Ye Jin Kim⁵, Byoungchol Chang^6,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4423-4439, 2022, DOI:10.32604/cmc.2022.030432
Abstract Due to rapid development in Artificial Intelligence (AI) and Deep Learning (DL), it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling. Such technique is sensitive to these models. Thus, fake samples cause AI and DL model to produce diverse results. Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further. In this regard, minor modifications of input images cause “Adversarial Attacks” that altered the performance of competing attacks dramatically. Recently, such attacks and defensive strategies are gaining lot of attention by the machine… More >

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Muneerah Al Nuwairan^1,*, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Maryam Alnami¹, Hanan Almuslem¹
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4441-4454, 2022, DOI:10.32604/cmc.2022.029432
Abstract In this paper, a fractional order model based on the management of waste plastic in the ocean (FO-MWPO) is numerically investigated. The mathematical form of the FO-MWPO model is categorized into three components, waste plastic, Marine debris, and recycling. The stochastic numerical solvers using the Levenberg-Marquardt backpropagation neural networks (LMQBP-NNs) have been applied to present the numerical solutions of the FO-MWPO system. The competency of the method is tested by taking three variants of the FO-MWPO model based on the fractional order derivatives. The data ratio is provided for training, testing and authorization is 77%, 12%, and 11% respectively. The… More >

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