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11.
Fuzzy geographic information systems is a newly emerging field of computational intelligence. It combines fuzzy logic with spatial context. Most of the natural phenomena are fuzzy in nature. They show a degree of uncertainty or vagueness in their extent and attribute, which cannot be expressed by a crisp value. Agriculture is one of the fields of the spatial domain that needs to be described in fuzzy terms. Fertilizer is a key input for the agriculture sector. In this article, the spatial surfaces of fertilizers are developed for the wheat crop using a fuzzy decision support system. The algorithm of our system takes soil nutrients and cropping time as input, applies fuzzy logic on the input values, defuzzifies the fuzzy output to crisp value, and generates a fertilizer surface. The resultant output surface of fertilizer describes the amount of fertilizer needed to cultivate a specific crop in a specified area. The complexity of our algorithm is \(O(mnr)\) , where \(m\) is the height of the raster, \(n\) is the width of the raster, and \(r\) is the number of expert rules. 相似文献
12.
Diagnosis and management may present difficult problems in patients with colovesical fistulas. Symptoms in the urinary tract are most common, and cystoscopy, and cystography are the most valuable diagnostic procedures. It may not always be possible to demonstrate the fistula by diagnostic tests, and a high index of suspicion should be maintained in patients with inflammatory or neoplastic disease of the rectosigmoid area or bladder with recurrent cystitis. Definitive treatment should include resection of the fistula and diseased segment of the intestine. Both one stage and multistage procedures have their place in the treatment of this condition. There are specific criteria for success for a one stage procedure. 相似文献
13.
In this work,pure nickel and Ni-based nanocomposite coatings(N1-Al_2O_3,Ni-SiC and Ni-ZrO_2) were produced on steel substrate by using pulse electrodeposition technique.The industrial performance tests were conducted to evaluate the wear resistance,corrosion resistance,adhesion strength and wettability behaviour of newly developed coatings.Rolling contact ball-on-disc tribometer was used to assess anti-wear behaviour of these coatings under waterlubricated contacts.The results showed that the wear- and corrosion resistance properties of nickel alumina and Ni-SiC composite coatings significantly improved than that of pure Ni and Ni-ZrO_2 coatings.The adhesion and wettability results of Ni-Al_2O_3 composite showed better performance when compared to the rest of the coatings.The effects of incorporating nanoparticles on the surface microstructure,interface adhesion and distribution of the particles were also investigated.The coatings were characterized by using scanning electron microscopy,X-ray diffraction analysis and 3D white light interferometry.The wear failure behaviour of these coatings was further examined by post-test surface observation under optical microscope. 相似文献
14.
This paper addresses the question of theoretical versus practical performance of algorithms and data structures. It describes an experiment the first author has been using in his data structures course to achieve the primary objective of comparing theoretical behavior of algorithms with their actual performance. It also presents enhancements in performance evaluation experiments and a general methodology that can be used to develop experiments in senior or first-year graduate courses in data structures, algorithm analysis, software engineering, and operating systems 相似文献
15.
16.
Julfikar Haider Mahfujur Rahman M. S. J. Hashmi Mohammed Sarwar 《Journal of Materials Science》2008,43(10):3368-3376
Significant progress has been made in the past decade in plasma nitriding with a majority of the research work focusing on improving hardness and wear resistance of the nitrided surface through the reduction of nitriding temperature, pressure or time. Hard-solid lubricating coatings have also been extensively studied for lowering the wear rate and coefficient of friction of traditional hard coatings such as TiN by the combined effect of hardness and solid lubrication. In this study, the wear characteristics of low-temperature plasma-nitrided steel substrate performed using a Saddle-field fast atom beam source and TiN + MoS x hard-solid lubricant coating deposited by a closed-field magnetron-sputtering technique have been investigated. The thin hard layer in plasma-nitrided substrates exhibited much higher hardness and lower wear compared to the untreated substrate in pin-on-disc wear testing. In addition, the study of the wear track morphology of the nitrided samples evidenced significant reduction of deeper ploughing and plastic deformation due to higher hardness and load supporting of the nitrided layer. On the other hand, due to the incorporation of MoS2 in TiN coating, the wear resistance and coefficient of friction were greatly improved in TiN + MoS x coating compared to pure TiN coating. In contrast to TiN coating, a relatively smoother wear track with less abrasive wear also supported the beneficial effects of adding MoS2 in TiN coating. 相似文献
17.
Approaches generally used in defining constitutive relations for concrete are reviewed. A computer program developed for the three-dimensional finite element analysis of complex reinforced, prestressed, and refractory concrete systems is described. The constitutive models based on isotropic elastic, orthotropic elastic, and plasticity formulations, which are implemented in that program, are discussed in detail. The program incorporates nonlinear material properties, cracking in concrete, shear transfer in cracked reinforced concrete sections, and time dependent effects such as creep, shrinkage, and transient temperature distributions. A wide range of structural problems are analyzed to demonstrate the applicability of the computer program. Comparisons between predictions with different constitutive models, and between predictions and test results are made. 相似文献
18.
Anwer Mustafa Hilal Jaber S. Alzahrani Ibrahim Abunadi Nadhem Nemri Fahd N. Al-Wesabi Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani 《计算机、材料和连续体(英文)》2022,72(1):333-348
In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%. 相似文献
19.
Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K. Nour Abdelwahed Motwakel Abu Sarwar Zamani Mesfer Al Duhayyim 《计算机、材料和连续体(英文)》2022,72(1):713-726
The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate. 相似文献
20.
Mesfer Al Duhayyim Heba G. Mohamed Saud S. Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I. Eldesouki 《计算机、材料和连续体(英文)》2022,73(3):5011-5024
Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results. This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media. The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context. Initially, the input data is cleaned and pre-processed to make it compatible for further processing. Followed by, independent recurrent autoencoder (IRAE) model is utilized for the recognition and classification of CBs. Finally, the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work. To assuring the improved outcomes of the TLGODL-CBC approach, a wide range of simulations are executed and the outcomes are investigated under several aspects. The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches. 相似文献