Reviewerly/exHarmony huggingface dataset:
Scientific Knowledge Graph Creation and Analysis

Reviewing the internet for research often tends to be a time-intensive process. With a rapid increase in data available on the internet, one’s research can easily be swayed as there is a high probability of less important papers being displayed as search results. A significant percentage of research papers published lack credibility thus leading to an overall decline in the standard of research. With the objective of making the methodology of research straightforward and providing academia with essential and accurate information, we propose a system with the application of Knowledge Graphs. We utilized the Aminer(v13) dataset for the creation of the Knowledge Graph. We have introduced a novel scoring approach, “Node Importance Score” (NIS), for research papers using graph centrality measures i.e., PageRank, EigenVector Centrality, and Betweenness Centrality, which is combined with the H-Index of the publication of the paper. This score is used to find the top-k influential and important papers in a given domain. Validation of the obtained NIS shows that our approach overcomes certain flaws present in the state-of-the-art scoring system.
System for Enhancing Accuracy of Noisy Text using Deep Network Language Models

Text from image documents must be recognized for its usage. Various tasks such as plagiarism & error check, language analysis, information capture rely on the accuracy of this text conversion. OCR systems convert the document images to their text equivalent. These OCR systems are prone to introducing errors during the recognition process.This work reports a system developed to ingest image documents which is converted to text using available OCR technologies. The recognized text, subsequently, is processed with deep network language models to enhance the accuracy of text. The system consists of a client server architecture with user interface available from web application as well as from mobile app. For the language models, encoder-decoder based BART & MarianMT are used. The results obtained demonstrate a 35% reduction in WER using the BART language model.
Analytical Performance of Traditional Feature Selection Methods on High Dimensionality Data

Dimensionality Reduction is a technique to select features or split contents from a dataset which reduces the dimension. Dimensionality Reduction techniques reduce the computational time to train the Machine Learning Model using the selected features to predict an outcome with higher accuracy. Feature Selection is a part of Dimensionality Reduction which reduces the number of features when developing a model for predictions. Wrapper method is used as Sequential Feature Selection to select the features from the dataset which contributes highly towards the accuracy of the model. Breast Cancer dataset, Vehicle Loan dataset and Loan Defaulter dataset have been used to compare four traditional feature selection algorithms. Once the features are selected from each of the four algorithms, we train the Logistic [15] Regression Model (ML Model) with those features which gives us the computational time and accuracy. Using computational time and accuracy given by the model, of the features selected, of all four algorithms; we put together a comparison.
Secure Content-Based Image Retrieval with Image Ambiguation

With the development of mobile devices, images and photos have become essential aspects of life for the majority of people. The storage space provided by these mobile devices frequently runs out due to the huge storage space requirements for the storage of these photographs. Because of this, the majority of users keep these data in the cloud. Additionally, the majority of service providers don’t use user-side encryption or ambiguation to protect the data. There needs to be another way to reduce the encryption overhead and reliably store the data without encryption and at the same time, preserve the image content. We use Image Ambiguation for this purpose. It aims at ambiguating the images with the help of a deep learning technique to distort the images in a way it can be securely stored on the cloud without any encryption. This technique reduces the encryption overhead and also reduces the chances of cloud providers gaining access to the stored data. We also need a reliable retrieval system. We propose an approach to solving the above problem by using bottlenecked autoencoders to reconstruct the images in such a way that the image on the cloud is unusable by the cloud provider and the storage used to store such images is kept to a minimum. To find the best matches for the query image, we use a retrieval technique based on VGG16 feature extraction and Euclidean distance with F1 scores of 0.902 and 1.0 on the COREL and CBIR Datasets respectively.
An Xception Model with Residual Attention Mechanism for Facial Occlusion Detection

Occlusions occur due to the presence of obstacles. It poses difficulty in localizing and detecting the facial region, resulting in substantial intra-expression variability caused by noise and outliers. Facial occlusions are one of the most common issues that exist in real-world images. Solving such issues is essential for improving face recognition. The main aim of this work is to detect the occluded face. This work proposes a modified Xception network along with a residual attention mechanism to detect occluded parts of the facial region. The recognition accuracy obtained with the proposed Xception network with residual attention (Xcep-RA) mechanism is 99.97%, 99.85%, and 98.95% using Webface-OCC, Labeled Faces in the Wild (LFW), and Real-World Masked Face Dataset (RMFD) datasets. Extensive experiments using Xcep-RA significantly achieved competitive results compared to state-of-the-art methods on Webface-OCC, LFW, and RMFD datasets.
Polyfund: Polygon - Based Crowdfunding Dapp

