Reviewerly/exHarmony huggingface dataset:
House Price Prediction Using Gradient Boosting and Linear Regression

Everyone dreams of buying and living in their own house, which is suitable for their lifestyle and suits their personality. The main objective for which people look at a house is the square footage of the house, the number of bedrooms and bathrooms, the location of the house, and the year it was built. This qualitative research helps to get an accurate price for the house that is suitable for their budget and will not affect them financially. It also helps a person buy a house according to his or her requirements. There are many machine learning algorithms out there, like Gradient boosting regression, linear regression, polynomial regression, and random forest regression. After researching on every algorithm, the best accuracy algorithm will be chosen to predict the house price.
Improving Efficiency of Panel Using Solar Tracker Controlled Through Fuzzy Logic

In this research, application of fuzzy logic as an intelligent quality strategy for controlling a two-axis sun tracking solar energy system. Solar panels are most effective when oriented perpendicular to the sun's journey across the sky. The two-axis sun tracking solar energy technology relies on software for its architecture. In this research, a method for optimizing the output power of a solar panel in a photovoltaic system by using a combination of two fuzzy controllers, one for tracking the sun and one for finding the point of optimum power production is applied. Using two strategically placed DC motors, the solar panels' horizontal and vertical orientations may be adjusted to follow the sun throughout the day. To obtain the maximum power output of the solar panels, a DC-DC converter is used. Resultantly, the proposed system uses innovative fuzzy logic to maximize total output by making the most of both the amount of photovoltaic energy consumed and the duration of time during which solar energy is available.
Unlocking the Market Insight Potential of Data Extraction Using Python-Based Web Scraping on Flipkart

Data scraping is not only to scrounge or extract data from websites but extracting data in an organized structured format so that it can be used for data analysis, collecting business data, enabling researchers and businesses to gain better insights and make informed choices etc. Since internet is the biggest storehouse of data but the data available on the web cannot be directly used for data analysis purposes and other techniques because of its unstructured form and this can be one of the biggest challenge users need to overcome in order to gain valuable information within a limited time frame. The process of web scraping requires the usage of machines just as computers to access web, extract data from its pages and then to store the extracted information. Also it is important to be careful and make sure these tools are used correctly because there are some websites that may prohibit website downloading and may even consider web scraping as illegal.
Automated Navigation System for News Videos: A Survey

News videos are a valuable tool for disseminating information. However, the number of newscasts every day on a single channel is enormous, and when multiplied by the number of news channels, the volume of information transmitted each day is enormous. In such instances, sorting news videos is critical for creating a well-organized database of news videos. Topics like Video Segmentation, Video Indexing, and Retrieval of the specific news from the news videos are the trending topics and points of interest for researchers from all over the world as these topics have various applications. This paper discusses segmentation of videos, as well as indexing and retrieval strategies of the videos, focusing on some of the methods for structure analysis of videos, such as SBD (Shot Boundary Detection), scene segmentation, story segmentation, key frame extraction, commercial filtering, and speech recognition. Finally, we look at potential research directions in the future.
Crowd Social Distance and Mask Detection Using Classical Machine Learning

Due to the ongoing COVID-19 pandemic's impact on public health and safety, there is an immediate requirement for creative measures to limit the transmission of the virus. In the paper, we present a computer vision-based system for detecting social distancing violations and mask-wearing compliance in crowded public spaces. The system uses a combination of deep learning algorithms and image processing techniques to analyze camera feeds and identify violations in real-time. We describe the architecture of the system, which includes a camera network, edge devices for image processing and analysis, and a central server for data management and reporting. We also evaluate the accuracy and efficiency of the system using a dataset of simulated crowd scenarios and real-world tests in public spaces.
A Novel Clustering Protocol Using Fuzzy Logic

The presented systems are efficient in terms of key sensitivity, decrypted picture quality, confusion and diffusion, and speed. The clustering approach use fuzzy-based criteria to choose data centre for the cloud while minimising the time required for data centre selection. For clustering, a centralised technique is employed. The fuzzy rules are built using Mamdani's fuzzy inference approach. Depending on the distance & closeness of the data centre in the cloud to the cloud server, the fuzzy rules are put into practise. Three major aspects must be considered to have a more effective clustering mechanism: achieving high coverage, time efficiency, and a small number of orphan nodes by the distribution of cluster heads. In these areas, the method for clustering produces effective results. The encryption algorithm is evaluated in comparison to the current algorithm. In addition, for encrypting and decrypting data, the time spent and choosing a cloud data centre are computed and compared..
A Deep Transfer Learning Approach for Enhanced Traffic Sign Recognition in Autonomous Vehicles with NVIDIA Jetson Nano

