Reviewerly/exHarmony huggingface dataset:
Using Classifier Ensembles to Predict Election Results Using Twitter Data Sentiment Analysis

A quick and easy technique to gauge public opinion on a political party and its leaders is through the use of sentiment analysis on Twitter. Prior sentiment analysis algorithms have struggled to identify the best classifier for a given classification challenge. As long as a single classifier is selected, there is no guarantee that it will perform the best on unknown data. We're combining the results of several classifiers in order to lessen the chance of picking the wrong one. Our method is to classify the twitter data on the basis of sentiments using a ML classifier that is combined with a lexicon-based one. We've added the numerous classifier such as SentiWordNet, Naive Bayes, and Hidden-Markov Model (HMM) to new set of classifiers. In this case, the majority voting principle is used to assess whether or not a tweet is positive or negative. Consequently, we have enhanced the accuracy of sentiment analysis utilising the classifier ensemble approach. In order to achieve high accuracy, we also make use of our method like word sense disambiguation and negation handling.
Credit Card Fraud Detection Using Machine Learning and Incremental Learning

With the onset of the digital age, people all around the world have come to heavily rely on the use of digital modes of payment, credit card payments being the most dominant. However, unauthorised use of credit cards by means of physical theft or data theft of card information can lead to fraudulent transactions. In this paper, we have leveraged the XGBoost algorithm, an efficient model to accurately predict fraud. Since the relative amount of fraudulent transactions is much lower than legitimate transactions, we ventured to fix this bias by leveraging sampling techniques such as undersampling, oversampling, and a combination of these two. It was observed that bias corrected using undersampling provided us with a lower false-negative rate. Dataset was then partitioned to apply incremental learning techniques using XGBoost as the underlying model to assess how the model performance is affected in a real-world scenario, where incoming data can impact the results. Experimental results concluded that the incremental learning approach led to a further reduction in the false-negative rates.
Information-Based Image Extraction with Data Mining Techniques for Quality Retrieval

The search engines are becoming unavoidable opening to an immense volume of data prevailing over the web. Due to the web users normally concentrating on the initial pages of the investigation outcomes the scoring schemes are initiated which resembles a momentous prejudice for perceiving the web. Retrieval of image with text has been in practice for several years since when the retrieval of an image has been on the deck of every major crop. While the retrieval has been done based on the text provided with the image sometimes leaves no clue of what the picture actually looks like. Hence it is considered that data mining techniques along with the color analysis of the image and the retrieval based on the content of the image would be more than an effective process to make the feature extraction along with prediction of nearest neighbor and estimation algorithms recognizably builds the proposed system. The resultant retrieval process can analyzed with proper color extraction and conversion of histogram equalization.
StakePage: Analysis of Stakeholders of an Information System Using Page Rank Algorithm

The requirements elicitation process is the sub-process of requirements engineering in which stakeholders are involved to identify the requirements of an information system. Various methods have been developed to identify and analyze the stakeholders like goal-oriented requirements elicitation process, traditional methods, etc. Based on our review, we found that little attention is given to the analysis of the stakeholders using different measures of social networks before the starting of the requirements elicitation process. Therefore, to address this issue, this paper presents a StackPage method for the analysis of the stakeholders using page rank algorithm of social network measures. The applicability of the StackPage method is discussed with the help of the stakeholders of an institute examination system. Finally, the results are compared with two state-of-the-art methods, i.e., StakeRare and StakeSoNet methods.
Tabular Data Extraction From Documents

Data is everywhere, and data is the new oil, meaning it is omnipresent and valuable. Unfortunately, much of this data is not digitized and that calls for a technology capable of translating a hardcopy into usable softcopy. Text extraction or “textraction” is the key. Using image processing and advanced machine learning, a technique to perform text extraction comes into the picture which is optical character recognition. But, the OCR’s scope is limited to just extraction of data. Making sense of that data by reorganizing it is a different story altogether. This project proposes a couple of methods to extract data from tables and then re-tabulate it in the database using the two approaches. The first method straightaway utilizes the processing power of Google’s vision API and later processes the data obtained from its OCR while the second method focuses on image processing algorithms to extract the data from the columns of the tables and use it to repopulate the digital database.
Histopathology Osteosarcoma Image Classification

