Reviewerly/exHarmony huggingface dataset:
ASL and Hand Gesture Emoji Translator Using Computer Vision

People with hearing loss communicate using a variety of sign languages, often using their hands. Regular people must learn these sign languages in order to interact with these individuals, which most do not do. As a result, the individuals with special needs may experience psychological and physical effects as a result. This project’s major goal is to address this problem and provide a simple means of communication for people with disabilities and their friends and family. However, in this world of digital people prefer social media platforms for communication and with that hand gesture emojis have gained significant popularity as a means of expressing emotions, actions, and ideas on digital platforms. These emojis, which depict a range of hand gestures, have been widely adopted due to their ability to convey nuanced meanings. Hand gesture emojis serve as visual representations of gestures commonly used in face-to-face communication, bridging the gap between physical gestures and digital interactions so this system can be used as ASL translator and also as emoji’s translator, this system requires only a laptop with an audio output and a camera to take live signs, process them, and compare them with the trained data to identify the perfect match and offer correct results. and it can also be upgraded to mobile application in the future. This solution is incredibly lightweight and has an accuracy rate of >96%.
Generation of Royalty-Free Images with NLP-Based Text Encoding and Generative adversarial networks

This paper introduces the AttnGAN, an Attentional Generative Adversarial Network that facilitates detailed text-to-image generation through multi-stage refinement driven by attention. The AttnGAN employs a unique attentional generative network that generates images with precise details in different subregions by focusing on relevant words in the textual description. The detailed image-text similarity loss is calculated using a deep cognitive multimodal resemblance model to further improve the training of the generator. Furthermore, we have evaluated the proposed AttnGAN on two widely used datasets, namely CUB and COCO. The investigational outcomes indicate that our model can accurately synthesis high-quality images on the CUB dataset. However, the performance of our model is not as satisfactory on the COCO dataset, which poses a more challenging text-to-image generation task because of the complex and diverse scenes in the dataset. Nonetheless, our proposed attention mechanisms have demonstrated their effectiveness, especially for generating fine-grained details in the images.
Colourify: Image Colourization using AI techniques

Converting a grayscale image into a coloured one is known as image colourization. This is a challenging task that needs human assistance to produce high-quality photos. Our model, which is an improved version of the VGG16, is what we suggest in this study. The dataset used is a subset of the MIT Places Dataset available on Kaggle. The architecture is divided into 2 parts, an automatic encoder and an automatic decoder. The model will predict the values of ‘a’ and ‘b’ in the Lab colour space. Architecture of the proposed model has been presented. The results have been compared with the existing models.
Real-time Indian Sign Language Recognition using Skeletal Feature Maps

Sign language recognition is a widely researched problem that has gained momentum since the advent of deep learning. Many researchers have designed models using different deep learning architectures claiming high validation accuracy but low real-time performance. The proposed model achieves consistent prediction in real time through Skeletal Feature Maps using keypoints extracted by MediaPipe. These images are passed to a novel 15-layer CNN architecture for classification. The proposed model can achieve a high validation accuracy of 99.84% over a self-created 106-word Indian Sign Language dataset, that is higher than other machine learning models such as SVM, Gaussian Naive Bias, Decision Tree, Random Forest and some pre-trained standard CNN architectures. The salient feature of the proposed model is it is consistent and accurate independent of the background, subject and lighting conditions. Further, the model is implemented as a desktop application and tested by different signers.
Unlocking the Power of Long Short-Term Memory Networks: A Text Classification Approach

Text classification typically suffers from the fundamental issue of latter-day data with a very high dimension, which hinders the model developed because training time grows exponentially with the number of features used and model risk of overfitting increases with increasing number of features. Since the recurrent structure is ideal for processing long varied texts, recurrent neural networks (RNNs) are among the most often used designs in natural language processing (NLP). RNN using the Long Short-Term Memory (LSTM) architecture is one of the deep learning techniques suggested in this study. In natural language processing for English, one element stores the subject's gender, while another element records whether the subject is singular or plural. Long Short-Term Memory (LSTM) supplies elements that are intended to be able to record an input characteristic. LSTM will discover these properties throughout the training procedure. The results of this study are most accurate when variable word sequence characteristics are taken into account.
Crime Detection System with Machine Learning using OpenCV, YOLO and CNN

