Reviewerly/exHarmony huggingface dataset:
Photo-to-Cartoon Translation with Generative Adversarial Network

Cartoons are a popular form of art in our daily lives, and the ability to automatically create cartoon graphics from photos is highly desired. Cartoon images have a more vibrant and lively appearance than traditional naive pictures. This study aims to explain the process of translating real-world photos into cartoon-like images. While converting photos to cartoons, there were a few difficulties, including fine hair edges, mismatched colors, and texture concerns. Conversion of photos to cartoon style images has been performed using generative adversarial networks (GAN). Various neural network-based GAN networks, DCGAN, CycleGAN and AnimeGan has been applied in this work for cartoon conversion. Among them CycleGAN performs better to transform actual photographs into colorful, eye-catching cartoons. This work is based on deep learning-based methodologies, which have lately gained popularity for stylizing imagesin artistic forms like painting. The results may be used to quickly convert real-world photographs to high-quality cartoon graphics. The proposed system provides a web API that contains training weights derived from the models outlined below. A web app has been created based on that API that converts real-world images into high-quality cartoon graphics for various cartoon styles. In these experiments, it outperforms state-of-the-art approaches to producing high-quality cartoon graphics from real-world photos. Numerical results show that, the CycleGAN approach has the lowest training time per epoch and requires the minimum number of trainable parameters.
An End to End Blockchain based Non- Fungible Token Platform for Buying and Selling Digital Arts

Blockchain is a revolutionary technology and will have great effects on our business in the future. NFT denotes Non-Fungible Token which can be considered as digital form of a real-world object. These objects can be art, music, in-game items, and videos. These are traded online, generally with different types of currencies, basically cryptocurrencies. Non-fungible tokens which are traded through aNon-Fungible Token marketplace, are different from fungible tokens that are bought or sold on multiple centralized or decentralized platforms. Each Non-Fungible Token has a digital signature of it which makes it impossible for them to be exchanged for another Non-Fungible Token over the internet. Each has its own value which is determined by various factors like meta data, creator, features, and many more. The majority of Non-Fungible Tokens in the present times are digital, and creators could improvise here in the future and will make more creative things for the users ahead. It is very clear that blockchain technology and Non-Fungible Tokens can offer the perfect opportunity for artists and content creators to obtain a reputation for their works and also financial support.
Improving Optical Character Recognition(OCR) Accuracy using Multi-Layer Perceptron(MLP)

Optical Character Recognition (OCR) is a cutting-edge application that has been made possible due to advances in technology. Optical Character Recognition involves using deep learning algorithms such as Multi-Layer Perceptron and Support Vector Machine to create a system that can recognize characters in an image. The Optical Character Recognition system first segments each character individually as part of the pre-processing step, after which it post-processes the image to compare each character to the pre-processed data. In the modern world, Optical Character Recognition is a crucial technology used in many industries to shorten turnaround times while increasing accuracy. However, one of the challenges faced with Optical Character Recognition is that it takes longer to match exact results and produces results with lower precision and character misreadings. To address this challenge, the pre-processed data used by Optical Character Recognition systems now includes practically all fonts. This means that the Optical Character Recognition system has to match the characters with a much larger dataset, making the process more time-consuming and less accurate. To improve the accuracy and speed of Optical Character Recognition systems, several methods have been employed. For instance, post-processing techniques such as error correction algorithms can be used to detect and correct errors in the Optical Character Recognition output. Additionally, the use of deep learning techniques such as Conventional Neural Networks has been shown to improve the accuracy of Optical Character Recognition systems. By employing these methods, Optical Character Recognition can become an even more powerful tool that can shorten turnaround times while increasing accuracy.
Coverless image steganography based on DWT approximation and pixel intensity averaging

Image steganography uses an image to send a secret message. One way to do it is to modify the image in such a way that the normal eye cannot see it, but the receiver can still extract the information. In traditional embedded steganography even though the human eye cannot detect the manipulation done to the cover image, statistical analysis can reveal the secret information. Coverless image steganography is another approach that solves this problem where instead of embedding secret information we find an image that already consists of a secret message. As no modification is done to the original image no modification traces are found leaving less space for steganalysis. But finding a suitable and optimal data set is a challenge here. Another use of coverless image steganography is to help in protecting the copyright of digital arts or copies of digital records. Since the Internet is not a secure medium these processes must be robust against these attacks to protect the secret message embedded inside the media. In due process of time, many frameworks are devised to increase the capacity of this method and also to increase the robustness against many image attacks. This study has developed a new framework using the Discrete Wavelet Transform of images combined with plane segregation and intensity averaging of sub-images that is robust and has a high capacity to pack information that is to be sent via a communication channel. This proposed method has demonstrated better capacity and robustness compared to previous methods based on DWT.
Comparing the Effectiveness of Data Visualization Techniques for Discovering Disease Relationships in a Complex Network Dataset

