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Mohammed A. Alzain
Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

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Research article
Published: 16 June 2021 in Mathematical Problems in Engineering
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Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop’s low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.

ACS Style

Sandeep Kumar; Arpit Jain; Anand Prakash Shukla; Satyendr Singh; Rohit Raja; Shilpa Rani; G. Harshitha; Mohammed A. AlZain; Mehedi Masud. A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases. Mathematical Problems in Engineering 2021, 2021, 1 -18.

AMA Style

Sandeep Kumar, Arpit Jain, Anand Prakash Shukla, Satyendr Singh, Rohit Raja, Shilpa Rani, G. Harshitha, Mohammed A. AlZain, Mehedi Masud. A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases. Mathematical Problems in Engineering. 2021; 2021 ():1-18.

Chicago/Turabian Style

Sandeep Kumar; Arpit Jain; Anand Prakash Shukla; Satyendr Singh; Rohit Raja; Shilpa Rani; G. Harshitha; Mohammed A. AlZain; Mehedi Masud. 2021. "A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases." Mathematical Problems in Engineering 2021, no. : 1-18.

Research article
Published: 18 May 2021 in Mathematical Problems in Engineering
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The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python’s to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy.

ACS Style

M. D. Kamrul Hasan; Sakil Ahmed; Z. M. Ekram Abdullah; Mohammad Monirujjaman Khan; Divya Anand; Aman Singh; Mohammad AlZain; Mehedi Masud. Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images. Mathematical Problems in Engineering 2021, 2021, 1 -8.

AMA Style

M. D. Kamrul Hasan, Sakil Ahmed, Z. M. Ekram Abdullah, Mohammad Monirujjaman Khan, Divya Anand, Aman Singh, Mohammad AlZain, Mehedi Masud. Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images. Mathematical Problems in Engineering. 2021; 2021 ():1-8.

Chicago/Turabian Style

M. D. Kamrul Hasan; Sakil Ahmed; Z. M. Ekram Abdullah; Mohammad Monirujjaman Khan; Divya Anand; Aman Singh; Mohammad AlZain; Mehedi Masud. 2021. "Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images." Mathematical Problems in Engineering 2021, no. : 1-8.

Journal article
Published: 06 May 2021 in Sustainability
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The urban flooding situations have arisen in the modern scenario of urbanization due to climatic changes. This work contributes to designing a planned and feasible urban rain flood ecosystem to promote the construction of a sponge city. It has various advantages of improving the water environment, controlling urban waterlogging, reducing runoff pollution, improving river and lake water quality, recycling rainwater resources, replenishing groundwater, and many more. This paper combines the design methods and advantages of the design results formed in decades using traditional regulation and utilizing it for the present study. It reconstructs and integrates the traditional regulation and sponge city construction requirements, thereby providing a feasible urban rain-flood ecosystem in the industrial and smart city scenario. Finally, the regulation of new paddy areas in Yanjin city of China is considered for experimentation, and the design of the regulation is applied using this setup. The design results obtained from the test of sponge city construction have operability and can improve the urban environment and enhance the vitality of the city. The control plan’s design results integrating the sponge city idea can provide effective technical support and guarantee the overall urban environment. The work presented in this article can assess and plan the flood mitigation measures to monitor this type of situation leading to flooding risk reduction in smart cities.

ACS Style

Yixin Zhou; Ashutosh Sharma; Mehedi Masud; Gurjot Gaba; Gaurav Dhiman; Kayhan Ghafoor; Mohammed AlZain. Urban Rain Flood Ecosystem Design Planning and Feasibility Study for the Enrichment of Smart Cities. Sustainability 2021, 13, 5205 .

AMA Style

Yixin Zhou, Ashutosh Sharma, Mehedi Masud, Gurjot Gaba, Gaurav Dhiman, Kayhan Ghafoor, Mohammed AlZain. Urban Rain Flood Ecosystem Design Planning and Feasibility Study for the Enrichment of Smart Cities. Sustainability. 2021; 13 (9):5205.

