Reviewerly/exHarmony huggingface dataset:
Image denoising analysis by Deep Learning Algorithms

The noise in the digital images will reduce its quality. These images are likely to get corrupted in complex environment. In order to reduce such corruption an image noise reduction technique should be implemented. An algorithm-based image denoising approach has achieve a high positive rate of denoising. Deep learning is neural network-based methods which consists of multiple layered design which is very helpful in implementing the deep learning models to handle complex input images and remove noise. As the deep learning models are ready to perform many repeatable tasks, they are used in almost every field. This research work utilizes an algorithm-based approach in removing noise from the images and obtain a clear picture. So, to perform this task images from the huge databases are considered. These images are then trained and the results are tested in order to appear a high positive rate of image without noise. The qualitative and quantitative evaluation is used in order to show the recovered image and obtain the noise free outcomes.
Blockchain based Effective Ledger and Decentralization in Healthcare System

On the subject of a decentralized open healthcare network, healthcare blockchains provide a novel way to information depository, transaction execution, along with trust building for data integration and sharing. In spite of that, problems relating to safety and solitude are the major focusing concerns in terms of working with technology like blockchain for sectors like healthcare. However, blockchain technology for healthcare has acquired widespread interest and recognition in business, administration, and education. This composition targets on the safety protocols and solitude concerns for the exchange of healthcare information via blockchain and examines the threats and obligation more in detail. Privacy, as well as methodologies and technical solutions. This research study initially reviews the prerequisites and the required security and privacy features to install a healthy blockchain and share electronic health data. Furthermore, this research study confers about the function involved in blockchain, including anonymous signatures, attribute-based encryption, proof-of-nothing, and verification techniques to secure smart contracts. This research study is finally concluded by discussing about other potential use cases in medical sector. This study will provide technical and insightful information on healthcare management system using blockchain technology to efficiently deal with the safety protocols, privacy concepts, threats, prerequisite as well as developments for healthcare professionals, healthcare providers, policy makers and health service developers.
Control the Movement of Mouse Using Computer Vision technique

Physically challenged people are feeling quite difficult to operate a mouse. The suggested method employs eye moments to direct the mouse pointer as a remedy for those who are physically unable to do so. The computer vision technique is useful for controlling the mouse on a computer using eye movements. It is an alternate way that enables a person to operate their computer using their eyes alone for those who are unable to use a mouse. For those with physical disabilities, eye moment might be seen as a crucial real-time input modality for human-computer communication. The suggested method explains how to utilize a webcam and Python to implement both eye moment and moment of cursor according to eye location, which may be used to control the cursor on the screen. Eye tracking is a sensor technology that can track what someone is looking at in real time while also detecting their presence. Eye motions are converted by the technology into a data stream that includes details like pupil position, gaze vectors for each eye, and gaze point.
Comparative Study of Autoencoders-Its Types and Application

Autoencoder was first proposed by LeCun in 1987. Autoencoders are a type of artificial unsupervised neural network used to study data encodings. The purpose of the autoencoder is to get an excessive dimensional representation (code text) of high-resolution records. Dimensions are mainly reduced by training in a way that the final outcome focus on the important component. Autoencoders support us for the same. Autoencoders are designed and pre-programed neural network that rescale the input in a presentable form and back to original scale. This research study provides an overview of the different types of auto encoders, its features and areas, where it can be applied or implemented.
An Enhanced Cryptography Technique (ECT) using Laplace Transform

Cryptography is the technique used to protect the digital data in various applications. Cryptography is the sub-domain in the computer science which is used to transform the data into various formats which is not recognized by the malicious users. Cryptography is the branch of security that converts the actual message to the secure message to protect the data from attackers. Today security becomes more important in many applications. Especially in network security it plays the major role in providing the security for the network and for the network components. In many domains and applications security becomes mandatory to overcome the issues for data. In this paper, An Enhanced Cryptography technique (ECT) is developing to provide the security for the network and data based on the transmission. To improve the proposed ECT the new mathematical model introduced called as Laplace transform is integrate and provide the better security for the data. Results show the performance of the ECT.
Development of an on-Site Earthquake Early Warning System for one Private Higher Educational Institution (HEI) and Its Nearby Community in Manila, Philippines

