Reviewerly/exHarmony huggingface dataset:
Blockchain-based Integrated Application for Forged Elimination of Hiring System using Hyperledger Fabric 2.x

Over time, how we used to keep track of our academic and work certificates has led to problems in terms of security and authenticity. The academic and experience certificate that a person gets over the course of their life are kept by centralized administrations with little to no connection with others. It becomes challenging to gather all these certificates from multiple institutions, arrange them together, and apply for a job. Because certificate forgery is so common, companies have a hard time getting official certifications, hurting the relationship between academia and business. The job market and educational institutions must be more efficient and open. Therefore, we made a blockchain-based integrated education-industry cooperative employment system where educational institutions and businesses can upload information and permit recruiters to use it. The recruiter can post job openings, and applicants looking for a job can apply by generating their CV. In our proposed method, we chose Hyperledger Fabric because of its ability to manage document permissions, transaction speed, scalability, no transaction fees, and other properties.
An Integrated Embedded System Towards Abusive Bengali Speech and Speaker Detection Using NLP and Deep Learning

Intelligible speech, while it provides an excellent means of communication for humans and sets us apart from other lifeforms, our abuse of speech creates deep and lasting issues in our society. The use of derogatory language has a significant impact not only on children’s mental health but also on adults, for instance, in an abusive work environment. Accountability for such actions is one of the key steps toward maintaining a healthy atmosphere or at least making it less frequent. In this paper, we describe our work on detecting abusive or hate speech in Bangla in real time. Our system converts the speech to text and then uses NLP and deep learning to detect such occurrences in real-time. Also, if the voice is registered on our system, it identifies the person engaging in abusive words, opening ways to greater workplace accountability. We also describe our mobile application and the microcontroller-based standalone embedded system that can be deployed in target places (for instance, daycare centers, schools, workplaces, etc.) to record audio and detect the abusive speech and the speaker in real-time. Several datasets have been deployed on the LSTM, Bi-LSTM, GRU, and BERT models to assess the system’s efficacy. Identification of the individual speaking the words is done using the audio signal extraction feature MFCC. The experimental results show that the BERT model provides the highest accuracy compared to other algorithms.
Prediction of Cryptocurrency Price using Machine Learning Techniques and Public Sentiment Analysis

Bitcoin and other cryptocurrencies are emerging markets that are growing more and more important in the financial world. Since the definition of money has changed and its price has fluctuated, cryptocurrencies like Bitcoin and others have grown in popularity. In this study, we suggest the use of machine learning technologies and readily accessible social media data for forecasting the price movement of the Bitcoin market. We used sentiment analysis and machine learning techniques to extract tweets from Twitter postings to examine the relationship between bitcoin price changes and tweet sentiment. We used a variety of machine learning methods to create a prediction model and insightful analysis of future market values. We use five distinct machine learning models, including Support Vector Regression (SVR), Prophet, An Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and XGBoost. Every model was tested on the last 30% of the data after training on the first 70%. The models' Root Mean Square Errors (RMSE) are compared. The expansion of the collection of significant characteristics retrieved from textual data using sentiment analysis employing long short-term memory is another way that this work adds to the body of knowledge on directed bitcoin price returns forecast (LSTM). The findings demonstrate that cryptocurrency markets may be predicted using machine learning and sentiment analysis, however other coins may be predicted using only Twitter data. The most impressive outcome from the LSTM model.
IoT Network Attack Detection Using XAI and Reliability Analysis

IoT has emerged as one of the most sophisticated techniques in recent years. But inadequate security controls are the most typical barrier to IoT expansion as the devices transmit a huge amount of data. Nowadays different machine learning and deep learning models are used to detect various IoT attacks. In our previous research, we applied Decision Tree, Random Forest, AdaBoost, XGBoost, ANN, and MLP to the IoT/IIoT dataset of TON IoT datasets to classify IoT network attacks. In binary classification, we got above 96% accuracy for all methods. In contrast, AdaBoost and ANN underperformed in multiclass classification. As accuracy improves, models get more complicated, and these models are frequently seen as black boxes that are difficult to interpret. Though these models give highly precise results, an explanation is required in order to comprehend and accept the models’ decisions. Here comes XAI which emphasizes a variety of ways for breaking the black-box nature of Machine Learning and Deep Learning models as well as delivering human-level explanations. In this article, we have extended our work by analyzing different machine learning and deep learning methodologies using XAI to explain the categorization of IoT network attacks. LIME, SHAP, and ELI5 approaches have been used to interpret and explain which will increase transparency and reliability.
Enhancement of Bubble and Insertion Sort Algorithm Using Block Partitioning

