Reviewerly/exHarmony huggingface dataset:
Age Estimation Based on Graph Convolutional Networks and Multi-head Attention Mechanisms

Age estimation technology is a part of facial recognition and has been applied to identity authentication. This technology achieves the development and application of a juvenile anti-addiction system by authenticating users in the game. Convolutional Neural Network (CNN) and Transformer algorithms are widely used in this application scenario. However, these two models cannot flexibly extract and model features of faces with irregular shapes, and they are ineffective in capturing key information. Furthermore, the above methods will contain a lot of background information while extracting features, which will interfere with the model. In consequence, it is easy to extract redundant information from images. In this paper, a new modeling idea is proposed to solve this problem, which can flexibly model irregular objects. The Graph Convolutional Network (GCN) is used to extract features from irregular face images effectively, and multi-head attention mechanisms are added to avoid redundant features and capture key region information in the image. This model can effectively improve the accuracy of age estimation and reduce the MAE error value to about 3.64, which is better than the effect of today's age estimation model, to improve the accuracy of face recognition and identity authentication.
Towards Automated Clustering

Automated Machine Learning enables many inexperienced users to generate good classification solutions solely by letting machines generate optimal pipelines. However, there are not as many possibilities to generate meaningful clusterings without further knowledge. As the clustering problem is context and domain dependent, a single solution can never be the sole best clustering for any dataset. To overcome this problem, in this paper we design a framework which uses clustering algorithms, CVIs, the skyline operator and multiple visualization techniques to generate diverse interesting clusterings and present them to a user, enabling him to take an informed decision without needing any experience in the field of clustering.
Audio to Sign Language Translation using NLP

Communication is an essential aspect of human life, but deaf people often face challenges in communicating with hearing people due to the language barrier. According to the World Health Organization, 466 million individuals, or 5% of the global population, have severe auditory loss. In India, there are roughly 18 million people who are deaf. Sign language is the primary way of communicating information for the deaf, but it requires significant time and effort to learn. To bridge this gap, we propose an Audio to Sign Language Translation solution that converts live voice or audio recordings through text forms and shows pertinent Animated GIFs or Sign Language images. The application uses frontend Easy Gui, PyAudio for microphone input, Google Speech API, and Sphinx for speech recognition, NLP for text preprocessing, and a dictionary-based machine translation approach for sign language generation. The algorithm follows a logical sequence of steps and provides an accurate and reliable solution to facilitate communication between deaf and hearing people. The purpose of this application is to give deaf individuals access to information and services in Indian sign language and to create a scalable project that can record the whole ISL vocabulary using both manual and non-manual signs. Overall, by enabling deaf people to communicate, the Audio to Sign Language Translation has the ability to significantly improve their quality of life to communicate easily and effectively with others.
IEEE BigData 2023 Keystroke Verification Challenge (KVC)

Institute, Warsaw, Poland This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.
Sentiment Analysis on Text data: Methods, Applications, Challenges and Future Directions

Sentiment analysis, or opinion mining, is the computer examination of text data thoughts, opinions, and emotions. Emotions analysis has gained popularity because to the growing volume of user-generated content on social media and the need to understand public mood and client feedback. This review article summarizes sentiment analysis's knowledge, methods, challenges, and advancements. Introduce sentiment analysis and its usefulness in many sectors before discussing how sentiment analysis approaches have grown from rule-based systems to machine learning and deep learning. Next, discuss how sentiment analysis improves politics, business, marketing, social media monitoring, and recommendation engines. This survey research examines the latest sentiment analysis methods, including supervised learning models, unsupervised methods, lexicon-based methods, and hybrid models. Examine each approach's pros and cons and applicability for different scenarios. Sarcasm and irony identification, context-dependent sentiment, noisy and unstructured text data, and multilingual sentiment analysis are all discussed. The methods, challenges, and advances of sentiment analysis are covered in this comprehensive study. It encourages innovation and cooperation in text data sentiment analysis by providing a comprehensive resource.
Research and Implementation of Parallel CART Algorithm Based on Distributed Database

This paper presents a parallel CART method for multivariate association query. The amount of communication information is reduced by performing two semi-joins. This method adopts a multi-node parallel method, which greatly speeds up the execution efficiency of the system. The sequence of operations generated by this algorithm has global optimization characteristics. One of its important development trends is to realize effective data query by optimizing the database. The simulation experiment proves that the method has fast processing speed, high node utilization rate and strong practicability. This method is suitable for large data processing.
Enhanced AI Voice Assistance using Machine Learning and NLP

