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| 1 | A survey on trust based detection and isolation of malicious nodes in ad-hoc and sensor networks显示文摘 | Adnan AHMED Kamalrulnizam ABU BAKAR Muhammad Ibrahim CHANNA Khalid HASEEB Abdul Waheed KHAN | 2015 | Frontiers of Computer Science2015，9，2： | 15 |
| 2 | Antibiotic resistance and cagA gene correlation:A looming crisis of Helicobacter pylori显示文摘AIM:To determine antibiotic resistance of Helicobacter pylori(H.pylori) in Pakistan and its correlation with host and pathogen associated factors.METHODS:A total of 178 strains of H.pylori were isolated from gastric biopsies of dyspeptic patients.Susceptibility patterns against first and second-line antibiotics were determined and trends of resistance were analyzed in relation to the sampling period,gastric conditions and cagA gene carriage.The effect of cagA gene on the acquisition of resistance was investigated by mutant selection assay.RESULTS:The observations showed that monoresistant strains were prevalent with rates of 89% for metronidazole,36% for clarithromycin,37% for amoxicillin,18.5% for ofloxacin and 12% for tetracycline.Furthermore,clarithromycin resistance was on the rise from 2005 to 2008(32% vs 38%,P = 0.004) and it is significantly observed in non ulcerative dyspeptic patients compared to gastritis,gastric ulcer and duodenal ulcer cases(53% vs 20%,18% and 19%,P = 0.000).On the contrary,metronidazole and ofloxacin resistance were more common in gastritis and gastric ulcer cases.Distribution analysis and frequencies of resistant mutants in vitro correlated with the absence of cagA gene with metronidazole and ofloxacin resistance.CONCLUSION:The study confirms the alarming levels of antibiotic resistance associated with the degree of gastric inflammation and cagA gene carriage in H.pylori strains. | Adnan Khan Amber Farooqui Hamid Manzoor Syed Shakeel Akhtar Muhammad Saeed Quraishy Shahana Urooj Kazmi | 2012 | World Journal of Gastroenterology2012，18，18： | 15 |
| 3 | Thymosin alpha 1:A comprehensive review of the literature显示文摘Thymosin alpha 1 is a peptide naturally occurring in the thymus that has long been recognized for modifying,enhancing,and restoring immune function.Thymosin alpha 1 has been utilized in the treatment of immunocompromised states and malignancies,as an enhancer of vaccine response,and as a means of curbing morbidity and mortality in sepsis and numerous infections.Studies have postulated that thymosin alpha 1 could help improve the outcome in severely ill corona virus disease 2019 patients by repairing damage caused by overactivation of lymphocytic immunity and how thymosin alpha 1 could prevent the excessive activation of T cells.In this review,we discuss key literature on the background knowledge and current clinical uses of thymosin alpha 1.Considering the known biochemical properties including antibacterial and antiviral properties,timehonored applications,and the new promising findings regarding the use of thymosin,we believe that thymosin alpha 1 deserves further investigation into its antiviral properties and possible repurposing as a treatment against severe acute respiratory syndrome coronavirus-2. | Asimina Dominari Donald Hathaway III Krunal Pandav Wanessa Matos Sharmi Biswas Gowry Reddy Sindhu Thevuthasan Muhammad Adnan Khan Anoopa Mathew Sarabjot Singh Makkar Madiha Zaidi Michael Maher Mourad Fahem Renato Beas Valeria Castaneda Trissa Paul John Halpern Diana Baralt | 2020 | World Journal of Virology2020，9，5： | 11 |
| 4 | e-antigen-negative chronic hepatitis B in Bangladesh显示文摘BACKGROUND:Bangladesh is a densely populated country where about 10 million people are chronically infected with hepatitis B virus(HBV).The aim of the present study was to evaluate the biochemical,virological and histological characteristics of HBeAg-negative chronic hepatitis B(CHB). METHODS:Patients were included in this study if they were chronically infected with HBV with detectable DNA.The patients who were co-infected with human immunodeficiency virus,hepatitis delta virus or hepatitis C virus,and previously subjected to antiviral treatment,and those with hepatocellular carcinoma were excluded.The study was conducted during the period of January 2001 to December 2007.During this period 2617 patients with CHB were studied.HBeAg-positive cases were included to compare the characteristics.Among them,237 cases underwent liver biopsy. RESULTS:2296 patients(87.7%)were male,with a mean age of 28.9±13.7 years.2375 patients(90.8%)had CHB,and 242(9.2%)were cirrhotic.HBV DNA levels were 7.6±1.5 copies/ml,ALT was 111.3±212.5 U/L,and AST was 91.5± 148.9 U/L.The number of HBeAg-negative CHB cases was 1039(39.7%).HBeAg-negative patients with a lower DNA load were older,and they had more fibrotic changes in the liver than HBeAg-positive patients.The two groups did not differ in necroinflammatory activity,but the former had lower ALT and AST values.Cirrhosis was more common in e-antigen-negative patients.CONCLUSIONS:e-antigen-negative CHB patients are older and have more hepatic fibrosis patients than HBeAg-positive patients,although they have similar necroinflammatory activity. | Nooruddin Ahmad Shahinul Alam Golam Mustafa Abul Barkat Muhammad Adnan Rahat Hasan Baig Mobin Khan | 2008 | Hepatobiliary & Pancreatic Diseases International2008，7，4： | 6 |