The rise of information technology has led to a surge in interest of investors and businesses in advanced futuristic technologies like Blockchain and Cryptocurrency. Blockchain is the heart of web3 technologies. One of the fields that could be revolutionized using it is crowdfunding. Crowdfunding is the process of supporting a project by obtaining modest contributions from a large number of individuals, usually online. Modern day crowdfunding websites are centralized, prone to cyberattacks, charge high amounts of transaction fees, have print rules & regulations and intellectual property risk. Blockchain has emerged as a technology which promises decentralization of a system along with a highly secured database. Blockchain is a distributed immutable ledger used to record transactions. Our project proposes a model to switch traditional crowdfunding processes on a Blockchain Network called Polygon. Apps made on Polygon use the same frontend as that used in traditional web2 websites but the backend instead of being a centralized database is an open peer-to-peer network. Our proposed work has a promising future and lots of room for development.
Hand Gesture Detection using Transfer Learning with Deep Neural Networks

Large segments of the population value sign language as one of the many languages used for communication. Each sign in each sign language differs in terms of hand shape, motion profile, and positioning of the hand. These differences will have a substantial influence on the accuracy and reliability of the identification process. Therefore, one of the key techniques for detecting gestures is sign language identification utilizing deep learning. In this paper, we have considered four major algorithms of Image classification EfficientNet- B0, GoogleNet, ResNext 50 32x4d and MobileNet-V2 which were tested on GPU system. To determine the optimum model that uses the fewest GPU cores, many factors have been considered, including training time, training accuracy, total loss and inference. GoogleNet performance is better as compared to other models.
Optimized Recognition Of CAPTCHA Through Attention Models

Information retrieval from the CAPTCHA is a crucial part, this CAPTCHA always contains some unwanted information along with required information, so attention technique comes in handy to select such useful information discarding the unwanted part. The attention concept has become a very important part in the field of deep learning which uses Natural Language Processing(NLP) and Computer Vision(CV). Attention mechanism is rigorously used in OCR based applications which requires generating of selected information rather than every information available. Our work includes implementation of general, global and local Attention mechanisms used with two different models which were transfer learning model and the parameter search model. As OCR with attention technique is computationally costly it is required to optimize the entire process so we suggest optimized retrieval of information from CAPTCHA using parameter search algorithm. This retrieval includes using weights that reduced the training time from 4.03 minutes to 3.33 minutes and the number of training images which were used for training were reduced than before. We obtained the highest accuracy of 87.34% for general attention with parameter search model and local attention model with parameter search model proved to have less computation and less training time than the general attention with parameter search model.
Quantum Modular Multiplication: A New Frontier in Quantum Computing

Quantum modular multipliers are a promising development in the field of quantum computing that aim to perform modular multiplication faster and more efficiently than classical algorithms. This is achieved by encoding the numbers being multiplied into quantum bits (qubits) and using quantum gates to manipulate them in a way that represents the calculation. Since quantum physics principles like superposition and entanglement dramatically speed up processing compared to conventional computing, many calculations may be run simultaneously. Quantum modular multipliers have the potential to have a significant impact in a range of fields, including cryptography and beyond, since they offer a faster and more efficient way to do modular multiplications. Several fields, including simulation, optimization, and cryptography, can benefit from the use of quantum computing.One of the crucial operations in many of these applications is modular multiplication, a sort of multiplication that requires obtaining the remainder of the product of two numbers when divided by a third number. In this paper, we provide a novel modular multiplication method based on a quantum algorithm. To examine its complexity and compare it to that of current adders, we employ a novel form of adder that is intended to not retain the final carry. This adder's decrease percentage in terms of gates and depth is roughly 70%. Our method takes advantage of the special qualities of qubits to complete the computation significantly more quickly than conventional algorithms. It is based on the fundamental ideas of quantum physics, such as superposition and entanglement.
Empirical Method for Thyroid Disease Prediction Using a Deep Learning Techniques

Thyroid issues are quite general, effecting millions of people across the globe. The thyroid gland is susceptible to a wide range of illnesses, including hypothyroidism, hyperthyroidism, and malignancy. The symptoms of hypothyroidism are often very unpleasant. Timely identification of thyroid disorders is greatly aided by accurate categorization and machine learning. Treatment times for patients will be impacted by this rapid categorization. If we want to save time and have fewer mistakes made by radiologists, automatic and exact thyroid nodule recognition in ultrasound images is essential. The use of medical imaging has increased dramatically as a trustworthy and significant data source for the growth of machine learning (ML) strategies. In this study, we apply numerous diverse ML strategies to the dataset and accomplish a comparative examination of their efficiency so that we can more perfectly anticipate the disease utilizing the factors recognized by the dataset. In addition, the dataset has been tweaked to improve classification prediction accuracy. Both the sampled and non-sampled datasets were classified so that the two could be contrasted more precisely. With some data tweaking, we were able to boost the random forest algorithm's accuracy to 99 percent, with 97 percent specificity.
A Comprehensive Review of Image Colorization Methods

This review paper focuses on different methods that are already in use for Grayscale Image Colorization. Image Colorization can be done using various methods. In today’s world, Convolutional Neural Networks(CNNs), Autoencoders, Generative Adversarial Networks, etc are the modern techniques that are used for Image Colorization. This paper gives a comparative study of the above methodologies/architectures. Along with this, a review of different Loss functions is categorized into three categories viz. Error-based, GAN-based, Distribution-based Loss functions are described in detail. We also discuss different methods for the evaluation of an image colorizer. Finally we summarize the results of different methodologies.
Performance Metrics Evaluation Towards The Effectiveness of Data Anonymization

A supplementary method for ensuring that private data is inaccessible to outside parties is data anonymization. Anonymization might affect the outcomes of data mining procedures since it may make it more difficult for commonly used algorithms to analyze the data. This practical experience report compares the performance impact of current data anonymization algorithms to the suggested k-anonymization methods utilizing both original and anonymized data in order to assess the correctness and execution time. Through the use of kanonymization, l-diversity, t-closeness, and differential privacy techniques, a sample of genuine data produced by a healthcare facility was made anonymous. Contrary to predictions, the Hadoop framework was able to handle anonymization approaches, improving accuracy and performance while speeding up execution. These findings show that data anonymization techniques, when properly implemented through Hadoop ecosystems, can help to increase the effectiveness of data anonymization. Furthermore, the suggested method can produce the data anonymization with the necessary utility and protection trade-offs and with a performance scalable to large datasets.
K-Nearest Neighbour Classifier for Big Data Mining based on Informative Instances

K-Nearest Neighbour (KNN) a well-known method that is using in several real-life applications that involve machine learning and data mining in the real world. It finds the nearest neighbors from the training data and classifies the test data using majority voting. However, KNN does not build a model or decision line based on training data, and it can become computationally expensive with large datasets. To address this issue, our proposed strategy divides the Big Data into clusters and extracts informative instances from each cluster based on the cluster center, the closest neighbors of the center, and randomly selected instances. The informative instances are then used for KNN classification, resulting in significant savings in time and storage space without compromising the classification performance. Experimental results demonstrate that our proposed strategy improves the effectiveness of the clustering and KNN algorithms, making it suitable for real-world applications with large-scale datasets.
A Novel approach to Handle Imbalanced Dataset in Machine Learning

The world has seen an exponential rise in machine learning and artificial intelligence since the 1990s. We apply machine learning models to solve various real life problems like regression and classification. However, class imbalance is a very common issue faced for classification problems in machine learning. In this study, we propose new greedy resampling techniques to solve the problem of class imbalance. We shall also compare the results of these techniques with the Synthetic Minority Over-sampling Technique (SMOTE).
A-VAE: Attention based Variational Autoencoder for Traffic Video Anomaly Detection

Video surveillance systems are essential for an intelligent and smart traffic monitoring system. Detecting and recognizing traffic anomalies is the foremost task for the safety and security of human lives. The advancement of computer vision technology, especially in deep learning, has paved the way for accurate anomaly detection. This paper presents a novel attention-based variational autoencoder (A-VAE) architecture for detecting anomalies in traffic videos. A-VAE is designed with the help of 2D CNN and BiLSTM layers with an attention mechanism for representation learning. In addition, a shortcut connection is employed between the spatial encoder and the spatial decoder, which helps for better decoding. A-VAE is trained end-to-end on regular video sequences in an unsupervised manner. To check the effectiveness of the proposed A-VAE, two challenging real-world traffic datasets (Crossroad1 and Pedestrian) have been utilized. A-VAE delivers the AUC and EER of 87.4% and 27% in Crossroad1, and 82.2% and 31% on the Pedestrian dataset, respectively. During testing, an improved runtime of 0.0149s (∼ 67 fps) and 0.0192s (∼ 52 fps) has been achieved for the Crossroad1 and Pedestrian datasets, respectively.
American Sign Language Fingerspelling Recognition using Attention Model

