Reviewerly/exHarmony huggingface dataset:
Research on image stitching algorithm based on improved SURF features

To address the problems of uneven feature points extracted by traditional SURF algorithm in image stitching, low correct matching rate and high time complexity, this paper proposes a fast image stitching algorithm based on improved SURF features. Firstly, we use the advantages of robustness and low complexity of SURF algorithm to detect feature points; secondly, we use the advantages of FLANN algorithm to adjust parameters for accuracy improvement to match feature points; thirdly, we use RANSAC algorithm to remove feature points with incorrect matching and perform the calculation of single response matrix to find the best model matching pair and reduce the error; finally, we use OpenCV Stitcher is used for image stitching and fusion. The experiments show that the algorithm has better robustness than the traditional SURF algorithm under various complex situations such as image angle rotation, brightness change, scale scaling, etc., and can better improve improve the matching accuracy and matching efficiency.
Study on Implementation of Electronic Health Records using Blockchain Technology

The recent technological innovation, Blockchain system is used to store data in a way that makes it difficult or impossible to edit, hack, or steal the data. It is an important part of technology with a decentralized architecture, distributed database, and continuously expanding records. There are many other technologies that are used along in blockchain. After doing critical study on many documents and understanding the technologies used by various studies, its necessities, and the issues encountered by it while building their respective models. The purpose of this study is to examine various tools and technologies that can be used in blockchain. Around 200 research works have been collected and up to 40 research studies are filtered. Research studies are filtered based on various factors. Some research studies were excluded based on title, few were excluded based on abstract and titles. This study is categorized in terms of Blockchain using Ethereum, Electronic Health records, Smart Contracts and Health monitoring.
Crime Hotspot Detection using Optimized K-means Clustering and Machine Learning Techniques

Over the world, crimes are becoming increasingly complicated and technologically advanced. It has become essential to prioritize actions targeting crime categories within every area. Nowadays, people are more concerned and stressed by the unprecedented increase in city crimes and violations. Large numbers of crimes are perpetrated frequently each day. Crime data analysts can aid law enforcement officials in discovering criminals more quickly. Crime analysis is an organized method of categorizing locations into crime hotspots and non-hotspot zones. The dataset utilized is the Kaggle-obtained UCI Crime and Communities Dataset. The algorithm can categorize the areas into hotspots and non-hotspot areas by examining crime records and associated variables. The suggested system uses data mining and machine learning techniques, such as optimized K-means clustering and the elbow approach, to explore datasets and analyze crimes committed. Three classification algorithms - Decision Tree Algorithm, Support Vector Machine, and Random Forest Algorithm are included in the model. They are trained on the dataset used to determine if an area is a hotspot based on the criteria that influence the occurrence of crimes in a region.
Virtual Mouse with hand gestures using AI

This research presents a novel system to control a mouse using hand gestures. Traditional mouse controls require the user to use a physical device, such as a trackpad or a mouse. By using hand gestures, the user can interact with the virtual mouse in a more natural manner. The proposed system uses hand tracking techniques to capture and track hand gestures, and uses a set of customizable rules to interpret them into actions. Without using a hardware mouse, the computer can be operated remotely based on hand gestures and can perform left-click and right-click operations. It is based on artificial intelligence for detecting the hands. So, the usage of this virtual mouse will reduce the rapid spread of corona virus by reducing the human-computer interaction.
Recommendation of Contemporary Fashion Trends via AI-Enhanced Multimodal Search Engine and Blockchain Integration

The evolution of advanced technology paved way for various automations and innovations in the manufacturing and production sectors in clothing industry. The fashion is a form of expressing one expression in the form of color and designs. The interest of clothing for a particular model in a particular intervals of time are termed as fashion trend. It is formulated and evaluated through the sentiment analysis. This helps to extract the people’s interest towards the particular design and theme. The innovation of fashion trend is increasing rapidly due to the advancement of digital environment. This helps to access the information from anywhere through online platform. This is implemented through augmented and virtual reality environments through artificial intelligence. The influence of clothing and its brand are enhanced through the social media interconnections. Proposed model helps to easily access one’s interest through the search engine modalities. The entire functioning and transactions in the online platform are done through the block chain technology. This helps to obtain safe and secure transactions and storing of information without any external consequences.
Augmenting Transparency and Reliability for National Health Insurance Scheme with Distributed Ledger

Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana is a chance to improve the health of hundreds of millions of Indians and get rid of or lessen medical poverty in the nation. Many individuals are excluded from the plan because of dishonest actions taken by the administrators or the recipients. In this research article, a blockchain-based solution for implementing a national health insurance scheme is proposed to improve the transparency and reliability of the Ayushman Bharat healthcare program. To publish to the blockchain or distributed ledger, the Aadhar database is synchronized with the Ayushman Bharat database. At numerous points, including registration, pre-authorization at hospitals, claim reimbursement, and uploading the patient’s medical bills on the blockchain ledger, this database is used for user eligibility and verification. The implication of NFC, IPFS and distributed ledger enhances the data privacy and security in the process, which can maintain the versions of the transactions. The proposed methodology covers all the aspects of the national health insurance scheme and hence the existing procedure can be shifted without any alterations in the rules of the health insurance system.
A lightweight face detection algorithm for wearing masks

The emergence of a new coronavirus pneumonia in 2019 has had a major impact on the world, this virus can be transmitted through airborne droplets and other carriers, and wearing a mask correctly in public can effectively prevent the transmission of the virus. Therefore, this paper proposes a lightweight face detection algorithm for wearing masks that can be applied to embedded devices. The algorithm introduces a contextual attention module based on the PP-PicoDet algorithm, which enhances the connection between feature contexts, makes the model pay more attention to face mask wearing-related features, and uses migration learning to help the model learn face mask wearing features better. A dataset of 8967 images, including both normal faces and mask-wearing faces, is created and manually labeled for network training. The experimental results show that the proposed method can effectively perform fast face-mask wearing detection, and can run as fast as 59 frames/second on the NVIDIA Jeston Xavier NX embedded device.
Breast Cancer Detection in Deep Learning based Architectures using Mammogram Images

Breast cancer is a very devastating type of cancer that typically develops in breast cells and in the past few decades, the incidence rate of breast cancer has risen steadily. The diagnosis mostly depends on the radiologists’ experience, questionable diagnoses are common, and there are worries about lawsuits due to incorrect diagnoses or missed lesions. Therefore, developing deep learning models for breast cancer identification is urgently needed, despite substantial advancements in the treatment of primary breast cancer during the past ten years. In Deep Learning, the convolutional neural network can be a useful technique for classification problems.In this study, some of the Transfer learning models are applied, which are pre-trained models that are trained on huge datasets such as the ImageNet dataset, which contains about 1.2 million images. However, these models are VGG16 VGG19 Mobilenet V2, and it is known that these models are used for classification problems, and therefore it will be used in this study in addition to developing a Convolutional Neural Network, which achieved the highest accuracy and best performance, compared to the applied models. These models were applied to the presented Mammogram Images dataset, which contains eight classes, each of which represents a form of mass that can be cancerous or benign. And since the dataset is unbalanced in terms of the distribution of samples for the classes, the Oversampling technique was applied, which had a key role in improving the performance of the models, especially for the CNN network, which obtained the highest accuracy of 96%.
Using Machine Learning to Detect and Classify URLs: A Phishing Detection Approach

It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. The training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. This study has implemented a parallel data processing pipeline so that handle the large dataset. This pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users.
Graptolite image classification based on feature transfer and mixup data enhancement

Aiming at the problem of special shape of graptolites, insufficient training samples and manual identification of graptolites consumes a lot of time and energy, a new Graptolite image classification based on feature transfer and mixup data enhancement is proposed. First, we expand graptolite dataset by using traditional data enhancement and mixup to generate mixed samples, then use the deep learning model pretrained on the large-scale ImageNet dataset to construct a new transfer learning model through feature transfer. Finally, tenfold cross validation method is used to test the recognition accuracy of the model. The experimental results show that the classification accuracy of the proposed method on the graptolite dataset is 88.65%. Compared with the traditional data enhancement method and the pretrained model trained by ImageNet directly, the classification accuracy of graptolites is improved by 1.19%, which shows that the method has better recognition effect.
Smart Mirror Monitoring using Raspberry Pi

