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Classification of human's activities from gesture recognition in live videos using deep learning

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第10期34卷

作者： Khan, Amjad Rehman Saba, Tanzila Khan, Muhammad Zeeshan Fati, Suliman Mohamed Khan, Muhammad Usman Ghani CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Res Lab AID Riyadh 11586 Saudi Arabia Al Khwarizmi Inst Comp Sci UET Intelligent Criminol Res Lab NCAI Lahore Pakistan

The automated behavior analysis is a significant problem in the examination. In the presence of invigilators and examiners, examinees use various unfair means to cheat the staff. Examinees may use different gestures like moving their head, eyes and using different suspicious objects (mobile phone, calculator) and so forth. This research is intended to automatically identify and distinguish examinees in live videos who cheat by their activities in examination hall during exams. Hence, we took the privilege of ensemble learning using deep learning-based algorithms. The suspicious head movements and prohibited objects have been monitored using fine-tuned Faster RCNN algorithm. At the same time, interactive use-age of prohibited objects find out using Open-Pose architecture. For this purpose, intersection over union (IOU) has been calculated between the region of interest (ROI) of detected objects and pose points of hands and face. To get the identity of a specific examinee, we stored the features of facial ROI after the last convolution layer and matched them at the time of testing for statistical analysis. We achieved state-of-the-art results using standard evaluating measures. Moreover, for experiments, dataset of automatic examinee invigilation is available currently. So, this research also contributes toward the generation and annotation of the dataset.

关键词： disaster risk reduction effective institutions ensemble learning object detection RCNN ROI smart invigilation technological development

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data Augmentation and Optimizer Tuning for Polyp Segmentation

Data Augmentation and Optimizer Tuning for Polyp Segmentatio...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Zeeshan Haider Ahmad Taher Azar Tanzila Saba Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Artificial Intelligence & Data Analytics (AIDA) Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Automatic segmentation of polyps is a very challenging problem in the scope of medical imaging. This challenge is often faced due to a lack of quality datasets. In this research, we explore the effect on the accuracy of colorectal cancer polyp segmentation due to augmentation and various optimizers while training the segmentation model. The effect of augmentation and optimizers on the polyp segmentation is studied separately. The augmentation effect is observed by changing the percentage of augmentation in each experiment whereas the optimizer effect is studied by changing the optimizer for each experiment. The experiments are performed with 8 optimizers and 10 different augmentation strategies.

关键词： Training Image segmentation Automation Accuracy Training data data augmentation Tuning

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IoT-Edge technology based cloud optimization using artificial neural networks

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MICROPROCESSORS AND MICROSYSTEMS 2024年 106卷

作者： Rehman, Amjad Saba, Tanzila Haseeb, Khalid Alam, Teg Jeon, Gwanggil CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 12435 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Ind Engn Al Kharj 11942 Saudi Arabia Incheon Natl Univ Coll Informat Technol Dept Embedded Syst Engn Incheon 22012 South Korea

In recent decades, artificial intelligence techniques have been adopted for many real -time applications. The Internet of Things (IoT) network comprises many sensing devices and physical objects for information gathering and further transmission. In addition to being sent to the receiving nodes, the collected data also needs to be received promptly. Also, many solutions have been proposed for IoT-based embedded systems using edge computing but they are not fully protected against unidentified communication threats. In such circumstances, such systems decrease the trust ratio, and communication performance is compromised. In this research, we describe an optimization model based on IoT-edged technology that incorporates cloud computational intelligence. Furthermore, edge nodes employ artificial intelligence algorithms to provide the optimal outcome for selecting trustworthy forwarded data and lengthen the connected time for smart devices. Firstly, the edge devices extract useful information from the IoT nodes, and accordingly, it provides a decision module based on optimization computing. Secondly, utilizing cryptographic approaches, edge technology secures the multi-layers of the IoT system and ensures data privacy with integrity. Finally, the proposed model is tested and verified for its performance than other related studies in terms of energy consumption, packet delivery ratio, and data delay.

