Reviewerly/exHarmony huggingface dataset:
Bangla Handwritten Words Recognition using Convolutional Neural Network

Convolutional Neural Network has become the advanced stage of technology for image recognition. It has been widely used for offline recognition of handwritten words of different languages such as English, Arabic, etc. This paper proposes a system for recognizing Bengali handwritten isolated characters and words using Convolutional Neural Network (CNN). To introduce an automated process that can be used to recognize a particular Bengali handwritten word to find the equivalent machine-readable format of that word is the actual purpose of this system. Based on the internal construction of the system, three different recognizing models have been introduced in this experiment. The 2-Layer CNN model, the Pure CNN model, and the 3-Layer CNN model have undergone several evaluation processes throughout the whole experiment to determine a stable model with satisfactory recognition accuracy. The 2-Layer CNN model has the highest accuracy of 72% when trained with the selected dataset. When trained with the same dataset, the Pure CNN model has produced 80% recognition accuracy for only ten epochs and the 3-Layer CNN model has produced a maximum 97% recognition accuracy. The models have also been trained and evaluated using the MNIST and Bangla-Lekha-Isolated datasets.
Bangla Handwritten Digit Recognition using RNN-CNN Hybrid Approach

The automatic recognition of handwritten English material has seen a lot of progress. However, research on automatic Bangla handwriting numerals recognition is far behind. Even the most effective recognizers now in use do not produce an adequate performance for real-world applications. This paper suggested a strategy based on deep neural networks. In this paper, we have used the BanglaLekha-Isolated handwriting dataset along with ResNet50 and DensNet201 models for benchmarking process. Then we proposed two new models one is a Gated Recurrent Unit (GRU) based and another one is a Hybrid of Convolutional Neural Network (CNN) and Convolutional Long Short-term Memory (ConvLSTM). As for our proposed GRU model it performs closely to the DensNet201 and REsNet50 models while requiring very few parameters compared to these two models. On the other hand, our proposed Hybrid ConvLSTM model outperforms both of the aforementioned benchmarking models. Finally, we have developed a new Bangla Handwriting Numerical dataset containing a total of seven thousand training, one thousand validation, and two thousand test images. Our proposed best-performing model (Hybrid ConvLSTM) achieves 98.84% accuracy in the test data of our dataset while the GRU model gained 91.33% test accuracy without any help of image preprocessing steps.
SMOTE Based Credit Card Fraud Detection Using Convolutional Neural Network

Nowadays, fraud correlated with credit cards became very prevalent since a lot of people use credit cards for buying goods and services. Because of e-commerce and technological advancement, most transactions are happening online, which is increasing the risk of fraudulent transactions and resulting in huge losses financially. Therefore, an effective detection technique, as the quickest prediction option, should be developed to deter fraud from propagating. This paper targeted to develop a deep learning (DL)-based model on SMOTE oversampling technique to predict the fraudulent transactions of credit cards. The system used three popular DL algorithms: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory Recurrent Neural Network (LSTM RNN), and measured the best performer in terms of evaluation metrics. However, the results confirm that the CNN algorithm outperformed both ANN and LSTM RNN. Additionally, compared to previous studies, our CNN fraud detection program recorded high rates of accuracy in identifying fraudulent activity. The system achieved an accuracy of 99.97%, precision of 99.94%, recall of 99.99%, and F1-Score of 99.96%. This proposed scheme can help to reduce financial loss by detecting credit card scams or frauds globally.
XTest: A Parallel Multilingual Corpus with Test Cases for Code Translation and Its Evaluation*

The advancement of artificial intelligence has significantly improved the automated generation, summarization and translation of source code. Researchers are working together to improve the automated coding tasks to bring the next generation of programming systems. Currently there are few corpora available for automated code generation, summarization and translation tasks but these corpora and models are limited within common languages like C, C++, Python, Java, etc. Also none of the existing corpus contains appropriate test cases to evaluate a program properly. This paper introduces XTest: A Parallel Multilingual Corpus with Test Cases for Code Translation. Our dataset contains parallel programs in 9 languages, Problem statement and test cases. Also in this work, we have built 30 systems to translate code between some high-resourced(C++, Python, etc) and low-resourced (Go, Ruby, etc) programming languages.
Design an Information Security Framework for University Automation System

Information Security frameworks can be denoted as the heart of protecting the information of any system. These frameworks ensure information security on a very broad scale, thus adopting them should also be sustainable. University automation system is one of such large-scale system having information of different confidentiality. The shared resources of a university automation system have been identified in this research, and as a result, a framework for information security has been proposed to ensure the system's overall safety.
Recognizing Bangladeshi Traffic Signs in the Wild

Traffic sign detection is an indispensable part of autonomous driving and transportation safety systems. However, accurate detection and recognition of traffic signs remain challenging, especially under extreme conditions, such as various weather and geo-social features. Though a lot of work has been done in the domain of Traffic Sign Detection and Recognition (TSDR), only a few of them focus on a dataset that comprises a wide variety of real-world challenges. Moreover, in the context of Bangladeshi traffic sign detection, the research is in a very preliminary stage, whereas, the geo-social features of Bangladesh add some unique challenges that are not seen in most parts of the world. In this regard, we have curated a dataset containing 2986 images belonging to 15 different classes of Bangladeshi traffic signs collected under conditions like varying distance, occlusion, blurry conditions, geological variations, varying lighting conditions, etc., reflecting several real-world scenarios. We have provided a thorough performance analysis with different state-of-the-art object detection algorithms where the YOLOv7 architecture has been found to be the best-performing model with a mAP value of 0.889, making it a suitable model for real-life applications.
An approach to build a Mars Rover: Mongol Ovizatrik 71

On the surface of Mars, autonomous rover navigation can considerably enhance everyday travel, especially when venturing into uncharted territory distant from the lander. Making a prototype for an effective and autonomous rover is the goal of this endeavor. The implementation of multiple perceptual blending together with the dissemination of the visuals, synthetic Mars environment, and atmospheric components. In this study, a Mars rover design was built that has a sensor array on the main body and a transportable structure for picking up soil samples and storing them for onboard testing. In addition, manual control is carried out with the aid of video that is broadcast from the rover to the base station, along with sensor arrays that include temperature, PH, and humidity sensors. Artificial intelligence is also addressed, along with speech recognition technology and real-time position sharing from the rover to the base station utilizing GPS. An overview of the development reveals that it is readily capable of carrying out all the required general activities. The rover is also able to traverse all of the aforementioned terrains, lift a substantial amount of mass without failing, and successfully recognize voices, enabling a far longer daily trip than is possible with purely ground-planned methods.
Largest Shift String Matching Algorithm: Blend of Berry Ravindran, Zhu-Takaoka and Back &amp; Forth Matching Algorithm

With the exponential growth of biological databases, it is a highly challenging and essential task to find the exact string from these databases. Various string pattern searching strategies played an essential role in solving string search problems. Mainly, the string pattern matching algorithm aims to reduce the running time which depends on attempts and character comparisons. Our paper proposed a new hybrid strategy, the Largest Shift Algorithm (LSA), to solve these string pattern matching problems more effectively. Our suggested new hybrid algorithm combines the most advantageous characteristics of Berry Ravindran, Zhu-Takaoka, and a customized Back & Forth Matching (BFM) algorithm. These three algorithms were chosen as they perform better in the tests for counting attempts and character comparisons. Three distinct types of algorithms were tested to analyze the performance of our proposed LSA algorithm, discussed in the literature which are BRR, maximum-shift, and quick-search algorithms. We have used English text, DNA, and protein sequences as data for their different nature. The proposed algorithm outperforms the previous algorithms in terms of performance such as runtime, total attempts, and comparisons of character.
Semantic Clustering of Bangla Natural Word using Different Word Embedding Techniques

Natural language processing is referred to as NLP that applies computational techniques for inter-communication between human and computer through human natural language on the basis of computer science, computational linguistic and artificial intelligence. The progression of NLP in different revolutionary techniques, word embedding has brought magnificent changes in the field of computational linguistic, statistical inference and so on. Semantic clustering can be interpreted as classify the group of identical objects that are semantically analogous. The main focus of the work is to manifest different word embedding techniques for semantic clustering of natural Bangla words. Earlier N-gram models were applied for the relevant field but dynamic word clustering models are currently popular due to the advancement of deep learning techniques because they speed up memory retrieval and decrease processing time. We discuss the effectiveness of Word2Vec, TF-IDF, FastText and GloVe word embedding models in this work and appraise the performance based on the models accuracy and competence.
Intelligent Door Controller Using Deep Learning-Based Network Pruned Face Recognition

Nowadays, our home is designed with various technologies which have increased our living comfort and offering more flexibility. Installing various technology in our Home makes it a smart home and we also call this installation process Home Automation. The popularity of Home Automation systems is increasing rapidly and it develops the quality of living. Home automation offers automatic light, fan, temperature, etc. control and also an automatic alarming system to alert the people, etc. Already there are various techniques have been used for implementing Home Automation. Here, in this paper, an intelligent door controller, an application of home automation is presented by using deep learning techniques. An intelligent door basically opens automatically and closes after a predefined time based on the person coming in front of the door. If a person is known then the door will be opened and after his/her entrance the door will be closed automatically. And if the person is not known then the door will remain closed. Here to identify the person, the person’s face is recognized by using deep learning. As well ass, Arduino and Servo motors are used to control the door opening or closing.
Performance Analysis of Hybrid Deep Learning Models in Sarcasm Classification

