Reviewerly/exHarmony huggingface dataset:
An effective architecture for Image colorization using Adaptive GAN

More attention has recently been paid to image colorization. In order to produce realistic results, traditional reference-based methods frequently rely on external colour pictures. An extensive picture database or internet search engine will be required to obtain such instances. At a reasonable cost, recent deep-learning-based algorithms may colorize photos automatically. On the other hand, inconsistent hues and poor artifacts are always present. Utilizing the extensive and diverse colour priors stored within a pre-trained Adaptive Generative Adversarial Network (GAN), our goal in this study is to restore vibrant colors. In particular, we incorporate matched features comparable to exemplars into the recoloring procedure by using feature modulations and a GAN encoder to “extract” them. Our system could generate vivid colors with just one forward pass thanks to the dynamic generative colour prior and sensitive designs. Additionally, changing the GAN hidden codes makes it extremely simple to obtain a variety of outcomes. By traversing GAN latent space, our method enabled controlled and smooth transitions while maintaining the advantages of Adaptive GAN interpretable controls. Our approach outperforms previous efforts in terms of execution, as evidenced by extensive testing and client research.
Image Captioning Using Deep Learning Techniques for Partially Impaired People

Image captioning is used to describe an image based on the features and actions that are present in that image. The existing image captioning primarily uses an encoding and decoding structure, with the encoder which extracts image features using CNN model as well as the decoder uses LSTM model. The current encoding and decoding scheme makes extensive use of the attention mechanism. Gradient explosion is a problem with the current image caption models, which are based on recurrent and convolutional neural networks, and therefore are not particularly good at extracting valuable information from images. To overcome this problem, the YOLOv5 and Bidirectional LSTM model is proposed. YOLOv5 is used to identify the objects in a given image and a bidirectional LSTM (Bi-LSTM) layer is used to extract the features of the given image. This proposed algorithm gives the optimized result with good accuracy. The Flickr8k benchmark dataset is used to test this approach. From the results it is known that the trained model performs better than alternative encoder-decoder methods that depend solely on global image features. The metric which has been used to evaluate the model is BLEU which is mainly used for machine translated text evaluation. This model gave a 0.7 BLEU score.
Artificial Intelligence Application for Effective Customer Relationship Management

Machine learning and artificial intelligence (AI) allow businesses to make better use of the data they already have and to find solutions to difficult problems, both of which make it possible for them to expand their operations in a timely and cost-effective manner. In recent years, artificial intelligence (AI) and process automation have emerged as two of the most sought-after innovations in customer relationship management (CRM). This is because businesses put in significantly more effort to guarantee complete and total happiness for their customers (CRM). AI in CRM is aimed to decrease the amount of boring, routine labour that people must do by analysing standard operations and freeing up time for employees to focus on things that are more important and demanding. With the use of artificial intelligence (AI), customer relationship management software may streamline and automate a variety of labour-intensive responsibilities, including following up with clients, sending thank-you messages, scheduling appointments, and managing calendars. When artificial intelligence (AI) is linked with typical customer relationship management (CRM) systems, it is anticipated that AI would increase lead creation and visibility and improve customer experience, procedures, and team productivity. In customer relationship management, the goal of artificial intelligence is to do more than gather data; this has the potential to increase sales and keep existing customers satisfied.
Recognizing Handwritten Offline Tamil Character using VAE-GAN &amp; CNN

Handwritten character recognition (HCR) is a pattern recognition problem. The HCR complication has been extensively learned for several decades, but there has been tiny research on independent script models. This is due to a variety of variables, lack of datasets, the concentration of most traditional research on character recognition approaches are language specific and unattainable. Convolutional Neural Networks are deep learning models that take input and process it through multiple layers to produce the desired output based on training. In order to increase the volume of the dataset, GAN (Generative Adversarial Network) framework was adapted on the HP Labs e TAMIL handwritten character dataset. Despite the fact that GAN improves the dataset, processing the images takes a long time, the proposed work hybrid the models Variational Autoencoder (VAE) and GAN model to speed up processing. A GAN tries to produce new data that cannot be identified from real data, whereas a VAE learns to encode the provided data and then reconstructs the new images from the encoding. IN VAE-GAN, we employ the latent representations produced by an encoder for a variety of purposes. The proposed system uses VAE-GAN to enlarge the dataset and Convolutional Neural Networks to recognise handwritten Tamil characters. The accuracy of VAE-GAN with CNN is found to be 86%.
Cloud Computing Based (Serverless computing) using Serverless architecture for Dynamic Web Hosting and cost Optimization