Chicago/Turabian Style

Yixin Zhou; Ashutosh Sharma; Mehedi Masud; Gurjot Gaba; Gaurav Dhiman; Kayhan Ghafoor; Mohammed AlZain. 2021. "Urban Rain Flood Ecosystem Design Planning and Feasibility Study for the Enrichment of Smart Cities." Sustainability 13, no. 9: 5205.

Journal article
Published: 14 April 2021 in Sensors
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Long-range radio (LoRa) communication is a widespread communication protocol that offers long range transmission and low data rates with minimum power consumption. In the context of solid waste management, only a low amount of data needs to be sent to the remote server. With this advantage, we proposed architecture for designing and developing a customized sensor node and gateway based on LoRa technology for realizing the filling level of the bins with minimal energy consumption. We evaluated the energy consumption of the proposed architecture by simulating it on the Framework for LoRa (FLoRa) simulation by varying distinct fundamental parameters of LoRa communication. This paper also provides the distinct evaluation metrics of the the long-range data rate, time on-air (ToA), LoRa sensitivity, link budget, and battery life of sensor node. Finally, the paper concludes with a real-time experimental setup, where we can receive the sensor data on the cloud server with a customized sensor node and gateway.

ACS Style

Shaik Akram; Rajesh Singh; Mohammed AlZain; Anita Gehlot; Mamoon Rashid; Osama Faragallah; Walid El-Shafai; Deepak Prashar. Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management. Sensors 2021, 21, 2774 .

AMA Style

Shaik Akram, Rajesh Singh, Mohammed AlZain, Anita Gehlot, Mamoon Rashid, Osama Faragallah, Walid El-Shafai, Deepak Prashar. Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management. Sensors. 2021; 21 (8):2774.

Chicago/Turabian Style

Shaik Akram; Rajesh Singh; Mohammed AlZain; Anita Gehlot; Mamoon Rashid; Osama Faragallah; Walid El-Shafai; Deepak Prashar. 2021. "Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management." Sensors 21, no. 8: 2774.

Journal article
Published: 02 April 2021 in Biosensors
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A plasmonic material-coated circular-shaped photonic crystal fiber (C-PCF) sensor based on surface plasmon resonance (SPR) is proposed to explore the optical guiding performance of the refractive index (RI) sensing at 1.7–3.7 μm. A twin resonance coupling profile is observed by selectively infiltrating liquid using finite element method (FEM). A nano-ring gold layer with a magnesium fluoride (MgF2) coating and fused silica are used as plasmonic and base material, respectively, that help to achieve maximum sensing performance. RI analytes are highly sensitive to SPR and are injected into the outmost air holes of the cladding. The highest sensitivity of 27,958.49 nm/RIU, birefringence of 3.9 × 104, resolution of 3.70094 × 105 RIU, and transmittance dip of −34 dB are achieved. The proposed work is a purely numerical simulation with proper optimization. The value of optimization has been referred to with an experimental tolerance value, but at the same time it has been ensured that it is not fabricated and tested. In summary, the explored C-PCF can widely be eligible for RI-based sensing applications for its excellent performance, which makes it a solid candidate for next generation biosensing applications.

ACS Style

Kawsar Ahmed; Mohammed AlZain; Hasan Abdullah; Yanhua Luo; Dhasarathan Vigneswaran; Osama Faragallah; Mahmoud Eid; Ahmed Rashed. Highly Sensitive Twin Resonance Coupling Refractive Index Sensor Based on Gold- and MgF2-Coated Nano Metal Films. Biosensors 2021, 11, 104 .

AMA Style

Kawsar Ahmed, Mohammed AlZain, Hasan Abdullah, Yanhua Luo, Dhasarathan Vigneswaran, Osama Faragallah, Mahmoud Eid, Ahmed Rashed. Highly Sensitive Twin Resonance Coupling Refractive Index Sensor Based on Gold- and MgF2-Coated Nano Metal Films. Biosensors. 2021; 11 (4):104.

