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Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

computers, Materials & Continua 2023年第6期75卷 5307-5319页

作者： Khalid M.O.Nahar Mohammad alauthman Saud Yonbawi Ammar almomani Computer Science Department Yarmouk UniversityIrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Software Engineering Department University of JeddahJeddahKSA Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our *** is a social media network issue,a global crisis affecting the victims and society as a *** results from a misunderstanding regarding freedom of *** this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet *** addition,we used accuracy,precision,recall,and F1 score to assess prior *** show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

关键词： Cyberbullying social media naïve bayes support vector machine natural language processing LDA

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Dynamic Lévy-Brownian Marine Predator algorithm for Photovoltaic Model Parameters Optimization

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SSRN 2023年

作者： Bouteraa, Yassine Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia

This research suggests a newly-proposed optimization technique known as MPA for creating precise and trustworthy PV models. BW and LF are the two leading search mechanisms in the classical MPA, where the transition between them is random. This paper introduces a novel dynamic transition function to adjust the relation between LF and BW mechanisms. The proposed dynamic LF-BW improves the changover pattern between the exploration and exploitation phases of MPA, so the overall MPA's performance is improved significantly. Additionally, a constraint handling technique is employed to guarantee that the DLBMPA is resilient in coping with the parameterization constraints of PV Modeling techniques. Ten well-known optimization techniques are used to evaluate the results of Dynamic DLBMPA, including the STOAWHHOA, GTLBOA, PGJAYA, IJAYA, WSOA, EHHOA, QRGNMPO, NLBBO, AOAHOA, and standard MPA. According to the experimental findings, DLBMPA performs better than comparable algorithms in terms of time complexity, accuracy, and performance. © 2023, The Authors. all rights reserved.

关键词： Dynamics

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Chi-Square and Cuckoo Search Based Feature Selection for Heart Disease Prediction

Chi-Square and Cuckoo Search Based Feature Selection for Hea...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Elizabeth, Bolanle Adejumo Muhammad Usman, ali Hussein, Ahmad MohdAziz Ossai Ebinum, Wallace Maigari, Timothy Umar Usman Abdullahi, ali Ibrahim, Jalo Abubakar Joda Usman, Mohammed Philips, Jacob Dawaki, Muhammad Department of Computer Sciences Gombe Nigeria Umm Al-Qura University Deanship of E-Learning and Distance Education Makkah21955 Saudi Arabia Kaduna Polytechnic College of Engineering Department of Computer Engineering Kaduna Nigeria Bayero University Faculty of Computer Science and Information Technology Kano Nigeria Gombe State University Department of Computer Sciences Gombe Nigeria

ISBN: (纸本)9798350358155

In many countries across the globe, heart disease is the main root of death. It's only that it has a lot of features that have been validated by numerous sources and incorporate distinct diagnostic techniques. Thus, physicians find it difficult to correctly identify the features that contribute to heart disease for each patient. Besides, most of these features are redundant and irrelevant that create noise towards the description of the target class. Nowadays, machine learning approaches are being used to aid physicians to correctly identify heart disease from a patient. In this study, the chi-square statistic is applied to rank the attributes in the heart disease data set while the cuckoo search optimization algorithm selects the most useful features that contribute significantly to heart disease diagnosis. The selected features are trained using the k-nearest neighbour (KNN) along with support vector machine (SVM). The investigational outcomes displayed that the features are minimized to the lowest level, mainly on the high dimensional data sets. Therefore, contributed positively to the heart disease diagnosis system. SVM outpace KNN in terms of accuracy after the proposed feature selection. On the other hand, KNN performed slightly better than SVM without feature selection on the majority of data sets. The proposed method performed better than other existing approaches especially correlation-based FS with PSO together with fuzzy analytic hierarchy process-based feed-forward neural network, modified differential evolution, on both percentage accuracy and number of chosen features (feature size). © 2024 IEEE.

关键词： Support vector machines

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Enhance Cancer Text Classification Using Multi Word Embedding and Ensemble Learning 17

Enhance Cancer Text Classification Using Multi Word Embeddin...

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

作者： Mahdi, Mohammed Fadhil Abd, Dhafar Hamed Abdalkafor, Ahmed Subhi Hussain, Abir Jaafar Al-Mansour University College Baghdad Iraq College of Computer Science and Information Technology University Of Anbar Ramadi Iraq Department of Computer Sciences College of Computer and Information Technology University Of Anbar Anbar Iraq Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350368697

The explosion of medical literature over the past decade has resulted in efficient and accurate techniques for text categorization to handle huge amount of data. This work combines ensemble learning methods with coupled multi-word embedding techniques to improve cancer text classification. The intricate semantic links present in medical tests are frequently outside the scope of traditional word embedding models, resulting in not ideal categorization results. To address this problem, we employ e continuous bag-of-words and Skip-gram approaches that yield more complete word representations capturing multiple linguistic nuances. Subsequently, such embeddings are passed through LGBM, CatBoost, and NGBoost ensemble learning classifiers to enhance classification accuracy. With 99.868% accuracy rate, LGBM and CatBoost were the most successful ensemble approaches examined. These approaches provide solid foundation for future work on the use of ensemble methods with complex word representations and for advancing the field of medical text classification. © 2024 IEEE.

关键词： Embeddings

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IoT-Enhanced Bionic Hybrid AI for Smart Education in Ideological and Political Courses 2

IoT-Enhanced Bionic Hybrid AI for Smart Education in Ideolog...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Abbas, Jamal K. Safi, Hassan Kadhim, Hajar Nader, Azad Salh Mohammed, ali ali Saber Al-Nisour University College Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Medical Laboratory Techniques Baghdad Iraq Knowledge University College of Administrative and Financial Sciences Department of Business Administration Erbil Iraq Al Kitab University Department of Computer Engineering Technologies Kirkuk Iraq

ISBN: (纸本)9798331534950

In today's world, science and technology are our society's fundamental productive forces, while transformation in all forms, notably in education and employment, cannot be distinguished from science and technology. Many technical college graduates may lack solid work habits as a result of poor classroom comprehension and failure to learn views. To solve these issues, a model for theoretical and political classes in Chinese Higher Vocational Schools is proposed based on Artificial Intelligence (AI) algorithms. Additionally, in the context of information and communication technologies, the incorporation of artificial intelligence (AI) and the Internet of Things (IoT) into the political education system is noteworthy. As a result, the Internet of Things (IoT)-assisted artificial intelligence (AI) smart educational system plays an important role in this study, in which the Bionic Hybridized Artificial Intelligence (BHAI) algorithm is established and developed for ideological and political education. Concerned research goals employing BHAI include (1) Thinking about the usability and efficacy of IoT assisted learning methodologies on political education platform for information transfer using smart frameworks. (2) Investigate the impact of AI technology on political education through interactive teaching methods. (3) Maintaining a strong engagement with pupils based on political studies and probabilistic concepts. This research helps educators comprehend how positive relationships between teachers and students would enhance the learning process while utilizing AI for political and ideological education in smart education. © 2024 IEEE.

关键词： Bionics

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Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

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Intelligent Automation & Soft Computing 2023年第5期36卷 1767-1782页

作者： M.P.Thiruvenkatasuresh Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia Corresponding

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient *** a novel ML algorithm speciﬁc to Diabetic Retinopathy(DR)is a chal-lenge and need of the *** images include several challenges,including relevant feature selection,class variations,and robust classiﬁ*** the cur-rent research in DR has yielded favourable results,several research issues need to be *** is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-ﬁ*** is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classiﬁer(SKPD-PSC)*** novelty of this method relies on the appropriate non-linear classiﬁcation of exclusive image processing models in har-mony with the Platt Scale Classiﬁer(PSC)to improve the accuracy of DR ***,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background ***,a Platt Scale Classiﬁer(PSC)is applied to the localized features for robust classiﬁ*** the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classiﬁ*** results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to

关键词： Diabetic retinopathy retinal images machine learning image localization Platt Scale classiﬁer accuracy

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Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

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computers, Materials & Continua 2022年第12期73卷 4501-4518页

作者： Muhammad ali Jamal Hussain Shah Muhammad Attique Khan Majed alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Departmnt of Computer Science HITEC University TaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of EE COMSATS University IslamabadWah CampusPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Tumor detection has been an active research topic in recent years due to the high mortality *** vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI *** automated detection process is simpler and takes less time than manual *** addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for *** proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this *** tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)*** preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation ***,PSO-based segmentation is *** the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer *** a serial approach,features are extracted from both trained models and fused features for final *** classification,a variety of machine learning classifiers are *** dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of ***,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

关键词： Magnetic resonance imaging(MRI) tumor segmentation deep learning features extraction classification

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Irreversibility analysis of magnetized flow of engine oil hybrid nanofluid (Ti6al4V-ZnO/EO) over a stretching surface

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alexandria engineering Journal 2025年 127卷 214-227页

作者： Hussain, Syed M. Faridi, Aftab Ahmed Ahmad, Hijaz ali, Kashif Iqbal, Muhammad Rashid Jamshed, Wasim Guedri, Kamel almaliki, Abdulrazak H. Department of Mathematics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia Department of Mathematics The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Near East University Operational Research Center in Healthcare TRNC Mersin 10 Nicosia99138 Turkey Department of Technical Sciences Western Caspian University Baku1001 Azerbaijan Department of Mathematics College of Science Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Basic Sciences and Humanities Muhammad Nawaz Sharif University of Engineering and Technology Multan60000 Pakistan Department of Mathematics and Statistics Institute of Southern Punjab Multan32100 Pakistan Islamabad44000 Pakistan College of Engineering Al-Ayen Iraqi University An Nasiriyah64001 Iraq Department of Computer Engineering Biruni University Topkapi Istanbul Turkey Mechanical Engineering Department College of Engineering and Architecture Umm Al-Qura University P. O. Box 5555 Makkah21955 Saudi Arabia Department of Civil Engineering college of Engineering Taif University Taif21944 Saudi Arabia

