DATA MODEL FOR BIGDEEPEXAMINATOR
Janusz Bobulski and Mariusz Kubanek Department of Computer Science, Czestochowa University of Technology, Poland
Big Data is a term used for such data sets, which at the same time are characterized by high volume, diversity, real-time stream inflow, variability, complexity, as well as require the use of innovative technologies, tools and methods in order to extracting new and useful knowledge from them. Big Data is a new challenge and information possibilities. The effective acquisition and processing of data will play a key role in the global and local economy as well as social policy and large corporations. The article is a continuation of research and development works on the design of the data.
Big data, intelligent systems, data processing, multi-data processing.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91602.pdf
CHINESE & IRANIAN ARTIFICIAL INTELLIGENCE IN LOW EARTH ORBIT TO THAAD SPACE WARS
Rory Lewis Department of Computer Science University of Colorado Colorado Springs, Colorado, 80919, USA
This paper addresses what the role of artificial intelligence will be in low earth orbit (LEO) and space war is and specifically, what China and Iran’s latest research in artificial intelligence on unmanned aerial vehicles (UAVs) for LEO space war domains has been, is, and strives to become. The author first presents testimony from scholars and space research scientists from many countries who all categorically state, without a trace of doubt, that all future space warfare will be in satellites and unmanned UAVs and how they in turn will rely heavily on artificial intelligence. This paper includes an analysis on China’s strengths in space artificial intelligence and, Iran’s systematic approach or arming its UAVs. The second portion of this research drills down into what are the specific mathematical theoretical research areas of artificial research for LEO space wars in various countries, including China. The author concludes with research strategies that will combat China’s dominance of space wars.
Artificial Intelligence, Machine Learning, Space War, Chinese artificial intelligence.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91406.pdf
AUTOMATED GENERATION OF COMPUTER GRADED UNIT TESTING-BASED PROGRAMMING ASSESSMENTS FOR EDUCATION
Sébastien Combéfis1, 2 and Guillaume de Moffarts2 1ECAM Brussels Engineering School, Brussels, Belgium 2Computer Science and IT in Education ASBL, Louvain-la-Neuve, Belgium
Automatic assessment of code, in particular to support education, is an important feature included in several Learning Management Systems (LMS), at least to some extent. Several kinds of assessments can be designed, such as exercises asking to “fill the following code”, “write a function that”, or “correct the bug in the following program”, for example. One difficulty for instructors is to create such programming exercises, in particular when they are somewhat complex. Indeed, instructors need to write the statement of the exercise, think about the solution and provide all the additional information necessary to the platform to grade the assessment. Another difficulty occurs when instructors want to use their exercises on another LMS platform. Since there is no standard way to define and describe a coding exercise yet, instructors have to re-encode their exercises into the other LMS. This paper presents a tool that can automatically generate programming exercises, from one single and unique description, and that can be solved in several programming languages. The generated exercises can be automatically graded by the same platform, providing intelligent feedback to its users to support their learning. This paper focuses on and details unit testing-based exercises and provides insights into new kinds of exercises that could be generated by the platform in the future, with some additional developments.
Code Grader, Programming Assessment, Code Exercise Generation, Computer Science Education
For More Details :
https://aircconline.com/csit/papers/vol9/csit91308.pdf
AN INTELLIGENT INTERNET-OFTHINGS(IOT) DOOR BELL SYSTEM FOR SMART NOTIFICATION ALERT
Melissa Qian1, Yu Sun2and Fangyan Zhang3 1Northwood High School, Irvine, CA 92620 2Department of Computer Science, California State Polytechnic University, Pomona, CA, 91768 3ASML, San Jose, CA, 95131
This paper presents an innovative redesign of a doorbell system in order to eliminate unnecessary ringing noise from users’ daily life. Employing artificial intelligence for face recognition, the IoT doorbell system define the visitors as complete strangers or someone who is expected. The next step operates based on this result; the doorbell system will either ring or send out notification to the users’ phone depending on the familiarity of the visitor and the user.
