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Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers

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MICROSCOPY RESEARCH AND TECHNIQUE 2022年第11期85卷 3600-3607页

作者： Alyami, Jaber Rehman, Amjad Sadad, Tariq Alruwaythi, Maryam Saba, Tanzila Bahaj, Saeed Ali King Abdulaziz Univ Dept Diagnost Radiol Jeddah Saudi Arabia King Abdulaziz Univ King Fand Med Res Ctr Anim House Unit Jeddah Saudi Arabia King Abdulaziz Univ Smart Med Imaging Res Grp Jeddah Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Int Islamic Univ Dept Comp Sci & Software Engn Islamabad Pakistan Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj Saudi Arabia

Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning-based classification. The employment of deep features through AlexNet architecture with local optimal-oriented pattern can accurately predict skin lesions. The proposed model is tested on two open-access datasets PAD-UFES-20 and MED-NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier.

关键词： classification handcrafted and deep features human and disease medical images melanoma nevus public health WHO

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Automated Long-Term Monitoring of Stereotypical Movement in Polar Bears Under Human Care Using Machine Learning

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SSRN 2024年

作者： Zuerl, Matthias Stoll, Philip Brehm, Ingrid Sueskind, Jonas Raab, René Petermann, Jan Zanca, Dario Simon, Ralph von Fersen, Lorenzo Eskofier, Bjoern Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91052 Germany Animal Physiology Department of Biology Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Behavioral Ecology and Conservation Lab Nuremberg Zoo Nuremberg90480 Germany

The welfare of captive animals is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant *** behaviour monitoring methods, however, are labour-intensive and subject to observer *** study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in captive animals, exemplified by a longitudinal study of two polar *** designed an animal tracking framework to localise and identify individual animals in the *** determining their position on the enclosure map via homographic transformation, we cleaned the resulting trajectories using a particle ***, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %.The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological *** work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated monitoring of stereotypical behaviour, paving the way for its application across different species and settings. © 2024, The Authors. All rights reserved.

关键词： Enclosures

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On the Performance of Jackknife Based Estimators for Ridge Regression

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IEEE ACCESS 2021年 9卷 68044-68053页

作者： Shah, Ismail Sajid, Faiza Ali, Sajid Rehman, Amjad Bahaj, Saeed Ali Fati, Suliman Mohamed Quaid I Azam Univ Dept Stat Islamabad 45320 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj 11942 Saudi Arabia Hadhramout Univ Dept Comp Engn Al Mukalla Yemen Prince Sultan Univ Coll Comp & Informat Sci Riyadh 11586 Saudi Arabia

Regression techniques are generally used to predict a response variable using one or more predictor variables. In many fields of study, the regressors can be highly intercorrelated, which leads to the problem of multicollinearity. Consequently, the ordinary least squares estimates become inconsistent and lead to wrong inferences. To handle the problem, machine learning techniques particularly, the ridge regression approach, are commonly used. In this paper, we revisit the problem of estimating the ridge parameter "k" by proposing some new estimators using the Jackknife method and compare them with some existing estimators. The performance of the proposed estimators compared to the existing ones is evaluated using extensive Monte Carlo simulations as well as two real data sets. The results suggested that the proposed estimators outperform the existing estimators.

关键词： Ridge regression multicollinearity Monte Carlo simulations mean squared error Jackknife technique machine learning

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Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using Machine Learning

Prediction of Stress Coping Capabilities from Nightly Heart ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Linda Vorberg Siri Pflueger Robert Richer Katharina M. Jaeger Arne Küderle Nicolas Rohleder Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Chair of Health Psychology Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Stress is related to short- and long-term alterations in stress systems, including the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). While it is well established that stress experienced during the day can affect sleep quality, less is known about how it affects stress systems during the night. We assume that stress coping strategies can have an impact on how stress carries over into the night and that individuals with bad coping mechanisms show elevated activation of stress systems during sleep. For that reason, we recorded the heart rate (HR) and heart rate variability (HRV) of 21 healthy participants on two consecutive nights during sleep and the first hour after awakening and extracted cortisol and alpha-amylase from saliva samples collected in the first hour after awakening. Stress coping capabilities were assessed using self-reports. We performed backward stepwise multiple regression models to analyze the relationship between HR(V) and stress coping and trained different machine learning-based regression algorithms to predict positive (SVF Pos ) and negative (SVF Neg ) stress coping capabilities, respectively. Our results show that individuals with higher SVF Neg scores showed higher SNS activity during the night, whereas higher SVF Pos scores indicated lower SNS activity. SVF Pos was predicted with a mean absolute error (MAE) of 1.51 ± 0.73 and SVF Neg with an MAE of 2.79 ± 1.53. Our findings indicate an association between nightly HR(V) and the individual’s capability of coping with stress. This provides further information about how stress influences sleep and might be used for tailored intervention and feedback on successful stress coping.

