Reviewerly/exHarmony huggingface dataset:
ETL Technologies for Big Data: A Comparative Study

There is a significant increase in the generation of massive data worldwide. Various sources such as social media applications, blockchain technology, and numerous other systems are responsible for generating data. This information can be structured or unstructured, originating from different sources. In this context, the ETL (Extract, Transform, and Load) process plays a crucial role where demands for efficient business decisions in contemporary systems are grown.During the processing phase, the entire method is organized in a pipeline structure to ensure that the resulting data contains valuable and pertinent insights. Indeed, selecting an appropriate ETL technology can be challenging due to the abundance of options available. This article delves into several technologies used for performing ETL processes with the aim of examining their strengths when weaknesses. This can provide assistance to researchers and industry professionals in making informed decisions and choosing the most suitable technologies to meet their specific requirements.
Unveiling Novel Techniques for Human Activity Recognition with Smartphone Sensors

Due to the accessibility and capability of smartphones, human activity recognition (HAR) has attracted a lot of interest. HAR includes using sensor data gathered from cellphones to automatically recognise and categorise human behaviours. Labelled sensor data gathered from cellphones made up the benchmark dataset utilised in this investigation. A wide variety of human motions and activities were included in the dataset, which also included various postures and movements.In this work, we assess the performance of five well-known classification techniques for HAR: k-nearest Neighbours (KNN), Linear Support Vector Machines (SVM), Random Forest, Decision Trees, and Logistic Regression.We use a benchmark dataset made up of labelled sensor data gathered from cell phones to assess the algorithms. The dataset includes a variety of human motions, including sitting, standing, jogging, and other bodily actions.The early findings show that the algorithms' degrees of accuracy differ. With a high accuracy score of 0.954869, logistic regression demonstrates great predicting ability. With an accuracy score of 0.872413, Decision Trees perform slightly worse than Random Forest, which performs better overall with a score of 0.921955. The most effective method, with an outstanding accuracy score of 0.968442, is linear SVM. The accuracy score for k-Nearest Neighbours, which is 0.902273, is also good. This side-by-side comparison sheds important light on how various HAR algorithms for cell phones function. The results support the creation of more precise and effective HAR systems by assisting researchers and practitioners in choosing the best algorithm for their particular applications. The next sections of this study give further information on the methodology, experimental design, findings, and commentary.
UI/UX Design for Online Learning Approach by Predictive Student Experience

This study examines how an User Interface (UI) and User Experience (UX) design for online learning/educational platforms can be designed in a way that can be fully effective for achieving educational outcomes. In order to find out the importance of UI/UX design, a quick survey was conducted using Google Form about user experience in terms of UI of tools and websites, audio, video clarity, screen sharing, messaging chat, number of maximum participants, network adaptability, course, name, age, cost and demographic location. In this survey, 560 students participated from across the discipline. Out of 560 participants only 530 respondents, out of 530, 359(67.9%) were male and 171(32.1%) respondents are female. 470 (88.7%) respondents feel that UI design is vital for a tool or website while 401 (75.6%) respondents had bad experience of UI, 106 (26.4%) students continue with website even it has bad UX and 295 (73.6%) students leave the website. 350 (66.6%) respondents prefer to ease of use, 53 (10%) respondents prefer mobile accessibility, 111 (21%) students prefer technical support and 148 (28%) respondents prefer cost more than UI design of e-learning platforms. The survey shows that most of the respondents are male and mean (SD) age 21 years. Zoom app and Udemy are their most preferred tool and online learning platform respectively. In this study, a number of user studies are given that may be used to evaluate UI and UX and examine usability as well as utility. Additionally, applying ML techniques on curated datasets to find best UI design of tool and website for online platform.
Food Suggesting Chatbot: Detecting and Prescribing Perfect Food for Every Customer

Nowadays people are using the food delivery app on huge scale and day by day the demand for these apps are increasing on huge scale. The ultimate aim of this chat bot is to take care of the customers' health too. This research has used artificial intelligence and data science to work on the BMI of the customer to predict the perfect food for them. Apart from this it too takes care of the customer's diet if so he/she suffers from the long term diseases like Diabetics, Blood pressure and other medical conditions too with the help of the BMI. So by following healthy food habits, a healthy lifestyle can be maintained.
Classification of Web Pages: A Comparison of Recent Machine Learning Techniques