A list of components can be arranged in a certain order using a sorting algorithm, which is a fundamental concept in computer science. The temporal complexity of the two fundamental and widely used sorting algorithms, Bubble sort and Insertion sort is $\mathcal{O}\left( {{N^2}} \right)$, where N is the total number of items. When it comes to sorting a specific amount of items, it is superior. However, by adding more parts to its quadratic complexity, it loses efficiency. Because of this, it is less frequently employed in computer science’s practical and real-world applications, despite being widely utilized as a subroutine in other areas. Numerous extension techniques for the insertion sort and bubble sort algorithms have been put out in the literature, but none of them tries to combine the two to create a combination algorithm like ours. The bubble and insertion sort method was modified in this study, and its computational complexity was estimated to be $\mathcal{O}(N\sqrt N )$. The technique begins by dividing the input array into a few pieces, sorting each of the blocks using a modified bubble sort, and then merging all of the blocks together using a modified insertion sort. The suggested bubble and insertion sort outperform traditional bubble and insertion sorting as well as all other sorting algorithms with a computational complexity of $\mathcal{O}\left( {{N^2}} \right)$.
Credit Card Fraud Detection Techniques: A Comparative Analysis

In recent times, credit card payments are playing a crucial role in our day-to-day life applications. Despite its advantages, it also has certain fraud transaction challenges. Detection of fraudulent behavior demands high precision which might differ as per the payment method, country, and population segment. To achieve high precision of fraud detection existing resources should be reused smartly to remodel them in a divergent state of affairs given the big budget conventional systems. This paper explores several Machine Learning (ML) ensemble algorithms to monitor how accurate and precise they have been in detecting whether a transaction is a normal payment or a fraudulent one. Then the existing algorithms are compared with different performance evaluation parameters.
Non-Fungible Token (NFT): Analyzing Marketplaces and Non-User Perspectives

Non-Fungible Tokens (NFTs) have emerged as a disrupting revolutionizing technology, particularly for the digital art and collectibles industries. Understandably, they have received a huge attention from the digital as well as traditional artists. Powered by smart-contracts deployed in different blockchains, NFTs have provided more control and power to the content creators than ever before and gave birth to the core concept of verifiable digital ownership. These NFTs are traded in different online marketplaces. In order to ensure a wide-scale adoption of NFTs, it is important to identify potential hurdles that might hamper their wide scale adoption. Towards this aim, we present the findings of two studies in this paper. Firstly, we present an evaluation framework for different NFT marketplaces, which clearly illustrates the difference between their offered features. Secondly, we carry out a semi-structured interview to identify common drawbacks that are holding people back from NFT adoption. Our results reveal that the whole technology backing NFTs need to be revised for wider adoption. We sum up our findings from different point of views where the barriers exist but viable improvements are suggested for better and far-reaching promotion of NFTs.
CompTRNet: An U-Net Approach for White Blood Cell Segmentation Using Compound Loss Function and Transfer Learning with Pre-trained ResNet34 Network

Identification of White Blood Cells (WBCs) is a bold problem because of having different varieties of cells and the variability present in human perpheral blood smear samples. It is important and needed for detection of blood related diseases such as leukemia, malaria, anemia, syphilis, etc to help the hematologists. Old methods for this identification are weighty and imprecise. Therefore, In this paper, we propose an encoder-decoder based U-Net architecture with ResNet34 backbone for leukocyte image segmentation. Specially, Backbone ResNet designs a feature encoder to bring out multi-scale features and establishes skip connections on condensed convolutional layers. Here, we also proposed a compound loss function (Dice loss & Focal loss) that not only considers pixels, but it also accounts for precision loss and recall loss. The segmentation attainment is evaluated on test set. The proposed method was able to achieve ME, FPR, FNR and Dice-coefficient of 0.0131, 0.0116, 0.0317, and 0.9665 respectively. The extensive result analysis indicates that the suggested method outperforms numerous existing approaches in terms of WBC segmentation.
An Efficient Text Preprocessing and Classification Technique for Multilingual and Transliterated Data

Individuals express their opinions on a daily basis through various online platforms in the form of text, comments, and reviews, which is becoming a popular arena for text classification researchers. For a better cause, we aimed to ease the researchers’ workload by uncovering the true meaning of their perspective using a text preprocessing method and a text classification algorithm. Our goal was to discover a connection that could work well on multilingual and cross-language data. In this paper, we worked with five different datasets from different languages - English, Bangla, and Banglish (transliterated dataset) - on which we ran our experiment and discovered that byte-mLSTM outperforms with XGB classification model. We achieved more than 90% accuracy on English datasets, 93.43% on Bangla datasets, and 78% on transliterated datasets using mLSTM preprocessing, outperforming the existing performance of traditional approaches on English datasets. For preprocessing, mLSTM performs well across all languages.
Deep Learning-Based Colon Cancer Tumor Prediction Using Histopathological Images