Sarcasm as an apparatus for exhibiting dissatisfaction, vexation, cynicism, shrewdness, mockery of ignorance, and foolishness has become ubiquitous in textual and electronic media. A sarcasm detection model, an automated method for classifying sarcastic data from multiple sources, is needed as a result of this effect of sarcasm on human sentiment perception. But the hardships associated with textual data processing apply to this task as well. For that reason, our work can be considered a three-phase classification method, where, the first phase is concerned with the assortment of text data which consist of Noisy and Well Structured text data. In the second phase, a method was devised for the conversion of textual data into numerical entities. The third phase is concerned with structuring our deep learning method for the precise classification of sarcastic data. We used FastText in this work to solve contextual problem due to information removal from dirty text data. FastText was employed with a standard CNN model, which was very quick but had generalization issues with 90.17% accuracy. Later, FastText was combined with an LSTM model, which improved generalization with 92.16% accuracy but costly in time consumption. In this study, we proposed a FastText model with CNN and LSTM that makes use of CNN’s advantages in terms of efficiency and LSTM’s advantages in terms of generalization with an accuracy of 91.29%.
Performance Evaluation of Different Word Embedding Techniques Across Machine Learning and Deep Learning Models

Sentiment analysis is one of the core fields of Natural Language Processing(NLP). Numerous machine learning and deep learning algorithms have been developed to achieve this task. Generally, deep learning models perform better in this task as they are trained on massive amounts of data. This, however, also poses a disadvantage as collecting sufficient amounts of data is a challenge and training with this data requires devices with high computational power. Word embedding is a vital step in applying machine learning models for NLP tasks. Different word embedding techniques affect the performance of machine learning algorithms. This paper evaluates GloVe, CountVectorizer, and TF-IDF embedding techniques with multiple machine learning models and proves that the right combination of embedding technique and machine learning model(TF-IDF+Logistic Regression: 87.75% accuracy) can achieve nearly the same performance or more as deep learning models (LSTM: 87.89%).
A Machine Learning Approach to Predict Movie Success from Youtube Trailer Comments

Social media use has increased to such levels in recent years that it has transformed into a trend-setting powerhouse, introducing subjects that would have previously remained outside of the public eye. Through people’s shared opinions and responses about a trend on social media, we hope to determine how long it can hold an audience’s attention on its own. We will analyze the sentiment of individuals toward a particular topic using the information gleaned from social media comments. Our work will be based on unreleased films and make predictions about how they will turn out when they are released. In this work, we have processed and examined accumulated reviews about a film to see whether the general public feels positively or negatively about it and to calculate the likelihood that a certain film will be a success. From this, we can infer how the success of a movie or product is influenced by both positive and negative attention before its release.
Designing a Bangla Parser using TRIE Based on Deterministic Finite Automata

We describe a new method of parsing Bangla language based on Deterministic Finite Automata (DFA) and implement the parser using a TRIE data structure. Hence we call the parser as TRIE parser. TRIE parser successfully parses sentences faster than other important parsing schemes as it needs no formal rules, no parameters and no Context Free Grammars (CFG). the scheme stores the Bangla grammar symbols or Pasts Of Speech (POS) as a state of the DFA and process a sentence following the operations of a DFA. If the set of POS symbols reaches to final state, then parsing is successful otherwise unsuccessful. The parser uses the grammar rules in compressed form hence it becomes very less space consuming. Therefore, it can be implemented in light weight fashion in main memory. The TRIE parser is compared with two other parsers and it shows that the proposed TRIE parser outperforms others in terms of processing time with an increasing number of sentences in the input paragraph. Necessary figures and examples are used to properly explain the proposed TRIE parser.
Aspect-Based Sentiment Analysis of Bangla Comments on Entertainment Domain

Low-resource natural language processing is getting more attention nowadays. Aspect-Based Sentiment Analysis (ABSA) from a high-resource language such as English becomes unchallenging because of sufficient datasets and experimentation tools. However, Aspect-Based Sentiment Analysis from low-resource languages such as Bangla is quite hard. So, many researchers are investing their time and knowledge in low-resource natural language processing. In this paper, we are proposing a Bangla Aspect-Based Sentiment Analysis model using Bangla natural language processing. We have collected 4012 Bangla text comments related to cricket, drama, movie, and music from YouTube. We have applied some very prominent supervised machine learning techniques such as Support Vector Classifier (SVC), Random Forest (RF), and Linear Regression (LR). We have achieved more than 75% accuracy in classifying positive, negative, and neutral sentiments and 80% accuracy in extracting aspects from Bangla texts. Finally, we used publicly available datasets to test our proposed model's generalizability. Furthermore, we find that our proposed approach surpasses earlier related research.
An Intelligent Approach Towards Legal Text-Documents Retrieval

