Reviewerly/exHarmony huggingface dataset:
Fraudulence Detection of Medical Images using Pixel Level Algorithm

Healthcare is one of the important, sensitive and superior department for a country. While the healthcare sector is developing, security and privacy becomes our major concern. Trust between the doctor and patient is the major foundation in medical field. This type of fraudulence may put that trust at risk by providing wrong treatment on the basis of the forged images. Technologies like these makes the job easier, establish strong trust and secure client’s dignity. Our project gives a system of fraudulence detection of medical images for the healthcare department to verify that images related to health are not tampered, changed or altered. In our model we used the Hybrid Median-filter-based noise reduction technique. Our model constitutes SVM+ELM classifiers and their combined result is subjected to Bayesian sum rule and whether fraudulence is involved or not is decided.
Analysis of Government Flagship Programs using Public Feedbacks

In the last decades, a variety of government policies are introduced in the areas of teaching, healthcare, games, and social safety. The assessment and measuring of the impact of these programs on society is a difficult and tedious job. The real success of any of the government policies depends on how it makes a difference in public life. Citizen feedback on government policies plays an important role in deciding the success and failure of the scheme. In current times, the major concern is the unavailability of any digital medium to evaluate citizen feedback on various government programs. In this article, a government online platform is proposed in which citizens can provide their opinions about the functioning of various government programs. Along with this, the ministry of concern department is included to evaluate the public reviews to estimate the success or failure of the scheme. Feature-level sentiment analysis is performed on large public-produced reviews to find citizen overviews. Feature-level sentiment extraction was performed using a deep learning-based method to retrieve sentiments and feedback regarding various aspects mentioned in the reviews. The investigational findings were reviewed in the planned study to assess the effect of the public reviews’ feature-based analysis. The findings of this work can help individuals, companies, religious institutions, academics, or researchers as they think and rethink their decisions.
Involvement of Functional Programming in Language Processing and Machine Learning

The involvement of functional programming is allowed to be determined in this research article. Therefore, the impact of language programming is an important factor that has an impactful role within functional programming. Different objectives take place within this research article. Moreover, the proper methodology to understand the data collection process has to be highlighted in this portion. Impact of functional programming, therefore, different ways to understand the role of language programming has to be highlighted in this research article. Additionally, different challenges that have to be highlighted due to machine learning and language process have to be addressed in this research article. The problem statement is the important part that is allowed to be addressed within this research portion.
A Design of Face Recognition Model with Spatial Feature Extraction using Optimized Support Vector Machine

In recent times, the face recognition is regarded as the better technique in the computer vision process. It is also considered as one of the biometric processes because; it is easier as well as acquires a large range. But, the face recognition process is always considered as a difficult process in facial expression, pose, and illumination and in computer vision. In other words, it is defined as the system application in order to automatically detect the person over the still images. In this work, a new model for extracting the spatial features for face recognition is developed. Initially, the standard benchmark images are collected and fed to the pre-processing phase, which results in enhancing the quality of images. Then, the spatial features are extracted using the Local Binary Patterns (LBP). Next, the feature reduction is performed using Principal Component Analysis (PCA) for easier visualization and analysis of data. Finally, the gathered PCA-reduced features are fed to the optimized Support Vector Machine (OSVM) for performing the face recognition process, where the parameters of the SVM are tuned via a Rat Swarm optimizer (RSO). The recognition rate of the proposed approach has achieved higher accuracy than other approaches.
Sentiment Analysis for Real-Time Micro Blogs using Twitter Data

The basic purpose of sentiment analysis is to determine how someone feels when they comment or express their feelings or emotions. Positive, neutral, and negative emotions are the three categories into which emotions are divided. Everyone will use and apply this analysis on social media; online; everyone expresses their opinions by clicking on the like, remark, or share buttons. Using the Random Forest, SVM, and Nave Bayes algorithms, the Twitter tweets in this study were identified as positive or negative, with F1-Scores of 0.224, 0.410, and 0.702, respectively, and accuracy values of 50%, 52%, and 73%.
The Performance Analysis of Network Security Management Model in High Speed Computer Networks