关键词： Edge computing Embedded devices Privacy System optimization Cloud systems Innovative processes

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Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 1607-1637页

作者： Ullah, Amin Anwar, Syed Myhammad Li, Jianqiang Nadeem, Lubna Mahmood, Tariq Rehman, Amjad Saba, Tanzila Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Cent Punjab Fac Informat Technol & Comp Sci Dept Comp Sci Lahore Pakistan Childrens Natl Hosp Shaikh Zayed Inst Washington DC USA Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China Univ Engn & Technol Taxila Dept Telecommun Engn Taxila 47050 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan

This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities' various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities.

关键词： Smart cities Applications of smart cities Internet of Things (IoT) data acquisition technology Wireless and mobile networking Deep learning

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Combining stochastic tendency and distribution overlap towards improved nonparametric effect measures and inference

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Scandinavian Journal of Statistics 2025年第0期

作者： Beck, Jonas Langthaler, Patrick B. Bathke, Arne C. Intelligent Data Analytics (IDA) Lab Department of Artificial Intelligence and Human Interfaces (AIHI) Paris Lodron University of Salzburg Salzburg Austria Department of Neurology Paracelsus Medical University of Salzburg Salzburg Austria

A fundamental functional in nonparametric statistics is the Mann-Whitney functional (Formula presented.), which constitutes the basis for the most popular nonparametric procedures. The functional (Formula presented.) measures a location or stochastic tendency effect between two distributions. A limitation of (Formula presented.) is its inability to capture scale differences. If differences of this nature are to be detected, specific tests for scale or omnibus tests need to be employed. However, the latter often suffer from low power, and they do not yield interpretable effect measures. In this article, we extend (Formula presented.) by additionally incorporating the recently introduced distribution overlap index (nonparametric dispersion measure) (Formula presented.) that can be expressed in terms of the quantile process. We derive the joint asymptotic distribution of the respective estimators of (Formula presented.) and (Formula presented.) and construct confidence regions. Extending the Wilcoxon–Mann–Whitney test, we introduce a new test based on the joint use of these functionals. It results in much larger consistency regions while maintaining competitive power to the rank sum test for situations in which (Formula presented.) alone would suffice. Compared with classical omnibus tests, the simulated power is much improved. Additionally, the newly proposed inference method yields effect measures whose interpretation is surprisingly straightforward. © 2025 The Author(s). Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics.

关键词： confidence region nonparametric test resampling simultaneous inference

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Cancer Unveiled: A Deep Dive Into Breast Tumor Detection Using Cutting-Edge Deep Learning Models

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IEEE ACCESS 2023年 11卷 133804-133824页

作者： Arshad, Wishal Masood, Tehreem Mahmood, Tariq Jaffar, Arfan Alamri, Faten S. Bahaj, Saeed Ali Omer Khan, Amjad R. Univ Cent Punjab Fac Software Engn & Informat Technol Lahore 54000 Pakistan Super Univ Fac Comp Sci & Informat Technol Lahore 54000 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Univ Educ Vehari Campus Fac Informat Sci Vehari 61100 Pakistan Princess Nourah Bint Abdul Rahman Univ Coll Sci Dept Math Sci Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia

About 1.5 million women are diagnosed with breast cancer every year, making it the most frequent disease among women. In Pakistan, one woman in every nine has a lifetime chance of being diagnosed with breast cancer, making it the country with the highest incidence rate of breast cancer in Asia. The mortality rate from breast cancer in Pakistan was 22.7% in 2020. A lack of resources, such as competent pathologists, causes a delay in diagnosis and inadequate therapy planning, all of which contribute to a dismal survival rate. End-to-end solutions that may be implemented into computer-aided diagnostic (CAD) systems have been developed by medical professionals and researchers using domain-specific artificial intelligence (AI) technologies, most notably deep learning models, to address this critical issue. By increasing the amount of work for pathologists, these AI models may help in breast cancer detection and diagnosis. The goal of this research was to compare and contrast the effectiveness of many recent convolutional neural network (CNN) designs. Five pre-trained and fine-tuned deep CNN architectures, InceptionV3, ResNet152V2, MobileNetV2, VGG-16, and DenseNet-121, are tested to determine the best-performing model. The goal is to discover which models are preferable in terms of accuracy and effectiveness. Notably, the pre-trained InceptionV3 model outperforms the basic CNN model by 9%, with a high accuracy level of 94%. ResNet152V2 got 95% accuracy, and MobileNetV2 got 97% accuracy. The VGG-16 model outperforms the competition with a remarkable 98% accuracy rate. Following suit, the DenseNet-121 model achieves a remarkable 99% accuracy. These findings highlight the utility of deep learning models in the diagnosis of breast cancer as well as the range of model precision.