Autonomous vehicles are crucial for improving road safety by reducing human errors that cause accidents. This paper presents the development of an autonomous vehicle system for obstacle avoidance and traffic sign recognition, using the NVIDIA Jetson Nano platform. The system leverages the YOLO (You Only Look Once) object detection model for real-time traffic sign recognition and obstacle avoidance. The YOLO model is trained on multiple datasets of real-world scenarios, enabling it to detect various objects such as cars, pedestrians, and obstacles that includes various lighting conditions and environments. When an obstacle is detected, the vehicle's control system is adjusted to perform appropriate actions, ensuring collision avoidance. In traffic sign recognition, YOLO can be used, and it is designed and trained using TensorFlow. TensorFlow is a popular deep learning framework used for building and training YOLO models, making it an effective choice for traffic sign recognition tasks. The NVIDIA Jetson Nano's powerful GPU makes the YOLO object detection and traffic sign recognition models work much faster. This allows the autonomous vehicle to process information in real-time and make quick decisions.
Anomaly Detection Approach for Human Detection in Crowd Based Locations

Currently, it is challenging to find a solution for human identification in a busy area. To deal with security issues like theft, fire, or other strange incidents, private organizations also install security cameras on their property. Deep learning has now proven to be effective in a wide range of industries, from audio and video to NLP and image recognition. For Deep Learning techniques like Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), etc., abnormal event detection is a typical application case. Modern 3D convolutional neural network (CNN)-based models for anomaly detection will be used in this study. The Avenue collection's footage of both common and atypical crowd behavior is used in experiments. By comparing the percentage of correctly detected frames to the actual data, the model's efficacy is evaluated. In this paper, we present a reliable method for spotting unusual crowd behavior. The input from numerous sensors used by various models is used to calculate accuracy. Finding outliers and giving comparable estimates is the main objective. The proposed model has the maximum accuracy (92.6%), according to the findings of the trials that were carried out.
Advancements in Human-Machine Interaction: Exploring Natural Language Processing and Gesture Recognition for Intuitive User Interfaces

“This abstract explores the integration of natural language processing and gesture recognition for intuitive user interfaces, advancing human-machine interaction. It examines the utilization of voice commands, gesture-based controls, and multimodal interactions. Key considerations include accuracy, real-time processing, user experience, and seamless integration. By leveraging these advancements, intuitive user interfaces can enhance user engagement and productivity in various domains.”.
Customized Framework for Backend Using Node JS

Backend frameworks are software libraries or tools that provide a set of pre-written code to perform common tasks and functions in web development. These frameworks are designed to help developers build server-side applications more efficiently and effectively. Backend frameworks provide a structure and set of conventions that help developers quickly and easily build web applications, increasing development speed. Many backend frameworks are designed with scalability in mind [1], which makes it easier to build web applications that can handle large amounts of traffic and data. Security features like protection against frequent web attacks such as SQL injection and Broken Authentication are often included in backend frameworks[2]. Backend frameworks provide a consistent structure and set of conventions, making it easier to support and update applications of web over time. Since these are generally used, there are a large number of developers who are familiar with the framework, and many resources and tutorials available online, which helps to solve any problems you may encounter while developing the application[3]. Backend frameworks are software libraries or tools that provide a set of pre-written code to perform common tasks and functions in web development. These frameworks are designed to help developers build server-side applications more efficiently and effectively. In summary, backend frameworks are an essential tool for modern web development, as they provide a set of pre-built tools and functionality that can help developers create web applications more efficiently, effectively, and securely.
An Enhanced Secure Authentication Scheme for the Internet of Things

The Internet of Things (IoT) has led to the proliferation of interconnected devices, including smart appliances and industrial sensors. Nevertheless, the rapid expansion of the IoT ecosystem has given rise to apprehensions regarding security, as compromised devices have the potential to result in data breaches and cyber assaults. This paper presents an enhanced secure authentication scheme for the Io'T, which aims to tackle the issues above by building upon existing authentication methodologies. The multi-step system commences with an initial setup phase wherein each loT device is assigned a unique device ID and a confidential pre-shared key. A secure key exchange protocol generates a session key for each authenticated device. The authentication server generates a random attempt to join the network or execute specific tasks. The machine then signs the data with its private key to authenticate the challenge. The server verifies the answer by comparing it with the public key signature of the device. The proposed methodology enables devices to update their session keys periodically to safeguard against potential attacks.
Deep Learning for Handwritten Character Recognition

