Reviewerly/exHarmony huggingface dataset:
Romagi of Shan Language to English Using Phrase-Based Statistical Machine Translation Model

Machine translation methods now offer top results for certain foreign languages. Machine Translation is also called the automatic translation process which uses a computerized system to convert one natural language into another. There are numerous studies using machine translation software for both foreign languages and ethnic languages of Myanmar (lower source languages). This article offers a paradigm for translating Shan (Romagi) phrases into English along with phrase-based analysis. The Moses toolkit is suggested for use with the phrase-based statistical machine translation (PBSMT) system for language pairs of Shan (Romagi) and English. GIZA++ was used to align the word-segmented source language and word-segmented destination language. For training, the proposed system uses 2-gram, 3-gram, and 5-gram language models to translate Romagi to English, this system uses KenLM and SRILM. The Moses decoder was used to decode the data and MERT applied the decoder's parameters for the fine-tuning process. Finally, BLEU scores are used to measure and compare the experimental outcomes of the system.
Feature Ranking based Dimensional Reduction Algorithm in High Dimensional Data Classification

Features are the most important things and huge influence on machine learning models. The removing of noisy and irrelevant features causes to increase the efficiency and accuracy of the model. The recent increase in feature dimension presents a significant challenge to many current methods of feature selection in terms of learning accuracy. In this work, combined correlation with distance measures approach is presented for the selection of informative features in high dimensional classification problem. Firstly, Pearson Correlation Coefficient is used to measure the relevance between attributes and class. And then, to calculate the redundancy of two feature vectors, Euclidean Distance is applied. To evaluate the presented approach, various classifiers with ten-fold cross validation are utilized based on five well-known datasets from UCI machine learning repository. The results show that the attribute subsets of all five well-known datasets generated by the proposed algorithm are more likely to be truly representative of classifier performance compared with original attribute set.
Performance of Machine Learning Using Preprocessing and Classification for Intrusion Detection System

Extracting valuable information from data is a challenging task. Many times, because of massive, redundant, incorrect and noisy outcomes, an analyst will end up with an incorrect classifier. It can also be due to misinterpreting the results and using incorrect procedures for a particular scenario. In our research, we discussed Naïve Bayes, one of the principal approaches to data mining. We did a comparative study of this approach as well. WEKA, which is open source software, was the instrument we used. The datasets used in the NSL-KDD dataset are KDDTrain+.arff and the datasets used in the DARPA dataset are DARPAWeek3-1.arff. ARFF is an abbreviation for Attribute Relation File Format, the simple dataset format that has been adopted by WEKA. The training set sample size is the amount of attributes that are present in the dataset and the amount of documents. Classification models shall be evaluated on the basis of the amount of class labels included in the dataset, the accuracy, the quantity and duration of the legislation created, the error rate and the standing status of the classification. The results show that, based on the amount of experiments we have performed, Naïve Bayes provides better accuracy with discretization than without discretization.
Building Annotated Image Dataset for Myanmar Text to Image Synthesis

Text to image synthesis is the translation of images from the input language text. The learning process can become easier when the spoken words can visualize with the images. It is one of the popular research field in combination of NLP and computer vision. Generative Adversarial Networks (GAN) have growth in the generation of images from text descriptions. We build the baseline system of Myanmar text to image synthesis and a type of annotated images dataset because there is not efficient annotated image dataset to be used in this implementation. It was created by using partial part of Oxford-102 flowers dataset. Word2Vec algorithms is used to convert word to vectors for the input sentence to GAN. GAN is applied for generation of images from Myanmar language text. This is the first text to image generation using GAN in Myanmar. The two-evaluation metrics are used to measure the quality of images. The quality of the generated images is evaluated using Inception score. The Frechet Inception Distance (FID) is used to measure the distance between the real images (images from original dataset) and the generated images from the model.
Performance Analysis on Block Size Valuation of Hyperledger Fabric Blockchain

Blockchain is an innovative technology and a decentralized ledger of immutable digital data. Hyperledger Fabric (HLF) blockchain is a permissioned ledger framework. The Fabric is made up of different parts, including endorsers, committers, validators, orderers and chaincodes. Although HLF is a scalable modular architecture, the configuration parameters of block size valuation in the orderer section impact the performance of a blockchain-based system. The ratio of batch timeout and block size should be balanced to increase the system performance. The block size valuation also affects the committed block height in the blockchain. In this paper, the effect of configuration factors such as batch timeout and block size (batch size) is analyzed to implement the optimizing blockchain-based storage system. Hyperledger Caliper tool is used to measure the transaction throughput and average latency for the system performance. The block height are studied based on the different batch timeout and block sizes for the numbers of concurrent users in an organization.
Machine Learning Based Ecommerce Model for Myanmar Hotel Industry