Serverless is a cloud-based code execution model where cloud carrie copes with servers and computing useful resource control instead of builders. There are not any virtual machines or bodily servers: they’re deployed routinely inside the cloud by companies. Cloud carriers cope with provisioning, keeping, and scaling the serverless architecture. What’s more, the serverless structure permits launching apps as wished: you don’t pay for ‘constantly-on’ server components to run your app when it’s no longer getting used. Instead, on a few occasiotriggerers app code, and the resources are dynamically allocated for that code. You forestall paying as quickly as the code is carried out. So, in a nutshell, serverless architecture is a manner to construct your cloud-primarily based software without coping with infrastructure. It eliminates the want for ordinary duties like protection patches, ability control, load balancing, scaling, and many others. Still, serverless does not mean there are no servers in any respect. The time period is truly elusive. Servers are definitely removed from the app development seeing that they are managed by the companies.
Automatic Extractive Text Summarization of Hindi Text using Deep learning approach

In order to swiftly sift through the vast amounts of textual material available online, Automatic Text Summarization (ATS) is in great demand. In this research, the Real Coded Genetic Algorithm (RCGA) is used to the Hindi movie reviews available on the Kaggle dataset in order to suggest an ATS approach for the Hindi language. The approach consists of five stages: pre-processing, extraction of features, processing, paragraph ranking, and summary output. In a rigorous research on many feature sets, sentence similarities and semantic segmentation characteristics are merged with some other features to produce the evaluation metrics. Different compression rates are evaluated in order to extract the sentences with the greatest scores as the corpus summaries. The ATS extractive approach provides a summary reduction of 65% when compared to current summarization methods. A text summary tool condenses the text and shows the user only the crucial information. The significant sentences are chosen via the extraction approach based on a theme approach. Hindi stop-words were eliminated before choosing thematic terms, and the stemming procedure was used to find the sentences’ root words. Stop-word removal removes useless words from the input material, while stemming helps group together words that have the same numerical term. The method relies on how frequently the radix of theme terms appear in the sentences to determine a score for them. Sentences having the highest ratings are prioritized for inclusion in the summary. In order to make the produced summary more similar to human-generated summary, it is then further treated based on the elimination of superfluous terms from the selected summary sentences.
Accident Prevention System using Machine Learning

Most of the Accidents Occur in these days are during the Night time. As per the Reports of the Accident, Most of the Accidents are due to Improper vision of the Drivers during the Night time. In order to minimize and prevent the Accidents we came with a Machine Learning Model Which Continuously Monitors the Road in the Range of 20 Meters and Specifies if there are any people or Animals passing across the Road. The Significance of the project is to help the innocent people who might lost their lives due to the Accident without their intervention. The Model is also useful for the animals and passers. The main purpose of the project is to detect strollers or animals like dog at night and dim light conditions. As the light intensity during night is less, Even our human eye cannot detect a person. The existing system are less effective due to the less accuracy of algorithms. The night-vision systems indeed work on mainly image processing with assistance of camera and processing units.
Cyber-Crime and Digital Forensics: Challenges Resolution

In this technology-friendly era, technology and science govern the world so much. No matter what sector it is, here or somewhere all sectors depend on technology. This technical dependency helps all sectors to grow fast but also invites criminals to take advantage. Criminal cases which are based on technology are getting to hike drastically day by day. Cybercrime is now these days one of the key problems of mankind. There are so many organizations from different sectors which are facing down because of cyber-crime, so many individuals/private or governments facing problems due to cyber-crimes. Digital/computer forensics is a new pattern to find cyber-crimes. Digital forensic science requires new forensic tools and technology to overcome such kinds of crimes. In this paper, the authors discussed different scenarios of cybercrime and the crime rate of different states of India. In addition, the paper is focused on some preventive measures to reduce the risk of cybercrime and will also analyze statistical data provided by government organizations and forecast cybercrime for the next few years.
Analytical review of deep neural techniques for breast tumor detection.