In today’s world, technology has become an integral part of our lives. We rely on the internet to communicate, shop, and access information. With the increasing use of technology, security measures have become a paramount concern. Network security management is the process of protecting networks and the data they contain from unauthorized access, misuse, and destruction. In this paper, the performance analysis of network security management model has proposed in high speed computer networks. The Network security management includes a variety of measures that can be taken to protect networks from malicious attacks. These include the use of firewalls, antivirus software, encryption, and two-factor authentication. Firewalls are used to block unauthorized access to networks and help to limit the damage that can be done by hackers. Antivirus software scans for malicious programs and deletes them from computers. Encryption scrambles data so that it is unreadable to anyone without the correct key. Two-factor authentication requires the user to provide two separate forms of identification before accessing a network.
Using Sysbench, Analyze the Performance of Various Guest Virtual Machines on A Virtual Box Hypervisor

Virtual Machines are classified as computers program also called image, which acts as a whole computer. It is real time emulation of an OS which work on a host machine and has access to some portion of its resources but acts as a whole computer. In this analysis of VMs same host is used for all the testing. To test various machines three OS are used Ubuntu 21.04, LinuxMint and ZorinOS. These are all UNIX-Based Linux systems based on the same Distro: Debian. Analysis of the virtual machines has been done under the identical circumstances using benchmarking tool. In this paper various benchmarking operations have been performed using Sysbench Benchmarking Tool to compare their results. All these OS have been installed on Virtual Box Hypervisor.
A Novel Node Similarity Measure for Efficient Email Classification

Electronic mail emerged as a key communication medium even after various alternatives, such as social networks, mobile applications, and electronic messages. Continuous increase in the number of email users leads to an increase in unwanted emails for any individual, necessitating auto management. Auto-management of email is an automatic email system that classifies emails into a predefined category based on training. This paper presents a practical e-mail classification approach based on node similarity measures. The proposed method has three major phases: building a graph for each email where attributes of the email are represented as nodes of the graph with edges connecting between two nodes, finding the similarity between two graphs with the help of the Node Similarity method, and finally classifying emails based on the similarity between the graphs. The paper did a comparative analysis with related works to assess the performance of the proposed approach. The noticeable results were obtained by the presented method.
Applications of Blockchain Technology in Project Management — A Systematic Literature Review

Project implementation failures are often seen due to sustainable development problems. Developing countries suffer in terms of successful development projects due to a lack of transparency, poor information communication management, and many more that lead to poor project management. Blockchain applications aim to develop ecosystems that satisfy the high-security requirements of the users in enterprises such as finance, supply chain, and insurance which are more complex than the project management domain. This research reviewed several blockchain-based applications to fill this gap and identify the potential areas of blockchain systems in the project management field. Therefore, this paper first reviews the state-of-the-art blockchain use cases in project management to investigate the current status of blockchain applications in the domain of project management. Then presents an architecture based on the review results to guide researchers and developers in designing blockchain-based applications which are unique for the project management domain. The review reveals that blockchain technology has a wide range of unique functionalities and capabilities that contribute management of projects. This systematic review unveils the directions to new blockchain research in the domain of project management.
Question Answering Retrieval Method for Knowledge-Based with Dynamic Programming Algorithm

This paper provides a Chinese question and answer retrieval method based on dynamic programming algorithm. This paper aims to unify the question and answer process, which was originally divided into three sub steps, into the same process, and integrate the idea of state machine in formal language to model the entity recognition process and the question and answer retrieval process with the same model. Entity recognition is modeled as a transfer process from the initial state 0 to state 1 of the system, The question and answer retrieval process is modeled as a transition from state 1 to state 2. To ensure the accuracy and breadth of answers, and to unify the whole question and answer process from a macro perspective. In addition, this paper models the transfer process of the target entity in the question answering process as the state transfer process in the state machine, models the transit entity as the intermediate state, and models the final answer as the termination state. On this basis, the dynamic programming algorithm is used to support the overall decision making of the transition process between all states, while ensuring the operating efficiency of the system.
Multi-View Image Reconstruction Algorithm Based on Virtual Reality Technology