The project’s primary intent is to emphasize an Enhanced AI Voice Assistant that wields machine learning (ML) and Natural Language Processing (NLP) to carry through the tasks using voice commands. With a minimal, user-friendly graphical interface in the idea, the automated voice-controlled assistant has been designed to execute a comprehensive set of voice commands. This provides users with an openly and effective way to cop up with their computers. The achievement of this voice assistant is in alignment with the more general goal of enhancing user interface performance with computers. It promises to make common computer tasks simpler and more accessible to a larger number of users by creating a more natural and seamless interaction model. The purpose of the enhanced AI voice assistant is to serve as an intelligent assistant that enables users to carry out activities easily with voice commands by combining speech recognition, natural language processing, and machine learning. Enhanced productivity, user-friendly design, availability, and an overall improved computing experience are some of the implicit advantages it offers.
Classification of Breast Cancer: A Comparative Study using K-Means Clustering-Based Feature Extraction and Hybrid Classifier

In order to save a person’s life, it is essential to diagnose and identify cancer as early as possible. Breast cancer is one of the deadliest diseases women may have, and it’s a major focus of medical research. Breast cancer starts in the cells and tissues of the breast. A breast lump, along with other symptoms including an abrupt shift in breast size or shape, leaking fluid from the nipple, and a generally moist feeling to the skin, are all telltale markers of breast cancer. As the disease progresses, symptoms such as shortness of breath, bone discomfort, and swollen lymph nodes may become apparent. Breast cancer is the second biggest cause of death among women worldwide. It’s one of the most common forms of cancer among females. Since the root cause of the condition is unknown, early discovery, classification, and diagnosis are essential. If breast cancer is detected early, it is highly curable. In this work the breast cancer risk of patients is classified using a Gaussian Mixture Model (GMM), and the hybrid classifiers Firefly GMM, Cuckoo Search GMM, Harmonic Search GMM and PSO GMM the results are presented in terms of classification accuracy, error rate, sensitivity, specificity and Jaccard Metric. Among these five classifiers the hybrid classifier PSO GMM attained maximum accuracy of 94% with minimum error rate of 6%.
Neural Networks Voting for Projection Based Ensemble Classifiers

Ensemble learning has been proven effective in enhancing classification accuracy by aggregating predictions from multiple base classifiers. This paper introduces a novel approach to augmenting weak projection-based classifiers using a Neural Network within a stacking ensemble framework. The proposed method capitalizes on the diverse strengths of both linear and complex models, harnessing the interpretability of projection-based classifiers, while leveraging the pattern recognition capabilities of Neural Networks. We present a comprehensive algorithm involving dataset selection, preprocessing, base model training, meta-feature generation, and Neural Network architecture design and training. Extensive experiments demonstrate the efficiency of our approach on a variety of high-dimensional biomedical datasets. Our results showcase significant accuracy improvements over standalone projection-based classifiers and conventional ensemble methods. We analyze the interpretability of the hybrid ensemble, shedding light on the insights drawn from its Neural Network component. This work not only advances the field of ensemble learning, but also underscores the potential of combining disparate classifier paradigms to achieve superior predictive performance. The code for this study is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.</sup> https://github.com/panagiotisanagnostou/NNv-MRPV
Breast Tumor Classification using Transfer Learning with Adaptive Crow Search Optimization

Breast cancer is a prevalent form of cancer among women, with around 2,87,850 newer cases acknowledged in 2022. In addition, a substantial amount of female deaths, with 43,250 were accounted to this cancer type. Early detection plays a crucial role in reducing the higher mortality rate associated with breast cancer. However, precisely diagnosing this cancer type from mammography images involves the expertise of trained clinicians. For improving the diagnostic process, researchers have explored the potential of artificial intelligence techniques. The paper utilizes an Adaptive Crow-Search Optimization (AdCSO) algorithm to boost breast cancer diagnosis. The adaptability and premature convergence problems of conventional crow-search optimization algorithm is made improved using the AdCSO algorithm. The proposed framework consists of three main stages: data augmentation, feature extraction using EfficientNetV2 based on transfer learning, and optimized classification using a convolutional neural network (CNN). The mammograms from the INbreast dataset are employed for work evaluation. By leveraging transfer learning and an optimized CNN for classification, the proposed EfficientNetV2 with AdCSO-CNN provides a maximum classification performance of 97.05% accuracy as compared with other approaches. The proposed method is tested against the above problem with two output classes: benign and malignant.
Research on Intelligent Protection System for Computer Web Database Security Performance Optimization