Document retrieval is the process of discovering a set of documents related to a query or another document, as per comparable similarity to some significant extent. Such as legal law, cases, or judgments. The current work under the area of the Document retrieval domain focuses on creating a system that will examine various features or information contained in the supporting documents with the given query document and suggest the possible top-ranked documents which have a higher similarity score. This work is particularly useful in helping out the lawyers by suggesting similar cases or judgments and allowing them to explore significantly less number of previously recorded cases/judgments. The main focus of this work is to retrieve relevant documents for particular Supreme Court cases in India from a set of prior case documents. To categorize documents and do the document retrieval task, we have used a published data set of 2000 prior cases. For each distinct document, the current system lists a set of documents with their ranking scores using N similarity, Fast-text, and BERT. Then, the system returns the relevant documents based on their ranking score. In order to compare our system to other similar ones already in use, we computed the accuracy and recall of our system. We have discovered that various methods perform better in certain areas where a few systems have higher precision and others with higher recall values. Our developed system provides comparably better accuracy, recall, and mean average precision than other current systems.
Sentiment Analysis of Bengali Texts on Online Tech Gadget Reviews using Machine Learning

Online marketing and e-commerce companies are booming in Bangladesh in this age of internet technology. As more people were afflicted with the COVID-19 epidemic, internet purchasing became the primary channel for closure shopping and was considered the safest method. The enterprises were pushed to appear online. There are many online service providers, such beneficial for individuals, but it also calls into question the quality of the products with services. Therefore, it is simple for new clients to be deceived, when doing internet purchasing. The enormous volume of tech gadget review data that is generated online every day can be examined for the purpose of assessing public sentiment and assisting in market intelligence. While the study of sentiment classification has advanced greatly in languages with abundant resources, it is still in the preliminary stage for languages with limited resources, such as Bengali. This work proposes a model for classifying the sentiment on online Bengali tech gadget reviews into three basic categories- positive, negative, and neutral. For this purpose, around 6015 Bengali tech review data is collected. Various Machine Learning techniques are then applied along with different feature extraction techniques. After evaluating the performance, the Random Forest outperforms the rest of other techniques, having a maximum accuracy of 86.28%.
Abstractive Dialog Summarization using Two Stage Framework with Contrastive Learning

In the modern era, a large amount of text conversation data between two or more interlocutors is generated by different online service consumers every hour. Converting such a long conversation into a concise form is more useful for further analysis and can boost service quality when conducted in an efficient manner. Abstractive summarization models usually suffer from performance degradation due to the different objective functions used in the training and inference steps. Contrastive learning is a powerful technique for developing training objectives that are similar to evaluation metrics and thus improve performance. Two-stage framework with contrastive learning are gaining popularity to mitigate this gap but this approach is very daunting in the field because of its huge computation time and demand for memory usage. To address this issue, we propose an optimization in the two-stage framework architecture for dialog summarization using the ALBERT pre-trained model in the evaluator section which is more efficient with respect to the usage of resources. Our method significantly outperforms strong baselines on SAMSum and DialogSum dataset for abstractive dialog summarization task.
Video Captioning in Bengali With Visual Attention

Generating automatic video captions is one of the most challenging Artificial Intelligence tasks as it combines Computer Vision and Natural Language Processing research areas. The task is more difficult for a complex language like Bengali as there is a general lack of video captioning datasets in the Bengali language. To overcome this challenge, we introduce a fully human-annotated dataset of Bengali captions in this research for the videos of the MSVD dataset. We have proposed a novel end-to-end architecture with an attention-based decoder to generate meaningful video captions in the Bengali language. First, spatial and temporal features of videos are combined using Bidirectional Gated Recurrent Units (Bi-GRU) that generate the input feature, which is later fed to the attention layer along with embedded caption features. This attention mechanism explores the interdependence between visual and textual representations. Then, a double-layered GRU takes these combined attention features for generating meaningful sentences. We trained this model on our proposed dataset and achieved 39.35% in BLEU-4, 59.67% in CIDEr, and 65.34% score in ROUGE. This is the state-of-the-art result compared to any other video captioning work available in the Bengali language.