Bone malignant growth is one of the hazardous sicknesses which might make pass Ing numerous people. Most osteosarcomas occur in youths aged 10–14. Potentially, the most broadly perceived sort of a bone dangerous development in kids and a widely recognized sort of bone disease. There should be an exact identification and arrangement framework accessible to analyze bone malignant growth at the beginning phase. Early discovery of disease is by all accounts the significant component in expanding the shot at malignant growth patient endurance. It is a noxious and threatening infection, caused because of the uncontrolled division of cells in the bone. Convolutional brain organization (CNN a start to finish model) is known to be a choice to conquer the previously mentioned issues. Consequently, this work proposes a limited CNN plan that has been completely investigated on a little osteosarcoma histology picture dataset (an elegant imbalanced dataset). However, during readiness, class-imbalanced data can antagonistically impact the introduction of CNN. Accordingly, an oversampling system has been proposed to beat the previously mentioned issue and further foster hypothesis execution. In this cycle, a different evened out CNN model is arranged, in which the past model is non-regularized (due to thick design), and the last one is regularized, unequivocally planned for little histopathology pictures. In addition, the regularized model is Incorp-evaluated with CNN's basic plan to lessen overfitting. Trial output exhibits that oversampling may be a successful method for tending to the imbalanced class issue during preparation. The readiness and testing correctness of the non-regularized CNN model are 98 and 78% with an imbalanced dataset and 96 and 81% with a good dataset, independently. The regularized CNN model planning and testing correctness are 84 and 75% for an imbalanced dataset and 87 and 86% for a sensible dataset.
Classification of Yoga Poses Using Integration of Deep Learning and Machine Learning Techniques

Pose estimation is a classical problem in computer vision. With the recent change across the globe, there is much focus on self-care with the help of yoga. To derive the desired benefits of yoga, the poses must be done as per its correct posture. Information about the name of yoga pose gives one idea about its associated benefits. In this paper, the majority voting classifier is utilized for voting out the given yoga pose into five classes (goddess pose, downward dog pose, plank pose, tree pose, and warrior 2 pose). Voting classifier is explored to improve the accuracy of stacked individual ensemble classifiers (AdaBoost, bagging, and dagging classifier). Classification accuracy is evaluated with the help of standard machine learning evaluation metrics like precision, recall, F1-score, area under curve (AUC). Experimental results validate the improved performance of voting classifier as compared to individual classifiers. Higher average F1-score of 0.9755, as compared to scores of bagging classifier, classifier, and AdaBoost classifier, also confirms the better balance of precision and recall metrics and better tolerance to the imbalanced or small datasets.
Face Recognition Using EfficientNet

Communication is becoming increasingly vital as we move closer to a new transformation. Face features are crucial facial expressions qualities that also participate in human–computer interaction. They play an essential role in social interactions and are commonly used in the behavioral description of emotions. The work introduced in this research focuses on facial expression recognition from images to improve the identification accuracy of real-world images with challenges in expression datasets. This approach also applies to laboratory-trained dataset images trained in a controlled environment for cross-database evaluation studies. The feature extraction process is more difficult in real-world films than in films trained in a controlled environment. A convolutional neural network model based on in-depth learning technology using EfficientNet has been proposed in this research work. The novel concept of EfficientNet Architecture, introduced in 2019, uses compound scaling to enhance CNN's structure further. Literary review shows that no work has been done so far for facial expression recognition using this architecture. Various optimizers SGD, RMSprop, and Adam are applied to determine which optimizer performs best in identity accuracy. This concept works well with high-resolution images. It offers an in-depth learning prototype structure that enables better accuracy and processing speed.
Game Data Visualization Using Artificial Intelligence Techniques

Games usually have a tendency to have the properties of massive use of a location and excessive complexity, making them notable benchmarks for evaluating numerous strategies which consist of AI ones. Broadly talking, techniques are utilized in video games in a position to make them larger exciting, immersive, smarter, and many others. Both of these prove to be excellent types of AI recreation. Since there are few viewpoints on contemporary results in the activity AI discipline from the standpoint of the fundamental program, in our work we have taken a scientific look at a number of commonplace that seems to be up to software program fields of pastime AI: viable agents in non-player character studies and recreation stage technological know-how in procedural content material fabric generation. Here we have proposed a model which can provide the different game visualization in the form of terms and conditions using different AI-based algorithms. In addition, AI for everyday tasks and hybrid genius for different games are explored.
Implementation and Analysis of Decentralized Network Based on Blockchain

