Web Intelligence

Feedback on past queries is a valuable resource for improving retrieval performance on new queries. We introduce a modular approach to incorporating feedback information into given retrieval architectures. We propose to fusion the original ranking with those returned by rerankers, each of which trained on feedback given for a distinct, single query. Here, we examine the basic case of improving

The term "deep web" is a truly interesting point to be the discussion, specifically for researchers or observers of technology in the field of website applications. This is because the world of "underground" websites allows a wider range of website pages than "conventional" website pages such as Facebook, YouTube or Twitter that can be quickly searched or found using various search engines available today. From the 3 levels or layers especially the web surface, deep web and dark web (dark net), this article tries to review the pages of the website in the second layer, especially deep web, where all the web pages that are in the deep web layer cannot accessed using a "traditional" or "conventional" method that simply uses a standard browser or searches using an existing search engine. Furthermore, we also try to explain how the mechanism or mechanism that exists in the pages of the website that goes into the deep web area and learn how the internet users can access the pages of the website.

Gathering and analysing data about the usage of websites can assist in understanding customer behaviour, improving services and potentially increasing profit. However, changing technologies and evolving web analytics methods and processes present a challenge to organisations embarking on web analytics projects. In this paper we examine current theory and practice in the field of Web Analytics (WA) to identify methods and techniques, issues and challenges. Our analysis identifies the need to move from a data-centric view of WA towards a more user- and business-focused view. We propose a framework for establishing sustainable WA programmes, which widens the focus of the WA process to fully account for business requirements and to focus attention on achieving actionable outcomes.

The virtual world overflowing with the digital items which make the searching, choosing
and shopping hard tasks for users. The recommender system is a smart filtering tool for
generate a list of potential favorite items for the user to reduce the time needed by user to
choose among a huge number of choices in websites and facilitate the process.
In that context, this thesis presents a novel technique that combines the ideas of itembased
semantic similarity, n-criteria and multi-filtering criteria with the genetic-based
recommender system. The genetic algorithm is utilized in order to predict the best list of
items to the active user. Consequently, each individual in the population represents a
candidate recommendation list. Each list subjects to three tests to measure the quality of it.
The proposed system alleviates the effect of the sparsity and cold start problems and makes
the recommender system capable of generating recommendation without the need of using
a similarity metric or requires any additional information provided by the hybrid system.
Furthermore and due to the fact that there are many environments facing the
information overload problem, the author presents a new classification of the
recommender system based on the environment that is applied in.
The proposed system is evaluated against the state-of-the-art genetic-based
recommender system and the traditional techniques that used in collaborative filtering
recommender system. The results obtained show that the proposed method outperforms
these algorithms in prediction accuracy by 24.3%, recommendation quality by 33.5% and
performance (CPU time) by 45.4%. Moreover, the results showed that 69.5% of the
recommended items are truly favorite items to the active user. The remainders 30.5% of the
recommended items are potential favorite items.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

International Conference on Artificial Intelligence Advances (AIAD 2022) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.

The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence for presenting new advances and research results in the fields of Artificial Intelligence. The wide realms of research addressing any of the significant developments in the below-mentioned broad areas are welcome. The conference invites researchers to submit in the pivotal areas of publication interests.

Scope & Topics 3 rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and Machine Learning. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Machine Learning in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multidisciplinary and its applications will be covered during the conferences.

3rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and Machine Learning. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Machine Learning in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

12th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the
Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Many automatic translation works have been addressed between major European language pairs, by taking advantage of large scale parallel corpora, but very few research works are conducted on the Amharic-Arabic language pair due to its parallel data scarcity. However, there is no benchmark parallel Amharic-Arabic text corpora available for Machine Translation task. Therefore, a small parallel Quranic text corpus is constructed by modifying the existing monolingual Arabic text and its equivalent translation of Amharic language text corpora available on Tanzile. Experiments are carried out on Two Long ShortTerm Memory (LSTM) and Gated Recurrent Units (GRU) based Neural Machine Translation (NMT) using Attention-based Encoder-Decoder architecture which is adapted from the open-source OpenNMT system. LSTM and GRU based NMT models and Google Translation system are compared and found that LSTM based OpenNMT outperforms GRU based OpenNMT and Google Translation system, with a BLEU score of 12%, 11%, and 6% respectively.

Sentiment analysis is very recent trend in Web intelligence and information extraction. Now a days analysis of text is depend upon dictionary of emotions also depends upon creation of emotion dictionary, artificial design and mining from that dictionary. In this paper for classification and analysis we using Naïve Bayes and Support Vector Machine (SVM) techniques are used.

Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots cannot understand human intentions. This study aims at improving the robotic intelligence by training it to understand human intentions. Different from previous studies that recognizing human intentions from distinctive actions, this paper introduces a method to predict human intentions before a single action is completed. The experiment of throwing a ball towards designated targets are conducted to verify the effectiveness of the method. The proposed deep learning based method proves the feasibility of applying convolutional neural networks (CNN) under a novel circumstance. Experiment results show that the proposed CNN-vote method out competes three traditional machine learning techniques. In current context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

There is a critical need to design and develop tools that abstract away the fundamental complexity of XML-based Web services specifications and toolkits, and provide an elegant, intuitive, simple, and powerful query-based invocation system to end users. Web services based tools and standards have been designed to facilitate seamless integration and development for application developers. As a result, current implementations require the end user to have intimate knowledge of Web services and related toolkits, and users often play an informed role in the overall Web services execution process. We employ a self-learning mechanism and a set of algorithms and optimizations to match user queries with corresponding operations in Web services. Our system uses Semantic Web concepts and Ontologies in the process of automating Web services matchmaking. We present performance analysis of our system and quantify the exact gains in precision and recall due to the knowledge acquisition algorithms.

Abstract—Traditional movie gross predictions are based on numerical,and,categorical movie,data. But since the 1990s, text sources such as news have been proven to carry,extra and,meaningful,information,beyond,tradi- tional quantitative finance data, and thus can be used as predictive indicators in finance. In this paper, we use the quantitative news data generated by Lydia, our system for large-scale news analysis, to help us to

Journal of Emerging Technologies in Web Intelligence ISSN 1798-0461 Volume 4, Number 3, August 2012 Contents Special Issue: Web Data Mining Guest Editors: Richard Khoury Guest Editorial Richard Khoury 205 SPECIAL ISSUE PAPERS Query Classification using Wikipedia's Category Graph Milad AlemZadeh, Richard Khoury, and Fakhri Karray Towards Identifying Personalized Twitter Trending Topics using the Twitter Client RSS Feeds Jinan Fiaidhi, Sabah Mohammed, and Aminul Islam Architecture of a Cloud-Based Social Networking News ...

Disease Intelligence (DI) is based on the acquisition and aggregation of fragmented knowledge of diseases at multiple sources all over the world to provide valuable information to doctors, researchers and information seeking community. Some diseases have their own characteristics changed rapidly at different places of the world and are reported on documents as unrelated and heterogeneous information which may be going unnoticed and may not be quickly available. This research presents an Ontology based theoretical framework in the context of medical intelligence and country/region. Ontology is designed for storing information about rapidly spreading and changing diseases with incorporating existing disease taxonomies to genetic information of both humans and infectious organisms. It further maps disease symptoms to diseases and drug effects to disease symptoms. The machine understandable disease ontology represented as a website thus allows the drug effects to be evaluated on disease symptoms and exposes genetic involvements in the human diseases. Infectious agents which have no known place in an existing classification but have data on genetics would still be identified as organisms through the intelligence of this system. It will further facilitate researchers on the subject to try out different solutions for curing diseases.

9th International Conference on Artificial Intelligence and Applications (AIAPP 2022) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence forum for presenting new advances and research results in the fields of Artificial Intelligence.

Recommender systems usually propose items to single users. However, in some domains like Mobile IPTV or Satellite Systems it might be impossible to generate a program schedule for each user, because of bandwidth limitations. A few approaches were proposed to generate group recommendations. However, these approaches take into account that groups of users already exist and no recommender system is able to detect intrinsic users communities. This paper describes an algorithm that detects groups of users whose preferences are similar and predicts recommendations for such groups. Groups of different granularities are generated through a modularity-based Community Detection algorithm, making it possible for a content provider to explore the trade off between the level of personalization of the recommendations and the number of channels. Experimental results show that the quality of group recommendations increases linearly with the number of groups created.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional figures of speech via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper shows that a set of rules based on WordNet classes and an ontology representing human behaviour and properties, can be used to identify figures of speech due to the discrepancies in the semantic relations of the concepts involved. Based on this realization, the paper describes a method for determining poetic vs. non-poetic prepositional figures of speech, using WordNet class hierarchies. The paper also addresses the problem of inconsistency resulting from the assertion of figures of speech in ontological knowledge bases, identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem.