Reviewerly/exHarmony huggingface dataset:
Deep Learning for Uneven Data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network

In minority class and noisy data situations, supervised learning performs more favorably for the majority class but cannot generalize testing data. Performance in the aforementioned use cases might be improved with the help of neural-based data augmentation approaches for generating new data and deep convolutional models for classification. GANs (generative adversarial networks) have recently demonstrated impressive advancements in picture generation. To address the restrictions imposed by the distribution bias problem between the produced data and the unique data, and to provide a more vigorous data augmentation, the presented distribution bias aware collaborative GAN (DGAN) model for unbalanced deep learning in industrial IoT. By including an auxiliary classifier in the foundational GAN model, a comprehensive data augmentation system may be built. In particular, a provisional source of energy with random labels is envisioned and trained combatively with the classification model to appropriately augment the amount of data specimens in minority classes, and a mass fraction system is newly envisioned between two distinct feature extraction, allowing the cooperative adversarial training among some of the power source, voltage divider, and classification algorithm. The next step is to develop an augmentation approach for smart anomaly detection in class imbalance, which, by adjusting for dispersion bias using the properly balanced data, might significantly improve classification precision. Experiment assessments using real-world unbalanced datasets show the superior recital of the suggested model in addressing the distribution biased issue for multi-class classification in class imbalance for commercial IoT applications, as compared to five baseline techniques.
Hand-Gesture Based Emoji Identification and Chat Integration

Every aspect of human life is slowly getting computerized and gradually turning electronic. This has made a rangeof tasks a lot easier and accessible for people from all walks of life. Unfortunately, this rapid computerization and the ever changing landscape of electronics has given birth to a new classof computer - illiterate individuals. Such individuals exist due to the lack of accessibility to latest electronic gadgets, or humansocial problems like old age. There is a need to make technology fun and easier to use for individuals belonging to such classes. The very primary use of internet-based networking is text-based communications namely: e-mails, texts chats, blogs, etc. The text based interaction is mainly facilitated using a keyboard which makes it the most focal human-computer interaction involved. The modern usage of this text-based communication involves extensive usage of emoticons. While most handheld devices feature a customised emoji-keyboard, the emoji keyboard is difficult to access in personal computers. This can adversely affect the interactions the aforementioned computer-illiterates partake in. This project aims to deal with this problem by creating a hand-detection based emoji detection algorithm in python through OpenCV and Mediapipe for detection of hand landmarks and implementing it in one such communication channel. The algorithm consists of two modules: a Hand-Tracking Moduleand an emoji detector capable of matching the tracked hand to existing emoji database. The resulting application is consistent, allow frequent users to use shortcuts and make the user feel more in control. The work also strives to integrate error prevention and simple error handling in existing emoji detection technologies along with managing short term memory load.
Automated Virtual Character Detection System from Live Video Streaming using Deep CNN

Automatic character recognition (ACR) transforms scanned document images into a language that computers can understand and recognize text that appears in photos. The most recent technological developments have highlighted the necessity for reliable automatic character recognition techniques. There was a need for a more contemporary recognition architecture. Thus, character recognition is accomplished using deep learning methods in this study. Finding the ideal set of features and then giving them to the classifier helps solve a variety of artificial intelligence tasks. Finding the ideal combination of characteristics can be challenging for many activities. The solution to this problem is deep learning which outperformed basic AI models. Proposed work has tried multiple experiments to find best performing model to recognize Devanagari Numerals through virtual representation. In this work, normal and complex CNN both types of models have been used to train and test purposes. The suggested model has produced 97.8% accuracy and used at the backend to recognize Hindi letters virtually indicated by the user in front of their webcam, also the model predicted ‘’ with 92.6% and 96.30% respectively at different instances during experiments. This work can be utilized as fun game for children to learn any language easily.
Emotion Detection from Text: Classification and Prediction of Moods in Real-Time Streaming Text