In the current modern world of computation and technology, the Internet plays a very important role. Majority of the things on the Internet work on the concept of centralized networks which offer good accessibility and security along with innovations. But centralized networks are vulnerable to a fair share of cyber-attacks and security issues. Decentralized networks form the basis of blockchain technology. Blockchain offers a simple solution to ever increasing cyber and security attacks on networks by removing the concept of a sole point of failure from networks. This is what decentralized networks entail a decentralized network distributes information-processing workloads across multiple devices instead of relying on a sole central server. Each of these separate devices serves as a mini-central unit that interacts independently with other nodes. As a result, even if one of the master nodes crashes or is compromised, the remaining systems can continue providing data access to users, and the overall network will continue to operate with limited or zero disruption. In this paper, we have presented a way to create custom decentralized networks using Hyperledger Besu and established how this network can be used as a base to create smart contracts. This paper shows how different nodes can be created in a network and how these nodes communicate with each other. Peer-to-peer interaction is done with the network using P2P ports.
Content-based Image Retrieval in Cyber-Physical System Modeling of Robots

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for Image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR.
Ship Detection from Satellite Imagery Using RetinaNet with Instance Segmentation

Recognizing the centrality of ship detection and segmentation based on optical remote sensing images in the military and civilian domains, the sea surface remote sensing field is receiving a lot of attention. The deep learning-based ship detection approach can also extract vessel position and category information, crucial for maritime surveillance. Unfortunately, less research has been done in this area. The majority of known approaches for spotting ships perform object detection but don't perform semantic segmentation. Furthermore, earlier studies included limitations such as the inability to detect small ships or ships of diverse sizes and the higher rate of false positives. Convolutional Neural Networks (CNNs) benefits are also significantly reduced due to the complexities of SAR images. Then there are a few models that can't discern the difference between items that seem like ships, and these false alarms lower the total accuracy rate. The available models have also faced trouble while identifying and segmenting ships with a complicated background and dealing with light variations. Furthermore, some existing models performed somewhat worse than other state-of-the-art frameworks. Some models have a higher cost of computation. This paper uses RetinaNet to provide the ship detecting algorithm. Instance Segmentation was also included in the model. The proposed model was created by integrating these two strategies. RetinaNet is a fantastic one-stage object detection model that can precisely work with dense and small-scale objects. The Feature Pyramid Network (FPN) and Focal Loss are two improvements over conventional one-step object detection models. Then basically, an instance segmentation algorithm has been implemented in place of the object detector to increase RetinaNet's performance by removing background better. The proposed model can work more precisely than existing models due to these adjustments. We achieved a promising result using the described techniques on the Airbus dataset from Kaggle, with Average Precision (AP) score of 65.7 and APs of 51.8, respectively.
Performance-driven Practices and Employee Performance in Nepalese Commercial Banks

Human resource is the fundamental resources of the organization, which determine the performance of the organization as well as gives the life to the other resources and the organization. This study focuses on assessing the role of employee performance-driven practices on employees’ performance in Nepalese commercial banks. This study utilizes the quantitative research approach with a causal-comparative and analytical research design. The sample of the study is 385 respondents from Kathmandu valley. Reliability analysis, descriptive statistics, correlation, and regression analysis are utilized to draw the conclusion. Evidence indicates that leadership, working environment, motivation, and job satisfaction positively and significantly affect employee performance in the Nepalese banking sectors. Finally, this study suggests banks provide excellent leadership opportunities, a good working environment to the employees, rich motivation, and focus on job satisfaction through intrinsic and extrinsic reward systems that positively contribute to the bank employees’ performance and might lead to downsizing employee turnover also.
ICTCAM: Introducing Convolution to Transformer-Based Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation(WSSS) is a challenging task, which only requires category information for segmentation prediction. Existing WSSS methods can be divided into two types: CNN-based and transformer-based, and the ways of generating pseudo labels are different. The former uses Class Activation Mapping(Cam)to generate pseudo labels, but there is a problem that the activated areas are concentrated in the most discriminative parts. The latter one choose to use attention map from the multi-head self-attention(MHSA) block, but there also exist the problems of significant background noise and incoherent object area. In order to solve the problems above, we propose ICTCAM to help transformer block obtain the ability of CNN, which include two modules named deeper stem(DStem) and convolutional feed-forward network(CFFN). The experiment results show that our modules have improved the performance of the network and achieve 69.9% mIoU, which is a new state-of-the-art performance on the PASCAL VOC 2012 dataset compared with similar networks.
3D Pose Estimation by Synthesizing Motions