The Philippines, an archipelago situated along the Pacific Ring of Fire, has recorded active seismic activities over the recent years. Earthquakes ranked third in terms of occurrence and first in terms of death tolls over the past 20 years with any other types of natural hazards globally. Throughout the history of natural disaster occurrences experienced by Metropolitan Manila, earthquakes pose the greatest threat to life, property, and the economy. Thus, a significant earthquake event in the Capital of the Philippines will greatly affect the nation's economy. Due to earthquake hazards' frequency of occurrence, intensity, and variability in Metropolitan Manila, the government is compelled to adopt disaster risk reduction and management plans. In this study, the proponent developed an On-Site Earthquake Early Warning System (EEWS) with the development of a low-cost seismic monitoring prototype, a web-based earthquake event monitoring system, and a seismic arrival time prediction that can be used in the early detection of arriving seismic waves and could provide an on-site earthquake early warning notification within 3 seconds. Moreover, the overall system integration of various components of the On-Site Earthquake Early Warning System had been proven to be a usable system as a holistic approach in providing proactive earthquake risk preparedness response as agreed by experts and stakeholders. Moreover, establishing a perspective of a more resilient Metropolitan Manila in the event of an earthquake.
Human Activity Recognition Using Relative Relationship between Different Human Body Regions

The Human Activity Recognition (HAR) system can be used for various purposes, including video surveillance and Senior Care, by tracking daily activities. We propose the HAR system based on the relative relationship between several human body regions. The Region of Interest (ROI) is retrieved from the video signal of the subject conducting the activity from the area of the video frame. The ROI contains humans performing the action. The human body is divided into six different regions. The spatial-temporal features are extracted for each human region. As the spatial-temporal feature, Motion Vectors (MV) and Optical Flow (OF) are calculated, then quantized to form a histogram. A relative correlation is calculated between one body region and the remaining body region. This correlation is stored in a vector to create a feature vector for HAR. The dimension of the feature vectors is reduced using Principle Component Analysis (PCA) and Discrete Fourier Transform (DFT). The extracted feature vectors are used by Hidden Markov Model (HMM) to model human activities in the Dataset. For testing, the likelihood is calculated for the test signal. The class for the test signal will be the HMM model, for which we get the maximum likelihood. The HMM models are tested on KTH and SFB588 Basic Kitchen Tasks Dataset. The effect of feature selection methods on the system's overall accuracy is studied. An experimental result on the human activity datasets suggests that the proposed human activity model performs moderately well compared to the other human activity models.
Automatic Question Tagging using Machine Learning and Deep learning Algorithms

Stack Overflow is a well-known website which is utilized by nearly everyone who learns to code, share their knowledge and publicly participate in this question-answering forum. The questions posted on the Stack Overflow forum by a user requires a minimum of 1 tag to be manually entered in by them. Tagging most commonly means to associate some single word information about the context of given text or question. Tagging a question is useful in identifying the category that a question or text belongs. It is also beneficial in providing ease of access to a person having a requirement of specific categories of questions. On analysis of tags associated with the questions on the website, it was found that a large number of the questions are labelled by more than one tags, with many of them not being tagged accurately. Due to this situation, it becomes challenging for the users to search for relevant tags. So, the main aim of this research task is to explore methods and compare different techniques in order to create an auto tagging system with the aid of Machine learning and deep learning facilities, accompanied by data preprocessing steps. The dataset for this purpose was taken from Kaggle, known as StackSample dataset, which is a dataset containing 10 percent of the questions present on the website. The output of the research performed for this purpose provided satisfactory results with scope of improvement.
Research and Analysis on Public Opinion Monitoring of Product Quality and Safety Accidents in 2021 through Crawler Retrieval Technology and Manual Data Retrieval