Emotion detection is a method which can be used to determine publics' attitudes, feelings, and feelings towards a specific target, such as persons, groups, organizations, various services, and products. Sentiment analysis is a superset of emotion detection since it infers the specific emotion rather than just declaring if something is good, bad, or neutral. Recent studies have concentrated on the use of verbal and facial clues to identify emotional states. Since written language lacks non-verbal indicators like voice tone, face expression, vocal pitch, etc., it can be challenging to discern emotions. The extraction of emotions from text has been proposed using a variety of natural language processing (NLP) techniques, such as the keyword-based approach, the lexicon-based approach, as well as deep learning approach. The disadvantages of keyword and lexicon-based approaches are numerous despite their focus on semantic links. In this research we suggest a BERT-based deep learning system (Fake BERT) by combining the BERT with numerous parallel blocks of a single-layer deep Convolutional Neural Network (CNN). GRU and Bi-GRU comparisons from past works were used as deep learning approaches. The proposed BERT-1D CNN generated the best results with an F1 score of 83.5, followed by Bi-GRU and GRU.
Edge Computing-Based Crime Scene Object Detection from Surveillance Video Using Deep Learning Algorithms

Crime scene artifacts include a handgun, revolver, or gun are utilised in most criminal crimes. A human must intervene in the surveillance and control system to discover such illegal activity. The purpose of this study is to create an automated, real-time object detection system for use with surveillance cameras at crime scenes. The intention is to warn security personnel of potentially dangerous situations. The proposed Fully connected Convolutional neural networks (CNNs) combined with Edge Computing so it is called hybrid system, and faster region-convolutional neural networks (faster R-CNNs), and regression are all examples of deep learning algorithms. Among the most promising approaches for detecting objects now in use are linear-based detectors YoloV7. Real-time detection requires a faster detection speed than region-based algorithms can provide. On the other hand, real-time detection may be achieved using regression-linear-based detectors such as YoloV7. In this research, YoloV7 was used to identify crime objects quickly. Crime object, and then Fully Connected CNN was used to improve detection accuracy. The system is trained and tested using a crime object dataset consisting of 5,660 images of weapons and anomaly action that are both publicly and privately accessible, with an 80-20 split, and then assessed using live security camera footage. In addition to its 92% accuracy, 95% precision, 88% recall, and an F1 score of 88.5%. The resultant findings validate the use of the suggested hybrid system for real-time monitoring by indicating the presence of a suspect carrier in an area under observation.
Online Election Management System Using Machine Learning

The Online Election Management System is a web-based application. The system has a centralized database to keep the details or records of all the voters, candidates and final Results. The System was implemented based on image classification technique and sending the passwords to voters through email, for login. The System uses HTML and CSS for front-end designing and for backend python programming language and python's web framework called Django for authentication and authorization, database management. The major work in the system is image recognition, for image recognition Convolutional Neural Network (CNN) is used. CNN needs less data to train the model and efficiency is more compared with other classification algorithm YOLO. On using this application, the voter need not go to the booth. So, it is user convenient and also it is easy for the admin to manage the voters and candidate information. It is secure because it uses a two-step verification method. First the user has to login using credentials then using image classification, it recognizes the exact voter. The system prepares the dataset at the time of user registration the user credentials by generating the password randomly for the voter and sending the voter credentials to the voter registered mail id. This research work gives a complete solution for an online voting system with higher accuracy and lesser time.
HRC-CLSS: An Hybrid Resource Constrained Certificate-Less Signcryption Solution for Securing Crowdsourced IIoT Environments

The Industrial Internet of Things (IIoT) has become an essential component of modern industrial automation. However, the security of IIoT remains a significant challenge due to its distributed and heterogeneous nature. The proposed hybrid approach, called HRC-CLS, integrates symmetric and asymmetric encryption, Certificate-less Signcryption (CLS), and attribute-based encryption to provide confidentiality, integrity, authenticity, and access control in the IIoT environment. The proposed approach supports dynamic access control and fine-grained access policies. The results of the experiments indicate that the suggested scheme outperforms other CLS methods that are already in use in terms of a variety of performance measures. The strategy may find use in a variety of contexts, including the automation of manufacturing processes, the development of smart cities, and medical care.
An Efficient 3-D Model for Early Prediction of Breast Cancer Based on Hybrid ANN - Fuzzy Model through Lossless Medical MR Images

Rapid advancements in technology has aided in early prediction of Breast cancer which is a high mortality rate characterized condition. Fuzzy and Neural network-based models have been effective in prediction of early occurrences of breast cancer. Any method now in use must be able to detect breast cancer in women at an early stage to effectively bring down the mortality rate. The major goal of the suggested approach is to apply 3-D picture lossless compression in NN to determine the tissue size from an MRI breast scanning image. The use of a 3-D predictor to categorise the photos and the application of the NN approach to determine the degree of breast cancer accuracy are the primary features of this work.
An Intelligent Surveillance System for Weapon Detection Based on EfficientDet Algorithm

The number of criminal activities has been increasing rapidly in recent years. Violence committed by brandishing a knife, gun, or other weapons results in an unsafe and unsecure environment. To guarantee a safe and secure environment, smart video surveillance systems are becoming very prominent. As demand for safety and security increases, intelligent monitoring relies more on video surveillance systems that can identify and comprehend the crime scene and abnormal instances. This study proposes a computer vision-based method for weapon detection capable of recognizing knives and pistols for real-time security surveillance. Footages obtained from Closed Circuit Televisions (CCTVs) serve as the data for the neural network model. The EfficientDet model from Tensorflow2 Model Zoo is used in the proposed methodology to identify and detect weapons. The EfficientDet model has improved precision and accuracy while reducing the real-world constraints of model size and latency.
Credit Card Fraud Detection Scheme Using Machine Learning and Synthetic Minority Oversampling Technique (SMOTE)

Credit Card Fraud Detection is one of the vital issues nowadays which needs to be tackled urgently. In today's world, everyone is shifting to an online and cashless world for easiness in the transaction. However, a colossal fraud scheme is running on the other side of this easiness. Daily, many people fall into this trap. This research work is a little contribution to solving this issue. This academic study uses data from the real world to find fraudulent transactions using Machine Learning techniques such as Decision Trees, Logistics Regression, and Random Forest. Furthermore, Synthetic Minority Oversampling Technique is employed to solve the dataset's imbalance issue. Following that, the effectiveness of machine learning methods is compared by using the “With SMOTE” and “Without SMOTE” techniques.
Public Financing Through Decentralised Application

Blockchain technology can change how people's data is viewed as all transactions are logged with the timing and hash keys, thus making them irreversible. Smart contracts automate the transactions. Thereby, boosting productivity and speeding up processes. This eliminates human involvement in verification process and reduces human caused errors. Transparency and security are crucial in any organization, especially Public Financing platforms. Therefore, a blockchain-based Public Financing web application has been developed to provide a reliable, secure, transparent, and decentralized way of doing the work. The application ensures transparency between donators and fund raiser, allowing donators to stay updated with the progress of the fund raiser's project in which they invested their money. If the project is aborted midway, the money donated will be refunded on immediate basis. A multi-user application is developed for the good functioning. The two types of users are Campaign starter and Campaign donator. Campaign donators can watch upon the project's completion they funded in and general information about other listed projects and campaign starter can check progress of their project and see relative performance of the campaign too. This blockchain-based Public Financing web application provides an efficient, transparent, and secure platform for fundraising.
Influence of IoT technologies in Education

The Internet of Things (IoT) has the potential to transform education by improving teaching and learning experiences, increasing efficiency, and improving student results. IoT technology enables real-time monitoring and tracking of student progress, allowing for more personalized and adaptive learning experiences. This study looks at the impact of Internet of Things (IoT) technology in the education sector. With the expanding popularity of IoT devices and applications, there is a growing interest in investigating their potential to transform and improve teaching and learning experiences. The article presents an overview of Internet of Things (IoT) technologies and their prospective uses in education, such as smart classrooms, individualized learning, and real-time monitoring of student progress. This paper provides insights into the potential benefits and challenges of integrating IoT technologies into education and highlights the importance of thoughtful implementation and consideration of privacy and security concerns.
Comparative Analysis of Text Classification using SVM, Naïve Bayes, and KNN Models

In contemporary times, there has indeed been a substantial rise in the count of complicated texts and documents that demand a greater comprehension of Machine Learning (ML) approaches to identify texts in numerous areas effectively. Several ML algorithms have produced outstanding Natural Language Processing achievements. The ability of these learning techniques to comprehend complicated systems and non-linear correlations within the information is critical to their effectiveness. On the other hand, identifying appropriate patterns, architectures, and algorithms for text classification is difficult for scientists. Thus, this study comprehensively analyzes text classification ML techniques like K-nearest neighbor, Naive Bayes, and Support Vector Machine. Lastly, the shortcomings of every method are examined, as well as their use in real-world challenges.
Natural Language Processing based Automatic Making of Use Case Diagram