In this study, we compare various data visualization methods for exploring a complicated network dataset containing details on illnesses, symptoms, and safety measures. The dataset was obtained from Kaggle and split into train and test subsets at a 4:1 ratio. It has 269 nodes and 483 edges. To evaluate the network data, we used Neo4j and Gephi, two data visualization tools. The dataset was queried and visually analyzed using Neo4j, and graphical representations of the network were produced using Gephi. We tested the potency of different visualization methods for finding patterns and correlations in the data, including force-directed layouts, node-link diagrams, and matrix views. Moreover, Neo4j's querying capabilities allowed us to analyze sub-networks and their connections in greater detail. Overall, our study shows the value of using a variety of visualization methods to have a deeper understanding of complicated network data. Researchers, medical experts, and public health officials attempting to comprehend and manage illness linkages will find the findings of this study to be quite insightful.
Analysis and Implementation of Image Steganography by Using AES Algorithm

Popular cryptographic technology called Advanced Encryption Standard (AES) enables secure data transfer over the internet. It is used for a variety of purposes, such as secure data transfer, mobile phone encryption, and encryption of extremely sensitive data. It operates in terms of blocks, or chunks, of data. Steganography is the process of hiding data in another sort of data, such as a digital image. If a user wants to utilize a cryptographic method to conceal encrypted data in an image, such as AES, it is essential that the method be impervious to all possible attacks. This study has analyzed the possibility to hide data inside digital photos by utilizing encryption techniques like AES. The performance comparison of AES and Data Encryption Standard (DES), a full Python implementation of the AES for image steganography is also presented. To illustrate how each method performs, experimental results are provided. The Advanced Encryption Standard can be programmed in software or built with pure hardware.
Chaos Sine Cosine Algorithm with Graph Convolution Network for Sarcasm Detection in Social Media

Sarcasm is a procedure of verbal irony that is planned to convey ridicule, contempt or mockery with the aid of words that expresses the opposite of what is meant or through facial expression, tone of voice, or inflection. In another word, it is a way of saying something but meaning the opposite, often intending to be critical or humorous. Sarcasm is widely applied in social media, humour, and casual conversation. Sarcasm detection using deep learning (DL) includes training a machine learning (ML) algorithm for identifying instances of sarcasm and recognizing the pattern in language. The study presents a new Chaos Sine Cosine Algorithm with Graph Convolution Network for Sarcasm Detection (CSCA-GCNSD) technique in Social Media. The presented CSCA-GCNSD technique aims to recognize and categorize various kinds of sarcasm. Primarily, the CSCA-GCNSD technique involves different stages of data pre-processing. Next, the CSCA-GCNSD technique applies the GCN model for the detection and classification of various kinds of sarcasm. Finally, the CSCA technique is used to optimally choose the hyperparameter values of the GCN model and thereby resulting in improved detection outcomes. The simulation outcomes of the CSCA-GCNSD methodology was tested on different sarcasm datasets and the outcomes reported the betterment of the CSCA-GCNSD algorithms over other models.
Extracting The Essence of Video Content: Movement Change based Video Summarization using Deep Neural Networks

The security and safety of the general public is ensured by the use of intelligent video surveillance systems in a variety of applications to monitor events and evaluate activities. One or more monitoring cameras generate, analyze, and store a large volume of data for security reasons. Video surveillance is an important part of maintaining security. Yet, since there will only be motion for a small duration of the picture, it may be difficult to identify any motion or movement in a replay version. Analyzing the video would take a lot of time, and pinpointing the exact frame at which change took place wouldn't be easy every time. At present the state of art results are having different issues basis on the different events of the video these proposed model captures the main content better than already existing methods. Researchers investigate Open CV for key frame extraction and recognizing distinct frames to summarize the video. This study uses a DNN model, which is trained by using a Mobile-Net SSD Caffe model with proto-txt to recognize objects, and then that same DNN is used to identify humans. Due to the complexity of routine indoor and outside monitoring as well as the processing rate required for real-time detection, the accuracy of behaviour identification gets improved.
Key Generation using Curve Fitting for Polynomial based Cryptography

