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Conversion of Bangla language to another native language and another language to Bangla language using Universal Networking Language (UNL) is highly demanding due to rapidly increasing the usage of Internet-based applications. UNL has been used by various researchers as an inter-lingual approach for an Automated Machine Translation (AMT) scheme. This article presents a novel work on construction of EnConverter for Bangla language with a special focus on generation of UNL attributes and resolving relations of Bangla text. The architecture of Bangla EnConverter, algorithms for understanding the Bangla input sentence; resolution of UNL relations; and attributes for Bangla text/language are also explained in this article. This article highlights the analysis rules for EnConverter and indicates its usage in generation of UNL expressions. This article presents the results of implementation of Bangla EnConverter and compares these with the system available at Russian and English Language Server.

The people in Bangladesh and two states (i.e., Tripura and West Bengal) in India, which is about 230 million of the world population, use Bengali as their first dialect. However, very few numbers of resources and tools are available for this language. This paper presents a Bangla DeConverter to extract Bangla texts from Universal Networking Language (UNL). It explains and illustrates the different phases of the proposed Bangla DeConverter. The syntactic linearization, the implementation of the results of the proposed Bangla DeConverter, and the extraction of a Bangla sentence from UNL expressions are presented in this paper. The Bangla DeConverter has been tested on UNL expressions of 300 Bangla sentences using a Russian and English Language Server. The proposed system generates 90% syntactically and semantically correct Bangla sentences with a UNL Bilingual Evaluation Understudy (BLEU) score of 0.76.

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing.

Diabetic Retinopathy is a human eye disease that causes damage to the eye's retina and may ultimately result in complete blindness. Early detection of diabetic retinopathy is needed to avoid complete blindness. Physical tests, such as visual acuity test, dilation of pupils, optical consistency tomography, is used to detect diabetic retinopathy. However, it is costly in terms of time and might affect the patients. In these consequences, this paper detects the presence of Diabetic Retinopathy in the human eye using a machine learning algorithm. The proposed method applies classification algorithms on several features (e.g., optical disk diameter, lesion-specific (microaneurysms, exudates) or presence of hemorrhages) of an existing Diabetic Retinopathy dataset. Then the features were extracted and used for the final decision making to predict the presence of diabetic retinopathy. The proposed system used Decision Tree, Logistic Regression. Support Vector Machine for the prediction. The proposed method achieved 88% accurate results which is much better than the existing works. Moreover, the proposed method achieves a better score in precision and recall which are 97% and 92%, respectively compared to the existing result 72% and 63%, i.e., more the 25% in each category on average which proves the enormousness of the proposed method.

E-commerce reviews and comments about specific products disclose consumer’s perceptions as well as attitudes. This attitudes expressed by the consumer’s seem to be most useful for the new customers who is interested on any product. Meanwhile, an ever increasing number of reviews and comments are being stored daily and the amount of people buying goods online are increasing in a great extent. User emotions record associated with every products is beneficial for both the makers as well as customers. But with the increasing number of datasets in the e-commerce websites, it has almost become impossible for manual analysis and without automated machine learning approach it can’t be even imagined. Therefore, improvement in the field of machine learning approaches must be accomplished. Realizing the worth of this, this work proposes a hybrid machine learning approach by incorporating different machine learning approaches. Own Bangla StopWords database consisting of approximately 900words have also been concentrated in this work. Initially, input data are tokenized using python NLTK library and filtered using StopWords created. Then conversion of data to numerical from string are conducted using TF-IDF (Term Frequency–Inverse Document Frequency) information retrieval mechanism and finally trained using K-nearest neighbor (KNN) with Support Vector Machine (SVM). Our proposed approach Normalization along with StopWords filtering embedded KNN based SVM demonstrates superiority after a comparative study with Principle Component Analysis (PCA) with Convolutional neural network (CNN), Random Forest, Logistic Regression.

with the evolutionary improvement and establishment of social networking sites huge amount of data associated with user's perceptions, emotions, posts, comments, reactions etc. Meanwhile, extraction of valuable information like user's opinion, sentiment from those massive and higher dimensional dataset have become one of the most crucial, complex and convoluted tasks nowadays. Moreover, immediate realization of user's perspective regarding any topic or product can ensure feasible as well as user friendly environment throughout the social networking sites. In this respect, from current point of view available natural language processing approaches are less applicable because of their limitations. Therefore, effective machine learning approaches long with the concept of NLP need to be invented to negotiate with this sort of situation. Realizing worth of this situation and for negotiating with the concept of big data, we have proposed a machine learning approach by combining unsupervised machine learning approach (Self-Organizing Map) for clustering as well as dimensionality reduction and classification approach (Adam Deep Learning) together. Moreover, for better outcome we have adopted MaxAbsScaler initially. For further clarification we have also considered principal component analysis based deep learning, Logistic Regression, Normalization less SOM based CNN mechanism for comparative study. To ensure effective performance and better feasibility in case of big data we have also employed our approach on different sizes of social network datasets. Overall analysis and demonstration interprets superiority of our proposed approach with accuracy of 88.83%.