In Ecommerce world, social media becomes very important part for communicating customers to business because business can collect and prepare their industry according to the reviews of the customers through social media network. Ecommerce model is one important model from real business model in which customer can review products or services of the business companies. This model helps to predict and understand the behavior of customers where it supports the improvement quality of industry. In this research paper, Support Vector Machine (SVM), Reduced Error Pruning Tree (REPTree), and Convolutional Neural Network (CNN) classifiers are applied for classification of the travelers 'reviews to improve the Myanmar Hotel Industry. Reviews classification supports for the Ecommerce model to improve the performance of the business industry. Moreover, the performance analysis of the classifiers based on reviews classification are evaluated in this system. The classification result of the CNN classifiers reached 90.85% on this system.
Entrodeduper: Data Deduplication Using Entropy and Secure Hash Algorithm

Being a data science era, any bulk of data concerned with specific fields need to maintain in data centers with the help of rack server. For reducing storage for data center, the need to implement the data deduplication mechanism and keeping backup of those data in data center is becoming more important. In the case of reducing redundant data from the various servers in data center, the huge amount of data become the biggest challenge. As the data deduplication mechanism not only it can reduce the size of data to be stored or to be transmit but also it can give the backup and recovery features. This paper proposed the efficient data deduplication mechanism called EntroDeduper, which is the hybrid of entropy, secure hash algorithm and indexing. The input files are divided into blocks using file separator and the redundant block is removed by applying the entropy gain theory, which is used to get not only the significant reduction rate but also the full block coverage for reconstruction.
A Question and Answer System for Program Comprehension based on Encoder-Decoder Recurrent Neural Networks

Computer programming is an innovative cognitive tool that has transformed modern society. Two of the most widely used programming languages for web development are java and python. Therefore, source code comprehension of those programming is considered as an essential part and time-consuming task during software maintenance process. To support code comprehension process, the question and answer systems based on program comprehension have proposed. Recurrent Neural Network (RNN) based sequence-to-sequence model is one of the most commonly researched models to implement artificial intelligence question and answer system. However, it is not being applied widely in question and answer system for code comprehension. This system will learn using neural network where it uses bidirectional RNN as encoder and Luong Attention RNN as decoder. This system proposed a question and answer system to provide source code comprehension with encoder-decoder RNNs based on the code comprehension dataset: CodeQA.
Proposed Security Enhancement Conceptual Models Using Quantum Key Distribution for Future Cryptography

Today's Cryptographic Methods are based on Mathematical ideas and its complexity. Quantum Computers are getting increasing attention because of the tremendous power by harnessing the unique properties of Quantum Physics. When they will be able to run circles around today's computers, solving problems are infinitely faster than the world's most powerful Super Computers. So, for future Cryptographic Infrastructures, Quantum Cryptography is proposed by using Quantum Key Distribution (QKD). In this paper, it is constructed Enhancement Security Conceptual Models, QSC, Quantum Symmetric Conceptual Model and QAC, Quantum Asymmetric Conceptual Model so as to present an improvement of future cryptography by modeling being against to break Encryption Schemes from Quantum Algorithms combined with the Concepts of Mathematical and Physical Laws based on Classical Cryptography and Quantum Cryptography. Furthermore, the Security Analysis of the Performance Evaluation of the Proposed Approach is evaluated with Avalanche Effect in our Research.
Developing Scalable and Lightweight Data Stream Ingestion Framework for Stream Processing

According to the development of technology, enormous amount of data are being generated as a continuous basis from Social media, IOT devices, and web etc. This lead to big data era. Many researchers are paying attention on massive amount of data stream processing coming with a rapid rate to gain valuable information in real-time or to make immediate decision. Data Ingestion Stage is an important part in data stream processing system. It is responsible for the data collection from different locations and then deliver this data for processing. The most important requirement of data ingestion is to provide low latency, high throughput, and scalability with many data producers and consumers. It can influence on entire stream processing performance. In big data stream computing, speed at which data being created and explosive growth of data lead to new challenges. One challenge is to accurately ingest different stream data into a processing platform or data storage platform. Current existing data stream ingestion systems use a combination of Apache NiFi and Kafka. Apache Nifi is used for collection and preprocessing of structured and unstructured data feeds. Kafka is used for message distribution. However, processor such as MergeRecord in Nifi can be memory, I/O CPU intensive. As a result, when processing massive data streams creation with high speed can lead to a lot of memory effort, input/output bottleneck or central processing unit (CPU) bottleneck. It leads to impact on the performance of stream processing layer and it is not appropriate for time sensitive applications. In this paper, we propose to use a combination of StreamSets Data Collector and Kafka to collect and transform from various sources of structured and unstructured feeds.
E-Health System Based KED and DNA Cryptosystem