关键词： Breast cancer Deep learning Solid modeling data models Breast Training Computer architecture Histopathology Cancer breast cancer histopathological images deep learning MobileNetV2 VGG-16 DenseNet-121 inclusive innovation

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Microscopic handcrafted features selection from computed tomography scans for early stage lungs cancer diagnosis using hybrid classifiers

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MICROSCOPY RESEARCH AND TECHNIQUE 2022年第6期85卷 2181-2191页

作者： Alyami, Jabar Khan, Amjad Rehman Bahaj, Saeed Ali Fati, Suliman Mohamed King Abdulaziz Univ Fac Appl Med Sci Dept Diagnost Radiol Jeddah Saudi Arabia King Abdulaziz Univ King Fahd Med Res Ctr Imaging Unit Jeddah Saudi Arabia Prince Sultan Univ Riyadh Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj Saudi Arabia

Lung's cancer is the leading cause of cancer-related deaths worldwide. Recently cancer mortality rate and incidence increased exponentially. Many patients with lung cancer are diagnosed late, so the survival rate is shallow. Machine learning approaches have been widely used to increase the effectiveness of cancer detection at an early stage. Even while these methods are efficient in detecting specific forms of cancer, there is no known technique that could be used universally and consistently to identify new malignancies. As a result, cancer diagnosis via machine learning algorithms is still fresh area of research. Computed tomography (CT) images are frequently employed for early cancer detection and diagnosis because they contain significant information. In this research, an automated lung cancer detection and classification framework is proposed which consists of preprocessing, three patches local binary pattern feature encoding, local binary pattern, histogram of oriented gradients features are extracted and fused. The fast learning network (FLN) is a novel machine-learning technique that is fast to train and economical in terms of processing resources. However, the FLN's internal power parameters (weight and basis) are randomly initialized, resulting it an unstable algorithm. Therefore, to enhance accuracy, FLN is hybrid with K-nearest neighbors to classify texture and appearance-based features of lung chest CT scans from Kaggle dataset into cancerous and non-cancerous images. The proposed model performance is evaluated using accuracy, sensitivity, specificity on the Kaggle benchmark dataset that is found comparable in state of the art using simple machine learning strategies.

关键词： cancer CT scans fast learning network health care human and diseases K-nearest neighbors

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Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision

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IEEE ACCESS 2021年 9卷 100040-100049页

作者： Khan, Muhammad Zeeshan Khan, Muhammad Usman Ghani Saba, Tanzila Razzak, Imran Rehman, Amjad Bahaj, Saeed Ali Univ Engn & Technol Lahore Natl Ctr Artificial Intelligence NCAI Intelligent Criminol Res Lab Al Khawarizmi Inst Comp Sci KICS Lahore 54000 Pakistan Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Deakin Univ Sch Informat Technol Geelong Vic 3217 Australia Prince Sattam Bin Abdulaziz Univ Coll Business Adm Dept MIS Al Kharj 11942 Saudi Arabia

Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is to follow the rules and regulations that avoid further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of the coronavirus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area as the particular region on which the person touches more than some specified threshold. We further mark that area to some specific color to help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on object detection algorithms (YOLO-v3, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 88.72% accuracy on person detection in the local environment. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 86.7% accuracy using the confusion matrix.

