Reviewerly/exHarmony huggingface dataset:
American Sign Language Recognition System Using Wearable Sensors and Machine Learning

This paper describes a recognition system that is designed to aid users of American Sign Language. American Sign Language is growing in popularity, and such systems can be used to help improve the communication barrier between ASL sign language users and people who don't understand the language. The proposed preliminary system is designed to detect ten words in the ASL language. Sensors positioned on both wrists are used to collect data that will reflect the motions of the hands. The data is processed and trained using the Support Vector Machine, and Discriminant analysis classifiers using a selective combination of features were explored. Data was collected with Inertial Measurement Unit sensors strapped on the index and middle fingers to simulate a wearable ring. This data, combined with the data collected from the wrists, are trained using the Discriminant analysis classifier, and the system achieved a classification accuracy of 82%.
FPGA Implementation of Different Layers of Present Cipher

In the hardware solutions, cryptography is a tool which is used for viewing the message only to the sender and receiver. There were many lightweight cryptography algorithms namely AES, DES, RSA and many more. We are now exploring about a cipher which is constructed in a lab environment called the present cipher. So what is cipher? Cipher is generally a simple algorithm created inside the cryptographic field where it’s fundamental function lies to do the encryption and description. Since we now know what cipher is we will now explore present cipher. Present cipher is a model of cipher which belongs to the category called lightweight block ciphers. The layers present inside this cipher are six :- sbox, substitution, permutation, keyupdate, substitutiton-permutation, present encryptor top layers. Detailed explanation is being told in further sections which can be checked.
Image Forgery Detection using CNN

With the increasing use of digital images in various applications, the problem of image forgery has become more prevalent than ever. In this paper, we propose a novel image forgery detection system based on Convolutional Neural Networks (CNNs) that can detect various types of image manipulations, including copy-move, splicing, and retouching. Our proposed system integrates Error Level Analysis (ELA) with deep learning techniques to provide a more accurate and reliable solution to the problem of image forgery detection. We evaluated the proposed system on a dataset of real-world images and achieved a high detection accuracy of 93%. Our system outperformed existing methods for image forgery detection and demonstrated its potential for various applications, including forensics, security, and digital image analysis. Overall, the proposed CNN-based image forgery detection system offers a robust and effective solution to the growing problem of image manipulation and forgery in today's visual media landscape.
Deep Learning Implemented Communication System for the Auditory and Verbally Challenged

Sign language is a natural way of communication for challenged people with speaking and hearing disabilities. There aren’t a lot of text to sign language conversion system, due to the lack of abundance of sign language libraries including all collection of written texts and learning sign language is not an easy task to begin with. This shows a need for developing a system that facilitates the communication of people with such disabilities with the rest of the world. The research work here would bridge the gap between people untrained in sign language and people who rely only on sign language for their day to day activities, without having to assume or rely on someone who already knows sign language. This is a way to not only encourage communication with people with disabilities without worrying about miscommunication, but to even enjoy it.
Visualization and Creation of Image using Processed Dataset and Python Model Architecture

Due to high data availability, it is difficult to classify/process images with higher speed and accuracy. The generation of semantic and human-face images has been a very important problem in artificial intelligence and computer vision. This project aims to develop a machine learning-based face generator using sketches as input. The training data consists of thousands of images of faces of different races, ages, and genders. The network is trained to learn the spatial relationship between facial features such as the eyes and the nose. The generator network consists of a feature extraction network and an undersampling and oversampling network, both of which use skip connections to reduce the number of layers without affecting network performance. To determine if the created face has the needed characteristics, the discriminator network is built. Experiments show that it can generate a wide range of faces with high fidelity. This work provides a foundation for further applications in various artificial intelligence and computer vision tasks. Compared to state-of-the-art image translation methods, the performance of the proposed network is excellent.
Computer-Aided Detection of Nuclei from Histopathology Image: Evaluating the Effectiveness of U-Net and Modified Networks for Nuclei Segmentation

