|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Antiseptics,iodine,povidone iodine and traumatic wound cleansing显示文摘 | Muhammad N Khan Abul H Naqvi | 2006 | Tiss Viab Soc2006，16，4： | 1 |
| 2 | Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning显示文摘White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%. | Abdullah Alqahtani Shtwai Alsubai Mohemmed Sha Muhammad Attique Khan Majed Alhaisoni Syed Rameez Naqvi | 2023 | Computer Systems Science & Engineering2023，46，7： | 1 |
| 3 | Relative weight--age in textbook and examination of secondary school chemistry-- implications for selective study among students显示文摘 | Akhtar Hussain Muhammad Amir Hashmi Syed Imtiaz Hussain Naqvi Shafqat Naeem Akhtar | 2010 | Procedia Social and Behavioral Sciences2010，2，： | 1 |
| 4 | Hard exudates referral system in eye fundus utilizing speeded up robust features显示文摘In the paper a referral system to assist the medical experts in the screening/referral of diabetic retinopathy is suggested. The system has been developed by a sequential use of different existing mathematical techniques. These techniques involve speeded up robust features(SURF), K-means clustering and visual dictionaries(VD). Three databases are mixed to test the working of the system when the sources are dissimilar. When experiments were performed an area under the curve(AUC) of 0.9343 was attained. The results acquired from the system are promising. | Syed Ali Gohar Naqvi Hafiz Muhammad Faisal Zafar Ihsanul Haq | 2017 | International Journal of Ophthalmology(English edition)2017，10，7： | 1 |
| 5 | 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 |
| 6 | Bio-refinery system in a pulp mill for methanol production with comparison of pressurized black liquor gasification and dry gasification using direct causticiza- tion显示文摘 | Naqvi Muhammad Yan Jinyue Dahlquist Erik | 2012 | Applied Energy2012，90，1： | 1 |
| 7 | Plant Nitrogen Metabolism: Balancing Resilience to Nutritional Stress andAbiotic Challenges显示文摘Plant growth and resilience to abiotic stresses,such as soil salinity and drought,depend intricately on nitrogen metabolism.This review explores nitrogen’s regulatory role in plant responses to these challenges,unveiling a dynamic interplay between nitrogen availability and abiotic stress.In the context of soil salinity,a nuanced rela-tionship emerges,featuring both antagonistic and synergistic interactions between salinity and nitrogen levels.Salinity-induced chlorophyll depletion in plants can be alleviated by optimal nitrogen supplementation;however,excessive nitrogen can exacerbate salinity stress.We delve into the complexities of this interaction and its agri-cultural implications.Nitrogen,a vital element within essential plant structures like chloroplasts,elicits diverse responses based on its availability.This review comprehensively examines manifestations of nitrogen deficiency and toxicity across various crop types,including cereals,vegetables,legumes,and fruits.Furthermore,we explore the broader consequences of nitrogen products,such as N_(2)O,NO_(2),and ammonia,on human health.Understand-ing the intricate relationship between nitrogen and salinity,especially chloride accumulation in nitrate-fed plants and sodium buildup in ammonium-fed plants,is pivotal for optimizing crop nitrogen management.However,prudent nitrogen use is essential,as overapplication can exacerbate nitrogen-related issues.Nitrogen Use Effi-ciency(NUE)is of paramount importance in addressing salinity challenges and enhancing sustainable crop productivity.Achieving this goal requires advancements in crop varieties with efficient nitrogen utilization,pre-cise timing and placement of nitrogen fertilizer application,and thoughtful nitrogen source selection to mitigate losses,particularly urea-based fertilizer volatilization.This review article delves into the multifaceted world of plant nitrogen metabolism and its pivotal role in enabling plant resilience to nutritional stress and abiotic challenges.It offers insights into future directions for sustainable agriculture. | Muhammad Farhan Manda Sathish Rafia Kiran Aroosa Mushtaq Alaa Baazeem Ammarah Hasnain Fahad Hakim Syed Atif Hasan Naqvi Mustansar Mubeen Yasir Iftikhar Aqleem Abbas Muhammad Zeeshan Hassan Mahmoud Moustafa | 2024 | Phyton-International Journal of Experimental Botany2024，93，3： | 0 |
| 8 | Coronavirus: A “Mild” Virus Turned Deadly Infection显示文摘Coronaviruses are a family of viruses that can be transmitted from one person to another.Earlier strains have only been mild viruses,but the current form,known as coronavirus disease 2019(COVID-19),has become a deadly infection.The outbreak originated in Wuhan,China,and has since spread worldwide.The symptoms of COVID-19 include a dry cough,sore throat,fever,and nasal congestion.Antimicrobial drugs,pathogen–host interaction,and 2 weeks of isolation have been recommended for the treatment of the infection.Safe operating procedures,such as the use of face masks,hand sanitizer,handwashing with soap,and social distancing,are also suggested.Moreover,travel bans for cities,states,and countries have been put in place,along with lockdowns to control the outbreak.Travel restrictions,mask use,sanitizer or soap use,and avoidance of touching the face and nose have produced encouraging results,whereas the effectiveness of antibiotics has not been proved.The results of isolation for the recovery of infected people have also been promising.Travel bans and lockdowns have caused a slump in economies,and unemployment has risen sharply,resulting in an increase in mental health cases globally.To date,vaccines have been developed and are in use in certain countries,but following standard operating