Sign Language Recognition(SLR) is a complex gesture recognition problem because of the quick and highly coarticulated motion involved in gestures. This research work focuses on Fingerspelling recognition task, which constitutes 35% of the American Sign Language (ASL). Fingerspelling identifies the word letter by letter. Fingerspelling is used for signing the words which do not have designated ASL signs such as technical terms, content words and proper nouns. In our proposed work for ASL Fingerspelling recognition, we consider ChicagoFSWild dataset which consists of occlusions and images captured in varying illuminations, lighting conditions (in the wild environments). The optical flow is obtained from Lucas-Kanade algorithm, prior is generated, images are resized and cropped with face-roi technique to get the region of interest (ROI). The visual attention mechanism attends to the ROI iteratively. ResNet, pretrained on Imagenet is used for the extraction of spatial features. The Bi-LSTM network with Connectionist Temporal Classification (CTC) is used to predict the sign. It provides the accuracy of 57% on ChicagoFSWild dataset for Fingerspelling recognition task.
Utilizing the Point Feature Matching for Video Stabilization

A technique for video stabilization that maintains the subject steady while also eliminating hand shaking. Our network topology is especially made to stabilize both the background and the foreground simultaneously while giving the user the opportunity to adjust the stabilization emphasis. We additionally offer a real-time frame-warping stiff moving least squares grid approximation. To explicitly infer the stiff moving least squares warping, which implicitly balances between global rigidity and local flexibility, a linear convolutional network is utilised. Our method is fully automated and requires no user preparation or input. The use of video stabilization is crucial in both amateur and professional filming. As a result, there are several mechanical, optical, and computational solutions. Stabilization may be used to capture handheld photos with lengthy exposure durations in still image photography as well.
Modern Soft Computing Techniques and Their Various Applications

In contrast to traditional computing, soft computing can be used to adjust prediction models and locate answers to difficult, real-world issues. Soft computing is more forgiving of ambiguity than classical computing. For soft computing, the gold standard is the human brain. Soft computing includes innovations like machine learning, artificial neural networks, genetic algorithms, fuzzy logic, and expert systems. First proposed in the 1980s, soft computing is now a central focus of academic inquiry across disciplines. Soft computing is widely used in commercial, industrial, and household settings due to its low cost and high performance. Soft computing is finding more and more uses today. This article compares and contrasts the benefits of traditional computing with those of contemporary soft computing.
AI Approach for Minimizing The Energy Consumption of Servers Using Deep-Q-Learning

This paper focuses on minimizing energy consumption by servers in data centers. Server’s energy consumption can be impacted by numerous factors, such as the number of connected devices, the workload being processed, and the energy efficiency of the components.High energy consumption can be serious because of several reasons as it can impact the reliability of servers because high temperatures generated by energy consumption can lead to hardware failure and other technical issues. Therefore, reducing energy consumption in servers is important for improving the cost-effectiveness, sustainability, scalability, and reliability of data center operations. A type of reinforcement learning called Deep Q-Learning (DQL) can be used to address issues with server energy consumption. The basic idea behind DQL is to train an artificial agent, such as a neural network, to make decisions about energy consumption in real time. The agent is trained by frequently performing actions in a setting and earning rewards depending on the amount of energy consumed by specific actions. Over time, the agent learns which actions are most likely to lead to energy savings, and it can then be deployed to make real-time decisions about energy consumption in a server. Experimental results of the proposed research show an average of 66% power saving in the server’s consumption of energy using Deep Q-Learning (DQL).
Cricket Video Summarization Using Deep Learning

Video summarising is the process of choosing and presenting the most relevant or fascinating components from a longer video document in order to create a brief summary for potential consumers. Cricket is a highly regulated sport played for quite a longer duration than most other sports. This study presents a paradigm for recognising and clipping crucial occurrences in a cricket match that takes into account event-based attributes. Cues used to capture such moments include replays, audio intensity, player celebrations, and playfield scenarios. This project focuses on building a model which will summarise the original video into three types of summaries. Different Deep Learning models and Natural Language Processing techniques are used to build the model. Key frame extraction is a crucial task that is done to generate the cricket highlights. The project’s purpose is to show how to extract a high-quality summary out of an original video using a high-performance approach. This research presents a robust video summarization model for the cricket videos that is able to generate high-quality cricket highlight videos along with textual and audio summaries based on the user’s preferences. The focus is on the automation of video summarization process and provide a better quality of summary to the user. From the comparison of the generated highlights to the actual existing highlights manually, it was concluded that the methodology adopted gave a satisfactory result covering almost 80-90% of the events and captions with Bilingual evaluation understudy score-4 of 0.748953.