In this paper, dense convolutional self-coding block is proposed to extract multi-scale spatial features, and three connection modes are designed to realize feature reuse: intra-unit jump connection and inter-unit dense connection to realize short-term feature reuse mechanism; The inter-block hop connection realizes the long-term feature reuse mechanism. In this paper, a deep convolutional neural network for image super-resolution reconstruction is constructed based on dense convolutional self-coding block, and the network parameters are reduced through multi-scale supervised training, further improving the image reconstruction quality.
Three Level Password Authentication System using Cryptography Approaches

Threats to national private records, company private records, or individual private data might result from a security compromise. When it comes to security, text-based passwords are by far the most popular. However, such passwords may be easily cracked, and one’s private data might fall into the wrong hands. Security risks connected to logins and accesses have risen to the forefront as cybercrime has increased. Also, single-factor authentication isn’t enough to keep you safe from cybercriminals. Despite modern society’s best efforts, security flaws may still be seen almost everywhere. Moreover, we’ve been utilising a simplistic approach to validation criteria for the mystery keys from the get-go, which isn’t enough to provide any additional security. We’ll adopt the practise of using three separate passwords for increased security. Three-factor password authentication is presented and some possible experimental outcomes are painted in these investigations. By comparing the results with the current procedures, it has been shown that the three-factor authentication provides a much higher level of security.
Design of Medical Image Registration System Based on Deep Learning

Medical image registration is an important task and technical difficulty in the field of medical image processing. It is of great significance for clinical work such as image fusion and tumor growth detection. Image registration aims to find the spatial transformation that maps one image to another. The traditional registration method iteratively optimizes the objective function of each pair of images to solve the spatial transformation, which has the problems of long registration time and large amount of calculation. In recent years, with the wide application of deep learning in the field of medical image research, image registration based on deep learning has become a promising research direction. Although the supervised registration method based on deep learning has improved in registration speed and accuracy, it is still difficult to obtain the label information required by supervised learning, which promotes the development of unsupervised registration method based on deep learning. In this paper, the unsupervised registration model is embedded in the lung segmentation network. Through the lung boundary information obtained from the lung segmentation network, the smooth constraint and the non smooth constraint are weighted and fused to construct a spatial adaptive regularization constraint. The results show that the spatial adaptive regularization term can flexibly and flexibly constrain the learning of lung registration model, and can simultaneously achieve accurate registration of slip and smooth motion in lung 4DCT images.
Automated Script Evaluation using Machine Learning and Natural Language Processing

Correcting handwritten answer booklets manually can be a challenging task for professors, involving significant time and effort. To address this issue, the paper proposes an automated evaluation system that uses DL and NLP techniques. The suggested approach begins by extracting raw text from image files using a proven GCP OCR text extract model, which is well-known for its better accuracy and efficiency. Furthermore, Natural Language Processing methods like BERT and GPT-3 are used to extract keywords and summarize extensive answers. The suggested technique gives marks that are usually comparable to those issued by manual evaluation. Furthermore, the article suggests a web tool that simplifies the evaluation procedure. The application outputs the raw text of student answers and the answer key, a synopsis of the student’s response, and the marks gained based on the extracted keywords.
Digital Image Enhancement using Conventional Neural Network

This paper presents how this image super-resolution works in different areas in the real world. The technique of converting low-quality pictures into high-resolution images is referred to as Image Super Resolution. We can achieve this using different techniques. In this paper, we show how we can increase the quality of images using the SRCNN technique. We have given a brief introduction to the SRCNN technique. We have calculated the PSNR, MSE, and SSIM values for some images after using the SRCNN method.
Image Recognition and Enhancement using Multi Scale Retinex and Histogram Equalization

nowadays the need for a safe and at ease device is favored using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output
An evaluation on E-learning with Cloud Computing

