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

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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Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9311-9331页

作者： Kalsum, Tehmina Mehmood, Zahid Kulsoom, Farzana Chaudhry, Hassan Nazeer Khan, Amjad Rehman Rashid, Muhammad Saba, Tanzila Univ Engn & Technol Taxila Dept Software Engn Taxila Pakistan Univ Engn & Technol Taxila Dept Comp Engn Taxila Pakistan Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy Politecn Milan Dept Elect Informat & Bio Engn Milan Italy Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh Saudi Arabia Umm Al Qura Univ Dept Comp Engn Mecca Saudi Arabia

Facial emotion recognition system (FERS) recognize the person's emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively.

关键词： Facial emotion recognition histogram-of-oriented-gradients local intensity order pattern support vector machine texture features

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IoT- Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine

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IEEE ACCESS 2024年 12卷 52744-52758页

作者： Haider, Ali Kazmi, Rafaqat Alam, Teg Bashir, Rab Nawaz Nobanee, Haitham Khan, Amjad Rehman Aqsa Islamia Univ Bahawalpur Fac Comp Dept Comp Sci Bahawalpur 63100 Pakistan Islamia Univ Bahawalpur Fac Comp Dept Software Engn Bahawalpur 63100 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Ind Engn Al Kharj 11942 Saudi Arabia COMSATS Univ Islamabad Vehari Campus Vehari 61100 Pakistan Abu Dhabi Univ Coll Business Abu Dhabi U Arab Emirates Univ Oxford Oxford Ctr Islamic Studies Oxford OX1 2JD England Univ Liverpool Fac Humanities & Social Sci Liverpool L69 3BX England Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 12435 Saudi Arabia Univ Technol Malaysia Real Time & Embedded Software Engn Lab Johor Baharu 81310 Malaysia COMSATS Univ Islamabad Dept Comp Sci Sahiwal Campus Sahiwal 57000 Pakistan

The assessment of tomato firmness is pivotal in determining optimal harvest time, evaluating shelf life, and gauging ripeness. This attribute plays a crucial role in guiding the distribution and transportation processes. Post-harvest, tomatoes tend to lose firmness and can deteriorate into a rotten state during transportation within the supply chain, mainly due to environmental fluctuations. To mitigate such losses and uphold tomato quality, the cold supply chain, with its controlled environmental conditions, proves instrumental. Monitoring this cold supply chain is imperative to combat the adverse impact of ambient temperatures on tomatoes during logistics. This research introduces an innovative approach, employing an Internet of Things (IoT) framework and the Whale Optimization Algorithm for temperature prediction within the cold supply chain. Ambient and tomato temperatures, along with stable temperature calculations under variable conditions using the Whale Optimization Algorithm, were collected. The predictions were executed using the Extreme Learning Machine of artificial intelligence. The data is collected during tomato cold storage for experimentation. The proposed technique with mean average precision 84.957%, mean average recall 96.9% and accuracy 99.83%. Evaluation through precision, recall, and F-measure accuracy metrics demonstrates the superior performance of the proposed approach compared to conventional models such as Decision Tree, Linear Model, Naive Bays, Random Forest, and Support Vector Machine.

关键词： artificial intelligence cold supply chain deep learning Newton's law of cooling

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Securing Android IoT devices with GuardDroid transparent and lightweight malware detection

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AIN SHAMS ENGINEERING JOURNAL 2024年第5期15卷

作者： Wajahat, Ahsan He, Jingsha Zhu, Nafei Mahmood, Tariq Nazir, Ahsan Ullah, Faheem Qureshi, Sirajuddin Dev, Soumyabrata Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Lasbela Univ Agr Water & Marine Sci Dept Comp Sci Lasbela 80700 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 Univ Coll Dublin Sch Comp Sci Dublin D04 V1W8 Ireland

The Internet of Things (IoT) has experienced significant growth in recent years and has emerged as a very dynamic sector in the worldwide market. Being an open -source platform with a substantial user base, Android has not only been a driving force in the swift advancement of the IoT but has also garnered attention from malicious actors, leading to malware attacks. Given the rapid proliferation of Android malware in recent times, there is an urgent requirement to introduce practical techniques for the detection of such malware. While current machine learning -based Android malware detection approaches have shown promising results, the majority of these methods demand extensive time and effort from malware analysts to construct dynamic or static features. Consequently, the practical application of these methods becomes challenging. Therefore, this paper presents an Android malware detection system characterized by its lightweight design and reliance on explainable machine -learning techniques. The system uses features extracted from mobile applications (apps) to distinguish between malicious and benign apps. Through extensive testing, it has exhibited exceptional accuracy and an F1 -score surpassing 0.99 while utilizing minimal device resources and presenting negligible false positive and false negative rates. Furthermore, the classifier model's transparency and comprehensibility are significantly augmented through the application of Shapley's additive explanation scores, enhancing the overall interpretability of the system.