The data compression and migration algorithm of large-scale Web database has the problem of poor correlation, easy to fall into local extreme value, which leads to high cost and low efficiency of migration. Aiming at this problem, this project intends to study a performance optimization scheme of database system for cloud environment. Build cloud data association model to realize cloud data association analysis. By analyzing the load of each data node, the purpose of quick retrieval of each data node is achieved. The method has strong advantages for online transactions requiring less frequent changes. It can improve the reaction speed and competition of the network, adjust the influence of index grid elements on the network security performance of the database.
Classification of Wisconsin Breast Cancer Data with Extreme Learning Machine and Osprey Optimization Algorithm

The illness reported for higher mortality in humans is cancer. On top of this, breast cancer is the most deadliest one among global women. Several clinicians and researchers are working towards earlier detection and its appropriate treatment. Thus, the requirement of robust Computer-Aided Diagnosis (CAD) frameworks is always in demand globally for tireless and effective diagnosis of such cancer. The paper intends to propose a CAD framework for the classification of breast tumors promptly. The research begins with the employment of initial data taken from the Wisconsin Breast Cancer database. The data preprocessing and its associated visualization are then performed. Afterward, the predominant features are selected for improving the efficacy of the framework. For this, a recently evolved Osprey Optimization Algorithm (OOA) is adopted in this work. Here, the optimization is based on the effective tactic of ospreys while hunting fishes from the oceans. Next to feature selection, the significant feature vectors are classified using Extreme Learning Machine (ELM). In addition to this, the work utilized K-Nearest Neighbour (KNN) and Mixture Kernel SVM (MiSVM) models for performance analysis. Finally, OOA features with ELM architecture provides superior accuracy of 97.66% for breast cancer classification.
spryDEX:Decentralized Exchange for Cryptocurrencies using Blockchain

This study outlines the development and execution of a Decentralized EXchange (DEX) that utilizes blockchain technology. The DEX allows for peer-to-peer trading of various cryptocurrencies like Ethereum and Sprybucks (experimental new ERC-20 token) without the need for intermediaries, providing greater privacy, security, and autonomy for traders. By providing users with real-time data, news, and social media community insights, DEXs allow users to make informed trading decisions about cryptocurrencies based on up-to-date information. This information can help users identify emerging trends and opportunities, as well as manage risks in a rapidly changing market. The DEX is built on top of the Ethereum blockchain, utilizing smart contracts to handle the trading logic and provide security, immutability, and transparency. The DEX is implemented using a peer-to-peer network where traders can broadcast their orders and match with each other in a trust-less manner. To ensure that the order book is always up-to-date and reliable, we used the proof-of-stake consensus algorithm. Traders have the ability to add or remove liquidity from the implemented liquidity pool, which in turn provides the necessary liquidity for trades. The DEX is intended to offer a straightforward and user-friendly experience. The user interface allows traders to place orders, view their balances, and monitor their transactions in real-time.
Research on Human Action Recognition Model Based on Computer Laplacian Matrix and Convolutional Neural Network

This project intends to study a new human action recognition system based on Laplacian matrix multi-scale convolutional neural network (MCNN) algorithm, in order to overcome the shortcomings of the action recognition process in the recognition system. Firstly, a de-correlation network architecture is established by using matrix transformation to solve the correlation problem. When the network performance is poor, it can replace the existing BN layer to standardize the data. Finally, the dual flow characteristics of time and space are integrated to realize the automatic recognition of human behavior. Comparative experiments were conducted on UCF101 and HMDB51, and the results showed that the algorithm proposed in this paper achieved accuracy of 83.22% and 79.41% for UCF101 and HMDB51, respectively.
Minimizing the Energy Consumption using Coral Reef Based Energy-Efficient Framework in Internet of Medical Things

The internet of things (loT) and In enabling quick action in the event of a medical emergency. It is used to transmit medical records of patients to distant researchers and hospitals for analysis. Low-power biosensor node-based wireless body area networks (WBANs) have been proposed as a means of monitoring patient health; however, addressing the problems of excessive energy consumption and high communication costs is interesting and difficult. The unequal energy consumption among biosensor nodes has a detrimental impact on the healthcare system because it slows down the transfer of patient data to remote centres. The main objective of the research presented in this article is to suggest an IoMT- based Optimised Energy-efficient Framework (OEF) for e- healthcare that reduces biosensor consumption while enabling the transfer of medical data. The results of the simulation indicate that the proposed framework improves the presentation of medical schemes by 18%, 26%, and 29%.
FPGA Implementation of Multi-Spectral HDR Imaging

