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IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

Computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets ISIC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare IoMT public health

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Recombinator-k-means: An evolutionary algorithm that exploits k-means++ for recombination

arXiv

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arXiv 2019年

作者： Baldassi, Carlo Artificial Intelligence Lab Institute For Data Science And Analytics Bocconi University via Sarfatti 25 Milan20135 Italy

We introduce an evolutionary algorithm called recombinator-k-means for optimizing the highly non-convex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the k-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real-word) show that for fixed population sizes recombinator-k-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-k-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly, but generally better at escaping local minima. Moreover, it is algorithmically simpler and more general (it could be applied even to k-medians or k-medoids, for example). Our implementations are publicly available at https://***/carlobaldassi/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Population statistics

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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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Application of Business intelligence Solution Development and Implementation in a Small-Sized Enterprise

Application of Business Intelligence Solution Development an...

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2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH)

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

ISBN: (数字)9781728174075

ISBN: (纸本)9781728174082

Business intelligence (BI) is an approach to optimize business decisions by utilizing existing information. BI has been supporting the organization to form effective strategic and tactical business strategies. For the past 2 decades, BI hasn't only had a great impact on large enterprises, but also it has aided in forming strong core strategies of small to mid-sized enterprises. This paper details the full BI solution's cycle; planning, design, and development with a focus on small enterprises. Fig. 1 (“System Development Cycle”) summarizes the process followed to develop a BI solution in this paper. The study involves a case study of a fictitious company, created for the sole purpose of applying the suggested BI solution. This case study elaborates on all the phases required to develop a BI solution for each phase. The challenges and benefits of the BI solution are thoroughly considered in this study. To evaluate the functionality of the proposed BI solution, several testing iterations were performed and the results of these iterations are presented at the end of this case study. The paper concludes by highlighting the areas for future research in BI.

关键词： Companies Planning Business intelligence Testing

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Image Fusion Using Wavelet Transformation and XGboost Algorithm

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Computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

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Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

TechRxiv

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TechRxiv 2023年

作者： Küderle, Arne Ullrich, Martin Roth, Nils Ollenschläger, Malte Ibrahim, Alzhraa Moradi, Hamid Richer, Robert Seifer, Ann-Kristin Zürl, Matthias Sîmpetru, Raul Herzer, Liv Prossel, Dominik Kluge, Felix Eskofier, Bjoern Friedrich-Alexander-Universität Erlangen-Nürnberg Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Germany

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Deep Learning-Based Automatic Detection of Central Serous Retinopathy using Optical Coherence Tomographic Images

Deep Learning-Based Automatic Detection of Central Serous Re...

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

作者： Syed Al E Hassan Shahzad Akbar Sahar Gull Amjad Rehman Hind Alaska Riphah International University Faisalabad Campus Pakistan Artificial Intelligence & Data Analytics LAB (AIDA) Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781665430418

Central Serous Retinopathy (CSR), also known as Central Serous Chorioretinopathy (CSC), occurs due to the clotting of fluids behind the retinal surface. The retina is composed of thin tissues that capture light and transform into visual recognition in the brain. This significant and critical organ may be damaged and causes vision loss and blindness for the individuals. Therefore, early-stage detection of the syndrome may cure complete loss of vision and, in some cases, may recover to its normal state. Hence, accurate and fast detection of CSR saves macula from severe damage and provides a basis for detecting other retinal pathologies. The Optical Coherence Tomographic (OCT) images have been used to detect CSR, but the design of a computationally efficient and accurate system remains a challenge. This research develops a framework for accurate and automatic CSR detection from OCT images using pre-trained deep convolutional neural networks. The preprocessing of OCT image enhances and filters the images for improving contrast and eliminate noise, respectively. Pre-trained network architectures have been employed, which are; AlexNet, ResNet-18, and GoogleNet for classification. The classification scheme followed by preprocessing enhances the foreground objects from OCT images. The performance of deep CNN has been compared through a statistical evaluation of parameters. The statistical parameters evaluation has shown 99.64% classification accuracy for AlexNet using Optical Coherence Tomography Image database (OCTID). This shows the suitability of the proposed framework in clinical application to help doctors and clinicians diagnose retinal diseases.

关键词： Optical filters Optical losses Retinopathy Optical coherence tomography Coherence Medical services Retina

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Early Stage Kidney Chronic Diseases Diagnosis using Feature Fusion and Deep Learning Algorithms 17

Early Stage Kidney Chronic Diseases Diagnosis using Feature ...

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17th International Conference on Developments in eSystems Engineering, DeSE 2024

作者： Saba, Tanzila Mujahid, Muhamamd Ayesha, Noor Kolivand, Mahyar Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia School of Clinical Medicine Zhengzhou University Henan ZhengzhouRP 450001 China Department of Medicine University of Liverpool Liverpool United Kingdom School of Computing and Digital Technologies Staffordshire University Staffordshire United Kingdom

ISBN: (纸本)9798350368697

Chronic Kidney Disease (CKD) is a global health issue due to pre-existing conditions like anemia, diabetes, and hypertension. Early warning signals are commonly neglected, complicating diagnosis and treatment. This study uses high-performance data mining techniques to provide self-analysis and prognostic tools for medical professionals. Clinical decision support using domain experts and specialists aids CKD prognosis and rehabilitation. We use advanced data mining to evaluate demographic characteristics, test findings, and patient medical information to uncover insights and patterns. We used a modified artificial neural network to extract characteristics from a dataset and long-term and short-term memory to classify chronic kidney disease stages. LSTM algorithms improve the accuracy of CKD diagnosis and stage prediction. Initial validation tests demonstrate a 92.23% accuracy gain over traditional methods. The binary classification AUC was 99.95% and the multi-class classification was 94.13% with the proposed technique. LSTM algorithms use temporal dynamics in patient data to enhance diagnostic accuracy, early diagnosis, therapy customization, and healthcare costs. We proposed a novel CKD diagnostic and prognosis paradigm and test multiple classification algorithms for disease diagnosis and staging. Multiple methods improve reliability and account for CKD diagnostic peculiarities. © 2024 IEEE.

关键词： Chronic kidney disease (CKD) Classifier fusion Health rehabilitation Healthcare

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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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