| 5 | Estimating the basic reproductive ratio for the Ebola outbreak in Liberia and Sierra Leone显示文摘Background:Ebola virus disease has reemerged as a major public health crisis in Africa,with isolated cases also observed globally,during the current outbreak.Methods:To estimate the basic reproductive ratio R0,which is a measure of the severity of the outbreak,we developed a SEIR(susceptible-exposed-infected-recovered)type deterministic model,and used data from the Centers for Disease Control and Prevention(CDC),for the Ebola outbreak in Liberia and Sierra Leone.Two different data sets are available:one with raw reported data and one with corrected data(as the CDC suspects under-reporting).Results:Using a deterministic ordinary differential equation transmission model for Ebola epidemic,the basic reproductive ratio R0 for Liberia resulted to be 1.757 and 1.9 for corrected and uncorrected case data,respectively.For Sierra Leone,R0 resulted to be 1.492 and 1.362 for corrected and uncorrected case data,respectively.In each of the two cases we considered,the estimate for the basic reproductive ratio was initially greater than unity leading to an epidemic outbreak.Conclusion:We obtained robust estimates for the value of R0 associated with the 2014 Ebola outbreak,and showed that there is close agreement between our estimates of R0.Analysis of our model also showed that effective isolation is required,with the contact rate in isolation less than one quarter of that for the infected non-isolated population,and that the fraction of high-risk individuals must be brought to less than 10%of the overall susceptible population,in order to bring the value of R0 to less than 1,and hence control the outbreak. | Adnan Khan Mahim Naveed Muhammad Dur-e-Ahmad Mudassar Imran | 2015 | Infectious Diseases of Poverty2015，4，1： | 5 |
| 6 | Estimating the basic reproduction number for single-strain dengue fever epidemics显示文摘Background:Dengue,an infectious tropical disease,has recently emerged as one of the most important mosquito-borne viral diseases in the world.We perform a retrospective analysis of the 2011 dengue fever epidemic in Pakistan in order to assess the transmissibility of the disease.We obtain estimates of the basic reproduction number R0 from epidemic data using different methodologies applied to different epidemic models in order to evaluate the robustness of our estimate.Results:We first estimate model parameters by fitting a deterministic ODE vector-host model for the transmission dynamics of single-strain dengue to the epidemic data,using both a basic ordinary least squares(OLS)as well as a generalized least squares(GLS)scheme.Moreover,we perform the same analysis for a direct-transmission ODE model,thereby allowing us to compare our results across different models.In addition,we formulate a direct-transmission stochastic model for the transmission dynamics of dengue and obtain parameter estimates for the stochastic model using Markov chain Monte Carlo(MCMC)methods.In each of the cases we have considered,the estimate for the basic reproduction number R0 is initially greater than unity leading to an epidemic outbreak.However,control measures implemented several weeks after the initial outbreak successfully reduce R0 to less than unity,thus resulting in disease elimination.Furthermore,it is observed that there is strong agreement in our estimates for the pre-control value of R0,both across different methodologies as well across different models.However,there are also significant differences between our estimates for the post-control value of the basic reproduction number across the two different models.Conclusion:In conclusion,we have obtained robust estimates for the value of the basic reproduction number R0 associated with the 2011 dengue fever epidemic before the implementation of public health control measures.Furthermore,we have shown that there is close agreement between our estimates for the post-control value of R0 across the different methodologies.Nevertheless,there are also significant differences between the estimates for the post-control value of R0 across the two different models. | Adnan Khan Muhammad Hassan Mudassar Imran | 2014 | Infectious Diseases of Poverty2014，3，1： | 3 |
