Issues and Challenges in Advertising on the Web

One of the big surprises of the 21st century has been the ability of all sorts of interesting Web applications to support themselves through advertising, rather than subscription. While radio and television have managed to use advertising as their primary revenue source, most media – newspapers and magazines, for example – have had to use a hybrid approach, combining revenue from advertising and subscriptions.  A venue for on-line advertising has been search, and much of the effectiveness of search advertising came from the “adwords” model of matching search queries to advertisements. This paper presents the algorithms for optimizing the way of matching search queries to advertisements is done. The algorithms discussed are of unusual type; they are greedy and they are on-line which are used to tackle the adwords problem.DOI:http://dx.doi.org/10.11591/ijece.v4i5.662

A Predictive Model for Mining Opinions of an Educational Database Using Neural Networks

Assessing the performance of an educational institute is a prime concern in an educational scenario. Educational Data Mining (EDM) considers several tasks originated from an educational context. One of the tasks identified is providing feedback for supporting instructors, administrators, teachers, course authors in decision making and thereby enable them to take appropriate remedial action. In this research, we have developed a prototype Neural Network Model which is trained to predict the performance of an educational institution. A Multilayer Perceptron Neural Network (MLP) model had been developed for this proposed research. The network is trained by back propagation algorithm. Data was obtained from a well-defined questionnaire consisting of 14 questions in the domains namely Academic Schedule, International Exposure, Jobs and Internship, Quality of the college, and Life at Campus. The results of these questions have been taken as inputs and performance of the institute has been considered as the output. To, validate the results generated by the network, statistical techniques have been used for the purpose. In this proposed research performance of an educational institution has been predicted. The results generated by the Neural Network and the statistical techniques have been compared in this research and it is observed that, both the methods have generated accurate results. The results have been considered based on the Normalized System Error (NSE) values of the network. A prototype Neural Network model has been developed to assess the performance of an educational institution

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations