Reviewerly/exHarmony huggingface dataset:
Automated Youtube Video Transcription To Summarized Text Using Natural Language Processing

This paper proposes video transcription using python language. With the adding quantum of videotape data generated every day, it has come important to epitomize the videos for faster reclamation and quick surfing of vids so that druggies can select the more applicable videotape for viewing as per their demand. The significance of videotape summarization lies in the fact that it helps in effective storage and allows quick browsing through a large number of videos. We propose a system to induce summaries for videos by utilizing the audio element. Our videotape summarization approach involves generating the audio paraphrase, using speech recognition, if it isn’t readily available. It’s grounded on assigning scores to rulings in the reiterations and opting for the bones with the loftiest scores. Also from the original videotape, parts corresponding to the named rulings are uprooted and intermingled to gain the final summary.
Multi-Object Detection and Tracking Using Machine Learning

In today’s information-based economy, data serves as a replacement for oil. There have been shifts in how quickly and accurately benchmarks are measured as a result of efficient data. Industry buzzwords Computer Vision (CV) and Artificial Intelligence (AI) perform the data processing, making the improvement observable (AI). Two technologies have allowed for previously impossible endeavours, such as the identification and tracking of objects for traffic surveillance systems. The need for an effective algorithm to unearth concealed elements in images grows in tandem with the number of such features. Single-object detection in the urban vehicle dataset is handled by a Convolutional Neural Network (CNN) model, while multi-object detection in the COCO and KITTI datasets is handled by YOLOv3. Metrics are used to evaluate and chart the performance of the models (mAP). On traffic surveillance video, we use YOLOv3 and SORT to follow objects as they move between frames. This study argues that cutting-edge networks like DarkNet deserve to be treated as special cases. We see effective detection and tracking on a dataset of urban vehicles. The algorithms produce very reliable identifications that can be used in real time for traffic applications.
Hand Written Text Recognition and Global Translation

Handwritten appreciation is the capability of a machine to recognise handwriting on paper, in a photograph, on a digital writing device, etc. Among other places, financial institutions, corporate settings, and manufacturing facilities are just a few of the potential applications for handwriting and character recognition technologies. The key area of the research is to generate a method based on a convolutional neural network that can recognise each character in a particular type format with accuracy. The way a neural computer works defies conventional wisdom. The objective of training a neural computer is to get it to either classify input data into one of several categories or to let the data evolve so that its most desirable characteristics show up. Since neural computing is still a relatively new idea, its structural elements are less well defined than those of other architectures. In order to convert the handwritten text into digital text, this project categorises each handwritten word. To achieve this goal, we combined two methods: character segmentation and direct word classification. In the first method, we train a model that reliably categorises words according to their meaning using a Convolutional Neural Network (CNN) with various topologies. In the latter, we create bounding boxes for each character consuming Long Short-Term Memory (LSTM) networks with convolution. The classification by CNN of these segmented characters, each word is subsequently reconstructed using the results of the classification and segmentation. Additionally, we now have regionally-based worldwide translation that is adaptable for users and clients.
Unified Model of Entity Tracking Using Deep Learning

The act of identifying a moving object inside a video is referred to as “entity tracking.” It has applications in monitoring and robotics, as well as imaging medicine and tracking autonomous vehicles, among other areas. Single Object Tracking, Multiple Object Tracking, Video Object Segmentation, and Multi-Object Tracking and Segmentation are the four subtasks that make up the majority of the field of entity tracking. The vast majority of previously developed techniques are only produced for a single one or a portion of the subtasks. With the same set of model parameters, the unified model Unicorn is able to simultaneously solve all four different kinds of object tracking issues (SOT, MOT, VOS, and MOTS). The “Unicorn” model that we are currently working to implement uses a single network to solve all four tracking problems to the same degree or a higher degree than its explicit undertaking partners in eight different tracking datasets. These datasets include LaSOT, Tracking Net, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS.
Applicable Techniques for Image Steganography: A Survey

Steganography and cryptography are two different technical approaches preferred to meet data security over the communication network. Due to the increase of illegal hacking, information security needs alerts in high volume. With the growth of communications based on the internet, computer networks need specialized security which would be available to secure incoming broadcast messages from any threat producing hacker affecting data privacy and also ban access illegally. The process of transferring the secret data without letting the attackers know. It is the method of transporting the secret message without grabbing the notice of attackers. ‘Stegano’ and ‘graphy’ are two Greek words that mean ‘Covered’ and ‘Writing’ respectively it is beneficial for hiding the message, thus protecting it. In this paper, we’ve discussed various steganography techniques, methodologies, along with applications and categorization.
Incorporating of Security Methods into the Software Development Lifecycle Process (SDLC)