The number of people using the Internet for a wide range of reasons has increased exponentially during the past few decades. However, the overwhelming quantity of online pages causes it to be harder for a user to properly search for the desired material. Since the use of the online is more integral to daily life, “Web Pages Classification (WPC)” has emerged as one of its most difficult and crucial undertakings. As a result, web page preprocessing has become essential for removing inappropriate and unwanted information from website pages as well as for lowering unnecessary noisy material for superior WPC. Different methods along with features could be used to organize websites into various categories. Numerous “Machine Learning (ML)” oriented techniques are currently being explored in the field of automated WPC to overcome this issue. Still, comparing those techniques to identify a superior WPC architecture is lacking. This comparative study provides a comprehensive review of many state-of-the-art ML classification techniques for WPC, including “NWDL-STC”, “DOM-SST-ANN”, and “OKNN-FF”. The unique features of tag information generation, classification methods, and experimental datasets are explored as well. It also compares the aforementioned WPC techniques on several performance criteria, including “Text Categorization”, “Noise Classification Ratio”, and “Accuracy”. It also addresses the problems and difficulties that came up throughout the development of WPC. This is going to encourage researchers to do a new study on WPC, which could assist in addressing several current issues.
Impact of Expert Academic Teaching Quality and its Performance Based on BiLSTM-Deep CNN Network

Undergraduate and postgraduate students from eight different departments at a UK institution participated in organized conversations about the impact of teachers' research activities on their education. In both samples, positive responses greatly outnumbered negative ones. There was an increase in positive feedback on professors' research when the overall quantity and quality of research in a specific field (as measured by Research Assessment Exercise [RAE] ratings) improved. Undergraduate samples with higher RAE scores were more likely to have negative feedback on research than graduate student samples. Both graduate and undergraduate students agreed that lecturers' research increased the instructor's credibility, relevance, and knowledge, as well as piqued and maintained their own interest, engagement, and drive. Data processing, feature selection, and model training are the first steps in the proposed approach. The data are changed from their raw form into a form suitable for academic use during the data pre-processing phase. They are employing Information Gain and Symmetric Uncertainty for feature selection. Following the feature selection process, the models are trained using BiLSTM-CNN. Both the BiLSTM and the CNN methods are inferior to the proposed method.
Improving Image Inpainting with Emphasis on Texture, Structure and Semantic Features Using BioInspired Generative Adversarial Network

Generative models have recently produced good results in image inpainting. Generally, structure, texture, and semantics are essential aspects for an image to be visually appealing. Most of the existing methods fail to consider all these three aspects, producing unwanted artifacts and fuzzy textures, especially for images with large damaged regions. Being an ill posed inverse problem, it is appreciable to solve the problem of inpainting from the perception of prior information. So, the proposed work suggests an inpainting framework named TSSI-GAN: Texture, Structure, and Semantic-based Inpainting - Generative Adversarial Network, which aims to extract the texture, structure, and semantic context of the degraded input image and adopt this knowledge as priors to perform the inpainting process. To obtain photo-realistic images as output, the optimization of the discriminator used in the inpainting module is an essential factor. So, the presented work introduces a novel and persuasive classification approach named Dolphin-Chicken Swarm Optimizer (DCSO), which is used to optimize the discriminator of the inpainting module. The usage of DCSO enhances the inpainting process to produce optimal results such that the inpainted images look visually appealing, and also the similarity between the ground truth and inpainted images is highly appreciable. The proposed DCSO incorporates the Dolphin EchoLocation (DEL) Algorithm with the chicken swarm behavior of the Chicken Swarm Optimization (CSO) algorithm. The proposed framework is evaluated using Flickr Faces-HQ (FFHQ) and DIV2K datasets and the results prove that the suggested framework excels the state-of-the-art models.
Performance Evaluation of Sign Recognition Protocol Using Hand Gesture Mechanism

For people with hearing loss or for those using sign language (hand gestures) as their major form of communication, a sign language recognition system is used. The major aim of sign recognition utilizing hand gestures is to allow successful communication and accessibility. It was investigated which algorithms best distinguish a hand motion. The diagonal sum algorithm was discovered to have the highest accuracy rate. A custom algorithm eliminates each training gesture's background during the preprocessing stage. The image is then transformed into a binary image, and the sums of each diagonal element in the image are calculated. The process includes machine learning techniques particularly deep learning architectures like Convolutional Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), which are used to specify and interpret these gestures from images. Accuracy rates exceeding 95% are commonly achieved for well-defined and relatively basic hand motions, such as recognizing a restricted range of static signs, especially with well-curated datasets and powerful machine learning algorithms. Gesture recognition systems created for certain controlled situations, such as isolated hand motions under perfect lighting, may achieve even better accuracies. However, getting high accuracy gets increasingly difficult as the complexity of gestures grows, such as recognizing dynamic or continuous hand movements and gestures with fluctuations in illumination, backdrop, and user behavior.
Comparative Analysis of Various Elliptic Curve Cryptography Algorithms for Handheld Devices

