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However, the earlier isolated automation in each function have created a significant hindrance to smooth flow of information particularly because there has been a very high system incompatibility among the computerized systems. One of the most difficult problems in modern manufacturing is the instability of production systems to mimic the basis human capabilities such as adjusting appropriately to the ever-changing environment. From past studies, it has been possible to witness that advances in theory and application methodology of artificial intelligence techniques can overcome many of the obstacles existing in manufacturing discipline.

Today, the emergence of advanced computational methods in the artificial intelligence world such as genetic algorithms and neural networks, both inspired by the natural evolutionary process, has created a new field of research and application referred to as computational intelligence CI approach. Accordingly, this thesis focuses on the application of computational intelligence tools from two main perspectives. On the one hand, instead of the isolated automation of each manufacturing function, the CI techniques have been considered as powerful tools that allow all functions to operate within a fully integrated and intelligent manufacturing system.

Particularly, since process planning, is the main linking element between design and manufacturing functions, an automated and optimized process planning function creates a much more powerful environment that leads to the optimization of the whole process.

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Particularly, being able to integrate feature recognition and operation sequence optimization is an important element in the manufacturing system chain that can highly contribute to the automation and flexibility of the integrated design and manufacturing system. On the other hand, the computational intelligence techniques themselves have certain weakness of their own in solving the complex manufacturing process as a stand-alone form. In a hybrid form, however, they can either support or complement each other. To realize these two points, this thesis has focused on the development of theories and application methodologies of hybrid computational intelligence systems to model and optimize complex manufacturing processes.

The aim is to exploit the strong side of one computational intelligence tool and support or complement the weakness of the other. To this effect, qualitative analysis and reasoning of computational intelligence based hybrid systems are comprehensively discussed. The development theoretical backgrounds and methodologies are further used in key problem areas of the manufacturing system such as operation sequencing, machining economics analysis using multi-objective optimization approach and modeling and optimization of unstructured data collected from a non-conventional machining environment electro-discharge machining.

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The results from the hybrid CI application to model and optimize the electro-discharge machine show that the methodology is also important not only to the industrial activities using this technology, but also promotes further research and application in the discipline. The paper illustrates the implementation of Genetic Algorithm based knowledge creation technique which could be applied in higher education scenario quantifying knowledge on various parameters and eventually helping in making guided decisions in the environment.

Keywords: Classification, entropy, Genetic Algorithm, higher education, knowledge. Abstract: Cloud computing is the ubiquitous on demand service that has brought remarkable revolution in the commercialization of High Performance Computing HPC [1]. Quality of Service QoS is the vital factor that always seeks high attention. Efficient Resource allocation and management techniques along with advance load balancing approaches make a bigger difference in terms of total system throughput. Several frameworks and algorithmic approaches are proposed in these areas to improve the throughput.

CloudSim 3. Abstract: Early diagnosis of breast cancer can improve the survival rate by detecting cancer at initial stage. In this paper, an efficient content-based mammogram retrieval system is proposed, which helps in early diagnosis of breast cancer by classifying the current case mammogram and retrieving similar past cases mammograms already annotated by diagnostic descriptions and treatment results.

The proposed steps include cropping of mammograms for finding the region of interest ROI , feature extraction using wavelet based-complete local binary pattern W-CLBP and K-means clustering. Further, K-means generates the clusters based on this texture similarity of mammograms, and query mammogram features are matched with all cluster representatives to find the closest cluster.

Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure.

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So, at the searching time the query mammogram is searched only in small sub-set depending upon the cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Experiments on benchmark mammography image analysis Society MIAS database confirm that the proposed method has better say with respect to other four variants of texture features. Keywords: Computer-aided diagnosis, mammography, content-based image retrieval, 2D-DWT, local binary pattern, K-means clustering.

Recent Advances in Hybrid Brain-Computer Interface Systems: A Technological and Quantitative Review

Abstract: Abstract-Lives of Holy Messengers of Almighty Allah had been and still are the perfect code of conduct for their followers. Such divine quizzical order of narration and description is only discernable by scholars.

Many researchers concentrated on tracing logical connection among apparently diagonal scattered details pertaining to incidents, injunctions, accidents, and personalities. Today, the enormous expansion in field of computer sciences … and latest optimized algorithms, has truly enticed the researches to deal with such pristine subject matter.

To achieve the equilibrium of understanding was refuting the language and semantic barriers for miscellaneous users enjoying the diverse backgrounds and approaches. This study adorns the readers adequate critical evaluating and adhering approach to adjoin the scriptures. This study reduces gaps among the variety of followers of the divine religious, traces similarities, and ennobles the revealed stories and facts.

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This enterprise, unveils the differences among the followers of divine religions. This study anticipates amiably and ardently to set the fragmented descriptions in some straight description pattern for the understanding of the variety of users belonging to different divine religions.

This research collects the information from the sources of Holy Corpus. It sequences and completes the stories of the Holy Prophets. But a common average explorer often faces ordeals to build a simple story extracted from the Holy corpus.

2019: Recent Advances in big data machine learning in Hydrology

Till yet, there hardly exists a system available that could arrange the lives of Holy Prophets from different resources for their followers applying the latest tools, technology and algorithms. The existing studies were merely devoted to linguistics, words formatting semantics and pronunciation rules and usage. To overcome such challenges, a semantic network in conjunction with fuzzy cognitive maps FCM has been proposed which can trace, extract, collect organized and construct an intelligent story reducing the gaps among the multiple sources of Holy Corpus to cater similarity and interfaith harmony.

This study is an efficient conformity about life histories of Prophets, their regions, social life, law and impact over the humanity. Moreover, the robot should learn from its past experience. The principle of soft computing had been used in all the above areas of research by various researchers. A thorough literature review has been carried out in the present paper. Although a lot of studies had been reported in each of the above areas, there are scopes for further improvement. Some research issues have … been identified for future study.

Keywords: Intelligent robots, soft computing, robot vision, motion planning, adaptive controllers.

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Abstract: Semantic-based process mining is a useful technique towards improving information values of process models and analysis by means of conceptualization. The conceptual system of analysis allows the meaning of process elements to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge that can be used to determine useful patterns and predict future outcomes. Also, the paper quantitatively assess the level of accuracy of the classification results to predict behaviours of unobserved instances within the process knowledge-base by determing which traces are fitting or not fitting the discovered model by using a training set and test log for the cross-validation experiment.

Accordingly, the work looks at the sophistication of the proposed semantic-based approach and the discovered models, validation of the classification results and their influence compared to other existing benchmark techniques and algorithms for process mining. The experimental results and data validation ends with the supposition that a system which is formally encoded with semantic labelling annotation , semantic representation ontology and semantic reasoning reasoner has the capability to lift process mining analysis and outcomes from the syntactic level to a much more conceptual level, resulting in a mining approach that is able to induce new knowledge based on previously unobserved behaviours and a more intuitive and easy way to envisage the relationships between the process instances found within the available event data logs and the discovered process models.

Hybrid Intelligent Systems in Manufacturing Optimization

Keywords: Process mining, process modelling, semantics, annotation, ontology, fuzzy models, event logs. Shibboleth log in. IOS Press, Inc.