In the field of software engineering, the creation of use case diagrams is crucial for effectively capturing system requirements. The current traditional development approach utilizes textual functional requirements, however, it is timeconsuming and error-prone. In software engineering, our research suggests a novel way for automating the production of use case diagrams. It is well-recognized that conventional methods using written functional requirements are timeconsuming and prone to inaccuracy. Our approach takes advantage of natural language processing methods for analyzing and obtaining data from natural language specifications in order to address this. This study creates use case diagrams by using predetermined rules and heuristics, which speed up the process and lowers error rates. Implementation as well as evaluation against actual requirements show that the proposed automated approach is superior to existing methods. Our approach increases efficiency and accuracy while requiring less time and effort to create diagrams while maintaining consistency. By presenting a more effective and dependable method of collecting system requirements, this research makes a contribution to the discipline of software engineering.
Enhancing salary projections: a supervised machine learning approach with flask deployment

Salary prediction encompasses the intricate process of extrapolating or forecasting the anticipated remuneration or compensation for a specific job posting or occupational position. This research elucidates the intricate task of salary prediction for job postings by leveraging the amalgamation of cutting-edge web scraping techniques and sophisticated machine learning algorithms. An extensive corpus of job postings from diverse online platforms is meticulously amassed through meticulous data acquisition encompassing web scraping methodologies and comprehensive surveys. The resultant model, meticulously developed using Python and seamlessly integrated within the highly versatile Flask web framework, culminates in a practical, deployable solution.
Security and Efficient Proven Data Procure with Privacy in Cloud based storage

In Cloud computing, computing services are distributed over the internet to provide more rapid innovation, resilient resources and cost reduction. This method allows lower maintenance and cuts down storage costs for the data owner. A security risk also materializes when the owner of the data loses legal and physical control over it. Thus, a data inspection is necessary to guarantee cloud security and data integrity. Due to the need to confirm data ownership while retaining privacy, this is becoming a mainstream issue with grown difficulties for data owners. The paradigm, Security and Efficiency proven Privacy Data Property system (Security and EPDP) enables efficient privacy-preserving verification while also securing data captured during the labelling of these problems. The addition of multiple ownership, data complexity, and batch identification is now an added benefit. The most fascinating feature of this technique is that the examiner may confirm data possession with little computing effort. The system shows, even if it does not keep the whole file of the user, the malicious cloud may produce proof adequate to satisfy third-party auditor verification. This system analyses and demonstrates the security of the strategy to prevent cloud service providers (CSP) from forging responses without keeping the necessary blocks while defending isolation of data from third-party public auditing (TPA). The most desirable aspect of the suggested system is that it covers all crucial characteristics, including block less verification, privacy protection, batch monitoring, and behavior of data.
Secured Electronic Voting System with Appropriate Authentication Using Blockchain

As a means of making elections more accessible and more convenient for voters, the use of online voting systems has grown in recent years. However, maintaining these systems' accuracy and security remains a significant obstacle. A face-and-email OTP-based voting system based on Convolutional Neural Network (CNN) methodology is proposed in this study to address these issues. Facial recognition and email OTP verification are used to verify a voter's identity and capture their face for the system. A CNN model that has been trained on a database of faces belonging to registered voters is used to perform the facial recognition process. After their identification has been confirmed, the voter may utilize the technology to cast their ballot. An additional layer of security and protection against potential fraud or hacking is provided by the combination of email OTP verification and facial recognition. In addition, the privacy and accuracy of the voting process are guaranteed by making use of a secure database and a user-friendly interface. The legal and ethical ramifications of incorporating facial recognition technology into a voting system, including privacy concerns and the possibility of algorithmic bias, must be taken into account during system design and implementation. A face-and-email OTP-based voting system based on CNN methodology provides an effective and secure option for online voting while preserving voter privacy and accuracy.
Phishing Prediction on Website Updates with Novel Features Through Machine Learning

Phishing attacks are becoming increasingly sophisticated, making it difficult to predict them using traditional security measures. One technique that has gained traction is leveraging machine learning algorithms to identify phishing websites. This research study presents a machine learning rooted approach to possibly predict phishing attempts by thoroughly analyzing website updates. The proposed approach involves training a XGBoost classifier using a dataset of legitimate and phishing websites. For this approach, created a new dataset labelled as “phish_predict” that includes novel features to enhance the accuracy of the classifier in identifying changes in web pages that could indicate a phishing attempt. This study's goal was to identify phishing websites using ML techniques. Three well-known algorithms, K-NN, SVM, and XGBoost, were tested, and their effectiveness was analyzed using a range of measures, involving the performance metrics. Among these algorithms, XGBoost had the greatest accuracy of 100% in predicting phishing websites. This novel approach makes an effective addition to traditional security measures for predicting phishing attempts.