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| 1 | Mortality and rebleeding following variceal haemorrhage in liver cirrhosis and periportal fibrosis显示文摘AIM To investigate mortality and rebleeding rate and identify associated risk factors at 6 wk and 5 d following acute variceal haemorrhage in patients with liver cirrhosis and schistosomal periportal fibrosis.METHODS This is a prospective study conducted during the period from March to December 2014. Patients with portal hypertension presenting with acute variceal haemorrhage secondary to either liver cirrhosis(group A) or schistosomal periportal fibroses(group B) presenting within 24 h of the onset of the bleeding were enrolled in the study and followed for a period of 6 wk. Analysis of data was done by Microsoft Excel and comparison between groups was done by Statistical Package of Social Sciences version 20 to calculate means and find the levels of statistical differences and define the mortality rates, the P value of < 0.05 was considered to be significant. RESULTS A total of 94 patients were enrolled in the study. Thirtytwo patients(34%) had liver cirrhosis(group A) and62(66%) patients had periportal fibrosis(group B).Mortality: The 6-wk and 5-d mortality were 53% and16% respectively in group A compared to 10% and 0%in group B(P value < 0.000 and < 0.004). In group A;a Child-Turcotte-Pugh class C and rebleeding within 5 d were significantly associated with 5-d mortality(P value< 0.029 and < 0.049 respectively) and Child- TurcottePugh class C was also a significant risk factor for 6-wk mortality(P value < 0.018). In group B; mortality was significantly associated with rebleeding within the 6-wk follow-up period and requirement for blood transfusion on admission(P value < 0.005 and < 0.049). Rebleeding:The 6-wk and 5-d rebleeding rate in group A were 56%and 25% respectively compared to 32% and 3% in group B(P value < 0.015 and < 0.002). Clinical presentation with encephalopathy was a significant risk factor for 5 d rebleeding in group A(P value < 0.005) while grade Ⅲperiportal fibrosis and requirement for blood transfusion on admission were significant risk factors for 6-wk rebleeding in group B(P value < 0.004 and < 0.02).CONCLUSION The 6-wk and 5-d mortality and rebleeding rate were significantly higher in patients with liver cirrhosis compared to patients with schistosomal periportal fibrosis. | Sara Elfadil Abbas Mohammed Abdelmunem Eltayeb Abdo Hatim Mohamed Yousif Mudawi | 2016 | World Journal of Hepatology2016，8，31： | 3 |
| 2 | Ureteric injuries:diagnosis,management,and outcome显示文摘 | Ahmed MA Ghali MD Elfadil MA | 1999 | J Trauma1999，46，1： | 1 |
| 3 | Effect of transglutaminase treatment on the functional properties of native and chymotrypsin-digested soy protein 显示文摘 | Elfadil Elfadl Babiker | 2000 | Food Chemistry2000，70，： | 1 |
| 4 | Fractionation,solubility and functional properties of cowpea (Vignaunguiculata) proteins as affected by pH and/or salt concentration显示文摘 | Ragab DiaelDin M Babiker Elfadil E Eltinay Abdullahi H | 2004 | Food Chemistry2004，84，： | 1 |
| 5 | Effect ofpolysaccharide conjugation or transglutaminase treatment on theallergenicity and functional properties of soy protein 显示文摘 | ElfadilE B Hiroyuki A Matsudomi N | 1998 | Journalof Agricultural and Food Chemistry1998，46，3： | 1 |
| 6 | Fractionation,solubility and functional properties of cowpea(Vigna unguiculata)proteins as affected by pH and/or salt concentration显示文摘 | DiaelDin M R Elfadil E B Abdullahi H E | | 0，，02： | 1 |
| 7 | The influ- ence of Cu2o crystal structure on the Cu20/ZnO hetero- junction photovoltaic performance显示文摘 | Elfadill N G Hashim M R Chahrour K M | 2015 | Superlattices and Microstructures2015，85，： | 1 |
| 8 | Effect of malt pretreatment and/or cooking on phytate and essential amino acids contents and in vitro protein digestibility of corn flour显示文摘 | AISHA SMF ELFADIL EB ABDULLAHI HET | 2004 | Food Chemistry2004，，88： | 1 |
| 9 | Effect of Polysaccharide Conjugation or Transglu-taminase Treatment on the Allergenicity and Functional Properties of Soy Protein显示文摘 | ELFADIL E B AZAKAMI H | 1998 | J Agric Food Chem1998，46，： | 1 |
| 10 | Effect of malt pretreatment and/or cooking on phytate and essential amino acids contents and in vitro protein digestibility of corn flour显示文摘 | Aisha S.M Fageer Elfadil E Babiker Abdullahi H El Tinay | 2004 | Food Chemistry2004，，2： | 1 |
| 11 | Effect of transglutaminase treatment on the functional properties of native and chymotrypsin-digested soy protein显示文摘 | Elfadil Elfadl Babiker | 2000 | Food Chemistry2000，，2： | 1 |
| 12 | Effect of malt pretreatment and/or cooking on phytate and essential amino acids contents and in vitro protein digestibility of corn flour显示文摘 | Aisha S M Fageer Elfadil E Babiker Abdullahi H El Tinay | 2004 | Food Chemistry2004，88，： | 1 |
| 13 | Effect of malt pretreatment and/or cooking on phytate and essential amino acids contents and in vitro protein digestibility of corn flour显示文摘 | Aisha S M Fageer Elfadil E Babiker Abdullahi H E1 Tinay | 2004 | Food Chemistry2004，88，： | 1 |
| 14 | Fractionation, solubility and functional properties of cowpea( Vigna unguiculata) proteins as affected by pH and/or salt concentration显示文摘 | DiaelDin M R Elfadil E B Abdullahi H E | 2004 | Food Chemistry2004，84，2： | 1 |
| 15 | Drift DetectionMethod Using DistanceMeasures and Windowing Schemes for Sentiment Classification显示文摘Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products.Due to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for researchers.The majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed detections.To improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams.This paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment classification.To improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream.Whenever a drift is detected,the proposed method builds and adds a new classifier to the ensemble.To add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble.To this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification results.Several experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy measures.The proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection studies.In all cases,the results show the efficiency of our proposed method. | Idris