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An efficient hybrid system for anomaly detection in social networks

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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Universal linear intensity transformations using spatially incoherent diffractive processors

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Light(Science & Applications) 2023年第9期12卷 1830-1856页

作者： md Sadman Sakib rahman Xilin Yang Jingxi Li Bijie Bai Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

Under spatially coherent light,a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view(FOVs)if the total number(N)of optimizable phase-only diffractive features is≥~2N_(i)N_(o),where Ni and No refer to the number of useful pixels at the input and the output FOVs,*** we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output *** spatially incoherent monochromatic light,the spatially varying intensity point spread function(H)of a diffractive network,corresponding to a given,arbitrarily-selected linear intensity transformation,can be written as H(m,n;m′,n′)=|h(m,n;m′,n′)|^(2),where h is the spatially coherent point spread function of the same diffractive network,and(m,n)and(m′,n′)define the coordinates of the output and input FOVs,*** numerical simulations and deep learning,supervised through examples of input-output profiles,we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N≥~2N_(i)N_(o).We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths,operating ***,we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination,achieving a test accuracy of>95%.Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.

关键词： spatially intensity arbitrary

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Ensemble learning of diffractive optical networks

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Light(Science & Applications) 2021年第1期10卷 123-135页

作者： md Sadman Sakib rahman Jingxi Li Deniz Mengu Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine ***,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation *** deep neural networks(D^(2)NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D^(2)NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and ***,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble *** independently training 1252 D^(2)NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D^(2)NNs that collectively improved the image classification *** this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D^(2)NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D^(2)NNs within each *** results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.

关键词： networks collective successive

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Industrial IoT Based Condition Monitoring for Wind Energy Conversion System

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 654-664页

作者： md Liton Hossain Ahmed Abu-Siada S.M.Muyeen md Mubashwar Hasan Momtazur rahman Department of Electrical and Computer Engineering Curtin UniversityPerthAustralia School of Science Engineering and ITFederation UniversityAustralia Edith Cowan University Australia

Wind energy has been identified as the second dominating source in the world renewable energy generation after *** and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system(WECS)including multilevel inverters(MLIs).The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link *** paper proposes a simplified proportional integral(PI)-based space vector pulse width modulation(SVPWM)to minimize the output waveform ripples,resolve the voltage balancing issue and produce better-quality output *** experiences various types of faults particularly in the DC-link capacitor and switching devices of the power *** faults,if not detected and rectified at an early stage,may lead to catastrophic failures to the WECS and continuity of the power *** paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real *** most wind power plants are located in remote areas or offshore,WECS condition monitoring needs to be developed over the internet of things(IoT)to ensure system *** this paper,an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment.

关键词： Asset management condition monitoring fault diagnosis industrial internet of things(IoT) wind energy conversion system

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Analysis and modeling of hysteresis of piezoelectric micro-actuator used in high precision dual-stage servo system

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Control Theory and Technology 2015年第2期13卷 184-203页

作者： md.Arifur rahman Abdullah Al MAMUN Kui YAO Department of Electrical and Computer Engineering National University of Singapore Institute of Materials Research and EngineeringA*STAR (Agency for Science Technology and Research)

A dual-stage servo system consists of a primary coarse actuator for facilitating large motion and a secondary micro-actuator for small but precise motion to improve tracking performance. Piezoelectric micro-actuator made from lead zirconate titanate(PZT)has been a popular choice for the secondary stage. However, the advantage gained by the resolution of the secondary PZT actuator is reduced by its inherent hysteresis nonlinearity. Model based hysteresis compensation techniques are preferred due to their simplicity and fast response. Identification and modeling are two substantial parts in such model-based techniques. This paper presents a rigorous analysis and modeling of the hysteresis of PZT micro-actuator. Modified Generalized Prandtl-Ishlinskii and Coleman-Hodgdon models are studied. Identification of the model through nonlinear least square and particle swarm optimization are examined and compared. Several analyses are done through tuning of the model parameters and identification *** analysis and simulation results underscore the effectiveness of this modeling approach. Finally as a design example,a dual-stage simulation analysis is done to show the effectiveness of systematic modeling on hysteresis compensation.

关键词： Hysteresis particle swarm optimization(PSO) modified generalized Prandtl-Ishlinskii(GPI) inverse GPI

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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 Reference Free Adaptive Learning Process for Speech Enhancement Applications

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Computers, Materials & Continua 2022年第2期70卷 3067-3080页

作者： Girika Jyoshna md.Zia Ur rahman L.Koteswararao Deptartment of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationKL UniversityVaddeswaramGunturAndhra PradeshIndia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationKL UniversityHyderabadTelanganaIndia

In issues like hearing impairment,speech therapy and hearing aids play a major role in reducing the *** of noise signals from speech signals is a key task in hearing aids as well as in speech *** the transmission of speech signals,several noise components contaminate the actual speech *** paper addresses a new adaptive speech enhancement(ASE)method based on a modified version of singular spectrum analysis(MSSA).The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference *** MSSA adopts three key steps for generating the reference from the contaminated speech *** are decomposition,grouping and *** generated reference is taken as a reference for variable size adaptive learning *** this work two categories of adaptive learning algorithms are *** are step variable adaptive learning(SVAL)algorithm and time variable step size adaptive learning(TVAL).Further,sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning *** performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement(SNRI),excess mean square error(EMSE)and misadjustment(MSD).For cockpit noise these measures are found to be 29.2850,-27.6060 and 0.0758 dB respectively during the experiments using SVAL *** considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.