This paper addresses the problem of the insufficient generalization ability of 3D pose estimator to a single dataset. Existing pose estimators have different performance on different datasets. We attribute this to the insufficient diversity of 2D-3D pose pairs in the dataset. To this end, we propose MotionEnhance, an auto-augmentation framework that generates synthetic human 3D pose by learning the distribution of poses in the training set and uses them to fine-tune a 3D pose estimation. MotionEnhance introduces a human motion generator that synthesizes 3D pose with the inferred bone length and bone rotation and develops a discriminator accordingly to ensure the rationality of the action. The motion generator is based on Variational Auto-Encoder model to generate bone lengths and rotations. MotionEnhance learned the 3D pose distribution in the dataset to create the synthetic 3D pose and corresponding 2D coordinates. Motio- nEnhance is commonly used for different 3D pose estimators. Experiments show that the estimation error is reduced by 2.1mm MPJPE on the H3.6M dataset and by lO.3mm MPJPE on the 3DHP dataset after adding the MotionEnhance framework.
PRETRUST: A Framework for Fast Payments in Blockchain Systems

In this study, we propose PRETRUST, a new framework to address the problem of the efficiency of payment process based on blockchain systems. PRETRUST is based on the thoughts of consortium chains, supporting fast payments. To make parties address transactions efficiently and scale out the capacity, the main strategy is based on the guarantee mechanism and shard technology with a random arrangement of transactions and consensus groups. In PRETRUST, the guarantee process replaces the verification of a transaction on-chain to realize the optimization of efficiency. And the model of a trusted execution environment is utilized in the interactions between PRETRUST and public systems. Throughout the paper, we discuss several transaction scenarios, which show that PRETRUST can guarantee security while simplifying interactions with the external environment.
Transfer Learning for CNN Based Modulation Classification in Time-Variant A WGN Channels

Machine learning based modulation classifiers are often criticized for their complex training process. Although such training process can be performed offline and not impacting the actual classification speed, in time-variant channels re-training of the model is needed to optimize it for the now scenario. In this paper, a transfer learning framework is proposed to reduce the computational complexity of the re-training process. According to the simulated results, the proposed framework is able to significantly reduce the number of parameters to be re-optimized after change of noise level while maintaining a good classification performance.
Dataset Augmentation Strategies for Visual Activity Recognition in Deep Neural Networks

Dataset augmentation involves synthetically increasing the size of a dataset by introducing appearance variations that help a machine learning model to generalize when trained over the dataset. Several augmentation strategies have been proposed and evaluated in the context of image recognition problems [7], however empirical studies on effectiveness of data augmentation in video recognition are few and far between. In this paper, we explore a number of dataset augmentation strategies and quanti-tatively assess their value on the task of visual activity recognition in a highly controlled manner. We extend a number of image augmentation strategies to the video domain, and also propose an entirely novel augmentation approach drawing inspiration from the idea of “Dropout” [20] in neural networks directly to the data domain and calling it “Blackout” augmentation. We thoroughly evaluate these augmentation strategies on the recently proposed dataset from Kumar et al. [15] which includes a complex manufacturing assembly activity controlling for variations in camera pose and human actors. Our detailed evaluation reveals interesting trends hitherto undiscussed in literature, and a major performance boost of 20.0 percentage points.
Improved Color Balance and Fusion for Enhancement of Underwater Image

In underwater photography, the absorption and scattering of light may probably cause low contrast, blurred images, and color cast. We present an effective method for improving the quality of underwater images that have been degraded by medium scattering and absorption. It combines color compensation with multi-scale image fusion. Color compensation consists of independently correcting the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}$</tex> channel and G-B channel of the input image and adjusting the white balance of the corrected image. After color compensation, the color of degraded image is restored effectively, but the blurring of image edges and details receiving scattering effects cannot be remedied. This problem can be solved by multi-scale image fusion. In our method, we use Gaussian pyramid and Laplace pyramid images for fusion, and these two feature pyramids are constructed by using the image after the illumination adjustment. The experimental results illustrate that dark-area sensitivity, global contrast, and edge sharpness of images have been improved by our methods. Moreover, in terms of visual effects and assessment metrics of underwater images, our method excels over other methods.