procedures remain critical.The countries following the guidelines can eradicate this virus.New Zealand was the rst country to eliminate the virus from their territory. | Rizwan Ali Naqvi Muhammad Faheem Mushtaq Natash Ali Mian Muhammad Adnan Khan Atta-ur-Rahman Muhammad Ali Yousaf Muhammad Umair Rizwan Majeed | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 9 | Automated Brain Hemorrhage Classification and Volume Analysis显示文摘Brain hemorrhage is a serious and life-threatening condition. It cancause permanent and lifelong disability even when it is not fatal. The wordhemorrhage denotes leakage of blood within the brain and this leakage ofblood from capillaries causes stroke and adequate supply of oxygen to thebrain is hindered. Modern imaging methods such as computed tomography(CT) and magnetic resonance imaging (MRI) are employed to get an idearegarding the extent of the damage. An early diagnosis and treatment can savelives and limit the adverse effects of a brain hemorrhage. In this case, a deepneural network (DNN) is an effective choice for the early identification andclassification of brain hemorrhage for the timely recovery and treatment of anaffected person. In this paper, the proposed research work is divided into twonovel approaches, where, one for the classification and the other for volumecalculation of brain hemorrhage. Two different datasets are used for twodifferent techniques classification and volume. A novel algorithm is proposedto calculate the volume of hemorrhage using CT scan images. In the firstapproach, the ‘RSNA’ dataset is used to classify the brain hemorrhage typesusing transfer learning and achieved an accuracy of 93.77%. Furthermore,in the second approach, a novel algorithm has been proposed to calculate thevolume of brain hemorrhage and achieved tremendous results as 1035.91mm3and 9.25 cm3, using the PhysioNet CT scan tomography dataset. | Maryam Wardah Muhammad Mateen Tauqeer Safdar Malik Mohammad Eid Alzahrani Adil Fahad Abdulmohsen Almalawi Rizwan Ali Naqvi | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 10 | Robust Length of Stay Prediction Model for Indoor Patients显示文摘Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently. | Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 11 | WeiTsing:A guard of the stele显示文摘Soil-borne pathogens,e.g.,Plasmodiophora brassicae,causes devastating clubroot disease,and existing identified resistance(R)genes has been broken down with the co-evolution of pathogen,threatening the future of cruciferous crops.Thus,there is a dire need to identify new R genes for developing clubroot-resistant crops.Recently,Wang et al.,2023 have reported a new R gene,WeiTsing,that confers complete resistance to Plasmodiophora brassicae,providing an opportunity to deliver broad-spectrum,durable clubroot-resistant crop varieties. | Muhammad Arslan Mahmood Rubab Zahra Naqvi Shahid Mansoor | 2023 | Molecular Plant2023，16，8： | 0 |
| 12 | Ontology Driven Testing Strategies for IoT Applications显示文摘Internet-of-Things(IoT)has attained a major share in embedded software development.The new era of specialized intelligent systems requires adaptation of customized software engineering approaches.Currently,software engineering has merged the development phases with the technologies provided by industrial automation.The improvements are still required in testing phase for the software developed to IoT solutions.This research aims to assist in developing the testing strategies for IoT applications,therein ontology has been adopted as a knowledge representation technique to different software engineering processes.The proposed ontological model renders 101 methodology by using Protégé.After completion,the ontology was evaluated in three-dimensional view by the domain experts of software testing,IoT and ontology engineering.Satisfied results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for improvements.The Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices. | Muhammad Raza Naqvi Muhammad Waseem Iqbal Muhammad Usman Ashraf Shafiq Ahmad Ahmed T.Soliman Shahzada Khurram Muhammad Shafiq Jin-Ghoo Choi | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 13 | Ontological Model for Cohesive Smart Health Services Management显示文摘Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human life.There are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and telemedicine.These applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service management.The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users.The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services.There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model.The proposed ontological model is derived from different classes,relationships,and constraints to integrate health care services.This model is developed using Protégébased on each interrelated/correlated health care service having different values.Semantic querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge acquisition.The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model.Comparative to other smart health care services integration systems,the proposed ontological model provides more cohesiveness. | Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 14 | Roman Urdu News Headline Classification Empowered with Machine Learning显示文摘Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent.Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for writing.The communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information supply.English Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the past.This is due to the numerous complexities involved in the processing of Roman Urdu data.The complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured format.We have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be done.The author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),Long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different websites.The preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the news.After applying above mentioned different machine learning algorithms,results have shown that Multinomial Naïve Bayes classifier is giving the best accuracy of 90.17%which is due to the noise lexical variation. | Rizwan Ali Naqvi Muhammad Adnan Khan Nauman Malik Shazia Saqib Tahir Alyas Dildar Hussain | 2020 | Computers, Materials & Continua2020，，11： | 0 |