Numerous smart devices that improve our daily lives have been developed as a result of the quick development of technology. One such innovation is the smart mirror, which blends digital capabilities with the functioning of a conventional mirror. The smart mirror monitoring system suggests in this project makes use of a Raspberry Pi, allowing for real-time monitoring and interaction with various data and apps. This project’s main goal is to create and put into use a smart mirror that can show a variety of data, including the time, weather updates, calendar events, news feeds, and social network notifications. A Raspberry Pi, a two-way mirror, a display screen, and different temperature sensors are the hardware necessities for this project. The central processing unit, or Raspberry Pi, is in charge of collecting data from sensors, locating pertinent information on the internet, and displaying it on the surface of the mirror. Python will be used to create the smart mirror software system, together with libraries for image processing and data visualization like Open-CV and API for obtaining outside data. Graphical user interface (GUI) frameworks will be used to develop the mirror’s user interface in order to make it simple and easy to use.
Next-Gen Authentication and Tracking Public Transport using Smart QR Codes

In recent years, the transportation industry has undergone significant advancements, and the need for secure and efficient authentication systems has become increasingly important. Traditional ticketing methods, such as paper tickets or magnetic stripe cards, have several drawbacks, including high cost, inefficiency, and security concerns. To overcome these challenges, the use of smart QR codes in public transportation systems has emerged as a promising solution.This survey paper provides a comprehensive overview of Next-Gen Authentication Pass and Tracking for Public Transportation using smart QR codes. The various aspects of smart QR codes, including their architecture, working mechanism, security features, and benefits. The key focus is to identify the potential benefits and challenges associated with the implementation of smart QR codes in public transportation systems. Smart QR codes have the potential to revolutionize the public transportation industry by offering secure and efficient authentication and tracking systems. However, to fully realize the benefits of the technology, there is a need for collaboration between stakeholders, including transportation operators, technology providers, and regulatory a uthorities. The research concludes by identifying the key research gaps and future directions for the implementation of smart QR codes in public transportation systems.
Designing a User-Friendly and Responsive AI based Image Generation Website and Performing Diversity Assessment of the Generated Images

This research paper presents a website that leverages the power of the Image GPT engine for image generation. The website allows users to input a textual prompt and generate a corresponding image using Image GPT’s natural language processing and machine learning capabilities. The paper details the architecture of the website, the Image GPT API integration, and the algorithms used for image generation. Additionally, we present the results and evaluate its quality and discuss potential applications of this technology in various industries such as advertising, art and design. We also discuss how the performance of image generation can be potentially improved. Overall, the website demonstrates the potential of combining natural language processing and machine learning for image generation and opens up new avenues for future research in this field.
Cloud Computing and its Emerging Trends on Big Data Analytics

Cloud computing and big data analytics are two rapidly growing fields in technology industry. The way that organizations store, handle, and analyze their data has been changed by cloud computing. Big Data Analytics has become an essential part of utilizing the potential of cloud computing as a result of the growing volume and complexity of data. This research study explores the emerging trends in big data analytics within the context of cloud computing and examines the fundamentals of cloud computing and how it has altered the field of big data analytics. This study analyzes the effects of different developing cloud computing technologies on big data analytics, including serverless computing, multi -cloud computing, and edge computing. This study also discusses about the opportunities and problems associated with using cloud computing for big data analytics, such as security, scalability, and cost effectiveness. At the end of the seminar, participants will have a thorough understanding of how cloud computing and Big Data Analytics relate to one another as well as knowledge of the most recent developments in this quickly developing sector.
An Efficient DBSCAN with Enhanced Agglomerative Clustering Algorithm