As software usage increases and updates, Software development requires several essential elements, including industry security. As vulnerabilities are exploited, we’ve witnessed an upsurge in the demand for securities. There are many various reasons why security is not effectively maintained in software development processes, thuswe have seen some approaches like traditional security, CllCD, and robust security to make sure security is implemented right from the start rather than after process development. The various tools in this study were used to discuss various security implementation methods, and this discussion may be useful for examining various software
Face Mask Detection Using Support Vector Machine

According to a report published by the World Health Organization (WHO), the only two primary ways in which the COVID-19 virus can be spread are through breathing contaminated air and through direct physical contact. Wearing a mask is one of the bare minimum preventative measures that can be taken against the transmission of certain viral infections, including COVID-19. Therefore, it is much more important to wear a mask properly in very crowded areas such as public parks, supermarkets, and shopping malls. This is because the risk of spreading an infectious disease is significantly higher in these settings. Face Mask Detection is a technique that we developed so that we could avoid problems like these. It can determine whether or not a person is wearing a mask on their face. There have only been a handful of scant studies done on face mask detection.
Bitcoin Price Prediction Using Machine Learning

Bitcoin is a type of Internet currency that is both a digital asset and a payment method. It enables anonymous payment from one person to another, making it a popular payment mechanism for online illegal activity. Due to its recent price increase, Bitcoin has attracted a lot of attention from the media and the public. The purpose of this study is to discover the Bitcoin price’s predictable price direction. Machine learning models are likely to provide us with the information we require to Understand the future of cryptocurrency. It won’t tell us what will happen in the future, but it might show us the overall trend and direction in which prices are likely to move. The proposed Methodology aims to create a machine learning model that uses data to learn about the patterns in the dataset and then uses a machine-learning algorithm to forecast the bitcoin price.
A Blockchain Based Certificate Authentication System

The certificates of the Secondary School Leaving Certificate (SSLC), the Higher Secondary Leaving Certificate (HSLC), and academic certificates are all digitized at the academic institution and made available to the students in today’s digital environment. The rise in the number of security breaches creates a threat to the users’ right to privacy regarding their academic digital certificates. Validation and verification of the digital certificates is very challenging for the institution and the business. The system can able to give a digital certificate validation that is both safer and more efficient thanks to the use of blockchain technology. The purpose of this proposed system is to provide a suggestion for a certificate administration and verification system that, by using blockchain technology, has the ability to provide a viable answer for the problem of academic credential issuance and verification. Hash, public-private key cryptography, mining peer-to-peer networks, and proof of work are only few of the capabilities that are included in blockchain technology.
Performance Analysis of Blockchain Technology using Inter Planetary File System (IPFS) database in comparison with Block chain database

Blockchain, as the name suggest is a long chain of blocks which are connected using the hashed of the data which is stored in the previous blocks. In present scenario schools, colleges and universities are being forced towards the creation, maintenance, retrieval, safety and privacy of educational records. Challenge is not only to store and maintain the data but also ensures fast access of data to all the stakeholders. Blockchain based transcripts save the time and money and smart contracts help in the authentication via codes. But there are several limitations of blockchain database, When sharing blockchain data from a server to multiple clients, it is highly likely that multiple clients will need the same data. They are working on the same case and may be investigating the same wallet. Blockchain and IPFS based transcripts save the time and money and smart contracts help in the authentication. Transcripts are safeguarded by blockchain in the event of a server outage or natural disaster. This work analyses the performance of IPFS database that uses content based addressing and cryptographic hashes with other database. In this paper IPFS is compared with blockchain database. IPFS is hypermedia protocol that is both safe and easy to use. It uses the content based addressing with the merger of cryptographic hashes to work upon the distributed file system. This paper compares IPFS with Blockchain database upon the following factors like Size of files shared by nodes, Degree of linking through number of peers linked, Bandwidth used.
Data Analytics for Phishing Attack Detection using Deep Learning Techniques

As the process of digital transformation continues to advance, the challenges to global cyber security have increased. As a result of digitalization, hackers have more alternatives. Phishing efforts are the initial type of cyber-attacks, and they aim to get individual user passwords. Phishing is a common tactic used by hackers to trick people into giving them access to a company’s networks and digital assets. Phishing is a type of cyber-attack in which a victim is tricked into divulging sensitive information including account passwords, bank account details, ATM pin numbers, and other personal information. Phishing-style cyber-attacks hurt individuals by deceiving them into disclosing important information. In this paper, the phishing attack dataset will be taught and evaluated to see which deep learning method yields the best results.
Banking Data Migration from On-Premise to Cloud

Data Migration is a multi-step process that begins with analyzing old data and culminates in data uploading and reconciliation in new applications. With the rapid growth of data, banking sector constantly need to migrate data. Data migration can be a complex process as testing must be done to ensure data quality. Migration also can be very costly if best practices are not followed and hidden costs are not identified in the early stage.
Supervised And Unsupervised Neural Network Deep Analysis For Facial Recognition

In this technological era automation is playing a vital role in making things faster. Face Recognition is one of the examples of automation. Facial Recognition is categorized as a biometric method alongside fingerprint scan and iris scan. Back in the days facial recognition is only limited for safety and security purposes but nowadays it is widely used in various industries like Banking, Healthcare, and many more. One such industry is the educational industry. Various universities around the globe have already implemented biometrics through facial recognition not only for the staff but also for recording the student's attendance. Currently available Facial Recognition systems are able only able to track one person at an instance. The constructed system must track the individual attendance while he/she being recorded in a live video or while they were in a group photo using machine learning techniques. The system must analyze each individual in a group using the image provided and mark the individual's basic information like name, class, section, etc. In general, the system is trained with each individual's 180-degree facial view with the help of convolutional neural networks. And to improve accuracy the system must be trained using deep learning and deploying with a huge and distinct dataset. For achieving this we have conducted a study comparing five well-established algorithms to know which algorithm is reliable
Electronic Voting Machine Using Fingerprint Scanner with Image Enhancement Technique

Elections are very important to decide country’s future. Manual voting system have many consequences, to overcome these problems implementing biometric voting machine in society diminish problems. Hence, a flngerprint-based voting system has been proposed in this paper. Every person has unparalleled identity by using this bit new system works. In this proposed system, fingerprints are collected during enrollment phase, and it will be verified during polling. For this purpose, the hardware component Arduino Uno has utilized in this work. The Adafruit library tells the sensor to read fingerprints, store them as “templates”. The voter has to place his finger to register, that data will be stored in software. Persons who had registered or already existed in list can have access to cast their vote by placing their fingerprint. If any person tries to fake their vote by casting second time or nonregistered the buzzer rings. By this change voting system get rid of some issues.
Interfacing Kera’s deep learning technique for Real-Time Age and Gender Prediction

The 21st century has seen the humans achieved many unimaginable achievements in the world of technology. This technology can be used in a different way as individual desire. It can be used to identify a person’s age and also identify the gender of a particular person by detecting face of the person with help of a camera image or even with a real time video. In this research paper, a complete procedure, what are the different methodologies and algorithms that can be used, and which one is more accurate method to be used will be shown. The aim of this work is designing as well developing model which can detect faces thereby predict the age and gender from real time images. This model can be used in police surveillance, policing to dating applications and much more. Deep learning techniques are used in designing this model. Convolution neural network and computer vision is also being used in this paper to adapt to real time processing. Convolutional neural network along with the computer vision and keras is used in training the model. After extensive testing and training, accuracy of 85 percent is achieved for the developed model. This model can be broadly used in airports, age restricted areas or buildings and can be used in restricting the alcohol access to the under aged people.
Cervical cell classification using Deep Learning Techniques

Cervical cancer is a very common disease in women. Early detection and accurate diagnosis can save lives. Pap smear or pap test is the widely used technique for the early detection of cervical cancer. Manual analysis is time consuming process that relies on expert cytologist. Hence it is better to develop a computer aided system for the diagnosis. Depending on the level of abnormalities, the cells in papsmear cluster slides vary in colour, size, and morphology. Various machine learning and deep learning techniques are developed for the segmentation and classification of the pap smear image. In this paper five different deep learning networks Vgg-16,vgg-19,Resnet-50 and ResNet- 101 and Efficient Net v3 are compared on the SIPAKMED dataset. The architectures were also evaluated on the Herlev benchmark dataset.
Hand Sign Classification Techniques Using Supervised And Unsupervised Learning Algorithms

Gesture recognition is becoming a sore subject in computer vision captivating the idea of social interaction not only between humans but also create ergonomic systems that control devices ranging from time-of-flight cameras and controlling vehicles to virtual reality. Even though it is gaining ground recently, fast and robust recognition remains an unsolved problem. To our understanding, we are utilizing the Leap motion sensor captured near-infrared image dataset for gesture identification, which tides over word-level sign recognition encompassing a diverse set of hand gestures. We have suggested the approach of extracting the gesture from the image using PCA and image segmentation followed by the feature extraction stage. In this paper, we have analyzed and differentiated various methods of hand recognition counting Random forests, stochastic gradient descent, Naive Bayes, Decision tree, and Logistic regression algorithms and exploited our results
Scaling Blockchain Systems using DAG

Directed acyclic graphs are used for one primary reason: they make it easier to build several blocks at once. The number of forks in a blockchain naturally grows as the rate at which blocks are generated increases. As a consequence of this observation, several experts have come to the opinion that organising the resulting directed acyclic network is the most effective way to solve the scalability issue that plagues Bitcoin and other similar cryptocurrencies. A significant difficulty for this system arises from the fact that blocks created concurrently or in close proximity may contain similar or conflicting transactions, all of which must be handled and organised in linear order. The idea of using several blockchains, each of which processes transactions independently based on the Sender's address, is presented as a possible alternative method. Consensus algorithms continue to be the limiting factor in the size of a blockchain network and the speed with which data can be published, despite the advancements in blockchain technology and the advent of new use cases. Despite blockchain's ability to accommodate a greater variety of data, this remains the case. Immediate action is required to address the critical problem of how to boost alliance blockchain performance in a secure and efficient manner.
Youtube Comment Spam Detection

YouTube is the most commonly used application these days. There are both good users and bad users who use youtube. Spammers have realized that these types of social media are easier to lure the users into malicious activities. In the current project, spam detection was performed on selected youtube comments and we predict the entered comment as ‘SPAM’ or ‘NOT SPAM’. Google Safe Search and Youtube Bookmaker are two tools that can be used to block harmful links; however, they cannot immediately safeguard real-time users. Four Artificial Intelligence approaches have been employed by victimization to run the survey for the spam comment detection methodology: Call tree, Ada Boost, logistic regression, and Random Forest. We shall achieve a 91.65 percent associate degree of accuracy by using neural networks and also to beat the current action course by around 18%. The difficulty of spam and the efficiency of the most well-known AI approaches (SVMs, Bayesian representation, ANNs, k-NN).
Online Medical System Using ElGamal

Cost-effective and secure sharing of individual health records is now possible through the use of cloud services in the healthcare sector. Storing sensitive health information in the cloud raises concerns about its security, necessitating an assessment of the best means of protecting this information. As a result, we propose a strategy to facilitate the exchange of private health data in the cloud, which we refer to as Enhanced Security Services. These systems ensure the confidentiality of PHRs while also protecting the patient’s right to exercise autonomy over their own health information. Patients encrypt their data and store it on remote servers, allowing multiple parties access to specific sections of their medical records. Key pairs and re-encryption keys are initially generated and stored on a semi-trusted proxy and re-encryption server (SRS). This approach is safe because it uses both forward and backward access control and guards against insider threats. Using High-Level Petri Nets, we formally investigate and verify the efficacy of this strategy (HLPN). An evaluation of the personal health data methodology’s efficiency suggests it can exchange data safely via the cloud.
Movie Recommendation System Using Content Based Novel Feature Extraction Information

Content-Based Filtering and Collaborative Filtering are the two key techniques used by recommender systems to help them provide relevant recommendations. In order to create a report for this paper, we developed a hybrid technique that benefits from both content and collaborative filtering algorithms. The algorithm discussed in this article differs from other work on the topic since it uses a cutting-edge technique to identify content similarities between two items. The study in the paper shows why this new technique is valid and how it can result in useful suggestions. On existing person and object data, the aforementioned strategy outperformed the two most popular approaches, Pure Collaborative Filtering and Singular Value Decomposition, in terms of outcomes.
Image Reflection Removal using Iterative Boost Convolutional LSTM Network through Feature Loss

Unwanted reflections commonly degrade the clarity of images taken via glass windows. The elimination of that particular reflection is essential for enhancing image quality. Due to the poorly worded challenge, many solutions require many input photographs, which in practice looks to be difficult. Users using the alternate solution must take numerous images from various perspectives. To decrease this problem, we thought about employing the cascaded network that is a series network model for the transmission and reflection layers. A straightforward approach for a cascade model on IRR is to educate one network to produce a forecasted transmission that act because the auxiliary data of the following network by continuing the method iteratively utilizing subnetworks that enhance the forecast. After passing through all the layers of network, there will be some losses present in an image which produces blurry images. In order to remove losses, the image is moved to the initial step.