Today, technology change is very fast. Security and fast processing are necessary for data transformation. The health system is related to important roles in any country for its national interest. E-health care system include patient treatment result, diagnostic report. Patient Health Information (PHI). Patient health information is securely stored and accesses this data so that only authorized entities can update and retrieve the data over the Internet. Safety becomes an important issue when providing an electric healthcare system because confidential patient data is collected and shared by different users and organizations. Two types of cryptography: Symmetric Key and Asymmetric Key. Using the same keys is Symmetric key and Asymmetric key is using Separate key. In this paper, a Symmetric Key algorithm called as KED (Key Encryption Decryption) using modulo92 is used. Two keys are used in which one is a natural number which is relatively prime to 92. In this paper, KED (Key Encryption and Decryption) algorithm combines AES S-box and DNA cryptography for electronic healthcare security system. In this system, propose an MCS (Medical Center Server) that connects to the patient and the doctor. The proposed system is fast in computing and can withstand cryptographic attacks such as differential and linear cryptanalysis attacks.
Actionable Static Analysis Code Warnings Identification with SMOTE-based Classification

Automatic static code analysis warnings are produced by tools for static code analysis (like FindBugs), but they suffer from an excessive number of false alarms that are ignored by developers. Accurately identifying actionable static analysis code warnings helps developers be more efficient, and the static analysis tool can be made more useful. In reality, static analysis warning datasets have a class imbalance in actionable warning identification since there are a greater number of un-actionable (false alarms) than actionable alarms. Data sampling techniques address the issue of class imbalance in classification, which is a problem with the quality of the collected data. By producing synthetic instances that may be acted upon, the Synthetic Minority Oversampling Technique (SMOTE) and its variations assist in solving the problem of class imbalance. SVM, Random Forest, Naïve Bayes, and Decision Tree classifiers were created as classification models using the eight Java open-source software projects datasets (Cassandra, Jmeter, Commons.lang, Luence-solr, Ant, Tomcat, Maven, and Derby). The performance of the developed models is evaluated using precision, recall, f-measure, ROC and false positive rate. The analysis of the experimental findings revealed that the SMOTE not only addressed the issue of class imbalance but also enhanced the classification performance and reduced the false positive rate of the experimental models when it came to the identification of warnings.
Development of Online Banking System Based on Secure Captcha Image Using Visual Cryptography

Network Security and privacy are the main expected features in the field of online banking system. Internet banking (online banking) system provides banking services over the internet. It also provides convenience for users and plays a vital role in human lives. Securing online banking system is implemented with captcha image, BPCS steganography and visual cryptography. This system is constructed with registration and login processes between users and administrator of online banking system. Security input password text string is converted into an image by using captcha image generation process because other users are difficult to read not to see. This image is split based on the visual cryptography. The share 2 is hidden into the cover image by using BPCS steganography and sends to the user and the share1 is stored on the database. One time password (OTP) is sent to the user's mobile. This system provides high security for the online banking system.
Analysis of GPS Data Interpolation Methods for Driver's Behavior Detection

Interpolation of GPS points is one of the biggest challenges in the world of today. GPS signals may be lost because of many reasons including signal loss and battery loss of the transceiver. Our research work is intended to make an online detection and alert for driver's behavior. In order to do so, we need to find the current segment of the trajectory of the driving path, first. The segment is formed by joining the GPS points of the movement of the vehicle. If some between GPS points are lost, it is difficult to make a precise segment. Therefore, we need to find missing points between two consecutive points using interpolation methods. In this research work, erroneous GPS points of datasets are removed before doing any interpolation. After that, 7 different interpolation methods are analyzed and simulated using MATLAB.
An Adaptive Model with Blockchain Technology for Mortgage Industry in Myanmar

Attaining a mortgage can be very painful, costly and lengthy process especially when the policy and infrastructure is not easy to implement it. In Myanmar, the process of the origination for mortgage can be minimum 30 days to up to 90 days based on the bank that the customer chooses to apply. In addition, the documents that required to do all the process will take time to prepare and also there is no single truth of source that the bank can rely and validate it. The origination process involves more than 100 steps and do manual assessment and reconciliation among a variety of parties, including appraiser, borrower, lender, insurer and the list can go on. In Myanmar mortgage industry, there is no way to verify and validate the documents that are related to the apartment or real estate. On the other hand, it is not just origination process, the bank needs to consider the servicing, prediction of the financial loss based on customers' behavior that include both financial and non-financial information. Therefore, the whole process needs to be seamlessly going through from the very beginning for both customers (buyer and seller) and the bank. In addition, the bank needs to measure and validate every single information that collected from customers and external sources to predict how the customers' behaviors will look like in upcoming years. The proposed model is a solution to ensure high security while eliminating fraud, lowering operational risks and reducing administrative cost as well as a competitive advantage in the mortgage industry.
Colorizing Face Sketch Images for Face Photo Synthesis