Handwritten Character Recognition (HCR) has gained significant popularity as an Artificial Intelligence tool in today's world. Recognition of a particular character is one of the challenging tasks when every work is done digitally. This technology converts handwritten characters to machine readable or digital form by applying various machine algorithms. Since handwriting and the style of writing a particular character always varies from one person to another person. A person may write some character whose style may differ in size, shape, font and position. An implementation a Handwritten Character recognition technology that uses deep learning algorithm is used. The dataset used in this technology is EMNIST (Extended Modified National Institute of Standards and Technology) dataset. The format of this dataset is of CSV file and it contains handwritten characters, which includes both uppercase and lowercase letters along with digits and various symbols. The dataset used in this model is divided into two parts one is training and other is testing. We have performed an operation for reshaping the image into 28 x 28 pixels so that it can be fitted with Convolutional Neural Network (CNN). During the training process training data is iterated through multiple epochs. The accuracy of the model is measured simultaneously. During the testing process several grayscale images are used to test the trained model and the prediction is made accordingly This model presents the accuracy, potential and high performance of CNN model in prediction of a handwritten character. This system opens up possibilities for its usage in various other applications, as it speeds up our task of analyzing documents and digitization.
Handwritten Text Recognition Using Machine Learning

Handwritten text recognition is a difficult task with numerous applications in document analysis, postal automation, and historical document preservation. There has been substantial progress in handwritten text recognition methods in recent years, ranging from traditional machine learning-based approaches to deep learning-based methods. This survey paper provides an in-depth look at the current methods for recognizing handwritten text, with an emphasis on the most recent and promising techniques. The paper discusses a variety of topics, such as data pre-processing, feature extraction, model selection, and model fine-tuning. On benchmark datasets, the performance of various methods is evaluated and compared, and the strengths and weaknesses of each approach are discussed. In addition, the survey highlights current research challenges and future directions in the field of handwritten text recognition. The purpose of this paper is to provide a useful resource for researchers and practitioners working in this field, as well as to facilitate further progress in the development of robust and accurate handwritten text recognition systems. Aside from the foregoing, this survey paper delves into the various handwritten text recognition tasks, such as offline and online recognition, as well as the various types of handwriting, such as cursive, print, and mixed script.
Face Detection System Using Machine Learning Algorithm

This research paper proposes a face detection system utilizing machine learning techniques. The objective is to develop an accurate and efficient solution for detecting human faces in images or video streams. The system employs a deep learning approach, specifically convolutional neural networks (CNNs), to extract discriminative features from input data. A large-scale dataset comprising various facial images is used to train the model, enabling it to learn robust representations. The proposed system achieves high detection accuracy, even in challenging conditions such as varying lighting, pose, and occlusions. Experimental results demonstrate the effectiveness of the approach, showcasing its potential applications in various domains, including surveillance, biometrics, and human-computer interaction.
Lenrow: A Blockchain Based Peer-to-Peer Product Lending and Borrowing System

This research paper presents Lenrow, a mobile application designed to facilitate peer-to-peer lending of personal items within a community. The app features two main user types, lenders and borrowers, and allows lenders to post items they are willing to lend, including rental terms and conditions. Borrowers can browse items and make rental requests, and a negotiation system is implemented to allow for communication between lenders and borrowers. The app also includes a rating and review system to build trust and inform rental decisions. The research paper analyzes the adoption factors of peer-to-peer lending platforms and the user experience design choices that contribute to the success of the app. The ethical considerations of peer-to-peer lending, such as privacy and data security, are also discussed. The results of the study suggest that Lenrow has the potential to provide a safe and convenient platform for borrowing and lending personal items within a community.
Image Recognition Using Machine Learning Techniques

Image recognition has become a prominent area of research in recent years, and the development of deep learning models has significantly improved the accuracy of image classification tasks. This paper provides an overview of deep learning techniques using two models in image recognition, including deep belief network and convolutional neural network. Additionally, the paper examines some of the applications of image recognition, including object detection, facial recognition, and medical imaging. The research work in this paper provides a comprehensive overview of the state of the art in image recognition in machine learning and highlights some of the key challenges and opportunities for future research. Artificial neural networks are used in the “deep learning” branch of machine learning to mimic how the human brain learns.
Designing a Blockchain Token Distribution Model to Encourage Action on Greenhouse Gas Reduction