In this paper, we present a computationally efficient implementation of multi-spectral HDR (High Dynamic Range) image fusion on a Field Programmable Gate Array (FPGA), where an infrared HDR image stream is fused with a visible light HDR image stream. Each HDR frame in the video is composited from a set of differently exposed images of the same subject matter. This system runs in real-time and captures high-quality video for rugged industrial environments with difficult lighting conditions and fugitive dust/soot (e.g. in underground coal mining or arc welding). Firstly, the visible sensor is configured to stream video frames with alternated exposures. These differently exposed images are quantigraphically composited using an efficient implementation [1]. Then, the infrared image from a different sensor is synchronized and geometrically transformed to match the frames from the visible light sensor. Finally, the system fuses the HDR infrared video stream with the visible light HDR video stream to produce the output.
Non-uniform Sampling Methods for Large Itemset Mining

A well-studied problem in data mining is large itemset mining. To address this problem over very large datasets, several approximate algorithms have been introduced, where an important class of such methods relies on sampling. However in the literature, only methods that are based on uniform sampling are investigated. In this paper, first we discuss how different sampling methods can be described using a generic sampling algorithm and study a property desirable for sampling methods. Then we use this property to argue that some non-uniform sampling methods may work better. We accordingly propose methods that sample each transaction proportional to its number of items or proportional to its number of frequent items. Finally, by conducting extensive experiments over real-world datasets, we show that non-uniform sampling methods usually outperform the uniform method.
Image Encryption and Decryption using Symmetric Key Sequence of Elliptic Curve (EC) Over Prime Field

Recent days exchange of information in the form of text, image, video and audio over World Wide Web (WWW) through wired or wireless channel are more common. Information may be confidential data including military data exchange, patient information, data communication in social media etc. In proposed work, the novel idea of image encryption method that uses key sequence obtained from the elliptic curve points. The key sequence {Ki} of period ‘N’ is generated by elliptic curve points over prime field. Using the key sequence {Ki} image encryption is carried out. Different analysis such as i) Histogram ii) Correlation iii) Entropy performed on different images to study the performance of the scheme. Result shows that, histogram plot of encrypted image is nearly flat and hence pixels are highly uncorrelated with the original image. Correlation between the original and encrypted image pixels are found to be low. Also, entropy of encrypted image is found to be high. It indicates the proposed encryption scheme is immune to statistical and other attacks
Batch Mode Deep Active Learning for Regression on Graph Data

Acquiring labelled data for machine learning tasks, for example, for software performance prediction, remains a resource-intensive task. This study extends our previous work by introducing a batch-mode deep active learning approach tailored for regression in graph-structured data. Our framework leverages the source code conversion into Flow Augmented-AST graphs (FA-AST), subsequently utilizing both supervised and unsupervised graph embeddings. In contrast to single-instance querying, the batch-mode paradigm adaptively selects clusters of unlabeled data for labelling. We deploy an array of base kernels, kernel transformations, and selection methods, informed by both Bayesian and non-Bayesian strategies, to enhance the sample efficiency of neural network regression. Our experimental evaluation, conducted on multiple real-world software performance datasets, demonstrates the efficacy of the batch mode deep active learning approach in achieving robust performance with a reduced labelling budget. The methodology scales effectively to larger datasets and requires minimal alterations to existing neural network architectures.
Prediction of the positive psychology of college students using LightGBM algorithm based on SMOTE and Bayesian optimization

In our work, the LightGBM algorithm based on Bayesian optimization is employed to classify and predict the characteristic factors affecting the college students' positive psychology; the unbalanced data is processed using the SMOTE algorithm, a high-precision prediction model of positive psychology is constructed on the basis of the data preprocessing, and the commonly used classification assessment indexes are used for evaluation of the model performance; finally, the characteristic factors affecting positive psychology are ranked in accordance with their importance, and analyzed. The results show that the LightGBM algorithm based on SMOTE and Bayesian optimization performs optimally on the assessment indexes of Accuracy, Recall, Precision, and f1, and outperforms the Xgboost, lr, and catboost algorithms.