Machine Learning, Deep Learning, Artificial Intelligence, Wireless Network
For More Details :
https://aircconline.com/csit/papers/vol9/csit91205.pdf
TRUST MODELLING FOR SECURITY OF IOT DEVICES
LeiNaresh K. Sehgal1 , Shiv Shankar2 and John M. Acken3 1Data Centre Group, Intel Corp, Santa Clara, CA 2Chief Data Scientist, Maphalli, Bangalore, India 3ECE Department, Portland State University, Portland, OR
IoT (Internet of Things), represents many kinds of devices in the field, connected to data-centers via various networks, submitting data, and allow themselves to be controlled. Connected cameras, TV, media players, access control systems, and wireless sensors are becoming pervasive. Their applications include Retail Solutions, Home, Transportation and Automotive, Industrial and Energy etc. This growth also represents security threat, as several hacker attacks been launched using these devices as agents. We explore the current environment and propose a quantitative and qualitative trust model, using a multi-dimensional exploration space, based on the hardware and software stack. This can be extended to any combination of IoT devices, and dynamically updated as the type of applications, deployment environment or any ingredients change.
Edge Computing, Security, Adaptive learning, Trust model, Threats, Cloud Computing, Information Security
For More Details :
https://aircconline.com/csit/papers/vol9/csit90913.pdf
PREDICTING CUSTOMER CALL INTENT BY ANALYZING PHONE CALL TRANSCRIPTS BASED ON CNN FOR MULTI-CLASS CLASSIFICATION
Junmei Zhong and William Li Marchex Inc 520 Pike Street, Seattle, WA, USA 98052
Auto dealerships receive thousands of calls daily from customers interested in sales, service, vendors and jobseekers. With so many calls, it is very important for auto dealers tounderst and the intent of these calls to provide positive customer experiences that ensure customer satisfaction, deeper customer engagement to boost sales and revenue, and optimum allocation of agents or customer service representatives across the business. In this paper, we define the problem of customer phone call intent as a multi-class classification problem stemming from the large database of recorded phone call transcripts. To solve this problem, we develop a convolutional neural network (CNN)-based supervised learning model to classify the customer calls into four intent categories: sales, service, vendor or jobseeker. Experimental results show that with the thrust of our scalable data labeling method to provide sufficient training data, the CNN-based predictive model performs very well on long text classification according to tests.
Word Embeddings, Machine Learning, Deep Learning, Convolutional Neural Networks, Artificial Intelligence, Auto Dealership Industry, Customer Call Intent Prediction.
For More Details :
https://aircconline.com/csit/papers/vol9/csit90702.pdf
EVOLUTIONARY ALGORITHMS TO SIMULATE REAL CONDITIONS IN ARTIFICIAL INTELLIGENCE AS BASIS FOR MATHEMATICAL FUZZY CLUSTERING
Ness, S. C. C Evocell Institute, Austria
In present-day physics we may assume space as a perfect continuum describable by discrete mathematics or a set of discrete elements described by a programmed probabilistic process or find alternative models that grasp real conditions better as they more closely simulate real behaviour. Clustering logic based on evolutionary algorithms is able to give meaning to unlimited amounts of data that enterprises generate and that contain valuable hidden knowledge. Evolutionary algorithms are useful to make sense of this hidden knowledge, as they are very close to nature and the mind. However, most known applications of evolutionary algorithms cluster data points to one group, thereby leaving key aspects to understand the data out and thus hardening simulations of biological processes. Fuzzy clustering methods divide data points into groups based on item similarity and detects patterns between items in a set, whereby data points can belong to more than one group. Evolutionary algorithm fuzzy clustering inspired multivariate mechanism allows for changes at each iteration of the algorithm and improves performance from one feature to another and from one cluster to another. It is applicable to real life objects that are neither circular nor elliptical and thereby allows for clusters of any predefined shape.
In this paper we explain the philosophical concept of evolutionary algorithms for production of fuzzy clustering methods that produce good quality of clustering in the fields of virtual reality, augmented reality and gaming applications and in industrial manufacturing, robotic assistants, product development, law and forensics as well as parameterless body model extraction from CCTV camera images.
Artificial Evolution, Artificial Intelligence, Biology, Big Data, Cellular Automata, Data Interpretation and Analytics, Deep Learning, Features Selection, Genetic Algorithms, Generative Models, Machine Learning, Pattern Recognition, Robotic Process Automation,Simulation, Smart Systems, Virtual Machines, Visualization.
For More Details :
https://aircconline.com/csit/papers/vol9/csit90501.pdf