Product quality and safety are related to the personal and property safety of the people. In recent years, the “Xi'an Aokai cable incident” and the “Fujian Zhangzhou big head doll incident” and other related product quality and safety incidents have not only caused personal harm and huge economic losses, but also have a certain negative impact on the law enforcement image of government departments. Therefore, it is particularly important to monitor the public opinion of product quality and safety accidents and explore the risk early warning mechanism. From the perspective of public opinion on product quality, this study searches, analyzes and summarizes the product quality information of 31 provinces in the country through crawler search technology and artificial data search, points out the existing problems, and puts forward corresponding countermeasures and suggestions. The survey results show that the public opinion information on product quality and safety in 2021 is mostly displayed in the second quarter, the eastern region and the categories of food, drugs and daily necessities. Quality defects are the main reason for product quality and safety incidents. Therefore, we should strictly control the production quality, strengthen market supervision, promote industry standardization and standardization, and promote market participants to implement their sense of responsibility, Provide reliable product quality assurance for economic and social development and people's life.
IoT and Machine Learning based Peer to Peer Framework for Employee Attendance System using Blockchain

Many organisations and institutes continue to record staff attendance using conventional procedures such as attendance registers or through digital means such as bio-metric digital scanners. One of the challenges of these conventional attendance mechanisms is the lack of tamper-proof mechanisms. Paper proposes a computerised system design that automatically maintains the employee attendance process using Internet of Things (IoT) devices, machine learning (ML) models, and blockchain which act as a decentralised platform for employee attendance system. The proposed system used machine learning, blockchain, and IoT devices to maintain the reliability of the system, and provide security, transparency, etc. The proposed system provides high accuracy by using the machine learning models.
An Online Intelligent System based on Multi-Network Information Sharing Algorithm

An online intelligent system for the cultivation of entrepreneurial talents in colleges based on the multi-network information sharing algorithm is studied in the paper. With the continuous increase of the amount of information, the reliability of the information transmission of the traditional method is obviously improved. The main reason is that in a small-scale target network, when the amount of network information reaches a certain amount, the phenomenon of competition and conflict of information communication channels is less. The success rate of information sharing nodes accessing the communication link is relatively high. Swarm based intelligent algorithms have the ability to deeply explore the search space and return the global optimal solution, and have certain advantages in solving practical optimization problems. Hence, a combination of the mentioned ideas is considered to create the novel online intelligent system for the cultivation of entrepreneurial talents in colleges based on multi-network information sharing algorithm. Through the experimental test on the different datasets, the performance is validated.
A Novel Secure Apriori Honey based Machine Learning Mechanism for Securing the Transactional Data

Frequent Itemset Mining (FIM) is utilized for identifying frequent itemsets that is simultaneous to the transactional database (TD). However, the rapid increase of the applications based on big data introduces the attacks in the database from unauthorized persons. So, a novel secure Apriori Honey based Machine Learning (SAHML) model is developed in this research to secure the transactional data from the third users. Here, the proposed SAHML model is to detect the malicious activities and protects the information in the database. This model is simulated using Java and provide high performance. Also, the experimental results are compared with existing models, which proves the competence of the SAHML approach.
Feature Selection and Classification using a Positive Learning Approach Focused on Graph and Neural Network

Real-world knowledge is represented by a knowledge graph that provides assistance for various applications built on the basis of artificial intelligence. Awareness of the neighbourhood is obtained from the individuals and relationships of the Knowledge Graph. High-dimensional data analysis is a difficult task in many applications and this article discusses the dimensionality by specifying a limited collection of features that implies high-dimensional data without visible or substantial data loss. An unsupervised learning approach based on learning that uses the neural network principle and learns the features using the graph. The Positive Feature Selection approach using the Neural Network (PFSNN) approach in this paper defines features using a graph where the classification is carried out by the NN process and analyses the output of the proposed system. The efficiency of the PFSNN is evaluated by contrasting it with existing classification methods and using different datasets. Performance is measured using the classification performance metrics and it is defined from the observation that the proposed PFSNN algorithm has the best outcome.
Machine Learning based Object Detection with Theft Alert System

Object detection and tracking is popular area in computer vision. Because of its vast applications in different fields such as surveillance, tracking modules used for security and many other applications. This has allowed researchers to come up with a number of enhanced and focused methods. In the presented method live input video is used where objects are detected and it can be computer-generated for real-time through additional hardware added. An enhanced and well-organized algorithm for object detection and for security is presented. It is used to identify the objects in real time this also ensures accuracy of the system. By adding some more features in it, this system is found quietuseful for keeping a record of valuablebelongings or devices. Open CV isis used here. Thisis helpful to write complex programs using C/C++ and create a Python wrapper for it. Which can be used as modules of Python.
Application Programming Interface (API) based Student Activity Tracking System

