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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

Computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta rahman Mohammed Abbas Alkherallah Menhal Kamel Al-Bohassan Mustafa Mohammed Alawami Hayder Ali Alsebaa Jawad Ali Alnemer Ghazi Fayez Al-Mutairi May Issa Aldossary Dalal A.Aldowaihi Fahd Alhaidari Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),na飗e Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP

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Credit Card Fraud Detection Using Machine Learning Techniques

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Journal of Computer and Communications 2024年第6期12卷 1-11页

作者： Ananya Sarker Must. Asma Yasmin Md. Atikur rahman Md. Harun Or Rashid Bristi Rani Roy Department of Computer Science and Engineering Bangladesh Army University of Engineering & Technology (BAUET) Natore Bangladesh

Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.

关键词： Support Vector Machine Decision Tree nave Bayes Random Forest Matthews Correlation

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Chromium adsorption on surface activated biochar made from tannery liming sludge:A waste-to-wealth approach

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Water Science and Engineering 2022年第4期15卷 328-336页

作者： Abul Hashem Sofia Payel Sadia Mim Anik Hasan Shahruk Nur-A-Tomal Aminur rahman Majher I.Sarker Department of Leather Engineering Khulna University of Engineering&Technology(KUET)Khulna 9203Bangladesh Department of Public Health Engineering(DPHE) Zonal LaboratoryJashore 7400Bangladesh Global Centre for Environmental Remediation(GCER) College of EngineeringScience and EnvironmentThe University of NewcastleCallaghanNSW 2308Australia Sustainable Biofuels and Co-Products Research Unit Eastern Regional Research CenterAgricultural Research ServiceU.S.Department of AgricultureWyndmoorPA 19038USA

In a beamhouse,liming plays a key role in the removal of hair/wool and epidermis,but problems are created when waste liming sludge is discharged to the *** treatment of tannery wastewater is another major challenge to the *** this study,thermally-activated biochars derived from liming sludge were studied for their effective adsorption of chromium(Cr)from the tannery *** thermally activated biochars(B500,B550,B600,and B650)were prepared at different temperatures from the liming *** characteristics before and after the treatment were investigated using Fourier transform infrared spectroscopy,energy dispersive X-ray spectroscopy,Bru-nauer-Emmett-Teller,and scanning electron microscopy *** related functional groups(C-H,O-H,C-N,and=C-O)and chromium adsorption capacity were determined according to the surface morphology,element contents(C,O,Ca,na,Al,Mg,and Si),surface area(5.8-9.2 m^(2)/g),pore size(5.22-5.53 nm),and particle size(652-1034 nm)of the experimental *** biochar originated at 600°C from the tannery liming sludge(B600)had a greater surface area with a chromium adsorption capacity of 99.8%in comparison to B500,B550,and B650 *** study developed an innovative way of utilizing liming sludge waste to minimize the pollution load and wastewater treatment cost in the tannery industry.

关键词： Adsorption Waste recycling Biochar application Thermal modification Tannery Solid waste

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Aspect Level Songs Rating Based Upon Reviews in English

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Computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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Study on the Physical and Mechanical Properties of Al-Powder Reinforced Bioactive Glass: Ceramic Composite Material

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Advances in Materials Physics and Chemistry 2022年第2期12卷 19-32页

作者： Muhammad Hafizur rahman Khan Sazzad Hossain Mohammad Maksudur rahman Department of Physics International University of Business Agriculture and Technology Dhaka Bangladesh Department of Physics Jahangirnagar University Savar Dhaka Bangladesh

Glass-ceramic samples, having composition of SiO2-35, CaO-45, na2O-10 and P2O5-10 in weight ratio were prepared through sintering route. Glass powder was reinforced by Al powder. The strength of glass-ceramic composite was found to be temperature dependent, and it varies with the addition of Al powder. Flexural strength increases with the increase of powder addition and sintering temperature, however, decreases with the increase of sintering time. There is an optimum temperature (>1100℃) above which flexural strength of all samples decreases. Bulk density changes to a higher value as the addition of Al-powder increases up to 3% by weight above which density decreases slowly. On the other hand, apparent porosity and water absorption decrease with the increase of percentage of Al powder added. Porosity and water absorption also showed a dependent characteristic on sintering time and sintering temperature.