关键词： Android malware detection Machine learning RFE SHAP IoT Random forest

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COVID-19 artificial intelligence Based Surveillance Applications in The Kingdom of Saudi Arabia

COVID-19 Artificial Intelligence Based Surveillance Applicat...

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artificial intelligence and data analytics (CAIDA), International Conference on

作者： Safia Dawood Aisha Dawood Hind Alaskar Tanzila Saba Artificial Intelligence and Data Analytics (AIDA) Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781665415118;9781665430418

COVID-19 imposed huge burdens and obligations on public health and epidemiology centers to elevate the role of periodic surveillance and case tracing in order to cease the spread of the pandemic. As a result, nations globally are developing various digital solutions for accurate surveillance, reporting of new cases, tracing contacts, and monitoring public health. Traditional tracking and reporting methods have been replaced by intelligent solutions for accurate and efficient reporting. Tools such smart phones, portable devices, and drones have been incorporated in these solutions. These devices produce large amount of data on daily basis and need to be processed instantly to battle the spread of the virus and this is where AI is needed. While the need for AI in disease control and surveillance is clear, the application of AI methods and machine learning algorithms in this field needs further studies. This paper is a systematic review of using AI in COVID-19 surveillance literature to answer the following questions: 1. What AI-based methods are used globally for COVID-19 surveillance? 2. How effective are these methods, and 3. What are the methods used in the Kingdom of Saudi Arabia.

关键词： COVID-19 Systematics Pandemics Surveillance data collection Tools Security

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in artificial intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography

WebPPG: Feasibility and Usability of Self-Performed, Browser...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Michael Nissen Madeleine Flaucher Katharina M. Jaeger Hanna Huebner Nina Danzberger Adriana Titzmann Constanza A. Pontones Peter A. Fasching Bjoern M. Eskofier Heike Leutheuser Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Gynecology and Obstetrics Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser’s insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical *** relevance— WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation

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Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

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SENSORS 2022年第4期22卷 1552-1552页

作者： Awan, Mazhar Javed Rahim, Mohd Shafry Mohd Salim, Naomie Rehman, Amjad Garcia-Zapirain, Begonya Univ Teknol Malaysia UTM Fac Engn Sch Comp Skudai 81310 Johor Malaysia Univ Management & Technol Dept Software Engn Lahore 54770 Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia Univ Deusto eVIDA Lab Bilbao 48007 Spain

The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images.

关键词： artificial intelligence osteoarthritis biomedical images deep learning segmentation U-Net convolutional neural network knee bone knee mask prediction ACL MR images

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Secure and Energy-Aware Heuristic Routing Protocol for Wireless Sensor Network

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IEEE ACCESS 2020年 8卷 163962-163974页

作者： Haseeb, Khalid Almustafa, Khaled Mohamad Jan, Zahoor Saba, Tanzila Tariq, Usman Islamia Coll Peshawar Dept Comp Sci Peshawar 25000 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA CCIS Riyadh 12435 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj 16278 Saudi Arabia

Wireless Sensor Networks (WSNs) achieve much attention from various domains because of its easy maintenance, self-configuration, and scalability characteristics. It is comprised of small-sized sensors that interact with the Internet of Things (IoT) for observing and recording the physical conditions. The sensor nodes are autonomous and construct inter-communication topology with each other in an ad-hoc manner. However, the main restrictions of sensor nodes are their finite resources for energy management, data storage, transmission, and processing power. Different solutions have been addressed by researchers to overcome network performance due to bounded limitations of such battery-powered nodes, however, equalize the energy consumption and maintain the network throughput are the main research problems. Furthermore, due to the compromised nodes, the data is more prone to security vulnerabilities. Therefore, their security over the unpredictable network is other research concerns. Thus, the aim of this research article to propose a secure and energy-aware heuristic-based routing (SEHR) protocol for WSN to detect and prevent compromising data with efficient performance. Firstly, the proposed protocol makes use of an artificial intelligence-based heuristic analysis to accomplish a reliable, and intellectual learning scheme. Secondly, it protects the transmissions against adversary groups to attain security with the least complexity. Moreover, the route maintenance strategy is also achieved by using traffic exploration to reduce link failures and network dis-connectivity. The simulation results demonstrated the SEHR protocol improves the efficacy for network throughput by an average of 18%, packet drop ratio by 42%, end-to-end delay by 26%, energy consumption by 36%, faulty routes by 38%, network overhead by 44%, and computational overhead by 43% in dynamic scenarios as compared to existing work.

关键词： Routing Wireless sensor networks Sensors Routing protocols Energy efficiency Security artificial intelligence data privacy energy efficiency heuristic analysis wireless sensor network

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