It is a challenging task to convert a facial sketch into a photo image. There are numerous techniques for changing a face photograph into a sketch. But there hasn't been much research done on the conversion of face sketches into photos. Since the proposed system is ongoing research, a method for converting a face photo from a face sketch is presented in this experiment. For transforming a hand-drawn sketch into a color photo, we presented a method that uses a neural network model to convert. We intended to propose a novel generative adversarial network that performs well on transforming tasks. We applied effective loss functions by enforcing the quality of the produced photos. According to an analysis of the experimental data, the proposed method can produce accurate images from sketches, and the created photos have a big impact on how accurately people can be identified over a huge gallery set.
Myanmar-Wa Machine Translation using LSTM-based Encoder-Decoder Model

This work contributes to the quality evaluation of Machine Translation between Myanmar and Wa and provides the research on Long Short-Term Memory (LSTM)-based Deep Learning encoder-decoder mode. The Parallel Myanmar-Wa Corpus also includes over 20000 sentences based on Myanmar. According to previous research, Neural Machine Translation (NMT) is still needed for the development of Natural Language Processing (NLP) research field in Myanmar. Machine translation systems, especially statistical machine translation systems, require large amount of parallel corpora. The lack of a large parallel corpus for proposed system development is a major problem in development of machine translation. Myanmar and WA are very different languages not only in terms of basic sentence structure, but also in terms of syntax, grammar and morphology. This reason can cause great complexity in any NLP task. Furthermore, the analysis presented in this study provides valuable information for future studies using interethnic MT in Myanmar.
Detecting Malicious Users on Twitter Using Topic Modeling

Social networking sites like Twitter, Weibo and Facebook etc. have played a significant role in daily lives of people as they promote innovative ways to connect efficiently and exchange their knowledge. The benefits of these social network services cause them to expand their community rapidly. Most current social network sites face additional issue of coping with unauthorized users and their high levels of violence activities, which distribute fake news, worms and viruses, etc. to the genuine users. Spam distribution degrades user experience and also has a negative effect on server-side functions such as knowledge discovery, user activity analysis and service selection. In this paper, whitelist and blacklist are built which can help to distinguish malicious users and legitimate users. With the aid of blacklist and whitelist, we introduced two new features: malicious probability and legitimate probability. Evaluation has been carried out on the CRESCI-2015 dataset. Three machine learning classifiers like AdaBoost, Bagging and Random Forest. Random Forest obtained the highest 99.7% detection score.
DIAGNOSIS OF HIV-AIDS BY ADOPTING MULTI-LAYER MAMDANI FUZZY SOFT EXPERT SYSTEM

A Multi-layered Mamdani Fuzzy Soft Expert System is planned to diagnose HIVAIDS.&#x0D; The Proposed DHA-MLMFES System is an Automatic diagnose of HIV-AIDS exploitationMulti-layer Mamdani Fuzzy Soft Expert System (DHA-ML-MFES) knowledgeable System, willclassify the various stages of HIV-AIDS like No-HIV-AIDS, HIV Stage 1, 2, 3 and Aids. The ExpertSystem has seven input variables at layer-I and four input variables at layer-II. At layer-I input,variables are headache, fatigue, aching muscles, sore throat, swollen lymph nodes, red rashes, feverthat detects output condition of HIV-AIDS, infection, and different downside. The additional inputvariables at layer-II are PCR+, PCR+ P24+, P24+ Ab+ (ELISA), Ab+ Wb-inderminate that verify theoutput condition of HIV-AIDS like No-HIV-AIDS, HIV stage I, II, III or AIDS and alternativeinfection and downside. Results display investigation of the accuracy of the outcomes up to 94 percentof exploitation planned DHA- Multi-layer Mamdani Fuzzy Soft Expert Structure to set up theadvanced HIV-AIDS technique with help of physician assumptions and compile it to the medical Labof Roscommon University Hospital, Ireland.
AN APPRAISAL OF AUTOMATED HAND GESTURE RECOGNITION TECHNIQUES

Hand gesture segmentation for identification and prediction analysis has been aprevalent topic in the community of the researchers working in the field of image processing andcomputer vision. Hand gestures were used by humans to signify various expressions, moods andactions. The advent of intelligent devices has made hand gesture recognition even more interesting.Many researchers have worked on intelligent interfaces that use hand gestures for its operations.Various state of art techniques used for hand gesture recognition has been critically analyzed. Amethod using Hahn moments and neural networks has been proposed. A subsequent comparative studyhas been drawn in terms of performance of the proposed and other competitive techniques. It wasobserved that the proposed technique was quite accurate and efficient.