Rabiu Naomie Salim Maged Nasser Aminu Da’u Taiseer Abdalla Elfadil Eisa Mhassen Elnour Elneel Dalam | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 16 | Metaheuristics with Machine Learning Enabled Information Security on Cloud Environment显示文摘The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud environment.Cloud computing(CC)is used for storing as well as processing data.Therefore,security becomes important as the CC handles massive quantity of outsourced,and unprotected sensitive data for public access.This study introduces a novel chaotic chimp optimization with machine learning enabled information security(CCOML-IS)technique on cloud environment.The proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the network.The proposed CCOML-IS technique primarily normalizes the networking data by the use of data conversion and min-max normalization.Followed by,the CCOML-IS technique derives a feature selection technique using chaotic chimp optimization algorithm(CCOA).In addition,kernel ridge regression(KRR)classifier is used for the detection of security issues in the network.The design of CCOA technique assists in choosing optimal features and thereby boost the classification performance.A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several measures.The comparison study reported the enhanced outcomes of the CCOML-IS technique over the recent approaches interms of several measures. | Haya Mesfer Alshahrani Faisal S.Alsubaei Taiseer Abdalla Elfadil Eisa Mohamed K.Nour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 17 | An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing显示文摘Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testingmay help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However,a minimized and prioritized set of test cases may reduce the efforts and time required for testingwhile focusing on the timely delivery of the software application.In this research,a technique named Test Reduce has been presented to get a minimal set of test cases based on high priority to ensure that the web applicationmeets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research,the 100-Dollar prioritization approach is used to define the priority of the new requirements. | Rubab Sheikh Muhammad Imran Babar Rawish Butt Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 18 | QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities显示文摘In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart cities.It produces a drastic increase in the need for delivery of video services that are of high resolution,multiview,and large-scale in nature.However,this entity gets easily influenced by heterogeneous behaviour of the user’s wireless link features that might reduce the quality of video stream for few or all clients.The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences.Besides,multicast routing protocol with multiple constraints in terms of Quality of Service(QoS)is a Nondeterministic Polynomial time(NP)hard problem which can be solved only with the help of metaheuristic optimization algorithms.With this motivation,the current research paper presents a new Improved BlackWidow Optimization with Levy Distribution model(IBWO-LD)-based multicast routing protocol for smart cities.The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive.Besides,a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints.A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects.The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods. | Khaled Mohamad Almustafa Taiseer Abdalla Elfadil Eisa Amani Abdulrahman Albraikan Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Muhammad Imran Babar | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 19 | Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN显示文摘In recent days,internet of things is widely implemented in Wireless Sensor Network(WSN).It comprises of sensor hubs associated together through the WSNs.The WSNis generally affected by the power in battery due to the linked sensor nodes.In order to extend the lifespan of WSN,clustering techniques are used for the improvement of energy consumption.Clustering methods divide the nodes in WSN and form a cluster.Moreover,it consists of unique Cluster Head(CH)in each cluster.In the existing system,Soft-K means clustering techniques are used in energy consumption in WSN.The soft-k means algorithm does not work with the large-scale wireless sensor networks,therefore it causes reliability and energy consumption problems.To overcome this,the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic(LBC-COFL)algorithm is used.The main objective is to perform the lifespan by balancing the gateways with the load of less energy.The proposed algorithm is evaluated using the metrics such as energy consumption,throughput,central tendency,network lifespan,and total energy utilization. | Ahmed S.Almasoud Taiseer Abdalla Elfadil Eisa Marwa Obayya Abdelzahir Abdelmaboud Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 20 | Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification显示文摘The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal system.The understandings obtained from the data can assist municipal authorities handle assets and services effectually.At the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic.Besides,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability.Few of the commonly available wastes are paper,paper boxes,food,glass,etc.In order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and trash.Due to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of wastes.In this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart cities.The goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL techniques.The IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object classification.In addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a classifier.Moreover,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet model.In order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques with maximal accuracy of 0.993. | Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa Fahd NAl-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Radwa Marzouk | 2022 | Computers, Materials & Continua2022，，6： | 0 |
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