Chicago/Turabian Style

Kawsar Ahmed; Mohammed AlZain; Hasan Abdullah; Yanhua Luo; Dhasarathan Vigneswaran; Osama Faragallah; Mahmoud Eid; Ahmed Rashed. 2021. "Highly Sensitive Twin Resonance Coupling Refractive Index Sensor Based on Gold- and MgF2-Coated Nano Metal Films." Biosensors 11, no. 4: 104.

Journal article
Published: 10 March 2021 in IEEE Access
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Despite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the Infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the backend where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or underloaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature’s findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Makespan.

ACS Style

Dalia AbdulKareem Shafiq; Noor Zaman Jhanjhi; Azween Abdullah; Mohammed A. Alzain. A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications. IEEE Access 2021, 9, 41731 -41744.

AMA Style

Dalia AbdulKareem Shafiq, Noor Zaman Jhanjhi, Azween Abdullah, Mohammed A. Alzain. A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications. IEEE Access. 2021; 9 ():41731-41744.

Chicago/Turabian Style

Dalia AbdulKareem Shafiq; Noor Zaman Jhanjhi; Azween Abdullah; Mohammed A. Alzain. 2021. "A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications." IEEE Access 9, no. : 41731-41744.

Research article
Published: 22 February 2021 in Mathematical Problems in Engineering
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In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.

ACS Style

N. Yuvaraj; K. Srihari; Gaurav Dhiman; K. Somasundaram; Ashutosh Sharma; S. Rajeskannan; Mukesh Soni; Gurjot Singh Gaba; Mohammed A. AlZain; Mehedi Masud. Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking. Mathematical Problems in Engineering 2021, 2021, 1 -12.

AMA Style

N. Yuvaraj, K. Srihari, Gaurav Dhiman, K. Somasundaram, Ashutosh Sharma, S. Rajeskannan, Mukesh Soni, Gurjot Singh Gaba, Mohammed A. AlZain, Mehedi Masud. Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking. Mathematical Problems in Engineering. 2021; 2021 ():1-12.

Chicago/Turabian Style

N. Yuvaraj; K. Srihari; Gaurav Dhiman; K. Somasundaram; Ashutosh Sharma; S. Rajeskannan; Mukesh Soni; Gurjot Singh Gaba; Mohammed A. AlZain; Mehedi Masud. 2021. "Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking." Mathematical Problems in Engineering 2021, no. : 1-12.

Original research
Published: 12 February 2021 in Journal of Ambient Intelligence and Humanized Computing
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Recently, the digital multimedia cybersecurity has become a research topic of interest due to the fast development of real-time multimedia applications over public networks such as the Internet. Therefore, this research paper introduces an efficient cybersecurity framework for protecting the high-efficiency video coding (HEVC) frames. The suggested selective cybersecurity HEVC framework employs a robust hybrid technique based on watermarking and selective encryption for maintaining confidentiality and achieving copyright protection of the transmitted HEVC information. The watermarking method employs the Homomorphic transform and singular value decomposition in the discrete wavelet transform to increase the immunity of watermarked HEVC streams to attacks. Moreover, the selective encryption technique uses the Chaotic logistic map for encrypting the motion vector difference and the discrete cosine transform sign bits to provide the feature of HEVC format compliance with low encryption overhead cost. An extensive security investigation is carried out for the proposed selective HEVC cybersecurity framework. The obtained experimental outcomes ensure and validate the effectiveness of the selective HEVC cybersecurity framework for HEVC sequences transmission.

ACS Style

Osama S. Faragallah; Walid El-Shafai; Ahmed I. Sallam; Ibrahim Elashry; El-Sayed M. El-Rabaie; Ashraf Afifi; Mohammed A. AlZain; Jehad F. Al-Amri; Fathi E. Abd El-Samie; Hala S. El-Sayed. Cybersecurity framework of hybrid watermarking and selective encryption for secure HEVC communication. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -25.

