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Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features

Computers, Materials & Continua 2023年第9期76卷 3167-3188页

作者： Nida Aslam irfan Ullah Khan Salma Abdulrahman Bader Aisha Alansari Lama Abdullah Alaqeel Razan Mohammed Khormy Zahra Abdultawab AlKubaish Tariq Hussain SAUDI ARAMCO Cybersecurity Chair Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Engineering Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhouChina

One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’devices,stealing personal information and causing hardware *** previous studies,machine learning(ML)has shown its efficacy in detecting malware events and classifying their ***,attackers are continuously developing more sophisticated methods to bypass ***,up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile ***,this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface(API)-based features from a recent *** overcome the dataset imbalance issue,RandomOverSampler,synthetic minority oversampling with tomek links(SMOTETomek),and RandomUnderSampler were applied to the Dataset in different *** results indicated that the extra tree(ET)classifier achieved the highest accuracy of 99.53%within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler ***,the explainable Artificial Intelligence(EAI)technique has been applied to add transparency to the high-performance ET *** global explanation using the Shapely values indicated that the top three features contributing to the goodware class are:Ljava/net/URL;->openConnection,Landroid/location/LocationManager;->getLastKgoodwarewnLocation,and *** the other hand,the top three features contributing to themalware class are Receive_Boot_Completed,Get_Tasks,and Kill_Background_*** is believed that the proposedmodel can contribute to proactively detectingmalware events in Android devices to reduce the number of victims and increase users’trust.

关键词： Android malware machine learning malware detection explainable artificial intelligence cyber security

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Revenue-Constrained Combination of an Optimal Tariff and Duty Drawback

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Frontiers of Economics in China-Selected Publications from Chinese Universities 2018年第1期13卷 52-67页

作者： Tatsuo hatta Asian Growth Research Institute 11-40temachi Kokurakita Kitakyushu 803-0814 Japan

A duty drawback is an export subsidy determined as a percentage of the tariffs paid on the imported inputs used in its production. This paper examines the revenue-constrained optimal tariff structure in a small open economy including a duty drawback as a trade policy tool. This paper has two main aims. First, we show that the revenue-constrained optimal combination of tariff and duty drawback for a given revenue level is not unique. Second, we show that if the optimal import tariff rates are all positive when the duty drawback rate is zero, then the optimal import tariff rates are always positive when the duty drawback is positive.

关键词： revenue-constrained optimal tariff duty drawback export subsidy,uniformity

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Comparison of nutrient use efficiency,antioxidant assay,and nutritional quality of butter-head lettuce(Lactuca sativa L.)in five cultivation systems

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International Journal of Agricultural and Biological Engineering 2023年第1期16卷 95-103页

作者： Mazhar H.Tunio Jianmin Gao Tarek M.K.Mohamed Fiaz Ahmad irfan Abbas Sher Ali Shaikh School of Agricultural Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina

A greenhouse experiment was conducted to evaluate the comparison of nutrient use efficiency,antioxidant assay,and nutritional quality of butter-head lettuce in five cultivation *** this experimental study,a split-boxes experimental arrangement in a completely randomized design(CRD)with three aeroponics systems(high pressure(T1),low pressure(T2),and ultrasonic nozzle(T3)),hydroponic system(T4),and conventional cultivation system(T5)was *** analyses of variance(ANOVA)were employed to assess yield,chlorophyll content,relative water content(RWC),nutrient use efficiency(NUE),2,2-Diphenyl-1-picrylhydrazyl(DDPH),Hydrogen Peroxide(H2O2),Ferric Reducing Antioxidant Power(FRAP),total phenolic(TPC),flavonoid content(TFC)and nutritional quality of butter-head *** experimental comparative results indicated that the shoot and root(fresh and dry)weight,root-to-shoot ratio,chlorophyll contents,CWC,NUE,DDPH,H 2O 2,FRAP,TPC,TFC,nitrate,vitamin C,and protein level were significantly(p0.7)among the Shoot(FW),Chlorophyll,RWC,NUE,and among TPC,TFC,DDPH,H 2O 2,and *** study demonstrates that butter-head lettuce grown in aeroponics systems with high-pressure nozzles may serve as a potential dietary source and is rich in natural antioxidants.

