Reviewerly/exHarmony huggingface dataset:
Securing Image Information with LSB Steganography Technique Using MATLAB Tool

Digital media may now be easily transmitted via the internet, thanks to advancements in internet technology. As a result, communications can be conveyed discreetly by digital media utilizing the hidden method and then quickly sent into the internet. By encapsulating data in other data, steganography can hide the fact that communication is going place. The proposed system would save the user's created key, reducing the risk of the user losing the key and, as a result, their work. Providing a fluidic solution to the aforementioned issues is widely regarded as a difficult undertaking, which will be addressed in this project. In this paper the main focus is to achieve greater PSNR value and capacity so that there will be less distortion in the image.
Blockchain and Cryptography

Cryptography is one of the most intriguing and arising advances these days, with its scope starting from digital currencies to even being beneficial in small industries. This paper discusses various tools and functions which are important to understand the basics of this innovation and the underpinnings of its technology. Different components like digital signatures, Merkel trees and hash functions are assessed in the extent of their use as building blocks of this technology.
Video Deblurring with GAN

This paper proposed A new video deblurring method based on generative adversarial network and Markov discriminant network which is to solve the problem of video blur caused by camera shaking or target movement. We combined the loss function based on pixel and feature space, and designed a new discriminant network based on Markov discriminant network, which facilitated the learning of image texture details by the network, resulting in a significant improvement in the quality of the generated images. We have made qualitative and quantitative comparisons between the method in this paper and similar methods on the test set and real video set. The experimental results show that the deblurred image by the method in the paper has a higher peak signal-to-noise ratio and richer detailed information.
A Proposed Sentiment Analysis Model for Product Reviews on Social Media

E-commerce, social medias, forums, blogs etc. become more popular among general public. Nowadays people use this social media platforms to recognize "general public thinking what" and "what was their experience" before buying any product. Internet contains huge amount of user’s data which is available in various form like comments, opinions and reviews regarding various products, services and events. This trend of reading review data from the internet is constantly growing day by day. People’s reviews or opinions which are available on the internet are unstructured. So it creates difficulty at the time of reviewing huge amount of data for both customers and Business Organization to get cumulative result with high rate of accuracy. So extracting and analyzing the useful things from this reviews content becomes challenging task. As a result, customers and Business Organizations need an automated sentiment analysis system. The proposed sentiment analysis model helps customers to take quick decision about any product or services and the Business Organizations to increase the quality of the product by getting clear idea about their product from the customer point of view.
Development of Part-of-Speech tagger for a low-resource endangered language

India is one of the multilingual countries where large number of languages are spoken, major languages being Hindi, Bengali and Marathi. Indian languages have limited research done in the Natural Language Processing (NLP) domain. This is because Indian languages use Brahmic script alphabets, instead of Latin alphabets, which is very difficult for NLP to understand and process. Most of the Indian languages have many dialects and also have many distinct linguistic characteristics as compared to English. Also, there are many Indian languages which are on the verge of extinction and there is very little progress done on NLP for such languages in order to preserve them. The size of dataset available for such low resource languages is very small. One such language is Katkari, which is an endangered Indian tribal language, and a dialect of Marathi language. The purpose of this work is to develop a Part-of-Speech (POS) tagger for Katkari language. POS tagging is a technique in which each word in the text is assigned a POS label based on its context. The POS taggers for several Indian languages are developed, but for Katkari language, work is yet to be done. Hence, this paper presents a POS tagger for Katkari language which is built with the help of Hidden Markov Model (HMM) and Viterbi algorithm. The Katkari POS tagger was compared with POS taggers of other Indian languages and the accuracy of the Katkari POS tagger was obtained as 86.84%.
Spammer Detection Prediction and Identification by ML

Social networking platforms are used by millions of individuals all around the world. The effects of user interaction with social networking sites like Twitter and Facebook, which are both influential and unpopular in everyday life, are both influential and unpopular. Spammers have turned to well-known social networking sites to disseminate a significant volume of useless and delete able content. For example, Twitter has grown to become one of the most frequently utilized platforms of all time, allowing for a fictitious spam level. Spam detection and false identity detection on Twitter have recently been frequent study topics on modern online social networks (OSNs). We conduct a strategic evaluation in this study to identify persons who publish spam on Twitter. Furthermore, the group of Twitter spam detection algorithms divides them into categories depending on their capacity to trace false content, spam-based URLs, spam on popular subjects, and phone users. The methodologies are also contrasted in terms of other characteristics, such as user, content, graph, layout, and time characteristics. Unwanted tweets by fraudulent users disrupts authorized customers and impedes resource usages. Additionally, the potential to distribute information about phone identities to users has grown, leading in the distribution of inappropriate content.
Emoji Classification using Random Forest Classifier

The automatic classification of emotions based on emojis is used in several applications such as social media platforms like Twitter are able to communicate their thoughts on a subject. Emojis are visual symbols that are used to express feelings and emotions on social media. This paper will address the development of an automated learning model that allows the classification of emotions in emojis and small texts in English using Bag of Words (BoW), Ngrams, and VADER sentiment scores. Further emoji images are processed with Gabor wavelet to get the features for training with a Random Forest (RF) classifier. To obtain different emotions, different emoji images are further classified using an RF classifier. The suggested technique achieves 94.59% classification accuracy.
FlawFix:Automated Vulnerability Fixing Tool for Open Source Libraries

In recent years, software applications use open source software (OSS) libraries extensively to speed up the development. These open source software libraries are prone to increasingly prominent vulnerabilities that are publicly disclosed. It is consequently essential for software developers to identify dependencies on vulnerable libraries regularly, to concretely assess their impact, and to alleviate any potential risk. Vulnerability scan and assessment tools, therefore, are being used to keep track of vulnerable dependencies. Various tools are available in the market that scan, assess and provide remediations for various vulnerabilities caused due to open source library dependencies. Manual efforts are required to fix the vulnerabilities once the remediation is provided. Here, in this paper, we are going to discuss a tool "FlawFix" which scans, assesses and fixes the vulnerabilities caused by open source library dependencies automatically thereby, overcoming the limitation of relying on manual .By using this tool we not only reduce manual errors but also save many man days, which can be used to focus more on business logic. The paper is organized as follows, section 1 gives a brief introduction of the tool’s necessity and definitions of few important terms. Section 2 highlights the work that has been done in this domain, the methodologies and their limitations. The paper then gives the methodology of our proposed tool which highlights the features, steps to be followed etc. Finally we conclude our work by mentioning some of the limitations which lays potential scope for our future work.
Security Intensification using Blockchain coupled with Internet of Things: Proposal, Challenges and Anatomization

Internet of things is an important part of our day-to-day life where all things are connected in the network with the internet. The number of devices linked to the network grows steadily each day in recent years. The innovation in the manufacturing industry also the reason for the production of different devices that uses various technologies to make a possible connection between the devices. Even though the Internet of Things has been developing and demonstrating its potential in recent years, its security when connected to the internet is in doubt. Blockchain is a disruptive technology that provides security to their network without tampering with the data in the network. Researchers and experts have recommended using the blockchain to address security vulnerabilities in the Internet of Things. In this paper, we have analyzed some of the issues which are occurring while integrating blockchain into the Internet of things. The major issues were discussed and which will be helpful to move towards the research direction to solve those problems.
Phishing URL Detection Using Machine Learning: A Survey

Phishing is a kind of social engineering attack with the intention to lure the victim to give up their personal data such as financial information to the attackers through malicious websites which appear as a legitimate source. Attackers are coming up with new techniques hence detection of such links has become a crucial concern. Since most of the internet users are not able to differentiate between a legitimate website and a malicious one. There are several ways to identify a phishing website but they are extremely time consuming. Machine learning can be used to detect phishing web links. Thus, to mitigate phishing threats, researchers are working on improving the accuracy of phishing detection through a variety of list-based and machine learning-based methods that leverage host information, handcrafted capabilities of URLs and website contents. This work is a survey of latest trends in Phishing URL detection, with a special focus on Machine Learning based approaches.
Sentimental Analysis Using Deep Learning Techniques

There may be a brief development inside the area of evaluation mining as well as feeling research which focuses to locate the message or suppositions gift on the specific virtual enjoyment plat-systems through AI (ML) with extremity computations, opinion examination or subjectivity examination. Wistful research (SA) demonstrates the text affiliation that's applied to categorize the communicated sentiments or mentality in exclusive habits like awesome, approval, effective, horrible, disapproval, terrible, and so on. SA is a asking for and prominent errand that comprises I) regular language handling (NLP), ii) net mining and iii) ML. additionally, to address this test, the SA is converged with profound mastering (DL) processes because DL fashions are productive in mild in their programmed getting to know potential. This paper underlines re-penny research in regards to the execution of DL fashions like I) profound brain agencies (DNN), ii) profound conviction business enterprise (DBN),
Insights into today’s cyber trends and threats : Meta and Base Classifier Models for Network Traffic Classification

The interconnectivity between the computers that provide a single network with lots of advantages is termed as the network. A network is a scenario that allow to collect and connect a number of computers for exchanging the information and offering facilities to other resources. To detect malicious activities from the network various classification techniques are designed. The existing technique is SVM which is applied to classify data into malicious and normal classes. This research work designed a stacking technique in order to classify the network traffic. The base classifier which is used, is known as SVM and meta-classifier is KNN classifier. The proposed model is the hybrid for the network traffic classification. Python is employed to execute the designed technique and the metrics namely accuracy, precision and recall are considered to analyze the designed technique.
Augmented Reality Tool Based in the Cloud to Improve the Quality of SMAW and MIG Processes Through Data Comparison

The implementation of new technologies provides greater quality control in civil works, both structural and mechanical. In developed countries such as China and Australia, the virtual reality industry and data management have developed hand in hand with Industry 4.0, which means that the use of quality parameters in a process before, during and after making decisions, becomes a standard every day more accurate, such is the case of applications focused on IoT (Internet of Things), where the communication of devices and mechanical means, allows better control, increasing performance, reducing risks and streamlining production lines. In this sense, a virtual tool associated with welding parameters facilitates the operator's control in the processes of identifying manufacturing measures before, during and after each operation, allowing him to train and improve based on his data, with data that can be compared and evaluated, giving him a pattern of constant improvement, Ecuador seeks to adapt to the dizzying changes of the technological era and solve several drawbacks within the construction area, Among them, the complexity in the field of welding and the high costs involved in the acquisition of equipment, training and experts, being in most cases obsolete, for this reason the focus of this project is to focus on improving the SWAG and MIG processes, connecting data operations through a server hosted in the cloud and presenting it through an augmented reality tool that allows the user to train and measure their knowledge.
Breast Cancer Prognosis using Machine Learning Techniques: A Literature Survey

Globally, breast cancer (BC) is women's leading cause of death. Primarily breast tissues are involved in the development of breast cancer. The population growth in medical research has made it increasingly necessary to study cancer in its early stages. A growing population has resulted in an exponential increase in breast cancer deaths. Among women, it is the second most common cancer. Cancer prediction is crucial for updating treatment aspects and patient survival standards. Aside from being a research center and a good strategy, machine learning (ML) methods are particularly effective in predicting breast cancer and detecting it early. The Wisconsin Cancer Diagnostic dataset (WBCD) was used to compare five ML algorithms: decision trees (C4.5), support vector machines (SVMs), logistic regression (LR), random forests, and convolutional neural networks (CNNs). Through machine-learning algorithms, this study aims to identify which algorithms are best suited for the early detection of breast cancer and the prediction and diagnosis of the disease using predictive algorithms. As a result of this study, different machine learning algorithms were compared, and it was found that Random Forest had higher accuracy, with a 99.8% accuracy rate.
A Novel Method for Product Quality Control Based on Production IoT Data

This study proposed a method of factor analysis to select key impact factors according to the results of importance values in production and to conduct quality prediction in continuous production process. Three different models were employed to compare the effectiveness and accuracy of factor searching and quality prediction. Through data analysis, compared with the empirical model by human knowledge with production IoT (Internet of Things) sensor data, the proposed data-driven based model in this paper can discover impact factors ignored by empirical model. For the quality prediction, the proposed models could also perform well in the aspects of correlation coefficient and standard deviation, respectively. The study in the paper found that the model using effective feature and correcting impact factors can effectively improve the predicting accuracy.
Deep Anime Recommendation System: Recommending Anime Using Collaborative and Content-based Filtering