One of the most important problems in a large-scale distributed database is the detection of point outliers. Many studies have used clustering to address these issues, and DBSCAN appears to be the most effective method. The paper concentrated on reducing the high temporal complexity of the problem by reducing the data dimensionality of the data points. Due to these issues, previous techniques are not adequate for minimizing inter-and intra-cluster variation on real-world datasets that call for quick responses. In order to tackle this issue without requiring more time complexity or memory, the paper introduces a DBSCAN with Enhanced Agglomerative Clustering (DBSCAN-EAC) technique. This algorithm uses a concurrent execution of Reverse Nearest Neighbor graph traversals using density-based clustering.
Towards Detecting the Fake Profile in Social Media using Hybrid Model of LSTM and Extreme Learning Machine

The last decade has seen an explosion in the use of online social networks (OSNs), with many people relying on them for the majority of their social contacts. They use OSNs to maintain relationships, disseminate information, plan events, and even do business online. Attackers and imposters looking to steal personal information, distribute fake news, or engage in other forms of bad behavior have taken notice of the explosive expansion of OSNs and the wealth of information about its members. However, academics have begun exploring effective approaches to identify suspicious or fraudulent accounts by analyzing account data and applying classification algorithms. In addition, results from stand-alone categorization algorithms aren’t always accurate, and some of the exploited account attributes actually have a detrimental impact on the final conclusions or have no impact at all. The solution provided makes use of preprocessing methods like tokenization and the removal of stop words. Chi-squared features are used to select and extract features before an LS TM-ELM model is trained. The proposed method outperforms the previous two models significantly.
Indian Sign Language Character Recognition System

Sign language is a vital mode of communication for the deaf community worldwide. However, even to this day, there exists an evident communication gap between signers and non-signers. To address this problem, researchers have proposed automatic sign language recognition systems. Despite extensive research on American Sign Language (ASL), there is limited focus on Indian Sign Language (ISL). The approach in this paper tries to explore the challenges and opportunities involved in developing an automatic sign language recognition system for ISL. The aim was to narrow down the communication gap for the hearing impaired communities and provide a tool for better accessibility. The paper identifies the gesture recognition and skin segmentation challenges while trying to provide an end product capable of recognizing different characters of ISL. Deep learning models were experimented to obtain a good accuracy score while also keeping the process cost and resource efficient.
A novel speckle-robust feature map and research on rotation invariance of feature maps in the SAR image

The feature map is often used for describing the characteristics of key points in image registration applications. Due to feature information being sensitive to rotation and noise, the values of calculation methods for various feature maps would change in different rotation or noise conditions. Consequently, it is unfavorable to obtain the stable descriptor for the same feature point in different images. Specifically, speckle noise damages the synthetic aperture radar (SAR) image seriously, which makes SAR image registration more difficult. In this paper, a novel feature map, which is robust to speckle noise, is proposed. A fully size-adaptive sliding window method is embedded into the ratio of exponentially weighted average method for its noise robustness improvement. Then, a series of Monte Carlo experiments compare the rotation and speckle stabilities among various feature map methods comprehensively. The experimental results reflect that the proposed feature map method is stable while speckle noise increases, and it has less mean orientation difference than others. Moreover, the data of experimental results can be used to support many other analyses on feature maps.
Document Analysis using Deep Learning Techniques

Document layout analysis is an automated process that recognizes and extracts the content and structure of various documents, including books, newspapers, and forms, using computer vision and machine learning techniques. Its main purpose is to enable accurate and efficient processing of vast amounts of digital documents in fields like digital preservation, document management, and information retrieval. Document layout analysis is widely applicable and can help with tasks such as data extraction, document classification, and information retrieval, which makes it an essential component of many modern information processing systems. This implements the layout parser and CNN architecture. The layout parser is a Python library wherein the library contains all the algorithms like R-CNN, CNN, and Faster-CNN. CNN is used to classify different components of the document based on their position in the layout, Spacy is used to extract the document and perform the natural processing tasks on the extracted text. The result is a structured representation of the layout and contents of a document that can be used for better document understanding, and information retrieval.