Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classicatio

Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information,the document classication task becomes an interesting area for controlling data behavior.This paper presents a document classication multimodal for categorizing textual semi-structured and unstructured documents.The multimodal implements several individual deep learning models such as Deep Neural Networks(DNN),Recurrent Convolutional Neural Networks(RCNN)and Bidirectional-LSTM(Bi-LSTM).The Stacked Ensemble based meta-model technique is used to combine the results of the individual classiers to produce better results,compared to those reached by any of the above mentioned models individually.A series of textual preprocessing steps are executed to normalize the input corpus followed by text vectorization techniques.These techniques include using Term Frequency Inverse Term Frequency(TFIDF)or Continuous Bag of Word(CBOW)to convert text data into the corresponding suitable numeric form acceptable to be manipulated by deep learning models.Moreover,this proposed model is validated using a dataset collected from several spaces with a huge number of documents in every class.In addition,the experimental results prove that the proposed model has achieved effective performance.Besides,upon investigating the PDF Documents classication,the proposed model has achieved accuracy up to 0.9045 and 0.959 for the TFIDF and CBOW features,respectively.Moreover,concerning the JSON Documents classication,the proposed model has achieved accuracy up to 0.914 and 0.956 for the TFIDF and CBOW features,respectively.Furthermore,as for the XML Documents classication,the proposed model has achieved accuracy values up to 0.92 and 0.959 for the TFIDF and CBOW features,respectively.

A Novel Design of Octal-Valued Logic Full Adder Using Light Color State Model

Due to the demand of high computational speed for processing big data that requires complex data manipulations in a timely manner,the need for extending classical logic to construct new multi-valued optical models becomes a challenging and promising research area.This paper establishes a novel octal-valued logic design model with new optical gates construction based on the hypothesis of Light Color State Model to provide an efficient solution to the limitations of computational processing inherent in the electronics computing.We provide new mathematical definitions for both of the binary OR function and the PLUS operation in multi valued logic that is used as the basis of novel construction for the optical full adder model.Four case studies were used to assure the validity of the proposed adder.These cases proved that the proposed optical 8-valued logic models provide significantly more information to be packed within a single bit and therefore the abilities of data representation and processing is increased.

A New Action-Based Reasoning Approach for Playing Chess

Many previous research studies have demonstrated game strategies enabling virtual players to play and take actions mimicking humans.The CaseBased Reasoning(CBR)strategy tries to simulate human thinking regarding solving problems based on constructed knowledge.This paper suggests a new Action-Based Reasoning(ABR)strategy for a chess engine.This strategy mimics human experts’approaches when playing chess,with the help of the CBR phases.This proposed engine consists of the following processes.Firstly,an action library compiled by parsing many grandmasters’cases with their actions from different games is built.Secondly,this library reduces the search space by using two filtration steps based on the defined action-based and encoding-based similarity schemes.Thirdly,the minimax search tree is fed with a list extracted from the filtering stage using the alpha-beta algorithm to prune the search.The proposed evaluation function estimates the retrievably reactive moves.Finally,the best move will be selected,played on the board,and stored in the action library for future use.Many experiments were conducted to evaluate the performance of the proposed engine.Moreover,the engine played 200 games against Rybka 2.3.2a scoring 2500,2300,2100,and 1900 rating points.Moreover,they used the Bayeselo tool to estimate these rating points of the engine.The results illustrated that the proposed approach achieved high rating points,reaching as high as 2483 points.