This paper demonstrates the reversible logic synthesis for the unidirectional, bidirectional and universal barrel shifters. The proposed shifters are constructed using only Fredkin and Feynman double gates. Initially, these two gates are designed and schematized using standard low power p-MOS 901 and n-MOS 902 models with average channel length 45 nm, delay 0.030 ns and density 1200 kgates/mm$$^2$$2. These schemas can detect faulty signal in its primary outputs. Thus, all the proposed shifters inherently tolerate single level fault. Moreover, the presented generalized algorithm for the proposed unidirectional barrel shifter has further been used to build base structures of the proposed bidirectional and universal shifters. In addition, lower bounds on the numbers of constant inputs and garbage outputs of the reversible barrel shifter have been proposed. It has been evidenced that the proposed circuits are constructed with these optimum constant inputs and garbage outputs. The simulation results prove the functional correctness of the proposed circuits. The comparative results show that the proposed designs perform much better and have significantly better scalability than the existing approaches.

Multi-instance features measurement is an important step in identifying characteristics that are bound to various experimental events. In biological data processing, a set of critical factors is responsible for several diseases. Computational simulation will help to design an optimal tool for cost-effective drug design. In this regard, the processing of big data is valuable for efficient simulation. Recent experimental results generate huge amounts of related data. In the current work, noisy data have been treated with three filtering techniques: cross-validated committees filtering (CVCF), iterative partitioning filtering (IPF) and ensemble filtering (EF). A comparison was made of these three filtering approaches. The filtered datasets were normalized. The repeated application of three normalization techniques removed the irregularities and structured the datasets. Wide ranges of comparisons were made among these three normalization techniques. After being appropriately structured, these normalized datasets were transformed accordingly with three different transformation processes: rank transformation, nominal to binary transformation and Box-Cox transformation. To prevent false positive and false negative outcomes of the experiments, certain key aspects were considered: accuracy, sensitivity and F-measures. Accuracy of the experiments relates to the level of precise detection of certain factors; specificity allows the selection of the dominant factors; and sensitivity and F-measures are the ratio between the training and testing datasets. Detailed experimental analysis included a comparison study of the four classifiers for the deoxyribonucleic acid (DNA) dataset.

J. Conway's renowned evolutionary problem Game of Life attracts researchers for its capability to transfer the deputized patterns from a generation to its next. In this paper, we propose an efficient solution to this Game problem. In the implementation of the proposed solution a higher abstraction has been used to partition the program into serial and parallel regions. This abstraction improves the performances of the proposed scheme over the existing conventional schemes for all possible design approaches. The paper also illustrates the possible enhancements of the proposed solution to solve the complex combinatorial and biological problems of the real world.

Conversion from another language to native language is highly demanding due to increasing the usage of web based application. Firstly, the respective sentence of a native language is converted to Universal Networking Language (UNL) expressions and then UNL expressions can be converted to any native language. UNL system is developed for most of the languages already but a very little effort has been made to convert Bangla language to UNL expressions. In this paper, we have described Bangla verb, Verb root, Verbal Inflections and the finally developed the dictionary entries for the vowel ended roots and their respective verbal inflections. General Terms Universal Networking Language, Bangla Verb, Verb Root, Verbal Inflections

Primary Immune Thrombocytopenia (ITP) is a rare chronic disease. The registry of ITP contains all the necessary information for its research and analysis. The registry is thoroughly dependent on its underneath data collection platform. Generally, data collection for the ITP conducted on a routine basis and over a long period of time. However, most of the existing web based data collection approaches compromise the data security, data backup, flexibility and scalability. The REDCap based system can overcome these weaknesses as it has easier migration, validation, extraction and evolution processes. Thus, this paper develops a REDCap based data collection platform for the ITP registry. Then a detail comparative analysis between the two systems has been presented.

This paper presents the development of dictionary entries of Bangla repetition words to integrate them into an interlingua representation called universal networking language (UNL) processors. Here, we particularly emphasize on categorization and construction of dictionary for the repetition words with the goal to prepare a Bangla-UNL dictionary. We have outlined the format of dictionary entries of different types of repetition words that come from verb roots and assigned head words and attributes for those words based on the framework of UNL provided by the UNL center.

The Universal Networking Language (UNL) deals with the communication across nations of different languages and involves with many different related discipline such as linguistics, epistemology, computer science etc. It helps to overcome the language barrier among people of different nations to solve problems emerging from current globalization trends and geopolitical interdependence. Morphological analysis is applied to Bangla verbs in this paper and based on that some rules have been developed. These rules would be useful for converting Bangla sentences into Universal Networking Language (UNL).