Today, all functions like purchases, banking, payments, social media, games, etc. are carried out using mobile phones. While performing these functionalities data transmission and authentication through the mobile device are important parts. The paper defines elliptic curve cryptography as a PKC scheme that can satisfy those demands. The application of elliptic curve cryptography (ECC) in such a limited setting is examined in our research along with the security and effectiveness of ECC. This paper assesses the effectiveness of ECC by conducting a comparative analysis of various implementation methods. The primary objective is to determine the optimal approach for ensuring user identity verification, authentication, and digital signing on mobile devices. The effective approach is then evaluated for mobile applications. To determine whether ECC encryption and decryption can handle all restrictions and provide good security, it is implemented and tested on the user module.
An Efficient Implementation of AES Algorithm for Cryptography Using CADENCE

Modern-day technological advancements have led to the increasing amounts of data being transmitted and received. Security, being one of the major concerns, several data encryption algorithms have been employed to protect the data from fraudulent usage. Out of the several algorithms, AES has been widely accepted for providing higher degree of confidentiality and simplicity in implementation. Several modifications have been suggested by researchers in enhancing the security of this algorithm. This work presents a novel technique for dynamic S box generation in AES cryptography. The module used for key expansion of the AES implementation is modified to reduce the power consumption and the area occupied by the structure. The proposed method of implementation using NAND gates can be used in FPGAs for reduced power consumption when compared to the normal implementations. In this work, a novel method for implementation of Substitution BOX using NAND gates is presented and the effectiveness of hardware implementation and its power requirements are presented. This implementation of Advanced encryption standard is carried out with Cadence Virtuoso. The implementation of AES algorithm is being carried out in secure file transfer protocols like FTPS, HTTPS.
Enhancing Biomedical Image Interpretation Through a Hybrid Machine Learning Algorithm

In contemporary medicine, biomedical image interpretation is essential for disease diagnosis and the selection of appropriate treatments. However, manually scrutinizing these images is time-consuming and may lead to erroneous conclusions. The proposed work offers a novel approach to resolving these issues by utilizing a mixed machine-learning technique to improve the interpretation of biological images. The proposed system accurately evaluates physical images using machine learning techniques, including convolutional neural networks and decision trees. The algorithm aims to integrate the most beneficial aspects of multiple image analysis methods to enhance their overall performance. Using a carefully selected dataset, demonstrate the algorithm's precision and robustness compared to other approaches. The findings imply that the algorithm could substantially alter the interpretation of biological images in clinical and academic settings. This discovery has far-reaching implications, paving the way for improved diagnostic precision and further study of the human body. This hybrid method is a promising step toward automating image processing and paves the way for new research and implementation opportunities in healthcare technology.
Progression of Disease Forecasting in Recurrence Cervical Cancer Using Logistic Regression

Cervical cancer is the fourth most prevalent disease in women globally as well as main cause of cancer- related mortality among women. The overall survival rates of cervical carcinoma patients are significantly impacted by the detection of recurrence. The disease is entirely treatable, though, if it is found in its early, precancerous levels. According to current figures, the death percentage a few decades ago was excessive. Women who have previously had treatment with CIN3 are most at riskof developing cervical cancer once more. The dataset includes information on the effects of various treatments on post- recurrence survival. The progression of a disease is detected in this research. Of the 4913 patients who were diagnosed withcervical cancer overall, 260 women with the disease suffered recurrences. The Data has been roughly categorized into 27 characteristics that are important for illness staging and therapy. Thevery important attributes selected are FIGO 2009, chemotherapy,radiotherapy, recurrence confirmed by a pathologist, etc. Five different models are trained to predict the progression. Logistic regression is finally predicted with a high level of accuracy of 96.3%. Two hundred and sixty patients with 27 attributes consistof a number of missing values. Research is implemented through the Orange platform. The missing values are balanced through Average and Most frequent data imputation method. Important attributes are selected through the ReliefF technique, and then disease progression is predicted by 5 different algorithms Logistic regression, Support vector machine, Naive Bayes, Stochastic gradient decent and neural network. Finally logistic regression predict the progression of a disease with highest accuracy of 96.3%.
Advancing Heterogeneous Face Recognition Through Convolutional Neural Networks DenseNet