AMA Style

Osama S. Faragallah, Walid El-Shafai, Ahmed I. Sallam, Ibrahim Elashry, El-Sayed M. El-Rabaie, Ashraf Afifi, Mohammed A. AlZain, Jehad F. Al-Amri, Fathi E. Abd El-Samie, Hala S. El-Sayed. Cybersecurity framework of hybrid watermarking and selective encryption for secure HEVC communication. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-25.

Chicago/Turabian Style

Osama S. Faragallah; Walid El-Shafai; Ahmed I. Sallam; Ibrahim Elashry; El-Sayed M. El-Rabaie; Ashraf Afifi; Mohammed A. AlZain; Jehad F. Al-Amri; Fathi E. Abd El-Samie; Hala S. El-Sayed. 2021. "Cybersecurity framework of hybrid watermarking and selective encryption for secure HEVC communication." Journal of Ambient Intelligence and Humanized Computing , no. : 1-25.

Journal article
Published: 04 February 2021 in IEEE Access
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With the development of wind power generation in recent years, several studies have dealt with the active and reactive power control of wind power systems, along with the quality of energy produced and the connection to distribution networks. In this context, this research proposes a new contribution to the field. The major objective of this work is the development of a nonlinear adaptive backstepping control technique applied to a DFIG based wind system and an optimization technique that uses the rooted tree optimization (RTO) algorithm. The backstepping control strategy is based on the Lyapunov nonlinear technique to guarantee the stability of the system. It is applied to the two converters (i.e., machine and network sides) and subsequently improved with estimators to make the proposed system robust to parametric variation. The RTO technique is based on monitoring the behavior of the underlying foundation of trees in search of underground water in accordance with the level of underground control. The solution proposed for the control is validated using two methods: (1) a simulation on MATLAB/Simulink to test the continuation of the reference (real wind speed) and the robustness of the system and (2) a real-time implementation on a dSPACE-DS1104 board connected to an experimental bench in a laboratory. Simulation and experimental results highlight the validation of the proposed model with better performance compared with other control techniques, such as sliding mode control, direct power control, and field-oriented control.

ACS Style

Badre Bossoufi; Mohammed Karim; Mohammed Taoussi; Hala Alami-Aroussi; Manale Bouderbala; Olivier Deblecker; Saad Motahhir; Anand Nayyar; Mohammed A. AlZain. Rooted Tree Optimization for the Backstepping Power Control of a Doubly Fed Induction Generator Wind Turbine: dSPACE Implementation. IEEE Access 2021, 9, 1 -1.

AMA Style

Badre Bossoufi, Mohammed Karim, Mohammed Taoussi, Hala Alami-Aroussi, Manale Bouderbala, Olivier Deblecker, Saad Motahhir, Anand Nayyar, Mohammed A. AlZain. Rooted Tree Optimization for the Backstepping Power Control of a Doubly Fed Induction Generator Wind Turbine: dSPACE Implementation. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Badre Bossoufi; Mohammed Karim; Mohammed Taoussi; Hala Alami-Aroussi; Manale Bouderbala; Olivier Deblecker; Saad Motahhir; Anand Nayyar; Mohammed A. AlZain. 2021. "Rooted Tree Optimization for the Backstepping Power Control of a Doubly Fed Induction Generator Wind Turbine: dSPACE Implementation." IEEE Access 9, no. : 1-1.

Journal article
Published: 22 January 2021 in Sensors
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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

ACS Style

Mehedi Masud; Niloy Sikder; Abdullah‐Al Nahid; Anupam Kumar Bairagi; Mohammed A. AlZain. A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. Sensors 2021, 21, 748 .

AMA Style

Mehedi Masud, Niloy Sikder, Abdullah‐Al Nahid, Anupam Kumar Bairagi, Mohammed A. AlZain. A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. Sensors. 2021; 21 (3):748.

Chicago/Turabian Style

Mehedi Masud; Niloy Sikder; Abdullah‐Al Nahid; Anupam Kumar Bairagi; Mohammed A. AlZain. 2021. "A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework." Sensors 21, no. 3: 748.