This article analyzes the irreversibility of hydro-magnetic dynamics of hybridized nanofluid comprising of titanium alloy (Ti6al4V) and zinc-oxide (ZnO) submerged into engine oil (used as a fuel) through a porous stretching sheet with multipart effects of viscous dissipation and non-uniform heat source. An induced magnetic field exists in the flow field due to the conducting nature of the hybrid nanofluid. The equations of the flow model are transformed by the similarity transformations and then simulated through a numerical scheme constructed with a formulation of central differences that adopts successive over-relaxation methodology. In this thermodynamic system, entropy and Bejan number profiles corresponding to the prominent parameters are simultaneously analysed for both the nanofluid case and hybrid nanofluid case in the Cartesian coordinates. A comparison table is constructed to validate the numerical results with existing literature. The impacts of various parameters on Nusselt number and skin friction coefficient are graphed with reference to the magnetic field parameter. A significant reduction in irreversibility and a remarkable improvement in thermal characteristics of engine oil are observed due to induced magnetism effects. The engine oil hybrid nanofluid (Ti6al4V-ZnO/EO) exhibits a 13.89 % higher heat transfer rate over a porous stretching surface compared to the engine oil nanofluid (Ti6al4V/EO). © 2025 The Authors

关键词： Magnetic levitation

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High-Frequency Waves of the Fifth Generation and Their Effects on the Head and Their Consequences for New Perspectives 7

High-Frequency Waves of the Fifth Generation and Their Effec...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： al-Sabti, Saif Mohamed Baraa Mohammed, alaa Hamid Mustafa, ali Saadi, Abdulrahman Maadh Abdulateef, Aqeel Nafea Atilla, Dogu Cagdas Albayan University Technical College of Engineering Medical Instruments Techniques Engineering Department Baghdad Iraq Al-Maarif University College Department of Medical Instruments Techniques Engineering Iraq Al-Maarif University College Department of Computer Science Iraq Altinbas University No. 26 Bagcilar School of Engineering and Natural Sciences Istanbul34217 Turkey

ISBN: (纸本)9798350383065

This research focuses on the efficacy of electromagnetic field RF at high frequency 27.74 GHz on the human head, which includes modelling 5G communication networks to compute the SAR value via the head human layers' tissues and which layers the SAR is attained. Locating the source of the reflected power or electromagnetic signal. Most of the available power from the source is reflected under these conditions. The head serves as a parasitic element, and as a result, the reflected light has a radiation pattern that is 180 degrees out of phase with the original light. In the long run, it leads to being able to steer the reflected pattern. The radiation pattern of reflected power becomes similar to the head geometry. From this point on, estimating power density on the scalp will be less of a hassle. Energy may be collected by harvesting from two sources: reflected energy and sustainable energy. © 2023 IEEE.

关键词： Dipole antennas

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Security Threat and Vulnerability Assessment and Measurement in Secure software Development

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computers, Materials & Continua 2022年第6期71卷 5039-5059页

作者： Mamoona Humayun NZ Jhanjhi Maram Fahhad almufareh Muhammad Ibrahim Khalil Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufKSA School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Computer Science Bahria UniversityIslamabadPakistan

Security is critical to the success of software,particularly in today’s fast-paced,technology-driven *** ensures that data,code,and services maintain their CIA(Confidentiality,Integrity,and Availability).This is only possible if security is taken into account at all stages of the SDLC(software Development Life Cycle).Various approaches to software quality have been developed,such as CMMI(Capabilitymaturitymodel integration).However,there exists no explicit solution for incorporating security into all phases of *** of the major causes of pervasive vulnerabilities is a failure to prioritize *** the most proactive companies use the“patch and penetrate”strategy,inwhich security is accessed once the job is *** cost,time overrun,not integrating testing and input in SDLC,usage of third-party tools and components,and lack of knowledge are all reasons for not paying attention to the security angle during the SDLC,despite the fact that secure software development is essential for business continuity and survival in today’s ICT *** is a need to implement best practices in SDLC to address security at all *** fill this gap,we have provided a detailed overview of secure software development practices while taking care of project costs and *** proposed a secure SDLC framework based on the identified practices,which integrates the best security practices in various SDLC phases.A mathematical model is used to validate the proposed framework.A case study and findings show that the proposed system aids in the integration of security best practices into the overall SDLC,resulting in more secure applications.

关键词： Security secure software development software development life cycle(SDLC) confidentiality integrity availability

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