| 7 | Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches显示文摘Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels. | Abdul Hannan Khan Muhammad Adnan Khan Sagheer Abbas Shahan Yamin Siddiqui Muhammad Aanwar Saeed Majed Alfayad Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，5： | 2 |
| 8 | Concurrent dengue and malaria infection in Lahore, Pakistan during the 2012 dengue outbreak显示文摘 | Muhammad Zaman Khan Assir Muhammad Adnan Masood Hafiz Ijaz Ahmad | 2013 | International Journal of Infectious Diseases2013，，： | 1 |
| 9 | Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine显示文摘An epidemic is a quick and widespread disease that threatens many lives and damages the economy.The epidemic lifetime should be accurate so that timely and remedial steps are determined.These include the closing of borders schools,suspension of community and commuting services.The forecast of an outbreak effectively is a very necessary but difficult task.A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available.This work proposes and examines a prediction model based on a deep extreme learning machine(DELM).This methodology is used to carry out an experiment based on the recent Wuhan coronavirus outbreak.An optimized prediction model that has been developed,namely DELM,is demonstrated to be able to make a prediction that is fairly best.The results show that the new methodology is useful in developing an appropriate forecast when the samples are far from abundant during the critical period of the disease.During the investigation,it is shown that the proposed approach has the highest accuracy rate of 97.59%with 70%of training,30%of test and validation.Simulation results validate the prediction effectiveness of the proposed scheme. | Muhammad Adnan Khan Sagheer Abbas Khalid Masood Khan Mohammed AAl Ghamdi Abdur Rehman | 2020 | Computers, Materials & Continua2020，，9： | 1 |
| 10 | Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning显示文摘One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives. | Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 11 | Alzheimer Disease Detection Empowered with Transfer Learning显示文摘Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches. | Taher M.Ghazal Sagheer Abbas Sundus Munir M.A.Khan Munir Ahmad Ghassan F.Issa Syeda Binish Zahra Muhammad Adnan Khan Mohammad Kamrul Hasan | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 12 | Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication显示文摘Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one another.One of the main requirements in a VANET is to provide self-decision capability to the vehicles.Cognitive memory,which stores all the previous routes,is used by the vehicles to choose the optimal route.In networks,communication is crucial.In cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each vehicle.Resources are allocated in a distributed manner,which is known as Mode 4 communication.The support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this study.The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 system.The results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users. | Muhammad Adnan Khan Ahmed Abu-Khadrah Shahan Yamin Siddiqui Taher M.Ghazal Tauqeer Faiz Munir Ahmad Sang-Woong Lee | 2022 | Computers, Materials & Continua2022，，11： | 1 |
| 13 | High-temperature stress suppresses allene oxide cyclase 2 and causes male sterility in cotton by disrupting jasmonic acid signaling显示文摘Cotton(Gossypium spp.) yield is reduced by stress. In this study, high temperature(HT) suppressed the expression of the jasmonic acid(JA) biosynthesis gene allene oxide cyclase 2(GhAOC2), reducing JA content and causing male sterility in the cotton HT-sensitive line H05. Anther sterility was reversed by exogenous application of methyl jasmonate(MeJA) to early buds. To elucidate the role of GhAOC2 in JA biosynthesis and identify its putative contribution to the anther response to HT, we created gene knockout cotton plants using the CRISPR/Cas9 system. Ghaoc2 mutant lines showed male-sterile flowers with reduced JA content in the anthers at the tetrad stage(TS), tapetum degradation stage(TDS), and anther dehiscence stage(ADS). Exogenous application of MeJA to early mutant buds(containing TS or TDS anthers) rescued the sterile pollen and indehiscent anther phenotypes, while ROS signals were reduced in ADS anthers. We propose that HT downregulates the expression of GhAOC2 in anthers, reducing JA biosynthesis and causing excessive ROS accumulation in anthers, leading to male sterility. These findings suggest exogenous JA application as a strategy for increasing male fertility in cotton under HT. | Aamir Hamid Khan Yizan Ma Yuanlong Wu Adnan Akbar Muhammad Shaban Abid Ullah Jinwu Deng Abdul Saboor Khan Huabin Chi Longfu Zhu Xianlong Zhang Ling Min | 2023 | The Crop Journal2023，11，1： | 1 |