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Single and multiple regions duplication detections in digital images with applications in image forensic

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 10351-10371页

作者： Nawaz, Marriam Mehmood, Zahid Bilal, Muhammad Munshi, Asmaa Mahdi Rashid, Muhammad Yousaf, Rehan Mehmood Rehman, Amjad Saba, Tanzila Univ Engn & Technol Dept Comp Sci Taxila Pakistan Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan Univ Engn & Technol Dept Software Engn Taxila Pakistan Univ Jeddah Coll Comp Sci & Engn Jeddah Saudi Arabia Umm Al Qura Univ Dept Comp Engn Mecca Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh Saudi Arabia

With the help of powerful image editing software, various image modifications are possible which are known as image forgeries. Copy-move is the easiest way of image manipulation, wherein an area of the image is copied and replicated in the same image. The major reason for performing this forgery is to conceal undesirable contents of the image. Thus, means are required to unveil the presence of duplicated areas in an image. In this article, an effective and efficient approach for copy-move forgery detection (CMFD) is proposed, which is based on stationary wavelet transform (SWT), speeded-up robust features (SURF), and a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering. The SWT allows the SURF descriptor to extract only energy-rich features from the input image. The SURF features can detect the tampered regions even under post-processing attacks like contrast adjustment, scaling, and affine transformation on the images. On the extracted features, a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering algorithm is applied to detect forged regions with high accuracy as it can easily identify the clusters of arbitrary shapes and sizes and can filter the outliers. For performance evaluation, three publicly available datasets namely MICC-F220, MICC-F2000, and image manipulation dataset (IMD) are employed. The qualitative and quantitative analysis demonstrates that the proposed approach outperforms state-of-the-art CMFD approaches in the presence of different post-processing attacks.

关键词： Sparsely encoded features sDBSCAN clustering forensic analysis forgery detection

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VoStress – Voice-based Detection of Acute Psychosocial Stress

VoStress – Voice-based Detection of Acute Psychosocial Stre...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Marie Oesten Robert Richer Luca Abel Nicolas Rohleder Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Chair of Health Psychology Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Current stress assessment methods include self-reports and biomarkers which are evaluated in often complex, laboratory procedures. Due to that investigating new indicators for acute stress is crucial for the development of automatic stress detection systems. A promising extension might be provided by investigating speech, which has been shown to be affected by negative emotions and threatening situations. For that reason, we extracted verbal acoustics from audio data collected during a study where N=21 participants underwent the Trier Social Stress Test (TSST), the gold standard for laboratory stress induction, and a stress-free control condition (friendly-TSST) while concurrently collecting cortisol via saliva samples to assess the biological response to stress. Our results show that acute stress leads to significant (p < 0.05) alterations of acoustic features. A stepwise backward multiple linear regression model explained 58.8 % of the variance of the maximum cortisol increase. In addition to that, we performed classification experiments that distinguished stress from non-stress situations with an accuracy of 80.0 ± 12.7 %. While further research is needed to validate our approach, we are convinced that the information extracted from speech can be a valuable indicator for automatic stress detection systems and can even predict the biological response to stress situations.

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Authentication through gender classification from iris images using support vector machine

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MICROSCOPY RESEARCH AND TECHNIQUE 2021年第11期84卷 2666-2676页

作者： Khan, Amjad Rehman Doosti, Fatemeh Karimi, Mohsen Harouni, Majid Tariq, Usman Fati, Suliman Mohamed Ali Bahaj, Saeed Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Asharfi Isfahani Univ Dept Comp Engn Esfahan Iran Islamic Azad Univ Dolatabad Branch Dept Comp Engn Esfahan Iran Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj Saudi Arabia Prince Sattam bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj Saudi Arabia

Soft biometric information, such as gender, iris, and voice, can be helpful in various applications, such as security, authentication, and validation. Iris is secure biometrics with low forgery and error rates due to its highly certain features are being used in the last few decades. Iris recognition could be used both independently and in part for secure recognition and authentication systems. Existing iris-based gender classification techniques have low accuracy rates as well as high computational complexity. Accordingly, this paper presents an authentication approach through gender classification from iris images using support vector machine (SVM) that has an excellent response to sustained changes using the Zernike, Legendre invariant moments, and Gradient-oriented histogram. In this study, invariant moments are used as feature extraction from iris images. After extracting these descriptors' attributes, the attributes are categorized through keycode fusion. SVM is employed for gender classification using a fused feature vector. The proposed approach is evaluated on the CVBL data set and results are compared in state of the art based on local binary patterns and Gabor filters. The proposed approach came out with 98% gender classification rate with low computational complexity that could be used as an authentication measure.