Colorectal cancer is one of the deadliest diseases and one of the most difficult diseases to diagnose. A big reason for this is that it takes a long time to identify at an early stage. For treatment, a rapid and precise diagnosis of nodules is very crucial. In order to identify cancer in its early stages, a variety of techniques have been employed. Deep learning approaches were used in this work in order to identify Colorectal cancer tumors. In our this research, we used a dataset of same dimension of colon cancer tissues histopathological images. We proposed a deep learning model for predicting CRC tumors from histopathological images. CNN technique used for analyzing complex data. By CNN technique we analyzed our complex tumor images for identifying abnormal or suspicious tumor patterns. We made a five-layer deep neural network model. It consists of the input layer, four hidden layers, and the output layer. We used Rectified linear unit (ReLU) activation function in the hidden layer and the Softmax function in the output layer. We obtained an accuracy 99.70% from our deep learning model and our model loss was 0.0160. We calculate precision, recall, and F-score for the performance evaluation of our method. It is evident from our experiment that our proposed model produces a better result than some related works.
DeepSen: A Deep Learning-based Framework for Sentiment Analysis from Multi-Domain Heterogeneous Data

People usually express their emotions, views, or sentiment in textual form. The textual sentiment analysis (TSA) identifies or classifies opinions or feelings from texts in a predefined class. The TSA is complicated or infeasible manually due to its voluminous nature and unstructured or messy conditions. Therefore, the automatic sentiment analysis method quickly paves the way to identify the hidden sentiment polarity from the textual content. Although a few studies on sentiment analysis were conducted on a single or specific domain, developing the TSA method concerning multi-domains is unexplored in Bengali. This paper presents a deep learning-based framework called DeepSen to detect textual sentiment from Bengali texts into three polarities: positive, negative and neutral. Four benchmark corpora from available domains, Book, Restaurant, Drama and Cricket, have been used to analyze sentiment from multi-domain heterogeneous data. This work investigates six popular machine learning (LR, DT, MNB, SVM, RF, AdaBoost) and five deep learning (CNN, LSTM, GRU, BiGRU, BiLSTM) techniques using four benchmark Bengali corpora to perform TSA tasks. The evaluation result reveals that the BiLSTM method obtained the highest weighted f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-</inf> score (0.85) among all models.
Transfer learning based Facial Emotion Detection for Animated Characters

Since 1906 till today animation has been a popular method of storytelling. Animated characters portray unique stories in a more perceivable way by expressing diverse facial expressions. Detection of these emotions has not yet gained as much acclaim as detection of human facial expressions. This research aims to classify and predict emotions of animated characters using Deep Learning Techniques. Images of seven facial expressions namely Anger, Disgust, Fear, Joy, Neutral, Sadness and Surprise are classified using Residual Network (ResNet) and Transfer Learning. In order to conduct research on emotion identification in animated faces, we generated a new dataset with fewer images than the existing dataset [1]. Features from the faces are extracted using Uniform Local Binary Patterns (LBP) and fed to Convolutional Neural Network (CNN) model for classification. In our proposed models, the Transfer Learning-based ResNet50 and ResNet101,were trained on the ImageNet [10] dataset. Among the models ResNet101 achieved the highest detection accuracy of 94% and ResNet50 showed lowest time complexity.
Rate Insight: A Comparative Study on Different Machine Learning and Deep Learning Approaches for Product Review Rating Prediction in Bengali Language

In this contemporary era of digital marketing, ecommerce has emerged as one of the most preferred methods for day-to-day shopping. Ever since the COVID-19 pandemic, online shopping behavior has forever changed to less or no human-to-human interaction. As a result, it is getting more difficult for e-commerce enterprises to observe and evaluate market trends, particularly when done through consumer behavior analysis. To identify behavioral patterns and customer review-rating discrepancies, extensive analysis of product reviews is a substantial research field. Lack of benchmark corpora and language processing techniques, predicting review ratings in Bengali has become increasingly problematic. This paper thoroughly analyzes the approach to product review rating prediction for Bengali text reviews exploiting our own constructed dataset that was collected from an e-commerce website called DarazBD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We acquired product reviews with labels known as ratings of five sentiment classes, from "1" to "5". It is noteworthy that we established a well-balanced dataset using our automated scraping system and a significant amount of time and effort is spent to maintain quality standards through the human annotation process. Exploration of multiple approaches to machine learning models such as logistic regression, random forest, multinomial naïve Bayes, and support vector machine, the best classification accuracy score of 78.63% is achieved by SVM. Subsequently, using Word2Vec, FastText, and GloVe embeddings with three deep neural network(DNN) architectures: CNN, Bi-LSTM, and a combination of CNN and Bi-LSTM, CNN+Bi-LSTM gave the highest accuracy score of 75.25% among the DNN architectures.
A Deep Learning-Based Bengali Visual Question Answering System