Journal article
Published: 27 August 2020 in IEEE Access
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Multimedia cybersecurity is a prevalent research topic in the digital world due to the rapid progress of digital multimedia and Internet applications. Watermarking, encryption, and steganography schemes are employed to attain multimedia data confidentiality and robustness. However, these schemes are externally applied on trusted computers, and there has been a lack of similar schemes that can be effectively and efficiently embedded through an untrusted transmission medium. In this work, a self-embedding-based High-Efficiency Video Coding (HEVC) transmission and integrity verification framework is presented. This framework is robust and reliable for verifying the integrity of HEVC frames transmitted through insecure communication channels. Firstly, the transmitted HEVC frames are divided into a number of blocks with a certain block size. After that, a discrete transform is used for self-embedding of watermarks from each block into another block depending on a predefined mechanism. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Fourier Transforms (DFT) are tested for this task. The watermarked HEVC frames are transmitted through a wireless communication channel, and hence they become subject to different attacks and corruptions. At the receiver side, the secret watermarks in each block are sensed with a correlation-based method to discover dubious counterfeit operations. To verify the reliability of the suggested transmission framework for achieving high protection and robust content verification of the transmitted HEVC frames over insecure communication channels, different HEVC analyses and comparisons are performed. Simulation results demonstrate the suitability of the suggested transmission framework for different multimedia cybersecurity applications. Furthermore, the comparative analysis shows that the DFT is an efficient discrete transform that can be employed with the proposed transmission framework to guarantee a higher HEVC frame integrity. It has a higher sensitivity to simple modifications in the transmitted watermarked HEVC frames. This makes the suggested cybersecurity framework applicable, secure, and appropriate for multimedia integrity and verification purposes.

ACS Style

Osama S. Faragallah; Ashraf Afifi; Hala S. El-Sayed; Mohammed A. Alzain; Jehad F. Al-Amri; Fathi E. Abd El-Samie; Walid El-Shafai. Efficient HEVC Integrity Verification Scheme for Multimedia Cybersecurity Applications. IEEE Access 2020, 8, 167069 -167089.

AMA Style

Osama S. Faragallah, Ashraf Afifi, Hala S. El-Sayed, Mohammed A. Alzain, Jehad F. Al-Amri, Fathi E. Abd El-Samie, Walid El-Shafai. Efficient HEVC Integrity Verification Scheme for Multimedia Cybersecurity Applications. IEEE Access. 2020; 8 (99):167069-167089.

Chicago/Turabian Style

Osama S. Faragallah; Ashraf Afifi; Hala S. El-Sayed; Mohammed A. Alzain; Jehad F. Al-Amri; Fathi E. Abd El-Samie; Walid El-Shafai. 2020. "Efficient HEVC Integrity Verification Scheme for Multimedia Cybersecurity Applications." IEEE Access 8, no. 99: 167069-167089.

Journal article
Published: 14 May 2020 in IEEE Access
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Recently, massive research works have been accomplished for augmenting privacy and security requirements for cybersecurity applications in wireless communication networks. This is attributed to the fact that conventional security processes are not appropriate for robust, efficient, and reliable multimedia streaming over unsecure media. Therefore, this paper presents an efficient color image cryptosystem based on RC6 with different modes of operation. The proposed cryptosystem is composed of two phases: encryption and decryption. The encryption phase starts by decomposing the color plainimage with few details into its RGB components, which in turn are segmented into 128-bit blocks. These blocks are then enciphered with RC6 with an appropriate mode of operation. After that, the corresponding enciphered blocks of RGB components are multiplexed for constructing the final cipherimage. This scenario is reversed in the decryption phase. The performance of the proposed cryptosystem is gauged via simulation using a set of encryption quality metrics. The simulation results reveal that the proposed cryptosystem with cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can efficiently and effectively hide all information of the color images with few details even in the presence of some input blocks with similar data. On the other hand, the results show that the electronic codebook (ECB) mode is not effective at all in hiding all details of images. Finally, the obtained results ensure the applicability of the proposed cryptosystem and its efficiency in encrypting images in terms of security, encryption quality, and noise immunity.