One way to reduce greenhouse gas (GHG) emissions is to encourage consumers to become more environmentally conscious. There are examples where consumers' GHG-reducing behavior is converted into points, and trees are planted according to the points collected. In other cases, blockchain is used to prove agricultural products' origin and distribution routes. This study proposes a model in which blockchain tokens (Web 3.0-based tokens) corresponding to GHG reductions are distributed along with the distribution of low-carbon agricultural products to encourage consumers to take environmentally conscious actions through the tokens. The proposed model facilitates the visualization and verification of consumers' environmental actions caused by the distribution of tokens, by using non-fungible tokens (NFTs). A reference implementation of the proposed model and its preliminary experiments have revealed several issues that need further investigation for social implementation. The proposed model could be suitable as a Web 3.0 educational tool, since it is non-monetary and there is no risk of economic loss.
On multiple imputation-based reconstruction of degraded faces and recognition in multiple constrained environments

Recognition of degraded frontal face images acquired under occlusion constraints remain challenging despite the plethora of reconstruction mechanisms. Though recent works have leveraged on some imputation mechanisms in this regard, their robustness in multiple constrained environments may not be guaranteed and may be affected by the choice of pre-processing mechanism. This paper proposes enhancement mechanisms that augment or complement the use of three (3) multiple imputation mechanisms for facial reconstruction in the presence of multiple constraints (10% and 20% occlusions and varying facial expressions). Specifically, we propose the use of a Discrete Cosine Transform-based (DCT) denoising or a Discrete Wavelet-based denoising following Histogram Equalization (HE-DWT) of the reconstructed face images prior to recognition. Experimental results showed that the proposed augmented enhancements improved significantly the recognition rates (90.63% & 91.15% and 86.98% & 85.94% for DCT and HE-DWT at 10% and 20% occlusion levels respectively for Missforest de-occluded face images) as compared with DWT in recognizing degraded frontal face images under moderately low levels of occlusions and varying expressions.
Extended Reality, Augmented Users, and Design Implications for Virtual Learning Environments

Recently, new technologies have been developed that allow physical and virtual space to converge. We reviewed a range of innovative technologies that enable immersive and 3D interaction, which we believe are of particular interest to apply Universal Design for Learning (UDL) principles in teaching and learning practices, with a particular interest in higher education. We found limitations related to hardware and interactive systems that cannot be customised to meet the needs of all different students and some users may be marginalised. We draw attention to problems that lead to the risk of intentional exclusion while highlighting relevant inclusion opportunities. The main contribution of this paper is to present a selection of use cases to discuss how software applications can be designed to meet the guidelines for UDL, and improve the accessibility of 3D interaction for innovative Virtual Learning Environments (VLEs).
Achieving Reinforcement Learning Speedup via Quantum Kernel Estimation

Reinforcement learning is playing a crucial role in sustainable developments and enabling wide applications in our daily lives ranging from self-driving cars, robotics, automation, personalised product suggestions, user notifications and many more. Kernel-based reinforcement learning holds theoretical guarantees of convergence to a unique solution for decision function approximation. This has motivated the development of implementations that exploit the advantage of kernel methods whilst overcoming the ‘curse of dimensionality’. The theoretical properties of kernel-based methods also support the combination of learning agents in loosely coupled systems into a single combined model for composable learning. Learning speedup is achievable by introducing expert knowledge to the algorithm through the kernel, which reduces variance by increasing bias and thus learning speed. Despite the theoretical promise of kernel based reinforcement learning methods, and continued evolution of more efficient algorithms, practical implementation are not prevalent. Research in quantum machine learning has successfully applied quantum algorithms to achieve computational speed-ups for various machine-learning tasks, and has also identified potential quantum advantage through the identification of patterns not accessible to classical data mapping. To date, this has not been applied to kernel based reinforcement learning. Recent work in the field of quantum learning suggests an important duality between quantum machine learning and kernel machine learning methods. Therefore, this paper proposes a framework to complement kernel-based reinforcement learning with quantum generated feature maps for function approximation to improve learning speed. This has positive consequences for optimisation of reinforcement learning techniques already in use to support sustainability targets in applications such as industry innovation and infrastructure, power generation management, data centre cooling and self-driving cars. Our established framework enables the adoption of emerging quantum kernel methods for reinforcement learning applications. The achieved results show over 20 % improvement and positive impacts of quantum map selection on model performance for different problems.