关键词： Biomaterials Glass Ceramics Aluminum Powder Composite Material

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Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

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Intelligent Automation & Soft Computing 2023年第4期36卷 299-312页

作者： Abdullah A.Asiri Bilal Khan Fazal Muhammad Shams ur rahman Hassan A.Alshamrani Khalaf A.Alshamrani Muhammad Irfan Fawaz F.Alqhtani Department of Radiological Sciences College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Computer Science City University of Science and Information TechnologyPeshawarPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science Engineering University of Engineering and TechnologyMardan23200Pakistan Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause *** technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of ***(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic *** diseases that cause death need to be identiﬁed through such techniques and technologies to overcome the mortality *** brain tumor is one of the most common causes of *** have already proposed various models for the classiﬁcation and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classiﬁcation process with improved efﬁ***,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving *** the Kaggle dataset,these strategies are tested using classiﬁcation accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation *** results show that SVM outperforms other algorithms,with 95.3%accuracy.

关键词： MRI images brain tumor machine learning-based classiﬁcation

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A Cyber Kill Chain Approach for Detecting Advanced Persistent Threats

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Computers, Materials & Continua 2021年第5期67卷 2497-2513页

作者： Yussuf Ahmed A.Taufiq Asyhari Md Arafatur rahman School of Computing and Digital Technology Birmingham City UniversityBirminghamUK Faculty of Computing IBM CoEERASUniversity Malaysia PahangPahangMalaysia

The number of cybersecurity incidents is on the rise despite significant investment in security *** existing conventional security approaches have demonstrated limited success against some of the more complex *** is primarily due to the sophistication of the attacks and the availability of powerful *** devices such as the Internet of Things(IoT)are also increasing attack exposures due to the increase in *** the last few years,we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G *** technology brings processing power closer to the network and brings many advantages,including reduced latency,while it can also introduce vulnerabilities that could be *** cities are also dependent on technologies where everything is *** interconnectivity makes them highly vulnerable to cyber-attacks,especially by the Advanced Persistent Threat(APT),as these vulnerabilities are amplified by the need to integrate new technologies with legacy *** behind APT attacks have recently been targeting the IoT ecosystems,prevalent in many of these *** this paper,we used a publicly available dataset on Advanced Persistent Threats(APT)and developed a data-driven approach for detecting APT stages using the Cyber Kill *** are highly sophisticated and targeted forms of attacks that can evade intrusion detection systems,resulting in one of the greatest current challenges facing security *** this experiment,we used multiple machine learning classifiers,such as naïve Bayes,Bayes Net,KNN,Random Forest and Support Vector Machine(SVM).We used Weka performance metrics to show the numeric *** best performance result of 91.1%was obtained with the naïve Bayes *** hope our proposed solution will help security professionals to deal with APTs in a timely and effective manner.

关键词： Advanced persistent threat APT Cyber Kill Chain data breach intrusion detection cyber-attack attack prediction data-driven security and machine learning

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Expert insights on Malaysia’s residential solar-energy policies:shortcomings and recommendations

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Clean Energy 2022年第4期6卷 619-631页

作者： Lin-Sea Lau Yuen-Onn Choong Suet-Ling Ching Chooi-Yi Wei Abdelhak Senadjki Chee-Keong Choong Ai-na Seow Faculty of Business and Finance Universiti Tunku Abdul RahmanMalaysia