关键词： aeroponics high-pressure nozzle phenolic content antioxidants lettuce

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Combination of mesenchymal stem cells and three-dimensional collagen scaffold preserves ventricular remodeling in rat myocardial infarction model

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World Journal of Stem Cells 2022年第8期14卷 633-657页

作者： Rida-e-Maria Qazi irfan Khan Kanwal Haneef Tuba Shakil Malick Nadia Naeem Waqas Ahmad Asmat Salim Sadia Mohsin Stem Cell Laboratory Dr.Panjwani Center for Molecular Medicine and Drug ResearchICCBSUniversity of KarachiKarachi 75270SindhPakistan Dr.Zafar H.Zaidi Center for Proteomics University of KarachiKarachi 75270SindhPakistan Dow Research Institute of Biotechnology and Biomedical Sciences(DRIBBS) Dow University of Health and SciencesOjha CampusKarachi 74200SindhPakistan Cardiovascular Research Center Lewis Katz School of MedicineTemple UniversityPhiladelphiaPA 19140United States

BACKGROUND Cardiovascular diseases are the major cause of mortality *** of the damaged myocardium remains a challenge due to mechanical constraints and limited healing ability of the adult heart *** tissue engineering using biomaterial scaffolds combined with stem cells and bioactive molecules could be a highly promising approach for cardiac *** of biomaterials can provide suitable microenvironment to the cells and can solve cell engraftment problems associated with cell transplantation *** stem cells(MSCs)are potential candidates in cardiac tissue engineering because of their multilineage differentiation potential and ease of *** of DNA methyl transferase inhibitor,such as zebularine,in combination with three-dimensional(3D)scaffold can promote efficient MSC differentiation into cardiac lineage,as epigenetic modifications play a fundamental role in determining cell fate and lineage specific gene *** To investigate the role of collagen scaffold and zebularine in the differentiation of rat bone marrow(BM)-MSCs and their subsequent in vivo *** MSCs were isolated from rat BM and characterized morphologically,immunophenotypically and by multilineage differentiation *** were seeded in collagen scaffold and treated with 3μmol/L zebularine in three different *** analysis was done and cardiac differentiation was analyzed at the gene and protein *** and untreated MSC-seeded scaffolds were transplanted in the rat myocardial infarction(MI)model and cardiac function was assessed by *** tracking was performed by DiI dye labeling,while regeneration and neovascularization were evaluated by histological and immunohistochemical analysis,*** MSCs were successfully isolated and seeded in collagen *** analysis revealed that zebularine was not cytotoxic in any of the treatment *** differentiation analysis

关键词： Mesenchymal stem cells Myocardial infarction Cardiac tissue engineering Demethylating agent Collagen scaffold Zebularine

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Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

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Computers, Materials & Continua 2023年第7期76卷 711-729页

作者： Muhammad irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia College of Engineering Najran UniversityNajran61441Saudi Arabia

A brain tumor is a mass or growth of abnormal cells in the *** children and adults,brain tumor is considered one of the leading causes of *** are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)*** brain tumors as early as possible is essential,as this can improve the chances of successful treatment and *** this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain *** pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome *** dataset consists of 120 *** pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region *** have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient *** pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%*** contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

关键词： Brain tumor deep learning ensemble detection healthcare

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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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Computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Heart Rate Monitoring Sensor Based on Singlemode-Multimode-Singlemode Fiber

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Photonic Sensors 2020年第2期10卷 186-193页

作者： Ninik IRAWATI Agus Muhamad hatta Yoseph Gita Yhun YHUWANA SEKARTEDJO Department of Engineering Physics Institute Teknologi Sepuluh NopemberSurabaya 60111Indonesia Aston Institute of Photonics Technologies Aston UniversityAston Express WayB4&ET BirmighamUnited Kingdom Department of Physics Airlangga UniversitySurabaya 60115Indonesia