The number of crimes are rising daily in the current environment. The government has put CCTV cameras in several public locations to help identify suspects. It can be difficult to recognise offenders on the footage, though. This system has created a procedure to speed up this process and lower the rising crime rate. In comparison to conventional approaches, this technology provides superior solutions and automates the suspect detection procedure. With the aid of a distributed CCTV system, This system that records the faces, gestures, and emotions of the targeted people (criminals). Together with a time and location stamp, will keep a database containing this information. The generated database will be used to identify suspects using CCTV footage of crimes that was recorded by a number of systems situated along routes and close to the crime site. This study examines several techniques including face identification using the OpenFace algorithm, object detection using the YOLOv3 algorithm, and hand motion recognition using the CNN algorithm for locating suspects in CCTV footage and turning that data into information that may be utilised to analyse certain crime scenes in more detail. The findings of our investigation show how successful our method is at identifying suspects. For identifying the expressions of anger, happiness, and neutrality, the OpenFace algorithm obtains accuracy of 55%, 60%, and 80%, respectively. The YOLOv3 algorithm successfully and accurately identifies firearms. The CNN algorithm recognises hand movements precisely, making it possible to identify certain motions or indications. Our research shows that the distributed CCTV system that is presented, when combined with algorithms, offers greater options for suspect detection and identification. It improves the precision of suspect identification and lowers the incidence of false alarms by automating the process and using contextual information. Comprehensive analysis of CCTV video is made possible by the integration of facial recognition, object identification, and hand gesture recognition, enabling a more in-depth comprehension of crime scenes. The system sends a report to the police in the form of a database whenever a crime is discovered. The result is sent to the police through Email in this suggested approach. Simple Mail Transfer Protocol, the industry-standard protocol for internet communication and the transmission of electronic mail, is used to transmit mail. In conclusion, this study makes a significant addition to the fields of crime detection and surveillance technologies. Suspect identification is made more efficient and effective by using a distributed CCTV system using algorithms. This system provides law enforcement organisations a useful tool to fight crime and improve public safety by utilising computer vision. The ability to analyse and understand CCTV footage for criminal investigations might be revolutionised by further research and development in this field that results in ever more sophisticated systems.
Generation And Detection of Deepfakes using Generative Adversarial Networks (GANs) and Affine Transformation

Deepfake technology has gained significant attention and notoriety in recent years for its ability to manipulate visual and audio content, leading to widespread concerns about its potential for abuse. Deepfake videos can be created by using deep learning algorithms that enable the synthesis of facial and vocal features of a target individual onto another person, leading to convincing and often misleading videos. While the technology behind deep fakes is constantly evolving, there has been increasing interest in understanding and mitigating their potential negative impacts. Researchers and experts are exploring ways to detect and prevent the creation of malicious deep fakes while also developing ethical guidelines to regulate their use. This paper presents a deep fake project that utilizes Generative Adversarial Networks (GANs) and affine transformations to generate deep fake videos. The proposed approach takes a target image and a driving video as inputs and generates a realistic-looking deep fake video that mimics the facial expressions and movements of the driving video and combines it with the target image and also incorporates a classification model to detect whether the generated deep fake video is real or fake. The classification model will also be based on the same architecture as used in generating the fake videos and will be used to evaluate the realism and quality of the generated deep fake videos. It generates a novel approach for generating and detecting deep fake videos that can be applied to various applications, such as entertainment, education, and security. However, creating deep fakes also offers potential benefits, such as improving the entertainment industry and enhancing the quality of content creation. As technology advances, it is crucial to balance its potential benefits with its potential risks and take appropriate measures to ensure that deep fakes are used ethically and responsibly. The proposed method aims to identify the real identity of a person appearing in a video and use this information to detect whether the video is genuine or not. This identity-aware approach leverages a deep neural network architecture that combines facial recognition and face forgery detection techniques. The proposed solution has the potential to enhance the security of digital media and protect individuals from various forms of identity theft and cyber-crime.
Comparing Different Sequences of Pruning Algorithms for Hybrid Pruning

Most developers face two significant issues while designing the architecture of a neural network. First, the available dataset for many real-life problems is relatively small, leading to overfitting. Second, When a dataset is large enough, the computation cost to train the model on a given dataset is enormous. Thus most developers use transfer learning with a standard model like VGGNet, ResNet, and GoogleNet. These standard models are memory and computationally expensive during inference, making them infeasible to deploy on resource-constrained devices. The recent research trend is to compress the standard model used for transfer learning to reduce memory and computing costs. In CNN, approximately 10% parameters are present in the convolution layer, contributing to 90% of computational cost, while 90% parameters are present in dense, contributing 10% of computational cost. This paper focuses on the structure pruning of parameters in the convolution layer to reduce computational costs. Here we explore and compare the following pruning technique, 1) Channel pruning with a quantitative score, 2) Kernel pruning with a quantitative score, 3) Channel pruning with a similarity score, and 4) Kernel pruning with a similarity score. Finally, as mentioned earlier, we try several combinations of pruning to form a hybrid pruning.
Machine Learning-based Prediction of Postoperative Survival in Lung Cancer Patients

