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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid mohammed S.alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1427-1467页

作者： mohammed Abdalsalam Chunlin Li Abdelghani Dahou Natalia Kryvinska School of Computer Science and Technology Wuhan University of TechnologyWuhan430070China LDDI Laboratory Faculty of Science and TechnologyUniversity of Ahmed DRAIAAdrar01000Algeria Information Systems Department Faculty of ManagementComenius UniversityBratislava82005Slovakia

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

关键词： Artificial intelligence machine learning natural language processing data analytic DistilBERT feature extraction terrorism classification GTD dataset

Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1731-1748页

作者： Mohanad A.mohammed Hala B.Abdul Wahab Computer Science Department The University of TechnologyBaghdadIraq

Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges.Concurrently,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human lives.This paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain systems.By integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be achieved.Moreover,the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm,renowned for its homomorphic characteristics,to reinforce the privacy and security of IoT-generated data.The integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain data.This platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data privacy.The results validate the robust security of the proposed system,comparable to standard blockchain implementations,leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.

关键词： Blockchain IoT integration of IoT and blockchain consensus algorithm Okamoto Uchiyama homomorphic encryption lightweight blockchain

An Extensive Review of Vitiligo-Associated Conditions

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International Journal of Dermatology And Venereology 2024年第1期07卷 44-51页

作者： Premkumar Madhavi Kalarani Iyshwarya Bhaskar mohammed Vajagathali Veerabathiran Ramakrishnan Human Cytogenetics and Genomics Laboratory Faculty of allied Health Sciences Chettinad Hospital and Research Institute Chettinad Academy of Research and Education Kelambakkam Tamil Nadu 603103 India

Vitiligo is an autoimmune disorder caused by the destruction of melanocytes in various body regions. Numerous diseases may accompany vitiligo. The prevalence of vitiligo varies among geographical regions and shows no relation to sex or age. However, vitiligo most commonly affects people aged 10 to 30 years. The clinical symptoms of vitiligo include irregular patches on the skin and various diseases associated with this disorder. The present review focuses on the clinical effects and risk factors of vitiligo-associated conditions and the current therapeutics for treating vitiligo. This systematic review was performed based on the PRISMA guidelines and articles published from 2000 to 2023 in Web of Science, PubMed, PsycINFO, and Embase. The most clinically significant diseases associated with vitiligo are alopecia areata, autoimmune thyroid disease, Addison disease, and type 1 diabetes mellitus. No targeted population is specifically prone to vitiligo; it does not affect any particular race or sex. Hence, awareness of the diseases associated with vitiligo is necessary to control them through various medical and surgical treatments with the proper facilities.

关键词： vitiligo autoimmune disorder melanocyte Addison disease atopic dermatitis

Diagnostic accuracy of ePOS score in predicting DNR labeling after ICU admission:A prospective observational study(ePOS-DNR)

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Journal of Intensive Medicine 2024年第2期4卷 216-221页

作者： Omar E.Ramadan Ahmed F.Mady mohammed A.al-Odat Ahmed N.Balshi Ahmed W.aletreby Taisy J.Stephen Sheena R.Diolaso Jennifer Q.Gano Waleed Th.aletreby Department of Critical Care King Saud Medical CityRiyadhSaudi Arabia Anesthesia Department Faculty of MedicineAin Shams UniversityCairoEgypt Anesthesia Department Faculty of MedicineTanta UniversityTantaEgypt Faculty of Medicine Alexandria UniversityAlexandriaEgypt Nursing Department King Saud Medical CityRiyadhSaudi Arabia

Background Resuscitation can sometimes be futile and making a do-not-resuscitate(DNR)decision is in the best interest of the patient.The electronic poor outcome screening(ePOS)score was developed to predict 6-month poor outcomes of critically ill patients.We explored the diagnostic accuracy of the ePOS score in predicting DNR decisions in the intensive care unit(ICU).Methods This study was conducted at the ICU of a tertiary referral hospital in Saudi Arabia between March and May 2023.Prospectively,we calculated ePOS scores for all eligible consecutive admissions after 48 h in the ICU and recorded the DNR orders.The ability of the score to predict DNR was explored using logistic regression.Youden's ideal cut-off value was calculated using the DeLong method,and different diagnostic accuracy measures were generated with corresponding 95%confidence intervals(CIs).Results We enrolled 857 patients,125 received a DNR order and 732 did not.The average ePOS score of DNR and non-DNR patients was 28.2±10.7 and 15.2±9.7,respectively.ePOS score,as a predictor of DNR order,had an area under receiver operator characteristic(AUROC)curve of 81.8%(95%CI:79.0 to 84.3,Pal cut-off value>17 was associated with a sensitivity of 87.2(95%CI:80.0 to 92.5,Palue of 29.2(95%CI:24.6 to 33.8,Palue of 96.7(95%CI:95.1 to 98.3,P17 may help guide clinical decisions to withhold or commence resuscitative measures.