Computerized segmentation of nuclei from histopathology images is crucial for detecting breast cancer, the most common type of cancer in women. Early detection is key for effective treatment. Pathologists can track cancer by properly segmenting and analyzing nuclei since changes in the morphological characteristics of nuclei can be used as indicators of cancer progression. So, automatic nuclei detection will help pathologists to recognize abnormalities without much physical effort, alleviating the burden of pathologists. Deep learning techniques, such as U-Net, have reported great potential in solving this issue.In this study, we explore the effectiveness of U-Net and the variants of U Net model for nuclei segmentation from histopathology images. We observed that U Net variants that adopt Attention and Residual networks significantly improve intersection-over-union and accuracy (80%) of the baseline U-Net model. We also attempt to create an annotated nuclei mask for the publicly available BreakHis dataset using Attention Residual U-Net. Our results demonstrate that the ground truth generated by the Aattention Residual U Net model is consilient with pathologist’s annotations, promising an effective technique to generate ground truth for dataset lacking annotated nuclei.Overall, our study highlights the potential of deep learning techniques, particularly the Attention Residual U-Net, for automated nuclei segmentation in breast cancer detection. This could improve diagnosis efficiency while reducing human error, especially as computer-aided detection methods become increasingly necessary for precise and automated segmentation of nuclei.
Cyberbullying Detection using Deep Learning Models in Bengali Language

Social media has developed into a very powerful tool for teamwork, communication, and idea exchange. Due to the anonymity offered by these platforms, verbal abuse and cyberbullying cases have, sadly, proliferated globally. Researchers and academics from all around the world have conducted studies to create methods to automatically detect cyberbullying in response to this disturbing epidemic. Unfortunately, the research that is now accessible primarily focuses on more advanced languages, leaving low-resource languages like Bengali with a substantial research gap. As a result, in this study, we present a method for detecting cyberbullying in Bengali using Deep Learning Models such as Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN). Furthermore, we utilised the Bengali Cyber Bullying public dataset from kaggle.com to illustrate this. Also, when we examined the outcomes of the Bi-LSTM and CNN models, we discovered that CNN performed better. The precision, recall, and F1 score produced by the CNN model are on average 0.827, 0.850, and 0.855.
Facilitating Cryptocurrency Analysis and Watchlist Management with a Web-Based Platform

Cryptocurrencies are digital assets built on blockchain technology, known for their high volatility and lack of underlying assets to justify their intrinsic value. The trading and investing in cryptocurrencies heavily rely on price trends and a few supporting parameters. To facilitate cryptocurrency analysis, our platform provides a price chart that showcases the current short-term, medium-term, and long-term trends of each currency. This analysis is supported by additional parameters such as market capitalization, 24-hour percentage-wise price change, and concise information about each element.Moreover, with the wide array of cryptocurrencies available, individuals struggle to maintain a watchlist that reflects their prioritized selection. To address this challenge, we have developed a priority watchlist that assists users in both short-term trading and long-term investing. Our solution is web-based and utilizes various methodologies including an Application Programming Interface (API) for data retrieval, a library for displaying price charts, and an algorithm for implementing the priority watchlist functionality.
Detecting Criminal Activities From CCTV by using Object Detection and machine Learning Algorithms

Now, a day’s Crime in every country is increasing day by day. Generally, Every day we listen to the news of different crimes of different categories like rape, assault, Kidnapping ,Robbery ,ATM Theft, Murders etc happening in different states, cities , countries. Almost all the newspapers, TV channels’, social media are filled with the news of Criminal activities happening all around the Whole World. In earlier times there is no method to detect Crime. After That the CCTV cameras were used to detect Crimes. But Watching these Videos manually by humans for detecting crimes is a very time Consuming process especially in today’s world of Artificial Intelligence and Machine learning .So this crime detection in CCTV surveillance becomes an important area of research in the field of machine learning. So, there is a very urgent need of the intelligent system which will detect the crimes from the real time CCTV Feed and classify them and provides an alert system to the nearest police stations and ambulances etc. So, that system will help in reducing the crime rate in any country. This paper reviews all prior research in this area, including approaches for object recognition and finding priority frames, techniques and algorithms like Yolo used to detect crimes , various datasets used and algorithms used to analyze crime data and train the dataset .It covers the various recent trends in researches in this field and analyzing the challenges faced and various research gaps and this paper also discuss how we can overcome these gaps in research so as to develop a better intelligence surveillance system in ml field.
Facial Image Pre-processing for Enhanced Ethnicity Classification

