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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

Chemically Radiative MHD Flow of a Micropolar Nanofluid over a Stretching/ Shrinking Sheet with a Heat Source or Sink

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Fluid Dynamics & Materials Processing 2024年第2期20卷 257-274页

作者： Parakapali Roja Shaik mohammed Ibrahim Thummala Sankar Reddy Giulio Lorenzini Department of Mathematics Annamacharya Institute of Technology and SciencesKadapa516126India Department of Engineering Mathematics College of EngineeringKoneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Mathematics Annamacharya Institute of Technology and SciencesKadapa516003India Department of Engineering and Architecture University of ParmaParma43124Italy

This study examines the behavior of a micropolar nanoﬂuidﬂowing over a sheet in the presence of a transverse magneticﬁeld and thermal *** addition,chemical(ﬁrst-order homogeneous)reactions are taken into account.A similarity transformation is used to reduce the system of governing coupled non-linear partial differ-ential equations(PDEs),which account for the transport of mass,momentum,angular momentum,energy and species,to a set of non-linear ordinary differential equations(ODEs).The Runge-Kutta method along with shoot-ing method is used to solve *** impact of several parameters is *** is shown that the micro-rota-tional velocity of theﬂuid rises with the micropolar ***,the radiation parameter can have a remarkable inﬂuence on theﬂow and temperature proﬁles and on the angular momentum distribution.

关键词： Chemical(ﬁrst order homogeneous)reaction magnetohydrodynamics micropolar nanoﬂuid stretching/shrinking sheet heat source

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

A New Kind of Atomic Force Microscopy Scan Control Enabled by Artificial Intelligence:Concept for Achieving Tip and Sample Safety Through Asymmetric Control

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Nanomanufacturing and Metrology 2024年第2期7卷 50-59页

作者： Johannes Degenhardt mohammed Wassim Bounaim Nan Deng Rainer Tutsch Gaoliang Dai Physikalisch-Technische Bundesanstalt Bundesallee 10038116 BrunswickGermany Institut Für Prozessmess-und Sensortechnik Technische Universität Ilmenau EhrenbergstraBe 2998693 IlmenauGermany Institute of Production Metrology TU Braunschweig SchleinitzstraBe 2038106 BrunswickGermany

This paper introduces a paradigm shift in atomic force microscope(AFM)scan control,leveraging an artificial intelligence(AI)-based *** contrast to conventional control methods,which either show a limited performance,such as proportional integral differential(PID)control,or which purely focus on mathematical optimality as classical optimal control approaches,our proposed AI approach redefines the objective of control for achieving practical *** presented AI controller minimizes the root-mean-square control deviations in routine scans by a factor of about 4 compared to PID control in the presented setup and also showcases a distinctive asymmetric response in complex situations,prioritizing the safety of the AFM tip and sample instead of the lowest possible control *** development and testing of the AI control concept are performed on simulated AFM scans,demonstrating its huge potential.

关键词： Atomic force microscopy Artificial intelligence Deep reinforcement learning Optimal control

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 ***,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human *** paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain *** integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be ***,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 *** integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain *** platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data *** 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

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef mohammed Dilovan Asaad Zebari Mazin Abed mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

Exploring public perception and willingness to pay for a renewable source of energy: evidence from an emerging economy of renewable-energy development

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CLEAN ENERGY 2024年第2期8卷 177-188页

作者： Bashir, mohammed J. K. Abdelhak, Senadjki Salah, Wael A. Abuhelwa, Mai Cent Queensland Univ Sch Engn & Technol 120 Spencer St Melbourne Qld 3000 Australia Univ Tunku Abdul Rahman Fac Business & Finance Kampar Perak Malaysia Palestine Tech Univ Kadoorie Coll Engn & Technol Dept Elect Engn POB 7 Yafa St Tulkarm Palestine Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA

The study focuses on the transition from conventional energy sources to sustainable renewable energy (RE) in Palestine, particularly in the West Bank and Gaza Strip. The research aims to understand the relationship between key factors and residents' willingness to invest in the RE sector, with a specific focus on the impact of education. Using the unified theory of acceptance and use of technology, the study analyzes data from 400 residents through a questionnaire and employs ordinary least-squares regression analysis with Eviews 12. The key findings indicate that residents' inclination to invest in RE is significantly influenced by factors such as resource availability, knowledge level and power outages. Moreover, the study suggests that educated residents are more likely to invest in RE when factors such as knowledge, effort expectation, enabling conditions and resource availability are relatively high. The conclusion emphasizes the importance of policymakers' allocating sufficient funds to research and development to promote technological innovation in RE sources, thereby enhancing the population's energy efficiency. The study focuses on the transition from conventional energy to sustainable renewable energy in Palestine, particularly in the West Bank and Gaza Strip, to understand the relationship between key factors and residents' willingness to invest in the RE sector, with a specific focus on the impact of education. Graphical Abstract

关键词： renewable energy technology energy security public perceptions sustainable development UNIFIED THEORY RURAL-AREAS TECHNOLOGY ACCEPTANCE DETERMINANTS INTENTION RELIABILITY SERVICES SYSTEM ERRORS

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 *** proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice ***,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate *** 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 *** integration of voice-based interaction and impaired vision detection represents a novel approach to conversational ***,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual *** modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these ***,the solution places the user at its *** 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