关键词： COVID-19 Computer vision Feature extraction Object detection Diseases Task analysis Filtering algorithms Convolution neural network object detection person detection hotspot zone fine-tuning

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Breast microscopic cancer segmentation and classification using unique 4-qubit-quantum model

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MICROSCOPY RESEARCH AND TECHNIQUE 2022年第5期85卷 1926-1936页

作者： Amin, Javaria Sharif, Muhammad Fernandes, Steven Lawrence Wang, Shui-Hua Saba, Tanzila Khan, Amjad Rehman Univ Wah Dept Comp Sci Quaid Ave Wah Cantt 4740 Pakistan COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt Pakistan Creighton Univ Dept Comp Sci Design & Journalism Omaha NE 68178 USA Univ Leicester Sch Math & Actuarial Sci Leicester Leics England Prince Sultan Univ Artificial Intelligence & Data Lab AIDA CCIS Riyadh 11586 Saudi Arabia

The visual inspection of histopathological samples is the benchmark for detecting breast cancer, but a strenuous and complicated process takes a long time of the pathologist practice. Deep learning models have shown excellent outcomes in clinical diagnosis and image processing and advances in various fields, including drug development, frequency simulation, and optimization techniques. However, the resemblance of histopathologic images of breast cancer and the inclusion of stable and infected tissues in different areas make detecting and classifying tumors on entire slide images more difficult. In breast cancer, a correct diagnosis is needed for complete care in a limited amount of time. An effective detection can relieve the pathologist's workload and mitigate diagnostic subjectivity. Therefore, this research work investigates improved the pre-trained xception and deeplabv3+ design semantic model. The model has been trained on input images with ground masks on the tuned parameters that significantly improve the segmentation of ultrasound breast images into respective classes, that is, benign/malignant. The segmentation model delivered an accuracy of greater than 99% to prove the model's effectiveness. The segmented images and histopathological breast images are transferred to the 4-qubit-quantum circuit with six-layered architecture to detect breast malignancy. The proposed framework achieved remarkable performance as contrasted to currently published methodologies. Highlights This research proposed hybrid semantic model using pre-trained xception and deeplabv3 for breast microscopic cancer classification in to benign and malignant classes at accuracy of 95% accuracy, 99% accuracy for detection of breast malignancy.

关键词： 4-qubit-quantum circuit breast cancer deeplabv3 health care public health ReLU xception

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Color to Grayscale Image Conversion Based on Singular Value Decomposition

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IEEE ACCESS 2023年 11卷 54629-54638页

作者： Khudhair, Zaid Nidhal Khdiar, Ahmed Nidhal El Abbadi, Nidhal K. Mohamed, Farhan Saba, Tanzila Alamri, Faten S. Rehman, Amjad Univ Technol Malaysia Fac Engn Sch Comp Johor Baharu 81310 Malaysia Univ Kufa Fac Engn Dept Elect Engn Najaf 54001 Iraq Al Mustaqbal Univ Comp Tech Engn Dept Babylon 51001 Iraq Univ Teknol Malaysia Inst Human Ctr Engn UTM IRDA MaGICX Johor Baharu 81310 Malaysia Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh 11671 Saudi Arabia

Color information is useless for distinguishing significant edges and features in numerous applications. In image processing, a gray image discards much-unrequired data in a color image. The primary drawback of colour-to-grey conversion is eliminating the visually significant image pixels. A current proposal is a novel approach for transforming an RGB image into a grayscale image based on singular value decomposition (SVD). A specific factor magnifies one of the color channels (Red, Green, and Blue). A vector of three values (Red, Green, Blue) of each pixel in an image is decomposed using SVD into three matrices. The norm of the diagonal matrix was determined and then divided by a specific factor to obtain the grey value of the corresponding pixel. The contribution of the proposed method gives the user high flexibility to produce many versions of gray images with varying contrasts, which is very helpful in many applications. Furthermore, SVD allows for image reconstruction by combining the weighting of each channel with the singular value matrix. This results in a grayscale image that more accurately captures the actual intensity values of the image and preserves more color information than traditional grayscale conversion methods, resulting in loss of color information. The proposed method was compared with a similar method (converting the color image into grayscale) and was found to be the most efficient.

关键词： Decolorization grey image image conversion SVD technological development

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