The singlemode-multimode-singlemode(SMS)fiber structure for a heart rate monitoring is proposed and *** artificial electrocardiogram(ECG)signal is used to simulate the heart pulse at different rates ranging from 50 beats per minute(bpm)to 200 *** SMS fiber structure is placed at the center of a loudspeaker and it senses the vibration of the *** vibration of the pulse signal applied to the SMS fiber structure changes the intensity of the optical output *** proposed sensor shows a linear frequency of the heart rate sensing range that matches well with the relevant heart rate from the artificial *** work shows the capability of the SMS fiber structure monitoring the heart rate frequencies for a long term,high stability realization,and reproducibility,and being suitable for the observation in hospitals as well as in other environments.

关键词： Heart rate monitoring fiber optic sensor SMS fiber singlemode-multimode-singlemode fiber hear rate sensor

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Impairments Approximations in Assembled mmWave and Radio Over Fiber Network

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Computers, Materials & Continua 2022年第12期73卷 4885-4895页

作者： Muhammad irfan Farman Ali Fazal Muhammad Saifur Rahman Ammar Armghan Yousaf Khan Faisal Althobiani Rehan Shafiq Mohammed Alshareef Mohammad E.Gommosani Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Qurtuba University of Science and ITDera Ismail Khan29050Pakistan Department of Electrical Engineering University of Engineering TechnologyMardan23200Pakistan Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Faulty of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering Sarhad University of Science and Information TechnologyPeshawar25220Khyber PukhtunkhwaPakistan Ports&Maritime Transportation Faculty of Maritime Studies King Abdulaziz University JeddahSaudi Arabia Nautical Science Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddahSaudi Arabia

The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input *** channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN ***,addressing of these distortions is the basic objective for the 5G mobile *** this paper,the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber(mmWave-RoF).The analytical model is designed in terms of outage probability for the proposed mmWave-RoF *** performance of mmWave-RoF against fiber nonlinearity and PN is studied for input power,output power and length using peak to average power ratio(PAPR)and bit error rate(BER)measuring *** simulation outcomes present that the impacts of fiber nonlinearity and PNcan be balanced for a huge capacity mmWave-RoF model applying input power carefully.

关键词： Fiber nonlinearity phase noise radio over fiber network advanced modulation system

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Multi-Objective Vendor Selection Problem of Supply Chain Management Under Fuzzy Environment

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Journal of the Operations Research Society of China 2021年第1期9卷 33-62页

作者： Srikant Gupta irfan Ali Aquil Ahmed Department of Statistics and Operations Research Aligarh Muslim UniversityAligarhUPIndia

Survival of a company in today's competitive business environment depends mainly on its supply *** adequate supply chain gives a competitive edge to a ***,which is the initial stage of a supply chain,can be made efficient by making an appropriate selection of *** vendor selection results not only in reduced purchasing costs,decreased production lead time,increased customer satisfaction but also in improved corporate *** general,the vendor selection problem is a multi-objective decision-making problem that involves some quantitative and qualitative ***,we have considered a multi-objective ven-dor selection problem(MOV SP)with three multiple objective goals:minimization of net ordering price,minimization of rejected units and minimization of late delivered *** most of the cases,information about the price of a unit,percentage of rejected units,percentage of late delivered units,vendor rating value and vendor quota flexibil-ity may not be known precisely due to some *** this paper,imprecision in input information is handled by the concept of a simulation technique,where the parameter follows the uniform ***,stochastic,a-cut and ranking function approaches are used to get the crisp value of the simulated data *** four differ-ent algorithms,namely-fuzzy programming,goal programming,lexicographic goal programming and D1-distance algorithm,have been used for solving the *** last,three different types of simulated data sets have been used to illustrate the work.

关键词： Vendor selection problem Trapezoidal fuzzy number α-Cut and ranking function Fuzzy programming Goal programming Lexicographic goal programming

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Liver Ailment Prediction Using Random Forest Model

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Computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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