Every organization's primary concern is security. A cryptography method will remain safe in the system if the key is not cracked by a hacker through any kind of attacks. A conventional cryptography algorithm's strength relies on the key size and structure. Cryptography algorithms can be susceptible to brute force attacks since their keys have fixed lengths. The advantage of having a variable length key is leveraged to mitigate key-Size based attacks by using polynomials. This article outlines an algorithm for generating and maintaining the keys based on the polynomials and interpolation.
A Collaborative Filtering based Recommender System for Hospital Recommendation

People have struggled with getting older and sicker ever since the beginning of time. Hospitals are key players in this situation. However, certain hospitals excel at internal medicine, while others excel at surgery. As a result, how to select a hospital that is appropriate for patients becomes a crucial issue. This study has developed a novel recommendation system to assist the patients in selecting the hospitals based on the type of disease and the location of the hospital. In the proposed model, the user is prompted to enter the city and disease type, which enables them to make more informed choices.
Virtual Mouse Input Control using Hand Gestures

Input control using hand gestures is a growing area of research in the field of human-computer interaction (HCI), In this paper, we present the working of the virtual mouse by using hand recognition and its use of only a simple finger gap with no additional hardware requirements to control the cursor of the system. We had the webcam act as an input device that tracked the hand to perform an action. This paper needs a camera-based vision control system that uses finger gestures to control the system and also uses the color tracking technique. The virtual mouse will allow the user to move or navigate the system cursor using finger and hand gestures, and left clicking, right clicking, and dragging can be done using various finger and hand gestures. It will also help transfer files between two systems over the internet. The system uses a low-resolution in-built web camera that works as a sensor and is able to recognize or track the user's hand with colored caps in 2-D (dimensions). Hand gestures are one of the simplest and most interactive ways to perform communication with the user. The output from the in-built camera or webcam is displayed on the screen, and after that, it will track the reference point of the hand and perform movements of the hand to perform the activities. Gesture shape, distance, and position information will be gathered using the hand as a reference point. It helps to improve the interaction between human and computer and makes it more interactive and user friendly.
Automatic Subjective Answer Grading Software Using Machine Learning

One of the major challenges during online examinations is the assessment of answers, particularly of the subjective type. Subjective answers test a student's ability to retain information and express it in natural language. While objective questions have a correct fixed answer, subjective questions can have multiple correct answers. These answers can convey the same information while using a completely different language and grammatical syntax. This makes it difficult to automate the process of grading subjective questions and requires a lot of manual work hours. This study intends to automate the process of grading subjective questions using Machine Learning (ML) and Natural Language Processing (NLP). The study has compared the subjective answer with an ideal answer that is provided by the authority that creates the question. Based on the similarity between the two answers, a score is generated which can be mapped to an appropriate grade. The authors have provided a web application made using the Django framework for people to give online examinations and be automatically graded in near real-time. No machine learning model can be 100% accurate, so there is a functionality for admins to edit the grades.
Cosine Similarity Based Word2Vec Model for Biomedical Data Analysis

The conversion process of words to vectors involves mapping each word or term in the biomedical data to a unique numerical vector, called a word embedding. One of the key advantages of using word embeddings is that they capture the meaning and context of words in a numerical format, which can be used as input for various machine learning and data analysis tasks. The Milvus vector engine is an open source software that allows for the efficient and accurate representation of large-scale data in vector space. It is particularly useful for high-dimensional data such as biomedical data that contains a large number of unique words and terms. The use of the Milvus vector engine is to convert biomedical data into numerical vectors for machine learning and data analysis tasks. In the context of biomedical data, the use of word embeddings can enable new discoveries and insights by allowing for more accurate and efficient analysis of large datasets. This word embeddings can be used to identify patterns and relationships in biomedical literature, or to classify medical articles based on their content.
Food Supply Chain Management Using Hyperledger

This research study investigates the potential of blockchain technology, specifically the Hyperledger platform, in addressing challenges in food supply chain management, The aim of this study is to propose a comprehensive framework for the implementation of Hyperledger in food supply chain management. The paper begins by highlighting the issues faced in food supply chain management, including food safety, traceability, and transparency problems. It then provides an overview of the Hyperledger platform and its features, and explores how it can be leveraged to address these challenges, The study focuses on the implementation of Hyperledger in food supply chain management, examining its potential benefits, challenges, and limitations. It also discusses the key stakeholders involved in the food supply chain, and how the adoption of Hyperledger can benefit each of them. Furthermore, the paper presents a case study of a pilot project that used Hyperledger in food supply chain management, The research findings demonstrate that Hyperledger can be an effective solution for addressing challenges in food supply chain management, particularly in ensuring food safety, traceability, and transparency. However, the study also highlights some challenges and limitations of implementing Hyperledger in this context, such as the need for standardized data formats and the high cost of implementation. Overall, this paper provides valuable insights for organizations and policymakers considering the adoption of blockchain technology in food supply chain management.
An Enhanced Decentralized Social Network based on Web3 and IPFS using Blockchain

