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RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction

MULTIMEDIA SYSTEMS 2021年第2期27卷 177-189页

作者： Abbasi, Rashid Chen, Jianwen Al-Otaibi, Yasser Rehman, Amjad Abbas, Asad Cui, Weiwei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China King Abdulaziz Univ Dept Informat Syst Jeddah 21589 Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Univ Cent Punjab Lahore Fac Informat Technol Lahore Pakistan

Image contrast enhancement is a prerequisite and plays a very important role in many image processing field like medical imaging, face recognition, computer-vision, and satellite imaging. In this paper we proposed reversible data hiding based Limited Dynamic Weighted Histogram Equalization techniques for Abnormal Tumor regions which improve the contrast, transmit the hidden secret information, preserve its brightness intensity and original appearance of the image. We have implemented Otsu's method to segment the input image into two sub-histogram regions of interest (ROI) and non-region of interest;furthermore, the sub-histograms ROI region equalized independently without of over-enhancement and any loss of hidden and diagnostic data. Our proposed method is more efficient to precisely preserve the brightness of the image and extract the secret information with contrast image reversibly;besides, different classifiers are used to classify the brain cancer to check the performance of our proposed method.

关键词： Contrast enhancement Secure transmission Reversible data hiding Classification

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Development and evaluation of immersive VR laboratories of organic chemistry and physics for students education

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PHYSICA SCRIPTA 2024年第5期99卷 056101-056101页

作者： Naz, Zubaira Azam, Ayesha Khan, Muhammad Usman Ghani Saba, Tanzila Al-Otaibi, Shaha Rehman, Amjad Univ Engn & Technol Lahore 54890 Pakistan Univ Engn & Technol Lahore 54890 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 11671 Riyadh 84428 Saudi Arabia

In science education, the use of laboratory experiments has a critical role. However, experiments can cause excessive material waste, and safety issues must be taken seriously. Virtual laboratories and simulations have become a useful and effective alternative to in-person, hands-on laboratories as a result of the educational possibilities afforded by information and communication technology. With virtual reality (VR), students can depict scientific phenomena through virtual practical experiments that use computer simulation. In this work, VR-based lab simulations of physics and chemistry experiments were introduced. Chemistry lab includes 3 experiments from organic chemistry (identify aldehydes using Tollen's test, identify ketones using the 2,4-dinitrophenyl hydrazine test, and identify carboxylic acids using the sodium carbonate test) and physics lab includes 2 experiments on series and parallel current, accomplished through the use of the real-time 3D graphics and pre-rendered animations. Further, to assess the usability of the developed system, 20 students were surveyed as research subjects and they filled out the SUS survey after conducting the VR experiments in Oculus Quest 2. . The average score for the VR chemistry lab simulations was 92.63%, and, the average score for the physics lab was 93.38%, indicating a high level of performance and achievement among the participants. Further, in the comparative study evaluating knowledge retention and learning experience, the experimental group (VR, N=20) outperformed the control group (traditional, N=13), indicating a more favorable learning experience. All participants from the experiment group reported that virtual labs were easy to use. Overall, findings from the Knowledge Retention survey show that interactive laboratory activities enhance students' understanding of chemical processes and practical skills.

关键词： virtual reality lab simulations chemistry laboratory physics laboratory system usability scale series current organic chemistry

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Viral reverse engineering using artificial intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM)

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ENVIRONMENTAL TECHNOLOGY & INNOVATION 2021年 22卷 101531页

作者： Abu Haimed, Ahmad M. Saba, Tanzila Albasha, Ayman Rehman, Amjad Kolivand, Mahyar Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Univ Liverpool Dept Med Liverpool Merseyside England

This research presents a reverse engineering approach to discover the patterns and evolution behavior of SARS-CoV-2 using AI and big data. Accordingly, we have studied five viral families (Orthomyxoviridae, Retroviridae, Filoviridae, Flaviviridae, and Coronaviridae) that happened in the era of the past one hundred years. To capture the similarities, common characteristics, and evolution behavior for prediction concerning SARS-CoV-2. And how reverse engineering using artificial intelligence (AI) and big data is efficient and provides wide horizons. The results show that SARS-CoV-2 shares the same highest active amino acids (S, L, and T) with the mentioned viral families. As known, that affects the building function of the proteins. We have also devised a mathematical formula representing how we calculate the evolution difference percentage between each virus concerning its phylogenic tree. It shows that SARS-CoV-2 has fast mutation evolution concerning its time of arising. artificial intelligence (AI) is used to predict the next evolved instance of SARS-CoV-2 by utilizing the phylogenic tree data as a corpus using Long Short-term Memory (LSTM). This paper has shown the evolved viral instance prediction process on ORF7a protein from SARS-CoV-2 as the first stage to predict the complete mutant virus. Finally, in this research, we have focused on analyzing the virus to its primary factors by reverse engineering using AI and big data to understand the viral similarities, patterns, and evolution behavior to predict future viral mutations of the virus artificially in a systematic and logical way. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Viral reverse engineering COVID19 SARS-CoV-2 Long Short-term Memory (LSTM) Healthcare Public health

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Trust Management Frameworks in Multi-Cloud Environment: A Review 6

Trust Management Frameworks in Multi-Cloud Environment: A Re...