The platforms offered by cloud service providers, users may develop, set up, and run cloud-based applications. Additionally, the cloud provides computing power and storage. Because these functionalities are available on the cloud, it is the ideal platform for e-learning. Through e-learning, instructors and students may communicate online. This needs in both hardware and software systems. The computing infrastructure is used to do this. This article offers a summary of recent developments in the area of cloud-based e-learning and offers helpful knowledge for future research. The study’s findings suggested that cloud computing and cloud architecture are advantageous for e-learning.
Real Time Face Recognition Based Attendance System using Multi Task Cascaded Convolutional Neural Network

Facial recognition has been an important research direction in computer vision. There are countless algorithms presented in related disciplines, and the precision that may be achieved is increasing. However, the implementation of facial recognition technology is hard. In this paper, combination of facial recognition and facial recognition algorithms to build a video-based facial recognition system to efficiently and accurately mark participant attendance. Utilizing FaceNet to extract characteristics and use MTCNN to detect the image of the student for recognition. Lastly, the output is analyzed by a Support Vector Machine (SVM) that recognizes the person of interest in the image. Studies reveal that this technique still yields accurate detection results when the dependent variable has no data and the image quality is unreliable. On the self-generated data set used in this article, the accuracy of the procedure may reach 94.85%.
Doc-Handler: Document Scanner, Manipulator, and Translator based on Image and Natural language processing

Physical documents may easily be converted into digital versions in the modern digital era by employing scanning software and the internet. The day when this activity needed printers and scanners is long gone. Nowadays, even our smartphones and cameras may be used to quickly convert paper documents into digital ones. This is especially useful in the wake of the COVID-19 pandemic, where the ability to share and access documents online is more important than ever. This study proposes an application for illiterate people to quickly translate scanned papers or photos into their native language and save them in a digital format. The Application makes use of image processing methods and has capabilities including PDF conversion, image colour adjustment, cropping, and Optical Character Recognition (OCR). A user-friendly application, developed using the Flutter Framework and programmed in Python and Dart, serves as the interface for the system. The proposed application is cross-platform and works with a variety of gadgets. This method intends to increase accessibility and productivity for illiterate people in the digital age by integrating image processing with language translation.
Predicting Risky Environment for Child Inside House using Deep Learning

We place a high priority on making sure kids are safe. Yet in the modern world, there are a lot of threats to children's safety and challenges to achieving other safety goals. Parent can't always supervise their kids and leave them alone inside the house. So this is a big problem of our time as the small kids is unable to assess the risk due to any harmful activity or objects. The purpose of this study is to provide safety to the child by alerting the parent about the child's position and surroundings. This proposed model can be used to monitor child's movements. We proposed a children's video data set. The video data set contains every moment of the child in the form of frames, and 30 frames are created every second. Additionally, we train a baseline computer vision module that determines the level of dangerousness for a child using deep learning. The baseline consists of well-established methods like conditional and logistic regression for child's pose estimation, as well as cutting-edge deep learning image classifiers. We then integrate the stumbled-condition baseline into various learning platforms and evaluate performance in terms of accuracy and loss.
Advanced Healthcare Chat Bot using Python

Health care has grown to be a vital component of our living in the modern world. To see a doctor for advice on every health issue, meanwhile, has grown to be exceedingly challenging. The goal of this project is to create a medical chatbot using artificial intelligence to help with problem diagnosis. The project’s specific objective is to create a medical chatbot powered by artificial intelligence that can identify health issues, provide an overview of the ailment we have, and recommend us to a doctor for additional treatment. Since there will be no longer be a need to visit a doctor for minor health issues, this project will lead to lower medical costs. The bot will also function more effectively because it makes use of Natural Language Processing (NLP) to communicate with users and preserve their data. The chatbot can enhance itself using background information to respond to queries more quickly in the future. Depending on your needs, you may install the chatbot anywhere, whether on a site or in an app.
Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier.