Reviewerly/exHarmony huggingface dataset:
Aspect Based Analysis of AWARE Dataset using Machine Learning Algorithms

The use of application specifically the mobile application has increased manifold in the past few years. The reviews are important for the businesses to help analyse the product. The Aspect Based Sentiment Analysis (ABSA) helps to analyse the various aspects of the product which can help improve the specific aspects of the product and further increase the market of the product. The sentiment classification of the aspects will help to analyse whether certain changes in aspect will help to improve the overall features and market of the product. Also, the category classification of aspects will help to improvise the various factors like cost which can further be beneficial for the business. The reviews are generally extracted of multiple products belonging to different domains, so the classification helps us to find the domain of the application as well. Thus, all this leads to an overall improved analysis of the application. In this paper, Aspect Sentiment Classification (ASC) and Aspect Category Classification (ACC) and Domain Classification (DC) is performed on the AWARE dataset with the objective to overall improve the analysis of reviews in terms of aspect and their categories and their domain. Machine learning algorithms for used for the same in case of Support Vector Machine (SVM) algorithm the results were improved as compared to the accuracy values provided for the AWARE dataset.
Simplifying Structure and Pattern in Complex Social Networks Using Graph Isomorphism

In recent years, networks have grown in size and complexity, traditional network testing methods have become time-consuming and inefficient. As a result, there is a need for more efficient and reliable network testing methods. One important topic in the field of network testing is graph pattern matching. Network analysis relies on graph pattern matching. The paper discusses the use of graph isomorphism for network testing. This paper also gives an overview of recent advances in isomorphism testing, which determines if two networks are structurally identical despite potential labeling discrepancies between them. The combination of isomorphism and pattern matching in network analysis is used to identify equivalent structures in different networks. This approach is used to identify and compare common patterns in complex network structures, such as those found in social networks.
Image to Image Steganography using U-Net Architecture with MobileNet Convolutional Neural Network

Steganography conceals information inside another object, so information is not immediately apparent in the examination. Different from encryption, where a ciphertext is an encrypted object, steganography aims to hide information and the fact that a piece of information is concealed. In the case of image steganography, the secret is embedded into a cover image so that the cover image does not differ substantially, and the secret can be extracted without too many alterations. This paper proposes a method to embed an image into another image using U-Net architecture with MobileNet Convolutional Neural Network. The proposed method is trained and tested on a readily available image dataset, CIFAR10, StanfordCars, and STL10. The result shows that the proposed method can embed and extract images from CIFAR10, StanfordCars, and STL10 datasets with average PSNR of 28.165 dB, 28.539 dB, and 28.226 dB for steganographic images; 27.075 dB, 29.759 dB, and 28.767 dB for extracted images.
Multiple Flying Object Detection using AlexNet Architecture for Aerial Surveillance Applications

In the past few years, drones have been used in military applications. Flying objects, such as mini UAVs, are increasingly being used maliciously by terrorists, criminals, and smugglers. These more technologically advanced and affordable devices have a high probability of assault, occur frequently, and have the potential for disastrous impacts. The risk of bird strikes always exists, because birds and aircraft occupy the same space at low elevations. Although there are restrictions on flying drones close to airports and designated airspaces to prevent them from invading each other’s spaces, there are still a number of difficulties. To overcome such challenges, the detection and tracking of various flying objects are key tasks. In this study, Two Convolutional Neural Network(CNN) models were employed, that is (ResNet101 and AlexNet), and the trade-off between these two architectures gave us a highly efficient and accurate system with a hopping training accuracy of 93.68% that can not only easily track and detect but also classify it with high accuracy.
Auto-grading C programming assignments with CodeBERT and Random Forest Regressor

Grading coding assignments manually is challenging due to complexity and subjectivity. However, auto-grading with deep learning simplifies the task. It objectively assesses code quality, detects errors, and assigns marks accurately, reducing the burden on instructors while ensuring efficient and fair assessment. This study provides an analysis of auto-grading of the C programming assignments using machine learning and deep learning approaches like regression, convolutional neural networks (CNN) and long short-term memory (LSTM). Using a code-based transformer word embedding model called CodeBERT, the textual code inputs were transformed into vectors, and the vectors were then fed into several models. The testing findings demonstrated the efficacy of the suggested strategy with a root mean squared error (RMSE) of 1.89. The contrast between statistical methods and deep learning techniques is discussed in the study.
Colour Detection Using HSV and RGB Colour Models

This paper presents a colour detection application that utilises a deep learning model to predict the name of a colour based on its HSV value. The use of the HSV values rather than the RGB values makes for a more intuitive representation of colours. The application supports two modes of operation, real-time mode and static mode, which cater to the needs of visually impaired individuals and content designers in the varying scenarios that they may find themselves in. This paper presents a design that leverages the advantages of deep learning and the stability of the HSV colour scheme to provide a model with good accuracy in detecting colours and labelling them accordingly. The application’s integration with the Flutter Framework and Firebase database provides a scalable and robust platform for both managing the gathered data and collecting more relevant data for the improving the model further.
Interactivity Development Using Unity 3D Software and C # Programming

Unity provides a powerful tool for game designing and 3D development. This project explained how to interact with 3D design models using Unity. This article represents Unity Essentials and how to build applications for mobile. Using Unity 3D, we can create applications for AR and VR devices like Oculus, HTC vive and Google Cardboard etc. For creating any game in unity used a programming language, C Sharp. C# is popular and very powerful language used for development in Unity 3D. In this paper we will discuss about interactivity development project where we will learn about unity with C# code and we will create a driving simulator prototype where the user can steer a vehicle down a road to avoid obstacles in its path. This would be done by importing assets and adding objects to the scene window in unity and later on customization of the layout and creation of C# Script for each component would be done in the project window.
Blockchain Based Voting Decentralized Application

Elections are an important way of conveying one's choice in a democratic system. Many new technologies have been introduced to carry out the process of elections over the years. Even after so many developments in the voting system, there are still many issues faced regarding the results of voting. The voting approaches available are vulnerable to malfunctions and the results of voting are not reliable. This paper compares the different methods of voting while discussing their advantages and drawbacks. Blockchain based polling is a decentralized voting system which offers transparency and protection from any malpractice. The voting results obtained from this process are reliable. An Ethereum platform based decentralized application (DApp) along with a smart contract in solidity is proposed in this work to execute the process of voting through blockchain based technology.
Design a Blockchain Based Security Algorithm for IoT based Healthcare

Healthcare applications have made extensive use of smart contracts to ensure the safe and decentralized management of patient data, track medication adherence, and streamline the processing of insurance claims. Blockchain-based decentralized healthcare applications have the potential to significantly alter the healthcare system as we know it. A system's ability to offer high-quality services to its end customers is quantified by its QoS. Availability, dependability, performance, and security are all examples of Quality of Service concerns that may arise in the healthcare industry. The QoS is evaluated by how well it meets the needs of its customers. The accurate determination of QoS during medical data processing utilizing sophisticated measurement methods is a crucial criterion for healthcare applications. When providing medical treatment, it is often necessary to explain complicated concepts to patients. Therefore, it is crucial to maintain rigorous requirements for public network security and intelligence. Objective of research is enhancing the quality of service in blockchain for healthcare. Quality of service (QoS) in smart contracts, however, is essential to the widespread adoption of these technologies. Current studies on smart contracts have taken into account service quality and traditional healthcare solutions. In this research, we provide a unique method for improving QoS of healthcare smart contracts. To ascertain the state of the art and locate already established answers or methods, we have reviewed the aforementioned literature. In order to assess the efficacy of our strategy, we have devised a study question and specified our methodology. The conclusions of our study demonstrate that our method may greatly enhance the QoS of healthcare smart contracts, leading to better patient outcomes and lower overall costs. Simulation work is considering packet dropping rate, file size and accuracy. It is observed that proposed approach is providing more than 94% accuracy where as conventional approach provided accuracy of below 92%. On other hand, proposed approach has reduced the file size from 10% to 40%. Thus the packet delivery ratio of proposed work is 91% to 95 % that is 90% to 94% in case of conventional work.
Real-Time Virtual Mouse using Hand Gestures for Unconventional Environment

This research paper explores the implementation and potential benefits of a virtual mouse using hand gestures as an input device for individuals. The virtual mouse is a software-based interface, enabling users to control the cursor without physically touching a mouse. Hand gestures are recognized using sensors and machine learning algorithms that translate these gestures into cursor movements on the screen. The results show that the hand gesture method is accurate in recognizing different hand movements, providing individuals with new means of interacting with technology with a budget free and user-friendly solution. The developed system demonstrates significant potential for enhancing human-computer interaction in unconventional environments, such as underwater exploration, space stations, industrial settings, extreme weather conditions, and remote locations.
Quantum Kernel-aided Support Vector Machine Classifier for Improved Speech Classification

The field of machine learning has been revolutionized by quantum computing, as it has the capacity to tackle complex problems that are beyond the capabilities of classical computers. One such problem is the efficient classification of speech signals with limited data, employing extracted feature attributes like Mel-scale frequency cepstral coefficients (MFCCs). This study compares the performance of classical machine learning models with a Quantum Support Vector Machine (QKSVM) classifier on a data set with a limited number of audio samples. Interestingly, the quantum kernel method outperformed classical models despite the small sample size. This result can be attributed to the quantum kernel method’s ability to extract high-dimensional features efficiently, allowing it to learn more from fewer samples. The Quantum Kernel Circuit (QKC) is contrived in different variants comprising 13, 8, 7, and 4 qubits. This QKC is built with an Angle Embedding layer, an adjoint version of the Angle Embedding layer, and a Hermitian space observable layer. Our findings demonstrate that the hybrid quantum kernel-based technique outperforms existing classical solutions. Amongst variants, the Quantum support vector machine model with 13 optimal numbers of coefficients is efficient in speech classification with the highest accuracy of 87.32%.
Free Verse Figure Of Speech Calculator: An Aid To Poet Style Determination

Poetry writing and analysis are both qualitative subjects. To get over any possible biases of human perspective it is necessary to map these poetic features on a scale of real numbers. Free verse is a form of poetry that is unrestricted by the requirements of metre and rhyme. This article is focused on free verse compositions viewed through the lens of rhetorical properties or figures of speech. The state-of-the-art model FoSCal cannot handle free verse compositions. On the other hand, FVRCal is a state-of-the-art tool for free verse that computes rhyme (not FoS) on a numerical scale. The proposed tool, FVFoSCal, covers a broader section of alankāra than the state-of-the-art model. It can measure the alankāra score for any free verse composition. The scores generated by the proffered tool form a dataset, FoSSset, which is later used to determinethestyleofthepoetusingsuitablestatistical tests. The statistical analysis performed in this experiment establishes commonality and diversity among the four renowned poets of Hindi literature, namely; Bachchan, Dushyant Kumar, Nirala, and Agyey.
Maze Solver: A Federated Reinforcement Learning Approach

In today’s era where Machine Learning and Deep Learning are household names and Artificial intelligence is rapidly evolving people are neglecting a basic issue in this type of setting the privacy issue. The Machine Learning and Deep Learning models need a large amount of data to train and while they use the data from the internet, they also use the data collected from the users. All the data used for the training of such models are saved on a single server prone to lots of cyber-attacks such as SQL injection, DDOS, Data leakage, etc.… In order to solve this issue in 2016 Google released a new way to train a Machine Learning or Deep Learning model this approach is known as the Federated Learning letter Google implemented the same framework on a Google Keyboard to create a next-word prediction model without saving any user data on their central server. This framework proved to be a privacy-preserving as well as performance-boosting framework. In order to harness the power of multiple IOTs and edge devices in rather computational resources-hungry tasks such as reinforcement learning researchers started to move the focus to Federated Reinforcement Learning. In this research work an implementation of Q learning a reinforcement learning technique is done in a Federated Environment. Letter the results from both Q learning and Federated Q Learning are compared and analyzed to find out the advantages Federated Reinforcement Learning has over centralized Q Learning or any reinforcement Learning technique.
Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches.
Exploring Collaborative Filtering Methods for Product Recommendations in E-Commerce: A Study Using Amazon and BigBasket Datasets

