Materials Science-Technology and Intelligent Engineering

This paper analyses the peculiar acting mechanism of artificial neural network (ANN) tech, and explores the great immediate significence for the intelligent sci-tech (IST) to research and develop the nano-tech.

Exploration and practice of intelligent engineering in Dadu River hydropower construction

In view of the difficulties in promoting intelligent management in the field of engineering construction,based on the concept of intelligent enterprise and the actual situation of hydropower project-construction management,this paper puts forward the basic concept,main features and general idea of intelligent engineering.Based on a review of the exploration,pilot construction and comprehensive practice of intelligent engineering in the hydropower construction of Dadu River,the practical results of the intelligent engineering of Dagangshan,Houziyan,Shaping-II and Shuangjiangkou hydropower stations are summarized.The technical system and management model of engineering an early-warning decision centre,intelligent dam project,intelligent underground project,intelligent electromechanical project,intelligent security control,intelligent service guarantee,intelligent environmental and water protection,and intelligent resource control in Shuangjiangkou hydropower station are introduced.It is proposed that intelligent engineering is a high integration of information technology,industrial technology and management technology.And it is pointed out that intelligent engineering will lead in the new development of water conservancy and the hydropower industry,and even the engineering-construction field,throughout the world.

Intelligent Petroleum Engineering

Data-driven approaches and artificial intelligence(AI)algorithms are promising enough to be relied on even more than physics-based methods;their main feed is data which is the fundamental element of each phenomenon.These algorithms learn from data and unveil unseen patterns out of it The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology.As the oil and gas industry is in the transition phase to oilfield digitization,there has been an increased drive to integrate data-driven modeling and machine learning(ML)algorithms in different petroleum engineering challenges.ML has been widely used in different areas of the industry.Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry;however,lack of two main features is noticeable.Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable.Attention must be given to data itself and the way it is classified and stored.Although there are sheer volumes of data coming from different disciplines,they reside in departmental silos and are not accessible by consumers.In order to derive as much insight as possible out of data,the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.

Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management

Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.

DATA: Digital Archiving and Transformed Analytics

As cloud service becomes more and more capable, available and powerful, wiseCIO has emerged from an innovative roadmap toward archival Content Management Service (aCMS) and massive Content Delivery Service (mCDS) in support of Anything-as-a-Service (XaaS) via Digital Archiving and Transformed Analytics (DATA);DATA aims to automate UBC with FAST solutions throughout a feasible, analytical, scalable and testable approach. This paper, based on the novel wiseCIO (web-based intelligent service engaging Cloud Intelligence Outlet), presents digital archiving and transformed analytics via machine learning automata for intelligent UBC processes to liaise with Universal interface for human-computer interaction, enable Brewing aggregation (differing from traditional web browsing), and engage Centered user experience. As one of the most practical aspects of artificial intelligence, machine learning is applied to analytical model building and massive and/or multidimensional Online Analytical Processing (mOLAP) for more intelligent cloud service with little explicit coding required. DATA is central to useful information via archival transformation and analytics, and utilizable intelligence for Business, Education and Entertainment (iBEE) in support of decision-making. As a result, DATA orchestrates wiseCIO to promote ACTiVE XaaS that enables accessibility, contextuality and traceability of information for vast engagement with various cloud services, such as aCMS (archival Content Management Service), COSA (Context-Oriented Screening Aggregation), DASH (Deliveries Assembled for fast Search and Hits), OLAS (Online Learning via Analytical Synthesis), REAP (Rapid Extension and Active Presentation), and SPOT (Special Points On Top) with great ease.

Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

Research of CRM Based on Customer Intelligence Engine

The discipline of business intelligence addresses a broad range of functional activities from data mining and statistical analysis to predictive modeling and reporting, and customer intelligence is the actionable output from an intelligence eco-system. In order to focus enterprise's attention on their customers satisfaction in the customer relationship management and make CRM system run more efficiently, a new concept of customer intelligence engine(CIE) is proposed at first time in the paper, the architecture of CIE is structured, the trigger of CIE is defined and described, the CIE-based CRM eco-system is also discussed.

Development of anti-phishing browser based on random forest and rule of extraction framework

Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information,such as login credentials and credit and debit card information,etc.It is carried out by a person masquerading as an authentic individual.To protect web users from these attacks,various anti-phishing techniques are developed,but they fail to protect the user from these attacks in various ways.In this paper,we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture.In this system,we use the rule of extraction framework to extract the properties or features of a website using the URL only.This list consists of 30 different properties of a URL,which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website.A dataset consisting of 11,055 tuples is used to train the model.These processes are carried out on the client-side with the help of a redesigned browser architecture.Today Researches have come up with machine learning frameworks to detect phishing sites,but they are not in a state to be used by individuals having no technical knowledge.To make sure that these tools are accessible to every individual,we have improvised and introduced detection methods into the browser architecture named as‘Embedded Phishing Detection Browser’(EPDB),which is a novel method to preserve the existing user experience while improving the security.The newly designed browser architecture introduces a special segment to perform phishing detection operations in real-time.We have prototyped this technique to ensure maximum security,better accuracy of 99.36%in the identification of phishing websites in realtime.

Development of anti-phishing browser based on random forest and rule of extraction framework

Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information,such as login credentials and credit and debit card information,etc.It is carried out by a person masquerading as an authentic individual.To protect web users from these attacks,various anti-phishing techniques are developed,but they fail to protect the user from these attacks in various ways.In this paper,we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture.In this system,we use the rule of extraction framework to extract the properties or features of a website using the URL only.This list consists of 30 different properties of a URL,which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website.A dataset consisting of 11,055 tuples is used to train the model.These processes are carried out on the client-side with the help of a redesigned browser architecture.Today Researches have come up with machine learning frameworks to detect phishing sites,but they are not in a state to be used by individuals having no technical knowledge.To make sure that these tools are accessible to every individual,we have improvised and introduced detection methods into the browser architecture named as‘Embedded Phishing Detection Browser’(EPDB),which is a novel method to preserve the existing user experience while improving the security.The newly designed browser architecture introduces a special segment to perform phishing detection operations in real-time.We have prototyped this technique to ensure maximum security,better accuracy of 99.36% in the identification of phishing websites in realtime.