A tumor is an abnormal mass of tissue that develops when cell growth and division are accelerated. Cancerous tumors can develop, making them extremely harmful and needing early detection for safe excision The most difficult and crucial task in many medical image applications is tumor detection/segmentation because it typically requires a large amount of data and information. Deep learning techniques including CNN (convolutional neural networks), RNN (recurrent neural networks), and DBN are employed in this paper (Deep belief network). The BRATS 2019 dataset is used in this study, and machine learning methods such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forests are compared. Performance metrics such as Dice Score Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity are used to determine which technique performs the best overall based on these metrics. The findings of the segmented images yielded dice scores for the CNN technique of 0. S9, PPV of 0. S7, and Sensitivity of 0.90.
Image Captioning for Information Generation

In the AI applications for natural language definitions, image captioning is a field that is expanding quickly. It attempts to capture meaningful interpretations of the interactions between the acquired picture data from datasets and the sentence meanings. It combines Long Short-Term Memory type RNN over phrases and CNN (Convolutional Neural Networks) picture reading techniques so that the image can be understood by the system and output it in a common language. This study intermingles the vision of the computer and natural language processing (NLP) to create machine that enhances visual information such as photos by giving braille legible captions for those who are blind to better understand what is going on around them.
Early Detection of lung Cancer Using Machine Learning Technique

The study and diagnosis of the lung diseases is the investigating point of interest for the medical experts existing from the a foretime to the current day. A diagnostic system is required to overcome this approach to reduce the time in diagnosis of lung cancers that is putting human life at risk. Few projects are proposed for this purpose and many other are under trails. To identify the cancer cells in the CT images a neural network model is proposed. It is also used to recognize issues in therapeutic imaging applications. A framework is originated for the identification of lung cancer that includes application of deep neural network system and AI. For staging of lung cancer classification of CNN is required. Deep learning methods are used on supervised learning to obtain better results. The proposed framework includes neural network that comprises of many steps like collection of images, pre-preparation, pixel enhancement, segmentation of images, and feature extraction. To outline the present research, it explains that to attain accurate results in detection and treatment of lung cancer cases at a low cost machine learning plays an unpredictable role.
Product Recommendation System using Deep Learning based Recurrent Neural Network

Today’s marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women’s clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation.
Age and Gender Prediction using Adaptive Gamma Correction and Convolutional Neural Network

The classification of age and gender has drawn increased attention recently because of its significance in creating user-friendly intelligent systems. In the domains of image processing and computer vision, determining age from a single facial image has proven a challenging job. Convolutional Neural Network (CNN) based techniques have been frequently adopted for the classification problem in the recent past because of their precise results in facial analysis. This study incorporates an end-to-end CNN approach with the addition of a key pre-processing step for image contrast enhancement, which was done via an adaptive gamma correction technique to produce precise gender and age group classification of real-world faces. The complete feature extraction and classification processes are included in the two-level CNN architecture. The feature extraction task pulls features that are related to gender and age while the classification assigns the facial photographs to the proper gender and age group. The proposed network has been trained and tested on the Adience (original) dataset. The results of the experiment seem to back up the claim that the proposed model is better at classifying people by gender and age when the Adience benchmark for classification is used.
Analyze the political preference of a common man by using data mining and machine learning

Using learning algorithms and game theory, provide a procedure for estimating the likely political leanings of a given voting population. A combination of online and in-person polling was the first step in our strategy. With voters’ preferences for political candidates expressed via a linear hierarchy of qualities, a zero-sum game strategy may divide the total number of votes fairly. In a case study of the Major’s election in our city, which took place in September 2020, we put our approach to work for electoral prospection. The findings were quite promising. I suggest using this method in marketing campaigns for genuine products.
Blockchain Based Crowd Funding

Crowdfunding is a method of online fundraising process that was initially developed for public members to make modest contributions to support the projects of creative individuals. Crowdfunding uses blockchain technology to offer smart contracts for users. This allows us to offer crowdfunding in a secure, transparent, and safe manner. The task of this work is to provide interactive forms for campaign development and financial contributions. Both campaign makers and donors may develop and support the campaigns by viewing or submitting requests for approval and fulfilling requests using this system. In addition, the donor may be able to see the progress of the funds they provide. All transactions will be recorded on the blockchain and stored as blocks. It is alluring to use smart contracts in blockchain. Without the aid of a trustworthy third party, a blockchain-based agreement must be negotiated, carried out, and enforced amongst unreliable participants. It is essential to develop executable code that runs on the blockchain. Blockchain was initially primarily used as the basis for cryptocurrencies, but in recent years, it has expanded to various industries. Blockchain is anticipated to be the most widely used technology as a green way to conduct internet transactions One application area for blockchain technology is crowdfunding websites. The biggest problem with today’s global crowdfunding market is that campaigns are no longer under strict control, and some crowd- investment efforts have proven fake. By utilizing Ethereum smart contracts on the crowdfunding site, this work aims to allay these worries by assuring that the initiatives may be added within the designated time limit, eliminating fraud, and allowing the contracts to be fully mechanically performed.
Efficient Gesture-Based Presentation Controller Using Transfer Learning Algorithm