Malaysia has a long way before achieving the 20%renewable-energy penetration by ***,merely 2%of the country’s electricity is generated by renewable energy sources including solar *** the abundant literature about solar energy,qualitative studies that focus on experts’opinions on the weaknesses of residential solar-energy policies have received less attention,particularly in the context of *** the flaws in the existing policies would lead to creating a better policy framework for solar-energy ***,this study aims to identify the shortcomings of the current government initiatives and policies that deter solar photovoltaic adoption among households from experts’*** also provide recommendations for better future policy design and *** study employs a qualitative research approach(via semi-structured interviews)in collecting experts’*** concerns emerging from the interviews include insufficient financial support,lack of awareness programmes and subsidized electricity ***,interviewed experts suggest sufficient financial incentives,increased public awareness programmes and comprehensive legislation aimed at safeguarding consumer interests as a means to raising solar-energy adoption in the *** underlines the need for policymakers to create public awareness,provide financial support and develop regulatory measures aimed at managing solar companies for the sake of solar development in Malaysia.

关键词： qualitative study interview expert insights solar energy households policy shortcomings policy recommendations

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Interaction of cetyltrimethylammonium bromide with cefixime trihydrate drug at different temperatures and compositions: Effect of different electrolytes

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Chinese Journal of Chemical Engineering 2019年第8期27卷 1895-1903页

作者： Marzia rahman Md.Anamul Hoque Malik Abdul Rub Mohammed Abdullah Khan Department of Chemistry Jahangirnagar UniversitySavarDhaka 1342Bangladesh Chemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Excellence for Advanced Materials Research King Abdulaziz UniversityJeddah 21589Saudi Arabia

Herein,conductivity measurements have been carried out to explore the interaction between cetyltrimethylammonium bromide(CTAB,a cationic surfactant) and antibiotic drug(cefixime trihydrate(CMT)) in water and also in occurrence of inorganic salts(naCl,na2 SO4 and na3 PO4) over the temperature range of 303.15-323.15 K with an interval of 5 *** all cases,two critical micelle concentrations(c^*) were achieved for the CMT-surfactant *** of CMT drug to CTAB solution decreases the values of c^* which indicates the interaction between CMT and *** values of c^* for CMT-CTAB mixture in the presence of salts are lower in magnitude compared to the aqueous medium which indicates that micellization of the CMT-CTAB mixed system is favorable in salt *** values of △Gm^0 were obtained to be negative indicating the spontaneity of the micellization process and the extent of spontaneity further increases by means of rising *** obtained outcomes from the ΔHm^0 and ΔSm^0 values disclose that the interactions between CMT and CTAB are mostly electrostatic along with hydrophobic in *** thermodynamic parameters of transfer and enthalpy-entropy compensation phenomenon were also determined and discussed in detail.

关键词： Cefixime trihydrate CTAB Critical micelle concentration Compensation temperature Free energy of transfer

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An efficient hybrid system for anomaly detection in social networks

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Cybersecurity 2021年第1期4卷 136-146页

作者： Md.Shafiur rahman Sajal Halder MdAshraf Uddin Uzzal Kumar Acharjee Department of Computer Science and Engineering Dhaka International UniversityDhakaBangladesh Department of Computer Science and Engineering Jagannath UniversityDhakaBangladesh Department Computer Science and Information Technology RMIT UniversityMelbourneAustralia Internet Commerce Security Laboratory Federation University AustraliaBallarat VIC 3350Australia

Anomaly detection has been an essential and dynamic research area in the data mining.A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and *** social network refers to a forum used by different groups of people to express their thoughts,communicate with each other,and share the content *** social networks also facilitate abnormal activities,spread fake news,rumours,misinformation,unsolicited messages,and propaganda post malicious ***,detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social *** this paper,we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree(C5.0),Support Vector Machine(SVM)and naïve Bayesian classifier(NBC)for classifying normal and abnormal users in social *** have extracted a list of unique features derived from users’profile and *** two kinds of dataset with the selected features,the proposed machine learning model called DT-SVMNB is *** model classifies users as depressed one or suicidal one in the social *** have conducted an experiment of our model using synthetic and real datasets from social *** performance analysis demonstrates around 98%accuracy which proves the effectiveness and efficiency of our proposed system.

关键词： Anomaly Detection Machine Learning Hybrid Anomaly Detection Social Networks

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