关键词： Do-not-resuscitate(DNR) Resuscitation Intensive care unit(ICU) Diagnostic accuracy Sensitivity Specificity

Comparative efficacy of sodium glucose cotransporter-2 inhibitors in the management of type 2 diabetes mellitus:A real-world experience

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World Journal of Diabetes 2024年第3期15卷 463-474页

作者： Lubna Islam Dhanya Jose mohammed alkhalifah Dania Blaibel Vishnu Chandrabalan Joseph M Pappachan Department of Endocrinology and Metabolism Lancashire Teaching Hospitals NHS TrustPreston PR29HTUnited Kingdom Department of Community Medicine Goa Medical CollegeGoa 403202India Department of Family Medicine King Faisal Specialist HospitalRiyadh 11564Saudi Arabia Department of Data Science Lancashire Teaching Hospitals NHS TrustPreston PR29HTUnited Kingdom Faculty of Science Manchester Metropolitan UniversityAll Saints BuildingManchester M156BHUnited Kingdom Faculty of Biology Medicine and HealthThe University of ManchesterManchester M139PLUnited Kingdom

BACKGROUND Sodium glucose cotransporter-2 inhibitors(SGLT-2i)are a class of drugs with modest antidiabetic efficacy,weight loss effect,and cardiovascular benefits as proven by multiple randomised controlled trials(RCTs).However,real-world data on the comparative efficacy and safety of individual SGLT-2i medications is sparse.AIM To study the comparative efficacy and safety of SGLT-2i using real-world clinical data.METHODS We evaluated the comparative efficacy data of 3 SGLT-2i drugs(dapagliflozin,canagliflozin,and empagliflozin)used for treating patients with type 2 diabetes mellitus.Data on the reduction of glycated hemoglobin(HbA1c),body weight,blood pressure(BP),urine albumin creatinine ratio(ACR),and adverse effects were recorded retrospectively.RESULTS Data from 467 patients with a median age of 64(14.8)years,294(62.96%)males and 375(80.5%)Caucasians were analysed.Median diabetes duration was 16.0(9.0)years,and the duration of SGLT-2i use was 3.6(2.1)years.SGLT-2i molecules used were dapagliflozin 10 mg(n=227;48.6%),canagliflozin 300 mg(n=160;34.3%),and empagliflozin 25 mg(n=80;17.1).Baseline median(interquartile range)HbA1c in mmol/mol were:dapagliflozin-78.0(25.3),canagliflozin-80.0(25.5),and empagliflozin-75.0(23.5)respectively.The respective median HbA1c reduction at 12 months and the latest review(just prior to the study)were:66.5(22.8)&69.0(24.0),67.0(16.3)&66.0(28.0),and 67.0(22.5)&66.5(25.8)respectively(Pall comparisons from baseline).Significant improvements in body weight(in kilograms)from baseline to study end were noticed with dapagliflozin-101(29.5)to 92.2(25.6),and canagliflozin 100(28.3)to 95.3(27.5)only.Significant reductions in median systolic and diastolic BP,from 144(21)mmHg to 139(23)mmHg;(P=0.015),and from 82(16)mmHg to 78(19)mmHg;(Palso observed.A significant reduction of microalbuminuria was observed with canagliflozin only[ACR 14.6(42.6)at baseline to 8.9(23.7)at the study end;P=0.043].Adverse effects of SGLT-2i were as follows:genital thrush and ur

关键词： Sodium glucose cotransporter-2 inhibitors Empagliflozin Canagliflozin Dapagliflozin Type 2 diabetes mellitus Cardiovascular disease albumin creatinine ratio Diabesity

Computed tomography for prediction of esophageal variceal bleeding

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World Journal of Gastrointestinal Endoscopy 2024年第3期16卷 175-177页

作者： mohammed Elhendawy Ferial Elkalla Department of Tropical Medicine and Infectious Diseases Tanta UniversityTanta 31111Egypt

This letter to the editor relates to the study entitled“The role of computed tomography for the prediction of esophageal variceal bleeding:Current status and future perspectives”.Esophageal variceal bleeding(EVB)is one of the most common and severe complications related to portal hypertension(PH).Despite marked advances in its management during the last three decades,EVB is still associated with significant morbidity and mortality.The risk of first EVB is related to the severity of both PH and liver disease,and to the size and endoscopic appearance of esophageal varices.Indeed,hepatic venous pressure gradient(HVPG)and esophagogastroduodenoscopy(EGD)are currently recognized as the“gold standard”and the diagnostic reference standard for the prediction of EVB,respectively.However,HVPG is an invasive,expensive,and technically complex procedure,not widely available in clinical practice,whereas EGD is mainly limited by its invasive nature.In this scenario,computed tomography(CT)has been recently proposed as a promising modality for the non-invasive prediction of EVB.While CT serves solely as a diagnostic tool and cannot replace EGD or HVPG for delivering therapeutic and physiological information,it has the potential to enhance the prediction of EVB more effectively when combined with liver disease scores,HVPG,and EGD.However,to date,evidence concerning the role of CT in this setting is still lacking,therefore we aim to summarize and discuss the current evidence concerning the role of CT in predicting the risk of EVB.

关键词： Esophageal variceal bleeding Variceal upper gastrointestinal bleeding Portal hypertension Computed tomography Computed tomography angiography

Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

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Computers, Materials & Continua 2024年第3期78卷 3129-3150页

作者： Awais Ahmad Sohail Jabbar Sheeraz Akram Anand Paul Umar Raza Nuha mohammed alshuqayran College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudia Arabia 2 School of Computer Science and EngineeringKyungpook National UniversityDaegu41566South Korea 3 Department of EngineeringManchester Metropolitan UniversityManchesterM156BHUK

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection model.The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands.Additionally,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate assistance.This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader audience.The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI.Notably,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual impairments.The modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these advancements.Crucially,the solution places the user at its core.Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

关键词： Accessibility in conversational AI CNN-based impair vision detection ChatGPT voice-based interaction recommender system

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1469-1483页

作者： Laila M.Halman mohammed J.F.alenazi Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11451Saudi Arabia

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

关键词： Network resilience network management attack prediction software defined networking(SDN) distributed denial of service(DDoS) healthcare