Human lives now include a significant amount of social media usage. Social media is used to create communities, strengthen bonds, and stay in touch with distant friends. A single server, which performs all significant processing, serves as the hub of a centralized system. Thus, a centralized server becomes an easy target for hackers. Decentralized social networks can provide a more secure environment where users may exercise more privacy control through distribution of information processing across many machines. This study proposes a system where the Web3 Library is used to implement the decentralized network. This provides a social graph with all devices in the network. The Ethers library provides functionalities to engage the Ethereum framework. This way an OSN (Online Social Network) service based on blockchain is created and its decentralized operations are performed. Due to a decentralized autonomous organization that is designed for user autonomy, users can democratically self-manage the OSN.
Study of Spam Email Filtering Methods using Supervised Machine Learning Techniques

The development of the Internet paved the way for the commercial exchange of data-extensive messages in the form of E-Mails. But, a big issue is spam E-Mails which are unwanted and incessant emails that the user may or may not have signed up for. Hence, users need to have a filter that distinguishes spam emails from ham to avoid unnecessary and potentially harmful messages. This study proposes a framework for automatically detecting spam emails using supervised machine learning techniques. The models are trained on an openly available dataset with additional methods that help in gaining insight into the data. The performance and the accuracy of different models for segregating incoming emails are then tested and compared.
A Soundbite-Based Framework for Text and Object Detection Using OCR and YOLO Technique to Assist Blind and Deaf

Communication among individuals primarily relies on text or speech, but people with visual and hearing impairments face difficulties in accessing information. The SoundBite hearing system provides an incredible solution for these individuals to maintain awareness of their surroundings by sensing the text and objects located in front of them using embedded cameras in caps or specs. The camera captures the image and the text is separated from the text image by using OCR(Optical Character Recognition). The OCR technique is used to separate the text from the captured image, which recognizes the text and converts it into machine-readable format. The converted text is then fed into the e-Speak software, generating a corresponding voice that is amplified in the SoundBite vibrator. The SoundBite system utilizes a removable oral device to transmit sound through teeth, enabling deaf and blind people to sense sound. The SoundBite system also utilizes a deep learning algorithm called You Only Look Once (YOLO) to capture objects in the surroundings with high accuracy. This CNN-based algorithm identifies the objects, and the e-Speak software converts them into sound that is amplified and vibrated in the SoundBite device. The main focus of this project is to help deaf and blind individuals to live independently without requiring assistance. They can stay connected to their environment by sensing object labels such as medicine expiry dates or newspaper headlines. Thus, the SoundBite system provides a powerful tool for deaf and blind individuals to stay connected to the world around them, enhancing their independence and improving their quality of life.
Security Aware Resource Management Framework (SARMF) for Edge-Cloud Computing

In recent years, there has been an increasing demand for edge computing, which utilizes resources at the edge of the network to improve response times and reduce bandwidth requirements. However, existing methods for managing and securing edge computing resources have failed to meet the Quality of Service (QoS) and Service Level Agreement (SLA) requirements of many applications. This has led to a need for a new approach that can address these requirements while maintaining the security and reliability of the system. This survey presents an overview of a new Security Aware Resource Management Framework (SARMF), focusing on how it addresses the QoS and SLA requirements of edge cloud computing. The study begins by discussing the challenges faced by existing methods, and how the SARMF is designed to overcome these challenges. This study has provided a detailed description of the SARMF, including its architecture, key components, and how it operates. It describes how the SARMF uses machine learning algorithms to analyze data on resource usage and network traffic patterns, and how it adapts to changing circumstances to ensure that resources are allocated effectively and that the system remains secured against new and evolving security threats. This study also discusses about the benefits of the SARMF, including improved accuracy and efficiency of resource allocation, reduced risk of security incidents and disruptions, and the ability to meet the QoS and SLA requirements of different applications and organizations. Overall, this study provides an in-depth analysis of the SARMF, highlighting how it addresses the QoS and SLA requirements of edge cloud computing in a secured and efficient manner. By providing a comprehensive overview of the SARMF, this research study aims to help researchers and practitioners understand how this framework can be used to improve the management and security of computing resources in edge cloud computing environments.