Heterogeneous Face Recognition attempts to generate face image cropped over multiple techniques, such as visible, near-infrared and existing HFR techniques often rely on incomplete models or features artificially incapable of fully accounting for complex cross-domain differences Using a densely connected network structure called Convolutional Neural Networks (CNNs) and DenseNet to improve feature propagation and reuse, The study presents a deep learning for HFR in this study. A DenseNet-based model is build that can train robust and fully discriminative features for HFR. The study also develops a unique loss function that combines cross-entropy and center loss mixing to improve intraclass compression and interclass separation of learned features The study demonstrates that the proposed technique outperforms previous deep learning algorithms for HFR, 2005. state-of-the-art performance and achieves. The study also investigate how network configurations and loss functions affect the efficiency of the HFR. Proposed findings support the utility and superiority of our approach to HFR.
An Evaluation of Extreme Learning Machine Algorithm to Forecasting the Gold Price

Machine learning methods are going to carefully evaluate the gathered data along with produce structure that aid in efficient decision-making. The strategies that are utilised to successfully analyses the enormous quantity of information involve machine learning techniques. There is always a need for gold. The recent increase in gold prices demonstrates that certainly one of the strongest approaches to investing is gold. Predicting the trend of the gold rate is therefore essential. In recognition of the economy and financial markets, effective gold price prediction is necessary. Therefore, price prediction requires the use of machine learning prediction models. The paper suggests an innovative method for periodicity extreme learning machine to predict gold price data that was obtained via publicly accessible sources based on prospective collected consistently gold. This article has been founded on research done to better understand the association involving the price of gold and a few of the elements that affect it. These statistics had been analysed using machine learning methods, regression models, random forest statistical regression, and gradient enhancement regression models. It is discovered that there is a significant link between the various factors at this time. The models described have decent data fit through the time, however random forest regression modelling is proven to have greater period-wide accuracy in predictions.
SignSpeak - Sign Language Interconverter Using CNN-based Approach

This research paper is oriented towards the creation of a mobile app called SignSpeak, which serves as an all-encompassing solution for converting sign language to text and vice versa. It accomplishes this through a Convolutional Neural Network (CNN) approach. The paper begins with the evaluation of existing tools for sign language interpretation, identification of their limitations, and the elucidation of how SignSpeak offers an effective remedy for these issues. It then provides an extensive account of the system's implementation, covering the app's development, data gathering, model selection, CNN model training, and the integration of the app's front-end with the trained model's back-end. Subsequently, the paper offers an intricate description of the system's functional and operational aspects. Finally, it delves into the achieved results and outlines future research opportunities.
Тhe Positive Customer Experience as a Factor for Guest Satisfaction and Loyalty

In the contemporary hospitality industry, superior customer experiences are essential to gaining customer loyalty and achieving a competitive advantage. The guest experience lies at the heart of hospitality and is a major driver of loyalty and personal recommendations. Guest satisfaction reflects the experience guests have in the hotel establishment. But it also reflects the performance the hotel achieves through the services it offers and the way it differentiates itself from its competitors. That is why it is important that guest satisfaction and experience are at the very heart of a hotel's management strategy. Improving customer satisfaction is a critical component of the hospitality industry’s value proposition to the guests (Siguaw a. Enz,1999). Consequently, the hospitality industry spends millions of dollars per year to better assess guest satisfaction and understand the elements of loyalty. With an understanding of what causes guests to stay and to return, hoteliers can act to increase loyalty. This article aims to provide an overview of what is known so far about customer experience and identifies some emerging issues that play an important role in it. It will also describe ways that create excellent customer experience. This will help practitioners to understand the factors that influence customer experience and thus to choose the right satisfaction effective strategy which will turn their guests into loyal customers.
The SBC package - check your models before you wreck yourself

To celebrate a new paper out in Bayesian Analysis, let’s talk simulation-based calibration checking (SBC). SBC is a method where you use simulated datasets to verify that you implemented you model correctly and/or that your sampling algorithm work. It was introduced by Talts et al. and has been known and used for a while, but was considered to have a few shortcomings, which we try to address.
Realtime object detection and recognition device for blind and partially blind People (NanoEye)*

In present era, where technology is improving everyday wellbeing of mankind as a whole, some people are left behind due to their accessibility needs, such as blind and partially blind people. As a result, there is a need for a system that can assist visually impaired individuals in improving their lives and enjoying the benefits of contemporary technology. The proposed framework focuses primarily on the requirements of visually impaired persons by providing them with a head-wear device that can assist them in recognizing various objects, people, and pictures in real-time. The purpose of creating and implementing the object detecting device is to use modern technology to mimic how a normal person identifies and perceives things via his eyes and brain in the suggested framework for assisting blind and partly blind people. The object detection headwear device works as a substitute to human eye and brain that proves to be beneficial to a blind and partially impaired person. After identifying objects in front of the user, the NanoEye sends an audio signal to the user, indicating how far the object is and what sort of object it is.