Recommendation systems play a significant role in today’s age of technology. These systems use machine learning algorithms and act as an information filtering tool to help users discover relevant and personalized content, products, and services based on their past behaviours and interests. Applications of recommendation systems range from e-commerce, news and article recommendation, social media, healthcare and education. Online shopping has rapidly expanded, which has led to an exponential rise in the number of items and options available. Since the e-commerce sites generate huge amount of data users may find it difficult in selecting the right product for their needs. To address this issue e-commerce sites, need an effective and accurate recommendation techniques. Many techniques have been proposed for building recommender systems and its effectiveness varies depending on the application domain and the dataset used. This paper compares the performance analysis of different collaborative filtering algorithms which includes SVD, SVD++, Slopeone, KNN, KNN with Means and KNN with Zscore using two Amazon and Big Basket datasets. According to experimental studies SVD++ out performed the other algorithms with a reduced error rate. In this work we have used MAE and RMSE as evaluation metrics to analyse the algorithms performance.
Aspects to determine the acceptance of cloud service: A study of cloud professionals

Cloud computing is an essential innovation in communication and information technology. In today’s scenario, any business, big or small, can use cloud services because they are scalable, affordable, and flexible. It is beneficial for the growth and benefits of the business. Even though cloud computing is significant, companies have been slow to use it because they are worried about security. The decision to select any cloud service depends on the various service measurement index(SMI). This work employs a multi-theoretical approach to investigate the obstacles to cloud service adoption. A survey-based model highlights the most crucial SMI factors among the users and is tested by more than 500 professional cloud users. The collected data has been analyzed with the IBM SPSS 22 program using multiple regression techniques, a descriptive study, and various significance-level tests. It found the most important parameters, and the performance of cloud services is based on other SMI factors. Also, it adds new SMI parameters to make the evaluation, selection, and ranking of cloud services using a comprehensive model more accurate and precise.
Intelligent E-Learning Platform: A Fuzzy Logic-Based Adaptive Approach

This study suggests an efficient adaptive online learning system that makes use of a triangular membership function to display the student's conceptual understanding in a selected topic area. The level of understanding for each topic in the field is evaluated using a fuzzy logic system, which also generates feedback based on student performance to address any gaps in comprehension. To assess the learner's conceptual understanding, the system collects data through an initial pre-test before using the learning system, followed by a post-test and a practical exam. The aim of this research is to showcase the enhancement of student performance and comprehension through an innovative and personalized e-learning system.
Analysis of demographic groups for different markets using machine learning techniques

The internet is the dominant technology of the day, and its effects have resulted in massive amounts of daily data production. It makes processing by a single individual almost impossible. The sales of stores and supermarkets should be regularly analyzed, as should customer desire in the sales demography. Market analysis serves as the sole foundation for all global output. Presently, the manufacturing of the goods is determined by the overall demand. The analysis of sales across various stores and goods reveals the profit and loss of the supply and demand chain. Moreover, consider the customer's buying power. This research paper aims to conduct a comparative sales analysis of different stores, customers, and demographics in order to determine the factors that affect sales. It will focus on identifying patterns and trends in sales performance and understanding the underlying factors that drive sales in different stores and customer segments. A machine learning model will be trained in this study and will provide the correct analysis of the data in detail. This paper will give readers the critical assurance they need to launch new businesses in their respective industries and project their future profitability.
Cloud Based Credit Card Fraud Detection System in Banking Using Machine Learning and Deep Learning algorithms

Various purposes like shopping, booking tickets, bill payments, etc are the reasons for the usage of credit cards. Credit cards are mostly used for online transactions. As there is a spike in the credit card usage, credit card fraud rate has increased. Card holders and financial institutions suffer from financial losses. In this study, the focus is on ML and DL models. In order to discover the best model, a comparative analysis is performed. The first step is to consider a dataset and try to apply the ML and DL algorithms one by one. If at all there is any imbalance in the dataset, methods like under-sampling is performed. The proposed model makes use of CNN, SVM and ANN algorithms to predict fraudulent transactions. A comparative study is performed by comparing the proposed model against all the ML and DL algorithms based on performance-evaluation measures. Accuracy, precision, F1 score and recall are the performance-evaluation measures. It is found that Convolutional Neural Network (CNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) outperform all the other models. Cloud Computing provides services on the internet remotely with pay-as- you-go pricing to manage, access and store data. The users can gain access to cloud services with their credentials. Cloud security issues are: (a) Data Confidentiality and (b) Data Loss.