ACS Style

Osama S. Faragallah; Ashraf Afifi; Walid El-Shafai; Hala S. El-Sayed; Mohammed A. Alzain; Jehad F. Al-Amri; Fathi E. Abd El-Samie. Efficiently Encrypting Color Images With Few Details Based on RC6 and Different Operation Modes for Cybersecurity Applications. IEEE Access 2020, 8, 103200 -103218.

AMA Style

Osama S. Faragallah, Ashraf Afifi, Walid El-Shafai, Hala S. El-Sayed, Mohammed A. Alzain, Jehad F. Al-Amri, Fathi E. Abd El-Samie. Efficiently Encrypting Color Images With Few Details Based on RC6 and Different Operation Modes for Cybersecurity Applications. IEEE Access. 2020; 8 (99):103200-103218.

Chicago/Turabian Style

Osama S. Faragallah; Ashraf Afifi; Walid El-Shafai; Hala S. El-Sayed; Mohammed A. Alzain; Jehad F. Al-Amri; Fathi E. Abd El-Samie. 2020. "Efficiently Encrypting Color Images With Few Details Based on RC6 and Different Operation Modes for Cybersecurity Applications." IEEE Access 8, no. 99: 103200-103218.

Journal article
Published: 17 February 2020 in IEEE Access
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The need for cybersecurity increases to protect the exchange of information for improving the data privacy. This paper presents an investigation of the encryption efficiency of the chaotic-based image block cipher in the spatial and Fractional Fourier Transform (FrFT) domains. The main objective of this investigation is to study the performance of different chaotic maps , while considering the parameters of the FrFT as additional keys for encryption and achieving reliable cybersecurity for robust image communication. In this paper, Cat, Baker, and Logistic maps confusion approaches are applied in the spatial and FrFT domains to study and analyze the cybersecurity and ciphering efficiency of chaos -based image cryptosystems. The confusion features of the chaotic maps in spatial and FrFT domain s are investigated using information entropy, differential analysis, histograms, visual inspection, attack analysis, effect of noise, and encryption quality analysis. Simulation results prove that the chaotic -based image encryption in the FrFT domain increases the efficiency of the confusion process and achieves a high nonlinear relation between the plainimage and the cipherimage in a symmetric ciphering approach. Moreover, the results demonstrate that the Cat -FrFT scheme is more susceptible to channel noise attacks than the Baker-FrFT and the Logistic-FrFT schemes. Hence, they can be implemented efficiently in the scenarios of noisy channels due to their high robustness to channel noise.

ACS Style

Osama S. Faragallah; Ashraf Afifi; Walid El-Shafai; Hala S. El-Sayed; Ensherah A. Naeem; Mohammed A. AlZain; Jehad F. Al-Amri; Ben Soh; Fathi E. Abd El-Samie. Investigation of Chaotic Image Encryption in Spatial and FrFT Domains for Cybersecurity Applications. IEEE Access 2020, 8, 42491 -42503.

AMA Style

Osama S. Faragallah, Ashraf Afifi, Walid El-Shafai, Hala S. El-Sayed, Ensherah A. Naeem, Mohammed A. AlZain, Jehad F. Al-Amri, Ben Soh, Fathi E. Abd El-Samie. Investigation of Chaotic Image Encryption in Spatial and FrFT Domains for Cybersecurity Applications. IEEE Access. 2020; 8 (99):42491-42503.

Chicago/Turabian Style

Osama S. Faragallah; Ashraf Afifi; Walid El-Shafai; Hala S. El-Sayed; Ensherah A. Naeem; Mohammed A. AlZain; Jehad F. Al-Amri; Ben Soh; Fathi E. Abd El-Samie. 2020. "Investigation of Chaotic Image Encryption in Spatial and FrFT Domains for Cybersecurity Applications." IEEE Access 8, no. 99: 42491-42503.