Facial recognition and ethnicity detection is an important task in various applications such as security, marketing, and social sciences. However, the accuracy of Face recognition and ethnicity detection algorithms can be impacted by various factors such as lighting conditions, noise and irrelevant features in image background, variation in facial expressions, face size, pose and orientation across images. In this study, we present a novel face image preprocessing model in this work that aims to enhance the performance of deep learning models like ResNet, DenseNet and other state of the art Deep Learning models on facial recognition tasks, specifically ethnicity detection by addressing these issues. Our novel method for preprocessing facial images incorporates several techniques to enhance the quality of the input images. Firstly, we removed the background using DeepLab-v3. Secondly, we employed facial landmarks detection and facial pose and orientation correction using facial alignment based on the angle between the eyes. Thirdly, to facilitate race classification, we performed facial cropping to isolate the facial region and other critical features essential for the task. Finally, we ensured consistent lighting conditions across all images using the Multi-scale retinex algorithm. These techniques together enabled the deep learning model to better analyze and classify facial data. To assess the efficacy of our model, we carried out a series of experiments on the UTK Face dataset and compared the outcomes with those obtained when our preprocessing model was not utilized.The experimental outcomes show that our suggested pre-processing model improves the f1-score of ethnicity detection classifiers by up to 6%. The proposed facial image preprocessing model is efficient, fast, and easy to implement, enabling real-time applications, making it appropriate. Our research highlights the importance of preprocessing techniques in enhancing ethnicity detection performance and provides a practical solution for improving the accuracy of ethnicity detection and facial recognition systems.
A Real Time Conversion Model for Hand Gestures to Textual Content

Sign language is a form of communication that uses hand movements and gestures to convey meaning to deaf and mute individuals. We attempted to create a real-time finger spelling system using a convolutional neural network based on American Sign Language (ASL). The paper presents the recognition of 26 alphabet hand gestures in ASL. The system has several modules, including pre-processing, training, and testing, and achieved an accuracy of 95.8% in extracting, processing, training, and testing the model, as well as converting ASL into text. In this model, we utilized deep learning, OpenCV, and TensorFlow to identify hand gestures and found that our dataset yielded improved recognition results.
Machine Learning Methods for Detecting Car Insurance Fraud: Comparative Analysis

Due to the massive premium payments and millions of annual claims, the automotive insurance business has expanded into a multi-million dollar industry. A sizable portion of the expense of owning a car goes toward vehicle insurance. Due to the fact that the insurance sector is one of the least predictable and revolves heavily around risk thus it is heavily reliant on data science as it makes the work a lot easier. This study examines the efficiency and verifiability of the most popular machine learning (ML) algorithms like Random decision Forest, Support Vector Machine (SVM) algorithm, logistic regression, XGboost, Naives Bayes, Decision tree, k Nearest neighbors etc. for fraud detection. Using the aforementioned techniques, we calculated the efficiency, recall, precision F1 score, and AUC score.
Credit Card Fraud Detection using Neural Embeddings and Radial Basis Network with a novel hybrid fruitfly-fireworks algorithm

Financial institutions are highly concerned about credit card fraud due to the growing frequency of deceitful activities linked with credit card transactions. To address this issue, a novel fraud detection system is proposed that combines advanced feature engineering and neural network architecture. The proposed approach. FFFW-RBN, employs a hybrid swarm intelligence model comprising the Fruitfly Optimization Algorithm and Fireworks Algorithm, (FFO-FWA) for feature selection, and the Neural Embeddings Generator in combination with a Shallow Radial Basis Network (RBF) for localized pattern classification. The proposed system achieves a 99.97% accuracy, 0.91 recall, and a precision of 0.94. The experimental results demonstrate that our methodology effectively and practically detects fraudulent credit card transactions. The paper presents a comprehensive evaluation of the system, including various performance metrics and a confusion matrix, highlighting its potential for real-world implementation.
Secure E-Voting System

The use of blockchain technology in the Secure E-Voting System ensures that the voting process is honest, accurate, and highly secure. This system utilizes two separate blockchains to store the details of voters and their votes, which allows transparency in election results while protecting each voter’s privacy. The voter details are stored in one blockchain, providing greater security by requiring a PIN confirmation before the vote is counted. Additionally, votes are cast as transactions, creating a secondary blockchain that tracks the tallies of the votes. This allows for independent auditing of the ballot box and ensures that no votes were changed, removed, or added illegitimately. By moving the entire voting process online, the system reduces the effort required by staff members and allows voters to cast their votes from their own locations. Ultimately, this system is designed to make the voting process highly secure and more convenient for voters.
Unlocking the Potential of Machine Learning for Accurate Diagnosis of Breast Cancer