Website for Student Activity Tracking is a web-based application that enables the admin to track the students' activities through this web application and also allows the students to upload their activities through this web application. Currently, there is no website for tracking students activity. So the admin, who is finding it difficult to know about the students' activities, uses this website to get information about the students' activities. Here this website are digitizing to track the students activity, so the students can upload their activities anywhere, anytime using a device that reduces a lot of manual work and saves time. This web application has two modules: the admin module, which contains admin details; and the student module, which shows the updates on students' activities. This website provides a way to fast access and reduce complexity.
Intelligent Attitude Analysis Algorithm based on Computer Somatosensory Technology

The system captures live images in real time through cameras and superimposes 2D pictures or 3D models of clothing on the images of table tennis players combined with somatosensory detection devices, to realize the fusion of virtual clothing and athletes' postures, and successfully obtain depth images through the Unity3D virtual reality platform. Data and bone and iliac tracking data and preprocessed the data to obtain a method of customizing user poses in Unity3D combined with Kinect somatosensory devices. Develop a set of independent property rights, easy to operate, stable performance, and can be used directly the “Intelligent Table Tennis Special Technical and Tactical Video Analysis System” that is practiced in table tennis and can be put into the market.
Investigation on Shor Codes as Degenerate Codes but Correct All Single Quantum Errors

In this paper, we deeply investigate the capability of the famous 9-qubit Shor codes, and reveal whether the codes can correct fully single-qubit errors or not. This paper records all syndromes in a table to easily distinguish the correctable errors. We perform a series of computer simulations for quantum communications under the depolarizing channels for messages encoded using Shor codes to evaluate the performance. We found that Shor codes are degenerate codes having three 3-pair of similar syndromes involving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{Z}$</tex> errors, but the codes surprisingly can correct all single qubit errors although three 3-pair of error patterns have the same syndrome. We have successfully revealed the reason for this unique capability. Furthermore, for the given stabilizers of the Shor codes, we provide performance evaluations based on quantum word-error-rate (QWER) according to the syndrome-based error detection and error correction.
Embedded Photographic Tomography Using Raspberry Pi

A computerized tomography scan (CT scan) is a device that uses X-ray machines and computers that will compute the data gathered from patients to create cross-sectional images. A CT scan can be used to view the inside without cutting the body open, such as blood vessels and different organs. However, most CT scan is mainly located at the hospital because it is very expensive and must be supervised at all times. So, it is hard for students to learn and understand the mechanic behind the actual CT scan. In this paper, we will build a simulated CT that can be used for educational purposes by creating an embedded photographic tomography using raspberry pi. The proposed system is capable of successfully creating a 3D model of the test object.
Role of Machine Learning in Fake Review Detection

In today's culture the growing technology is promoting a lot of products and events in a very positive way. Technology usage in current generation has taken a new step in reaching great heights. But when a technology brings in so much positiveness it also has its own negative usage and one among them is the fake reviews. Fake reviews are weakening the actual worth of the product. To be more specific, the reviews can be divided into two categories: legitimate fake reviews and reviews written intentionally to decapitate the product or brand value. On the other hand, the machine learning algorithms are extensively used. The incorporation of machine learning techniques into the classification of the reviews is considered as an excellent combination. In this work, various datasets from different industries such as airline industry, movie industry and food industry are considered and fake reviews are classified using various algorithms including K-Nearest Neighbors, Naive Bayes, Random Forest, Decision tree, Support Vector Machine, Logistic Regression from Machine learning. There are reviews which can be decoded using the sentiment analysis from Natural Language Programming. Sentiment analysis is used to find the emotion in a text. The accuracy parameter result is analyzed for all the implemented models. The results demonstrate support vector machine technique giving high accuracy compared to other machine learning classification techniques.
Blockchain based Secure System for the Internet of Drones (IoD)

At present, drones are being used at a higher rate than ever. Frequently, they have been often used in military services. Therefore, it is the need of the hour to increase the smartness, privacy, and security of drones as they provide confidentiality and validity in the transmission of data. This research study has suggested and implemented a blockchain-based secure system for the internet of drones. It provides seamless authentication and registration for drones with better data storage security. The proposed method is evaluated on dummy drones to check the security and confidentiality of the system.