In the vast multitude of products and services sprawling in our world, it is often daunting to make a reasonable choice backed with statistical evidence addressing the various aspects that governs the reliability and durability of the things we opt for. As such Recommendation Systems that facilitates personalization, and always up to date filtering of products or services based on the user historical track are often celebrated as one of the most visible applications of Machine Learning and Deep Learning. It thus, usually does not come in a surprise that recommendation systems have become pivotal aspect of almost all the applications dealing with E-Business, leaving no grounds unexplored. One of such relatively uncharted examples for recommendation system, however, involves recommending anime based on user’s historical choices. The sheer potential of market sizing anime, and the long-tail nature of product or services served by online businesses thus, compelled us to propose a solution for collaborative filtering, and content-based filtering. We thus, proposed a model-based architecture that employs Gated Recurrent Neural Network along with a trivial memory-based recommendation employing Cosine Similarity to make prediction for the top K rated anime by an anonymous set of users. Moreover, since content-based filtering for recommendations tends to be inherently susceptible from loss in performance with sparse data or the data with missing values, we noticed that our Neural Network based architecture turns out far superior to Cosine Similarity based approach. Thus, our proposed research could be used by different authentic or aggregator service alike that provide anime streaming services to implement a reliable and efficient recommendation system into their existing products.
Review on Deep Learning Based Image Forgery Detection and Localization

In the last few decades, the chances of forgery in an image have been increased with the advancement in digital camera having high resolution and with the availability of several image processing software’s. These manipulations sometime are so pioneering that it is very difficult to detect forgery in the image. The proficiency to identify image forgery is present day research scenario. Therefore, the main emphasis of this research is to study numerous types of detection techniques for forgery for digital images using Deep Learning. The results may be compared to improve or generate a new approach for a future Machine Learning based forensic science investigation. The aim of the study is to learn about the work done in the detection of these forgeries using deep learning technique. This study also reviews the challenges faced by existing techniques and focuses on proposing few techniques which can be applied in future.
An Optimum Approach to Indian Sign Language Recognition using Efficient Convolution Neural Networks

The most important component of every discussion is language, and sign language is the most formal mode of communication for persons who are deaf or mute. Communication, as we all know, is one of the most important components of modern life. The importance of communication is increasing in tandem with our maturation in life. The purpose of this project is to develop a system capable of recognizing all of these signs and serving as a bridge for signers and non-signers to communicate and interpret sign meanings. Technology is rapidly evolving, and now that we have powerful systems, a wealth of data, and a diverse set of sources, we can design a system to assist people with disabilities. We will develop a user-independent framework for automatically recognizing Indian Sign Language in this project, which will allow for feature extraction, recognition, and interpretation of a variety of one-handed, dynamic isolated signals. The two most important and fundamental aspects of SLR research are isolated sign recognition and continuous sign recognition. Hand form, hand position, and so on are the primary hand traits that can be used in sign language. The extra-clung functionality can be implemented in two ways. Either one is tracking-based or it is not. This essay focuses on one such approach that we devised to overcome significant communication barriers. The technique is divided into three stages: pre-processing, texture extraction, and recognition.
Breast Cancer recognizable proof, classification and discovery utilizing neural networks

Artificial neural networks are widely used tools in various fields of medicine and technology. Breast cancer is the world’s second biggest cause of death for women. One of the key issues in medicine is determining the right diagnosis of breast cancer. More effective procedures for early and precise diagnosis are constantly being developed. More than 10% of women develop breast cancer after lung cancer. Breast tumor can be identified by the following features: Redness in skin, size change, pain in abdominal, etc. Changes in the texture. The main causes of breast cancer sometimes it is very vague, but the disease is difficult to stop. Neural networks have become a very popular tool in diagnosing breast cancer and classifying cancer datasets. Artificial neural networks are a field of artificial intelligence and are widely used as new technologies in the field of computer science.We have used neural networks to find out the tumor is Benign or Malignant. The input are just a bunch of measurement and features of tumor. Some of these parameters include measurement of the nuclei of cells in the tumors.
Classification of Galaxies using Transfer Learning

Deep Learning is changing our world in a way that we never imagined before. In order to solve and reduce the misclassification of galaxy due to lack of abundance of data we focus to classify Galaxies accurately. We studied different deep learning models for classification of galaxy using transfer learning approach. We tried to improve traditional classification algorithms like Convolutional Neural Network and K-Nearest Neighbor for morphology of galaxies with transfer learning approach. A lot of Machine Learning, deep learning traditional models were explored by us to find optimal algorithm which can yield better result for classification scenarios in terms of accuracy and speed.