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6th International Conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

作者： Alajroush, Arwa Latif, Rabia Saba, Tanzila Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia Prince Sultan University Artificial Intelligence and Data Analytics Lab College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9781665477239

The term "cloud computing"refers to a method of managing and using resources remotely. Networks, servers, applications, machines, and other resources and storage are all part of the shared and virtualized resource pool that is offered. In addition to providing the flexibility, stability, and scalability many organizations need the services of cloud computing, this reduces the need for setting up the private cloud and deploy separate hardware resources. However, multi-could settings presented several security and trust issues. This paper primary contribution is an examination of contemporary, serious trust challenges that arise in a multi-cloud context. And to put out a survey comparing trust management frameworks for Multi-Cloud environment to address the challenges identified. The suggested solution is given to have a more robust and secure trust framework based on data compliance. © 2023 IEEE.

关键词： Trusted computing

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Prediction of Environmental Earth Surface Temperature using hybrid machine learning model

Prediction of Environmental Earth Surface Temperature using ...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Aisha Dawood Arwa Alsehibani Safia Dawood Amjad Rehman Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia SE Department Alfaisal University Riyadh Saudi Arabia

Global warming increasingly became a research topic of high importance that have attracted attention of many scientists worldwide. According to the annual 2020 global climate report, global yearly temperature has increased 0.08C per decade since 1880. In this study, the global earth temperature has been analyzed including temperature records since 1850. And it has been observed that globally the average temperature has increased by almost 10C degrees in the last century. Also, this study developed a model for temperature prediction using linear regression and random forest regression. Also, the study reflected its finding on Saudi Arabia and found over 3C degrees over the last 100 years. The proposed models achieved accuracies 99.52 % and 99.58 %, respectively, with mean square error 0.28 and 0.24.

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Tumor Localization and Classification from MRI of Brain using Deep Convolution Neural Network and Salp Swarm Algorithm

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COGNITIVE COMPUTATION 2024年第4期16卷 2036-2046页

作者： Alyami, Jaber Rehman, Amjad Almutairi, Fahad Fayyaz, Abdul Muiz Roy, Sudipta Saba, Tanzila Alkhurim, Alhassan King Abdulaziz Univ Fac Appl Med Sci Dept Diagnost Radiol Jeddah 21589 Saudi Arabia King Abdulaziz Univ King Fahd Med Res Ctr Anim House Unit Jeddah 21589 Saudi Arabia King Abdulaziz Univ Smart Med Imaging Res Grp Jeddah 21589 Saudi Arabia CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Jio Inst Artificial Intelligence & Data Sci Navi Mumbai 410206 India Univ Wah Dept Comp Sci Wah 47040 Pakistan

Early diagnosis of brain tumors is crucial for treatment planning and increasing the survival rates of infected patients. In fact, brain tumors exist in a range of different forms, sizes, and features, as well as treatment choices. One of the essential roles of neurologists and radiologists is the diagnosis of brain tumors in their early stages. However, manual brain tumor diagnosis is difficult, time-consuming, and prone to error. Based on the problem highlighted, an automated brain tumor detection system is mandatory to identify the tumor in its initial stages. This research presents an efficient deep learning-based system for the classification of brain tumors from brain MRI using the deep convolutional network and salp swarm algorithm. All experiments are performed using the publicly available brain tumor Kaggle dataset. To enhance the classification rate, preprocessing and data augmentation such as skewed data ideas are devised. In addition, AlexNet and VGG19 are leveraged to perform specific functionality. Finally, all features merged into a single feature vector for brain tumor classification. Some of the extracted features found insignificant towards effective classification. Hence, we employed an efficient feature selection technique named slap swarm to find the most discriminative features to attain best tumor classification rate. Finally, several SVM kernels are merged for the final classification and 99.1% accuracy is achieved by selecting 4111 optimal features from 8192.