Visual Question Answering (VQA) is a challenging task in Artificial Intelligence (AI), where an AI agent answers questions regarding visual content based on images provided. Therefore, to implement a VQA system, a computer system requires complex reasoning over visual aspects of images and textual parts of the questions to anticipate the correct answer. Although there is a good deal of VQA research in English, Bengali still needs to thoroughly explore this area of artificial intelligence. To address this, we have constructed a Bengali VQA dataset by preparing human-annotated question-answers using a small portion of the images from the VQA v2.0 dataset. To overcome high linguistic priors that hide the importance of precise visual information in visual question answering, we have used real-life scenarios to construct a balanced Bengali VQA dataset. This is the first human-annotated dataset of this kind in Bengali. We have proposed a Top-Down Attention-based approach in this study and conducted several studies to assess our model’s performance.
Classification of Hotel Reviews Using Sentiment Analysis and Machine Learning

Social media has become an essential part for people all over the world. It has given a platform for people to share thoughts, emotions, opinions, and ideas, causing a huge deal of data upsurge. Such an amount of data could be analyzed based on sentiment analysis and text classification via construction of an effective machine learning model. The concept gets more insight into it through analysis of the data, which is nearly impossible to conduct manually due to its huge configuration. This research focuses on the user’s comments, and reviews about different hotels to predict their sentiment. As for the datasets, comments and reviews of hotels from online sites have been utilized. Moreover, text pre-processing techniques like tokenization, case folding, stopword removal, lemmatization, and duplicate data removal have been applied. TF-IDF and Bag of Words have been applied for word embedding. Furthermore, the effectiveness of supervised machine learning algorithms like, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression was evaluated and from the comparative analysis, it was observed that the Logistic Regression provided the most accuracy ranging from 86 to 89 percent.
HuZhouSpeech: A Huzhou Dialect Speech Recognition Corpus

In this paper, a new open source dialect speech corpus is proposed. It is by far the only open source Goetian dialect corpus, suitable for conducting speech recognition research and building a speech recognition system for Huzhou dialect. This corpus contains about 184 hours of speech and corresponding subtitle transcriptions. In addition, an automated method for building speech recognition corpus is proposed. Only the film and television materials with subtitles are needed to build the speech recognition corpus automatically. Tested on four classical speech recognition frameworks to demonstrate the effectiveness of the corpus for training different speech recognition systems.
Two-Directional Two-Dimensional PCA: An Efficient Face Recognition Method for Thermal Infrared Images

Compared with face recognition in the environment of visible light, thermal infrared face recognition has the advantages of being independent of light, working around the clock, and capable of detecting hidden targets easily. In this paper, we propose a thermal infrared face recognition method based on the two-directional two-dimensional PCA (2D2DPCA) and random forest classifier. We compared this with two deep learning networks: Alexnet, Three-dimensional Convolutional Neural Networks (3DCNN), and applied these with two databases: the Terravic Facial IR database (with different facial angles) and the NVIE database (with various emotional expressions). Among these methods, the accuracy of face recognition with the 2D2DPCA method achieves the best recognition effect, it reached 99.92% and 99.97% in both databases, respectively. We statistically verified that our method could not only accurately and robustly recognize thermal infrared faces with large variations in angle and expression, but also greatly reduce computational complexity and data dimension, improving the speed of face recognition. With the two sample sets tested, our work has demonstrated that 2D2DPCA has excellent potential for facial image compression and may broaden thermal face recognition applications.
An Improved Model Based on Deep Learning for Detecting Insulator Defects

Insulators are a vital portion of high-voltage transmission lines. Defective insulators will cause the power system to fail, resulting in significant losses. Although the traditional methods of detecting the insulator defect based on image detection have improved detection efficiency, the problem of low detection accuracy and poor real-time performance arises when the aerial image resolution is high and the environmental background is complex. In this paper, an improved YOLOv5 model is proposed to solve these issues. On the basis of the YOLOv5 model, BiFPN is used as the neck part to strengthen the feature extraction capability of the model to enhance detection accuracy. We also integrate Transformer and Coordinate Attention to capture important insulator edge information and reduce the interference of complex backgrounds. The model was tested several times on a dataset that we built ourselves. The AP value of the improved YOLOv5 model for insulator defect detection is 98.6%, and the average processing speed per image is 4.5ms. The experimental results indicate that the improved YOLOv5 model is more effective than the existing insulator defect detection methods.