Breast cancer is a major health concern affecting women globally, and early detection is crucial for successful treatment. A promising strategy for enhancing breast cancer diagnosis accuracy and lowering diagnostic mistakes is machine learning. This research aims to enhance the accuracy of breast cancer diagnosis by utilizing balanced data and comparing different machine learning algorithms for classification with and without the use of feature selection methods. In this study, we utilized the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, and to balance data both oversampling and undersampling techniques were utilized. We have used eight different classification models and five different feature selection techniques. We compared the performance of classifiers over undersampled and oversampled data, with and without feature selection. MLflow was utilized to monitor the effectiveness of algorithms and keep a record of their performance. Our results show that oversampling was more effective in improving the performance of our models compared to undersampling. When compared to other models, Logistic Regression achieved the highest accuracy on the oversampled data without feature selection. Our research showed that incorporating feature selection results in slightly lower accuracy compared to the base model which means that the results were not significant enough to compensate for the information loss caused by removing certain features. The study underscores the efficacy of machine learning in the diagnosis of breast cancer and draws attention to the potential of machine learning algorithms in enhancing the accuracy of cancer detection.
Breast Histopathology Image Classification: A Comparative Evaluation of SVM, Random Forest and CNN

Artificial intelligence (AI) uses the impressions of human intelligence to wire machines to think and mimic the human behaviour. Deep Learning, Machine Learning, Neural Networks, are a few concepts included in AI among many others. Convolutional neural network (ConvNet or CNN) is a category of machine learning commonly applied for image recognition and processing pixel data. Support Vector Machine (SVM) algorithm is a Supervised Learning algorithm used often for Regression problems and Classification problems. Random Forest is a collaborative algorithm which is also used for both Regression and Classification problems. This paper provides a comparative analysis of CNN, SVM and Random Forest Algorithms. The dataset used is the Breast Histopathology Image Dataset.
Secure Data Dissemination in Wireless Sensor Networks with The Help of Module Based Blockchain Technology

Wireless Sensor is small, limited energy storage capacity and also using for wide variety of application. Due to its limited power and storage capacities, it is required to collect the data from the sensed nodes. The nodes present in the network are always busy with sensing and transmitting the data towards Base Station with the help of several hoping. Here it is required for secure data transmission for increasing the wireless sensor network quality of service. In this paper, we discuss how we can ensure secure data transmission between the nodes in WSN using Blockchain. We examine a Blockchain-based WSN architecture that offers a distributed network that primarily addresses the privacy and security issues during data transfer between the nodes. By producing unique blocks for each node along the routing path excluding the dead nodes and combining these blocks using the hashing method to convey data between the sender and destination nodes, we adopt Blockchain technology for the WSN network.
Exploration Practice of Data Mastery Traceability Algorithm Based on Knowledge Graph in Data Governance of Electric Power Industry

In order to meet the requirements of digital transformation construction of State Grid Corporation Limited and complete the work of carrying out data source determination and responsibility on a regular basis, as well as to realize the standardized management of data authority source, data management policy system design and key technology research, this paper elaborates the technical concept of knowledge mapping, data master definition, link prediction algorithm, etc., and innovatively proposes the The application of the scenario of power grid data master system traceability based on knowledge mapping technology is innovatively proposed to provide a help to support the State Grid Corporation to refine the data management mechanism, jointly promote the implementation of data management system and promote the establishment of the concept that data is the core asset of the company.
Travelling Chatbot Using Android Application

In the majority of cases, a user of an Android application either receives the expected outcomes or receives unsatisfactory results. The paper's major objectives are to automate communication through the use of chatbots and to make hotel searching simple and convenient for system users. Creating a chatbot using android will help user to find out the different locations, and famous food available in different locations. Also, this system will identify the current weather of the different locations. Extensive research on existing systems demonstrated faults that the system attempts to address by creating a chatbot with the use of a Dialogue flow library and a number of algorithms, including String Similarity, Natural language parser, Spellchecker, and Keyword Matching. The implementation of this system has allowed for improving the use of resources and the reactivity of users. This system has been set up to incorporate all information about the hotel, food, weather information, and a lot more information.