Conversion from another language to native language is highly demanding due to increasing the usage of web based application. Firstly, the respective sentence of a native language is converted to Universal Networking Language (UNL) expressions and then UNL expressions can be converted to any native language. Already UNL system is developed for most of the languages, but there are no algorithms to convert Bangla sentence to UNL expressions. This paper presents newly developed algorithms for conversion of Bangla sentence into UNL expressions. Theoretically it proves its ability to convert from Bangla sentence to UNL expression.

The Universal Networking Language (UNL) is a world wide generalizes form of human interactive language in a machine independent digital platform for defining, recapitulating, amending, storing and dissipating knowledge or information among people of different affiliations. The theoretical and applied research associated with this interdisciplinary endeavor facilitates in a number of practical applications in most domains of human activities such as creating globalization trends of markets or geopolitical interdependence among nations. In our research work we have tried to develop Templates for Dictionary Entries of Bangla Roots, Krit Prottoy (primary suffix) and Kria Bivokti (verbal inflexion) which will help to create a doorway for converting the Bangla Sentence to UNL and vice versa and subside the barrier between Bangla to other languages.

Conversion from Bangla language to another native language using Universal Networking Language (UNL) is highly demanding due to increasing the usage of Internet based application. Since Bangla case structure plays a fundamental role in Bangla grammartical structures, this paper presents some rules for Bangla case structures that will be used to convert Bangla sentence to UNL expression. The theoretical analysis shows that the defined rules can be used successful conversion of Bangla sentence.

This paper describes a method for the development of Bangla Enconversion within the framework of the Universal Networking Language (UNL). We also discuss some issues and problems related to the UNL representation that affect the quality of generation. Additionally, the ling ware engineering is introduced as a technique to enhance the quality and increase the development efficiency. In this paper a pioneer work is proposes that analyzes the Bangla words morphologically from which we obtain Roots and Primary suffixes (Krit Prottoy) and develops some rules for Bangla Root, and Primary Suffix for the UNL. This paper also describes grammatical attributes for Bangla root and Primary suffixes and use of morphological analysis. UNL Expression of the Bangla attributes is also thoroughly discussed in this paper.

This paper focuses on morphological analysis of Bangla words to incorporate them into Bangla to universal networking language (UNL) processors. Researchers have been working on morphological structure of Bangla for machine translation and a considerable volume of work is available. So far, no attempt has been made to integrate the works for a concrete computational output. In this paper we particularly emphasize on bringing previous works on morphological analysis in the framework of UNL, with the goal to produce a Bangla-UNL dictionary, as UNL structures can provide, for any morphological analysis, a unified base to fit into already developed universal conversion systems of UNL. We explain the morphological rules of Bangla words for UNL structures. These rules tend to expose the modifications of parts of speech with regards to tense, person, subject etc. of the words of a sentence. Here we outline the morphology of nouns, verbs and adjective phrases only.

In this paper, we present a workable structure along with characteristic features of a subsystem that may become an integral part of a language server bridging Bengali and the Universal Networking Language (UNL). We try to assimilate the results of the research efforts of the UNL community and also of various machine translation projects. Vast information resources in different languages are available in the Internet, but the can not be shared (because of vastly due to the language barrier). And the UNL community is set to devise an effective and efficient system to diminish that barrier with an ultimate aim to allow automatic conversion of Web based resources in one member language to that in another member language. A good number of researchers in computational linguistics all over the world have already joined hands with the UNL initiators, and research groups representing most widely used natural languages are working intensively for the purpose. This paper is to demonstrate our pioneering efforts in the field of Bengali (Bangla). Here we here outline a possible Bangla-UNL dictionary, feature an annotation editor for Bangla texts, infer significant morphological, syntactic and semantic rules for parsing Bangla web documents in connection with conversion to the UNL, and show possible ways of future contribution towards the goal.

Deadlock is as condition that arises when multiple processes are waiting for resources held by other processes. A process cannot release the resources it holds until it can acquire the resources it is waiting for, but it cannot acquire these resources until another process releases them. There are few strategies to deal with the deadlock i.e. detection and recovery, deadlock prevention and deadlock avoidance etc. Deadlock can be avoided if certain information about processes is available in advance of resource allocation. For every resource request, the system sees if granting the request would mean that the system would enter an unsafe state, meaning a state that could result in deadlock. The system then only grants request that would lead to safe states. In this paper a method has been proposed to avoid the deadlock in multiprocessor system. A multiprocessor system with two processors has been used here. Two resources i.e. flash drive and printer have been used here. This paper explains how these processes request and release these resources and overcome unsafe state to enter the safe state. Finally, the deadlock avoidance algorithm for multiprocessors system is presented