| 15 | Detection of SARS-CoV-2 Eta VOI among international travelers using COVIDSeq-NGS显示文摘Rationale:SARS-CoV-2 has been identified as a highly infective and contagious viral infection.The SARS-CoV-2 pandemic has been spread worldwide and affected more than 210 countries.Globally,the fast spread of novel SARS-CoV-2 variants has been mostly attributed to international travel.Patient concerns:We are reporting the genomic evidence of SARSCoV-2 Eta VOI among two international travelers.Both travelers were males from Nigeria aged 24 and 34 years and both were asymptomatic.Diagnosis:The nasopharyngeal swab samples were in both travelers positive by real-time RT-PCR followed by COVIDSeq-NGS.Interventions:Paracetamol 3 times daily for 5 days.Outcomes:Patient recovered completely within 10 days and discharged after 14 days of quarantine duration.Lessons:This report highlights genomic variation of SARSCoV-2 among the travelers.For managing the present health crisis,molecular identification of viral variants present in different geographical locations will be very helpful. | Mati Ur Rehman Rooh Ullah Narmeen Arshad Muhammad Ammad Qurat Ul Ain Anam Razzak Muhammad Yousaf Shabana Perween Syed Sajjad Naqvi Tarique N.Hasan | 2022 | Asian Pacific Journal of Tropical Medicine2022，15，11： | 0 |
| 16 | Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine显示文摘The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries,including the transportation sector.The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features.One of these tasks is to ensure that vehicles are autonomous,intelligent and able to grow their repository of information.Machine learning has recently been implemented in wireless networks,as a major artificial intelligence branch,to solve historically challenging problems through a data-driven approach.In this article,we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field.Deep Extreme Learning Machine(DELM)framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments.The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions.It leads to the concept of vehicle controller making self-decisions.The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations.This overcomes inadequacy of the current in-vehicle route-finding technology and its support.All the relevant route-related information for the ride will be provided to the user based on its availability.Using the DELM method,a high degree of precision in smart decision taking with a minimal error rate is obtained.During investigation,it has been observed that proposed framework has the highest accuracy rate with 70%of training(1435 samples)and 30%of validation(612 samples).Simulation results validate the intelligent prediction of the proposed method with 98.88%,98.2%accuracy during training and validation respectively. | Dildar Hussain Muhammad Adnan Khan Sagheer Abbas Rizwan Ali Naqvi Muhammad Faheem Mushtaq Abdur Rehman Afrozah Nadeem | 2021 | Computers, Materials & Continua2021，，1： | 0 |
| 17 | Explainable Artificial Intelligence Solution for Online Retail显示文摘Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business. | Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 18 | Mobile Devices Interface Adaptivity Using Ontologies显示文摘Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs. | Muhammad Waseem Iqbal Muhammad Raza Naqvi Muhammad Adnan Khan Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 19 | Suitability of VVC and HEVC for Video Telehealth Systems显示文摘Video compression in medical video streaming is one of the key technologies associated with mobile healthcare.Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality.This paper presents a comparative study between High Efciency Video Coding(HEVC)and its potential successor Versatile Video Coding(VVC)in the context of healthcare.A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Denition(FHD)videos.The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos.Our results and ndings show that VVC clearly outperforms HEVC in terms of achieving higher compression,while maintaining high quality in FHD videos.VVC requires upto 40%less bitrate for encoding an FHD video at excellent perceptual quality.We have provided rate-quality curves for both encoders and a degree of overlap across both codecs in terms of perceptual quality.Overall,there is a 71%degree of overlap in terms of quality between VVC and HEVC compressed videos for eight different test conditions. | Muhammad Arslan Usman Muhammad Rehan Usman Rizwan Ali Naqvi Bernie Mcphilips Christopher Romeika Daniel Cunliffe Christos Politis Nada Philip | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 20 | Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou’s 5 Step Rule显示文摘Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation. | Shaheena Khanum Muhammad Adeel Ashraf Asim Karim Bilal Shoaib Muhammad Adnan Khan Rizwan Ali Naqvi Kamran Siddique Mohammed Alswaitti | 2021 | Computers, Materials & Continua2021，，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员