关键词： authentication digital security gender recognition iris image texture features oriented gradient histogram

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Future Prospects and Challenges of On-Demand Mobility Management Solutions

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IEEE ACCESS 2023年 11卷 114864-114879页

作者： Qureshi, Muhammad Mukhtar Yunus, Farizah Binti Li, Jianqiang Ur-Rahman, Atta Mahmood, Tariq Ali, Yahya Ali Abdelrahman Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Univ Malaysia Terengganu Fac Ocean Engn Technol & Informat Kaula Nerus Terengganu Malaysia Iqra Univ Dept Comp & Technol Chak Shahzad Campus Islamabad 45550 Pakistan Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci Dammam Saudi Arabia CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan Najran Univ Fac Comp Sci & Informat Syst Dept Informat Syst Najran Saudi Arabia

Generally, mobility management (MM) solutions are centralized, which means only one entity controls the mobility-related tasks of mobile nodes connected to the network. However, the massive number of users, rapid growth in the network traffic, and the recent developments regarding the architecture and capability of 5G networks forced multiple entities to perform the task of MM. Similarly, to fulfill the demand for the applications at the time of handoff, we have on-demand MM solutions. These solutions are designed to be adoptable to various service delivery use cases supported by future networks. In addition, they also have power-saving features that turn them off automatically, when all your devices are fully charged. Owing to stated features, we need to explore on-demand MM solutions and these solutions can provide divergent types of services according to the user's demand. Therefore, in this paper, the importance and comparison of on-demand MM solutions with legacy mechanisms are presented, by keeping in view the merits and constraints of their contributions. Furthermore, the significance of the MM as a service (MMaaS) model is discussed in detail, which is used for the on-demand MM mechanism. In this model, the substantial characteristics of the granularity of services are also elaborated, which are intricately tangled with the adoptability, extensibility, and power-saving requirements of future wireless networks. Finally, we discuss the challenges, future prospects, and application areas of on-demand MM solutions for researchers and application developers.

关键词： 5G beyond 5G MM MMaaS MN

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Internet of medical things embedding deep learning with data augmentation for mammogram density classification

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MICROSCOPY RESEARCH AND TECHNIQUE 2021年第9期84卷 2186-2194页

作者： Sadad, Tariq Khan, Amjad Rehman Hussain, Ayyaz Tariq, Usman Fati, Suliman Mohamed Bahaj, Saeed Ali Munir, Asim Int Islamic Univ Dept Comp Sci & Software Engn Islamabad Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Quaid I Azam Univ Dept Comp Sci Islamabad Pakistan Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj Saudi Arabia Prince Sattam bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj Saudi Arabia

Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Two pretrained deep convolutional neural network models called DenseNet201 and ResNet50 were applied through a transfer learning approach. A total of 322 mammogram images containing 106 fatty, 112 dense, and 104 glandular cases were obtained from the Mammogram Image Analysis Society dataset. The pruning out irrelevant regions and enhancing target regions is performed in preprocessing. The overall classification accuracy of the BD task is performed and accomplished 90.47% through DensNet201 model. Such a framework is beneficial in identifying BD more rapidly to assist radiologists and patients without delay.

关键词： breast density cancer computer‐ aided diagnosis healthcare Internet of Medical Things (IoMT) mammography masses

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Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN) 4

Covid-19 Detection by using Deep learning-based Custom Convo...

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4th International Conference on Innovative Computing (ICIC)

作者： Awan, Mazhar Javed Imtiaz, Muhammad Wasif Usama, Muhammad Rehman, Amjad Ayesha, Noor Shehzad, Hafiz Muhammad Faisal Univ Management & Technol Dept Comp Sci Lahore 54000 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou Henan Peoples R China Univ Sargodha Dept Comp Sci & IT Sargodha Pakistan

ISBN: (纸本)9781665400916

The early diagnosis and treatment of COVID-19 has been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects that either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98%. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic.

关键词： SARS-CoV-2 COVID-19 CNN SARS RT-PCR

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