关键词： Adaptive algorithm speech enhancement singular spectrum analysis reference free noise canceller variable step size

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Whole Leea macrophylla ethanolic extract normalizes kidney deposits and recovers renal impairments in an ethylene glycol-induced urolithiasis model of rats

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Asian Pacific Journal of Tropical Medicine 2012年第7期5卷 533-538页

作者： Abu Nasim Nizami md Atiar rahman Nazim Uddin Ahmed md Shahidul Islam Department of Biochemistry and Molecular Biology University of ChittagongChittagong-4331Bangladesh Department of Biochemistry School of BiochemistryGenetics and MicrobiologyUniversity of KwaZulu-NatalDurban 4000South Africa Bangladesh Council of Scientific and Industrial Research(BCSIR) Chittagong-4220Bangladesh

Objective:To investigate the antilithiatic effect of the whole Leea macrophylla(L macrophylla) Roxb(Leeaeeae) ethanol extract in ediylene glycol-induced urolithiasis model of ***: Forty two seven weeks old male wistar albino rats were randomly divided into two major groups namely:preventive(n=18) and therapeutic(n=24).Preventive group was further subdivided into 3 groups of 6 rats namely:preventive control(PC),preventive lithiatic control(PLC) and preventive lithiatic ***(PLLM).Similarly,therapeutic group was also subdivided into 4 groups of 6 rats namely:therapeutic control(TC),therapeutic lithiatic control(TLC).therapeutic lithitatic L macrophylla(TLLM) and therapeutic lithiatic cystone(TLCYS).The lithiasis was induced by 0.75%(v/v) ethylene glycol in the drinking water of all groups except the PC and TC *** urinary ionic parameters such as calcium,inorganic phosphate,oxalate,magnesium & creatinine and renal morphology were altered by ediylene glycol,which were partially recovered by 14 d preventive and almost fully recovered by 28 d therapeutic intervention trials with L macrophylla extract(500 mg/kg BW daily).Results:Significant difference on recovery was observed between preventive and dierapeutic interventional ***-urolithiatic effect of cystone was significantly(P<0.001) higher than extracts.L macrophylla extract was found nontoxic in the acute toxicity ***:The results of this study demonstrated very promising antiurolithiatic effect of *** extract with preventive and therapeutic treatments in this experimental condition.

关键词： Urolithiasis Ethylene glycol Leea macrophylla Urinary parameter Rats

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A Critical Analysis of Machine Learning and Deep Learning Methods for Cervical Cancer Screening

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Journal of Computer and Communications 2023年第12期11卷 64-85页

作者： Muhtasim mahmudur rahman Jakir Khan Abu Sale Mohammad Mostafizur rahman Redoanul Haque md. Sumon Ali Department of Computer Science and Engineering Bangladesh Army University of Engineering & Technology Natore Bangladesh

Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy of vagina tests. In this piece, we conducted a thorough review of 50 research studies that applied these techniques. Our investigation compared the outcomes to well-known screening techniques and concentrated on the datasets used and performance measurements reported. According to the research, convolutional neural networks and other deep learning approaches have potential for lowering false positives and boosting screening precision. Although several research used small sample sizes or constrained datasets, this raises questions about how applicable the findings are. This paper discusses the advantages and disadvantages of the articles that were chosen, as well as prospective topics for future research, to further the application of ml and dl in cervical cancer screening. The development of cervical cancer screening technologies that are more precise, accessible, and can lead to better public health outcomes is significantly affected by these findings.

关键词： Cervical Cancer Neoplasms Screening Machine Learning Techniques Deep Learning Techniques

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Investigation of accuracy of pavement mechanistic empirical prediction performance by incorporating Level 1 inputs

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Journal of Traffic and Transportation Engineering(English Edition) 2020年第2期7卷 259-268页

作者： md Mehedi Hasan A.S.M.Asifur rahman Rafiqul A.Tarefder Department of Civil Engineering University of New MexicoAlbuquerqueNM 87131USA Texas A&M Transportation Institute Texas A&M UniversityCollege StationTX 77845USA

The recently developed AASHTOWare pavement mechanistic empirical(M-E)design requires advanced characterization of pavement *** particular,M-E Level 1 analysis requires materials input parameters to be obtained from the laboratory test *** cooperation with the New Mexico Department of Transportation(NmdOT),six pavement sites were selected to compare M-E Level 1 predicted performances from laboratory tested data with the field *** materials such as subgrade,base,asphalt concrete,binder,aggregate,etc.,were collected from these pavements sites during *** dynamic modulus,fatigue endurance limit(FEL),dynamic shear rheometer,and resilient modulus tests were conducted on the collected *** these parameters,pavement sections,as constructed,were modeled and analyzed in M-E at Levels 1,2 and *** predicted distress quantities for international roughness index(IRI)and rutting were compared with the field collected data as well as among the three design *** analysis results demonstrated that M-E overpredicts IRI and rutting in the pavement compared to the field *** was also observed that Level 1 showed the lowest rutting,whereas the maximum rutting was found at Level *** dynamic modulus of AC layer was underpredicted at Levels 2 and 3 resulting in higher rutting and IRI.

关键词： Pavement M-E Rutting Validation IRI Design

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