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排序方式: 共有3171条查询结果,搜索用时 15 毫秒
31.
Hasan Idrees Ibrahim M. A. ElSherbiny Meagan Hecket Qirong Ke Christian Staaks Ahmed S. G. Khalil Mathias Ulbricht Stefan Panglisch 《化学,工程师,技术》2021,93(9):1408-1416
Reusing wastewater from oil-related industries is becoming increasingly important, especially in water-stressed oil-producing countries. Before oily wastewater can be discharged or reused, it must be properly treated, e.g., by membrane-based processes like ultrafiltration. A major issue of the applied membranes is their high fouling propensity. This paper reports on mitigating fouling inside ready-to-use ultrafiltration hollow-fiber modules used in a polishing step in oil/water separation. For this purpose, in-situ polyzwitterionic hydrogel coating was applied. The membrane performance was tested with oil nano-emulsions using a mini-plant system. The main factors influencing fouling were systematically investigated using statistical design of experiments. 相似文献
32.
Linear polyethyleneimine (L-PEI)-based nanoparticles were synthesized via hydrolysis of poly-2-methyl-2-oxazoline (PMeOx), which was prepared by cationic ring-opening polymerization of the oxazoline five-membered ring. Herein, a kinetic study of the ring-opening polymerization reaction is discussed. The nuclear magnetic resonance spectrum of PMeOx verified the presence of repeating units and terminal groups in the polymer's structure. Molar ratios of PEI and PMeOx were characterized using size exclusion chromatography with low-polydispersity polymer chains as the controlled polymerization reaction. PEI and PMeOx exhibited narrow particle size distribution with hydrodynamic radii of 89 and 67 nm, respectively, as determined via dynamic light scattering analysis. In addition, atomic forces and scanning electron microscopy were used to investigate the topography of the PEI thin films. Poly(glycidyl methacrylate) P(GMA) was grafted onto a PEI chain in the presence of trimethylolpropane triacrylate (TMPTA) as the crosslinking agent to synthesize the P(GMA–PEI–TMPTA) tripolymer via free radical polymerization using gamma irradiation. The thermal characterization of the P(GMA–PEI–TMPTA) tripolymer was conducted using thermogravimetric analysis and differential scanning calorimeter. Generally, the thermal stability of the P(GMA–PEI–TMPTA) tripolymer was improved at low-glycidyl methacrylate concentrations. The prepared tripolymer could be used as effective packaging materials for electronics industries. 相似文献
33.
Selcuk Cevher Mustafa Ulutas Ibrahim Hokelek 《Journal of Network and Systems Management》2016,24(4):944-973
Real-time services require reliable and fault tolerant communication networks to support their stringent Quality of Service requirements. Multi Topology Routing based IP Fast Re-route (MT-IPFRR) technologies provide seamless forwarding of IP packets during network failures by constructing virtual topologies (VTs) to re-route the disrupted traffic. Multiple Routing Configurations (MRC) is a widely studied MT-IPFRR technique. In this paper, we propose two heuristics, namely mMRC-1 and mMRC-2, to reduce the number of VTs required by the MRC to provide full coverage for single link/node failures, and hence, to decrease its operational complexity. Both heuristics are designed to construct more robust VTs against network partitioning by taking their topological characteristics into consideration. We perform extensive experiments on 3200 topologies with diverse structural properties using our automated topology generation and analysis tool. Numerical results show that the amount of reductions in VT requirements get higher up to 31.84 %, as the networks tend to have more hub nodes whose degree is much higher than the rest of the network. 相似文献
34.
Vishanth Weerakkody Zahir Irani Habin Lee Nitham Hindi Ibrahim Osman 《Information Systems Management》2016,33(4):331-343
Citizens’ satisfaction is acknowledged as one of the most significant influences for e-government adoption and diffusion. This study examines the impact of information quality, system quality, trust, and cost on user satisfaction of e-government services. Using a survey, this study collected 1518 valid responses from e-government service adopters across the United Kingdom. Our empirical outcomes show the five factors identified in this study have a significant impact on U.K. citizens’ satisfaction with e-government services. 相似文献
35.
Ibrahim Abdelaziz Sherif Abdou Hassanin Al-Barhamtoshy 《Pattern Analysis & Applications》2016,19(4):1129-1141
The success of using Hidden Markov Models (HMMs) for speech recognition application has motivated the adoption of these models for handwriting recognition especially the online handwriting that has large similarity with the speech signal as a sequential process. Some languages such as Arabic, Farsi and Urdo include large number of delayed strokes that are written above or below most letters and usually written delayed in time. These delayed strokes represent a modeling challenge for the conventional left-right HMM that is commonly used for Automatic Speech Recognition (ASR) systems. In this paper, we introduce a new approach for handling delayed strokes in Arabic online handwriting recognition using HMMs. We also show that several modeling approaches such as context based tri-grapheme models, speaker adaptive training and discriminative training that are currently used in most state-of-the-art ASR systems can provide similar performance improvement for Hand Writing Recognition (HWR) systems. Finally, we show that using a multi-pass decoder that use the computationally less expensive models in the early passes can provide an Arabic large vocabulary HWR system with practical decoding time. We evaluated the performance of our proposed Arabic HWR system using two databases of small and large lexicons. For the small lexicon data set, our system achieved competing results compared to the best reported state-of-the-art Arabic HWR systems. For the large lexicon, our system achieved promising results (accuracy and time) for a vocabulary size of 64k words with the possibility of adapting the models for specific writers to get even better results. 相似文献
36.
Emad Shihab Akinori Ihara Yasutaka Kamei Walid M. Ibrahim Masao Ohira Bram Adams Ahmed E. Hassan Ken-ichi Matsumoto 《Empirical Software Engineering》2013,18(5):1005-1042
Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects—namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1–78.6 % and a recall in the range of 70.5–94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs. 相似文献
37.
Adil Fahad Zahir Tari Ibrahim Khalil Ibrahim Habib Hussein Alnuweiri 《Computer Networks》2013,57(9):2040-2057
There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques. 相似文献
38.
39.
Khaled M. Alalayah Fatma S. Alrayes Jaber S. Alzahrani Khadija M. Alaidarous Ibrahim M. Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim 《计算机系统科学与工程》2023,46(3):3121-3139
With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches. 相似文献
40.
Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics. 相似文献