| 14 | Optimal control analysis of Ebola disease with control strategies of quarantine and vaccination显示文摘Background:The 2014 Ebola epidemic is the largest in history,affecting multiple countries in West Africa.Some isolated cases were also observed in other regions of the world.Method:In this paper,we introduce a deterministic SEIR type model with additional hospitalization,quarantine and vaccination components in order to understand the disease dynamics.Optimal control strategies,both in the case of hospitalization(with and without quarantine)and vaccination are used to predict the possible future outcome in terms of resource utilization for disease control and the effectiveness of vaccination on sick populations.Further,with the help of uncertainty and sensitivity analysis we also have identified the most sensitive parameters which effectively contribute to change the disease dynamics.We have performed mathematical analysis with numerical simulations and optimal control strategies on Ebola virus models.Results:We used dynamical system tools with numerical simulations and optimal control strategies on our Ebola virus models.The original model,which allowed transmission of Ebola virus via human contact,was extended to include imperfect vaccination and quarantine.After the qualitative analysis of all three forms of Ebola model,numerical techniques,using MATLAB as a platform,were formulated and analyzed in detail.Our simulation results support the claims made in the qualitative section.Conclusion:Our model incorporates an important component of individuals with high risk level with exposure to disease,such as front line health care workers,family members of EVD patients and Individuals involved in burial of deceased EVD patients,rather than the general population in the affected areas.Our analysis suggests that in order for R0(i.e.,the basic reproduction number)to be less than one,which is the basic requirement for the disease elimination,the transmission rate of isolated individuals should be less than one-fourth of that for non-isolated ones.Our analysis also predicts,we need high levels of medication and hospitalization at the beginning of an epidemic.Further,optimal control analysis of the model suggests the control strategies that may be adopted by public health authorities in order to reduce the impact of epidemics like Ebola. | Muhammad Dure Ahmad Muhammad Usman Adnan Khan Mudassar Imran | 2016 | Infectious Diseases of Poverty2016，5，1： | 1 |
| 15 | Projection of future streamflow of the Hunza River Basin,Karakoram Range(Pakistan)using HBV hydrological model显示文摘Hydrologiska Byrans Vattenbalansavdeling(HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System(IRS). HBV model performed fairly well both during calibration(R2=0.87, Reff=0.85, PBIAS=-0.36) and validation(R2=0.86, Reff=0.83, PBIAS=-13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during bothcalibration(R2=0.94, Reff=0.88, PBIAS=0.47) and validation(R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995-2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model(GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010-2039, 2040-2069 and 2070-2099, using 1980-2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitiveparameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010-2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21 th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010-2099. However, Can ESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010-2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region. | Ayaz Fateh ALI XIAO Cun-de ZHANG Xiao-peng Muhammad ADNAN Mudassar IQBAL Garee KHAN | 2018 | Journal of Mountain Science2018，15，10： | 1 |
| 16 | Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques显示文摘The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks. | Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 17 | Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning显示文摘Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate. | Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad Faheem Mushtaq Rizwan Ali Naqvi Dildar Hussain Amir Haider | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 18 | Convolutional Neural Network Based Intelligent Handwritten Document Recognition显示文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%. | Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 19 | Aspect Level Songs Rating Based Upon Reviews in English显示文摘With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold. | Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 20 | A Fused Machine Learning Approach for Intrusion Detection System显示文摘The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection. | Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
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