This research work explains how to control Presentations using hand gestures. Presentations are crucial in many aspects of our human lives. Everyone employee who has ever worked in a company or been a student has participated in a demonstration at any point in their lives. PowerPoint presentations might become irritating when you have to update and manipulate the slides using the keyboard or a dedicated device. So, this project focuses mainly on the development of interactive projection technology, which makes it more interesting and interactive by detecting the user’s motion simultaneously. Our team always wanted that users should be able to operate the presentation slideshow with hand gestures. Many functions of the PowerPoint slideshow have been attempted to be managed in research using gestures. This study attempts to map minute differences in movements using some simple PowerPoint slide show control Python applications. The motions were recognized using machine learning. Gesture recognition became more vital for operating software such as robotics, and games a few years ago. The hand gesture recognition system improves when surroundings are in well-lit conditions. Hand gestures make communication simple, useful, and technologically independent. The proposed technique is to help presenters provide more effective and interactive presentations by increasing their natural contact with the computer. The recommended approach, in particular, is more practical than a laser pen since the hand is clearer and may attract the audience’s attention more effectively.
Copy Move Image Forgery Detection Using Machine Learning

In the era of social media, where information can be shared with a single click, it is important to segregate data into real and fake. Since the majority of the population uses the internet it is easy to spread misinformation. Digital information in the form of photos is one of the primary modes of communication. Unfortunately it has become increasingly easy to manipulate them. In our paper, we survey the recent proposed methods of CMFD(Copy-move forgery detection), and explained the whole CMFD process done. Particularly, we characterize a CMFD workflow for feature extraction and the matching process using key points and descriptors.
Metaverse: Cryptocurrency Price Analysis Using Monte Carlo Simulation

Among the most recent and quickly growing industries worldwide is metaverse cryptocurrency. Despite the fact that the first cryptocurrency namely Bitcoin was only developed 13 years ago, the use and value of digital currencies have increased dramatically. Their marketplaces are among the most intricate and unpredictable markets over the globe. Despite being a decentralized system that does not have a single point of failure, it is commonly known that many individuals who invest in it lose their money. In this article, we attempt to use Monte Carlo simulation to forecast the price of a selected few well-known cryptocurrencies for the next 1000 days namely Doge coin (DOGE), Ethereum (ETH), Litecoin (LTC) and HEX.The mean return on a cryptocurrency and the standard deviation of past returns are the two key factors that affect how the simulations end out. Additionally, the study evaluates the projected returns for cryptocurrencies as well as the correlation between all the different currencies. It also looks into the statistics of cryptocurrency, average true range, volatility and cumulative returns. By calculating the growth rate and sharpe ratio, the entire research would also assist investors in deciding if they should purchase a specific cryptocurrency or not.
Electronic Health Records &amp; Data Management using Hyperledger fabric in Blockchain

Permissioned blockchain networks ensure safety and reliability by employing blockchain’s fundamental tenets, including the distributed ledger, smart contracts, and consensus. There is a potential for data privacy violations in the existing condition of patient data interchange across healthcare organizations if there is a lack of mutual trust. The introduction of blockchain technology has made it possible for healthcare providers to confidently share patient medical records. To ensure the safe and reliable transfer of patient information, a permission blockchain network built on the Hyperledger Fabric framework is proposed and implemented between patents and medical institutions. Blockchain’s effective management of EHRs is made possible by a Hyper Ledger Fabric network. Results from live deployments demonstrate that inaccuracies in data sharing between healthcare providers (such as hospitals, clinics, and public health agencies) are eliminated by using a blockchain based on the Hyperledger fabric. The accessibility of the network allows for the prompt, secure, and legally compliant transfer of patient information across organizations. By using smart contracts as chain code, the blockchain has enabled transparent and secure data exchanges while controlling access to private patient health information. Results indicate Hyper Ledger Fabric provides excellent functionality, security, and transaction privacy.