关键词： Brain tumor Deep learning MRI Transfer learning Health risks Public health

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Corrections to “A Modified Singular Value Decomposition (MSVD) Approach for the Enhancement of CCTV Low-Quality Images”

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IEEE Access 2025年 13卷 25663-25663页

作者： Shahzada Fahad Sami Ur Rahman Fakhre Alam Muhammad Yousaf Faten S. Alamri Naveed Abbas Saeed Ali Bahaj Amjad R. Khan Department of Computer Science and IT University of Malakand Chakdara Dir Lower Khyber Pakhtunkhwa Pakistan Department of Mathematical Sciences College of Science Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh Saudi Arabia Department of Computer Science Islamia College University Peshawar Pakistan MIS Department College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Artificial Intelligence and Data Analytics Laboratory (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia

Presents corrections to the paper, Corrections to “A Modified Singular Value Decomposition (MSVD) Approach for the Enhancement of CCTV Low-Quality Images”.

关键词： Singular value decomposition

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Properties of the geometry of solutions and capacity of multi-layer neural networks with Rectified Linear Units activations

arXiv

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arXiv 2019年

作者： Baldassi, Carlo Malatesta, Enrico M. Zecchina, Riccardo Artificial Intelligence Lab Institute for Data Science and Analytics Bocconi University Milano Italy

Rectified Linear Units (ReLU) have become the main model for the neural units in current deep learning systems. This choice has been originally suggested as a way to compensate for the so called vanishing gradient problem which can undercut stochastic gradient descent (SGD) learning in networks composed of multiple layers. Here we provide analytical results on the effects of ReLUs on the capacity and on the geometrical landscape of the solution space in two-layer neural networks with either binary or real-valued weights. We study the problem of storing an extensive number of random patterns and find that, quite unexpectedly, the capacity of the network remains finite as the number of neurons in the hidden layer increases, at odds with the case of threshold units in which the capacity diverges. Possibly more important, a large deviation approach allows us to find that the geometrical landscape of the solution space has a peculiar structure: While the majority of solutions are close in distance but still isolated, there exist rare regions of solutions which are much more dense than the similar ones in the case of threshold units. These solutions are robust to perturbations of the weights and can tolerate large perturbations of the inputs. The analytical results are corroborated by numerical findings. Copyright © 2019, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Microscopic melanoma detection and classification: A framework of pixel-based fusion and multilevel features reduction

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MICROSCOPY RESEARCH AND TECHNIQUE 2020年第4期83卷 410-423页

作者： Rehman, Amjad Khan, Muhammad A. Mehmood, Zahid Saba, Tanzila Sardaraz, Muhammad Rashid, Muhammad CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia HITEC Univ Dept CS&D Taxila Pakistan Univ Engn & Technol Dept Comp Engn Taxila Pakistan COMSATS Univ Islamabad Dept Comp Sci Attock Pakistan Umm Al Qura Univ Dept Comp Engn Mecca Saudi Arabia

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel-based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean-based function is implemented and fed input to top-hat and bottom-hat filters which later fused for contrast stretching, (b) seed region growing and graph-cut method-based lesion segmentation and fused both segmented lesions through pixel-based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy-based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

关键词： classification contrast stretching feature reduction lesions segmentation skin cancer

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An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform

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IEEE ACCESS 2022年 10卷 7520-7532页

作者： Adnan, Myasar Mundher Rahim, Mohd Shafry Mohd Khan, Amjad Rehman Saba, Tanzila Fati, Suliman Mohamed Bahaj, Saeed Ali Univ Technol Malaysia Fac Engn Sch Comp Johor Baharu 81310 Malaysia Dept Islamic Univ Najaf 54001 Iraq Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia

Every day, websites and personal archives generate an increasing number of photographs. The extent of these archives is unfathomable. The ease of usage of these enormous digital image collections contributes to their popularity. However, not all of these databases provide appropriate indexing data. As a result, it's tough to find information that the user is interested in. Thus, in order to find information about an image, it is necessary to classify its content in a meaningful way. Image annotation is one of the most difficult issues in computer vision and multimedia research. The objective is to convert an image into a single or numerous labels. This necessitates a grasp of the visual content of an image. The necessity for unambiguous information to build semantic-level concepts from raw image pixels is one of the challenges of image annotation. Unlike text annotation, where a dictionary links words to their meaning, raw picture pixels are insufficient to construct semantic-level notions directly. A simple syntax, on the other hand, is well specified for combining letters to form words and words to form sentences. The automatic feature extraction for automatic annotation was the emphasis of this paper. And they employed a deep learning convolutional neural network to build and improve image coding and annotation capabilities. Performance of the suggested technique on the Corel-5K, ESP-Game, and IAPRTC-12 datasets. Finally, experimental findings on three data sets were used to demonstrate the usefulness of this model for image annotation.

关键词： Convolutional neural networks Feature extraction Image annotation Annotations Deep learning Computer architecture Visualization Automatic image annotation deep learning features extraction slantlet transform technological development

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