The primary focus of this work is the utilization of machine learning classifiers to forecast the likelihood of survival in individuals with lung cancer who have undergone significant lung resections via thoracic surgery. Since the data is highly imbalanced, the variations in the synthetic minority oversampling technique (SMOTE), Random Oversampling, and near-miss under-sampling were used to prevent biased decisions in favor of the majority class by the machine learning classifier. Five-Fold Cross-validation is performed before and after balancing data for all the classifiers. The Random Oversampling method outperforms the majority of machine learning classifiers. Support Vector Classifier provides the highest evaluation metrics for the following data, with an accuracy of 85% for both imbalanced and balanced (B-SMOTE-SVM) data. Since the data is imbalanced, we considered the F1 score as a measure for evaluating all the classifiers while applying various data balance techniques, with the lowest score being zero and the highest being 0.34. By combining the benefits of classifiers along with Over sampling and Under-sampling and the identification and interpretation of risk factors, this proposed solution can assist medical practitioners in their diagnosis of the survival status of lung cancer patients.
Implementation of Panoramic Image Stitching using Python

Panoramic image stitching is a popular technique for combining numerous photographs to generate wide-angle views and high-resolution images, Image stitching entails matching the pictures, adjusting for camera location and orientation discrepancies, and mixing the images to form a smooth composite image. In this paper, we offer an implementation of panoramic image stitching using Image processing techniques in python. The suggested technique employs feature-based image alignment, which aligns pictures by utilizing feature points in the images. The detection of spatial differences between the pixels of the image is a key to detect feature points, which is resistant to changes in scale, rotation, and affine distortion. After aligning the photos, we generate a smooth composite image. The suggested approach is tested on a real-world picture collection, and the findings demonstrate that it can generate high-quality panoramic photos with good alignment and smooth blending. The suggested approach is simple and straightforward to apply, and it has applications in virtual reality, surveillance and security systems, and film and video creation.
A Detailed Analysis on Various Datasets using Machine learning and Deep Learning Techniques for Phishing URLs Detection

Phishing represents a deceitful interaction where an assailant attempts to acquire delicate data from the person in question. Phishing is a sort of digital assault, where the attacker takes the individual data of the user or victim and uses this information for malicious purposes. To resolve this problem many techniques are there but each technique has some advantages and the wall has some disadvantages. Phishing attack is increasing day by day so it's a more critical problem which faces by each and everybody who used the internet for various purposes. Phishing is a bogus cycle, where an aggressor attempts to get significant data from the person in question. By and large, these sorts of assaults are performing utilizing instant messages, messages and sites. Noxious sites, which are right now in a broad ascent, have the same look as real destinations. Alternately, their backend is intended to gather astute data that is inputted by the harmed party. Nonetheless, their backend is intended to gather delicate data that is inputted by the person in question. In this paper, we selected the dataset based on the performance of various ML and DL models. Also compare the accuracy of these models with existing work and select the best features of URLs to detect phishing attacks.
Analysis and Review of Image Super Resolution for Low Light Enhancement

Image Super Resolution is the enhancement of a low resolution image to obtain its high resolution counterpart. It is an important technique in computer vision and has many practical use cases such as medical imaging and surveillance. In this paper, we study the usefulness of applying Image Super Resolution for Low light image enhancement. While a lot of research has already been done in low light image enhancement, most of the methods proposed so far introduce artifacts in the images and fail to produce finer details due to the limited amount of data they are trained on. In this paper, we review and analyze state-of-the-art super resolution methods to enhance the results from low light enhancement models. To this end, we first provide a taxonomy of methods, datasets, and metrics for Super Resolution. Finally, we experiment with different combinations of Low Light Image enhancement methods and Super Resolution methods to establish the effectiveness of using the latter in low light enhancement and show that it produces visually better results.
An Automated Multi-Level Convolutional Neural Network Approach for Classification of White Blood Cells

Blood cells known as white blood cells (or leukocytes) are generated in the marrow of the bone marrow and circulate through the circulatory system and lymph tissue. White blood cells (WBC) are the cells responsible for defending the body against outside invaders. They aid the body in its defence against infections and other illnesses. There are three types of white blood cells: granulocytes (which include basophils, eosinophils and neutrophils), monocytes, and lymphocytes (which include T cells and B cells). Usually, a total blood count test includes checking the blood's white blood cell count. Infection, inflammation, allergies, and leukaemia. White blood cells have a lifespan of 13 to 20 days before being eliminated in the lymphatic system. The first time immature WBCs leave the bone marrow and enter the peripheral circulation, they are known as bands or stabs. Phagocytosis is the method through which leukocytes combat infection. This work’s primary goal is to find the type of white blood cell from the provided input of white blood cells images and to categorize cell type according to its variety. Based on the progress made in developing the Deep Learning algorithm, this study has the potential to aid in the prediction of cell kinds and in assisting medical professionals in the diagnosis of a wide range of abnormalities and disorders. Inceptionresnetv2, VGG-16, CNN, are few deep learning model that can automatically assess white blood cell shots from different angles and circumstances of illumination in order to determine what kind of cell they are. The dataset that is used in the project contains four categories or classes with values ranging from 0 to 3, where 0,1,2 and 3 denotes particular variety of white blood cell. Among the models that have been utilised for the classification of images of blood cells, the accuracy obtained by proposed custom-built CNN framework is 98.70% that outperform the other framework.
Reducing Cloud Workload Costs in Geographically Distributed Data Centers with GeoSched

In this research paper, we present GeoSched, a job provisioning framework for geo-distributed data centers. Our approach takes into account parameters such as temperature and electricity prices to intelligently schedule jobs across different data centers. Through extensive simulations, we evaluate the performance of GeoSched and observe significant improvements in energy efficiency. Specifically, our meta-scheduler reduces energy consumption by 8.2% and decreases the cost of operation by 11.7%. These results highlight the effectiveness of our approach in minimizing data center operating costs while meeting workload demands and maintaining Service Level Agreements (SLAs). For future work, we plan to investigate additional factors that impact energy consumption, including the availability of renewable energy sources and the energy required for data transfer. We also aim to develop more comprehensive SLAs that can capture the complex and diverse requirements of cloud workloads, such as response time, availability, throughput, and data consistency. Furthermore, we recognize the importance of considering workload diversity in our meta-scheduling approach. Different types of workloads have varying requirements and priorities, and accommodating them is crucial for optimizing resource allocation and job provisioning. Additionally, we intend to explore the scalability of our framework for larger and more intricate data center networks. This would involve developing more sophisticated resource management strategies and algorithms to handle the complexities of scaling up. By addressing these areas of future research, we aim to further enhance the energy efficiency and performance of geo-distributed data centers while providing robust SLA adherence.
Efficient Real-time Breed Classification using YOLOv7 Object Detection Algorithm

This work aims to develop an accurate and reliable solution for real-time breed recognition and categorisation of dogs and cats using the YOLO-v7 object detection algorithm. The Oxford IIIT pet dataset, consisting of a large number of images of different dog and cat breeds with various poses and backgrounds, is utilized for training and validation of the model. This model achieved a high F1 score of 85.6% and mAP of 82.57%, superior than other latest models. The findings illustrate the potential of using the YOLO-v7 algorithm for real-time breed detection and classification of dogs and cats, which can be used in a wide range of fields, including veterinary clinics, animal shelters, and pet stores.
Indian Sign Language Generation – A multi-modal approach

As social beings, communication is an important skill for us humans. But some people who have hearing issues face problems while communicating in their day to day lives. They use Sign Language as a way to express themselves with the help of hand gestures and facial expressions. In today’s times when we have the whole world on our palms, online content in multiple forms is a large source of entertainment and infotainment for us. The accessibility of online content is quite high, but for people with hearing loss, it is still not accessible enough. In this research, we propose a method of generating Indian Sign Language gestures from text, subtitles of video content, audio and image with text as inputs. For the video content we have taken Youtube videos as a case study. After further processing and with help of SiGML files, we get the animated gestures as output. Another method we propose is for recognizing the ISL gestures with the help of spatial and temporal feature extraction using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These will give the word for the ISL gesture being played. We used accuracy and confusion matrix as performance metrics for this methodology. We recorded an accuracy of 96% in ISL Recognition methodology.