Reviewerly/exHarmony huggingface dataset:
SignNet: A Deep Learning Architecture for Accurate Sign Language Recognition from Images

Sign Language, as the word is self-explanatory, it means using the signs, or herein we can also call them as gestures for inter-communication between people who are specially-abled and even with those who try to express themselves with gestures. Sign Language System is a treading road for the field of deep learning and communication. There are models made every year that surpass all others and succeed state of the art mark achieving best results. Convolutional neural networks have evolved a lot in past years providing unbelievable outcomes and results. Thus, using ASL dataset and state of the art models like VGG16 and ResNet50, making a SLR system via transfer learning as well as understanding in-depth and detailed convolutional neural network structure and steps involved in it. It is an irrefutable fact that there are problems when it comes to communicating with disabled people, thus research regarding sign languages could be a great help for disabled people as well as for deep learning as a whole.
News Article Topic Classification Using Embeddings

The rate at which data is produced has increased dramatically in recent days. Without a suitable category or tag, having a lot of data is merely useless information. The consumer may quickly find the content they’re looking for and search for older news stories with ease when news items are properly categorized or classified. The classification of News Article Topics into proper categories aids in many tasks such as finding older data in a faster way, useful organization of data, enabling quick fetching of required data, etc. The proposed system analyses the Antonio Gulli’s (AG) News Classification Dataset using different embeddings. The proposed model also experiments with different types of Machine Learning and Deep Learning Classifiers for classifying the embeddings.
An Enhanced Mammogram Classification to Detect Breast Cancer using Boosted PSO Feature Selection and Multi-Feature Analysis

Breast cancer is a globally recognized public health issue, particularly impacting women. While mammogram screening is a widely used method for identifying cancerous cells, accurately distinguishing these cells from the surrounding tissue remains a challenge. A novel approach is presented in this study, to categorize mammograms. Initially, preprocessing is done by median filter with Contrast Limited Adaptive Histogram Equalization (CLAHE). Multi-Feature Analysis (MFA) is used for feature extraction to extract texture, shape, and volume information from the mammograms. A Modified Weighted Particle Swarm Optimization (MWPSO) algorithm selects the optimal features, which are fed to the Light Gradient Boosting Machine (Light GBM) for classification. The model's performance is evaluated through k-fold cross-validation and demonstrates high accuracy in mammogram classification. By combining the strengths of image processing and machine learning algorithms, the proposed methodology provides a powerful tool for mammogram classification.
Sensible Tweets: Speech Act Filtering for Effective Sentiment Analysis

Existing sentiment analyses of twitter data often develop ad hoc rules of selecting or discarding tweets. To move towards a rigorous methodology for the selection of short messages or so-called microtexts in sentiment analysis, we develop a sentiment analysis framework based on Speech Act theory with a focus on speech act filtering to identify sensible tweets – the sentiment-bearing microtexts that are perceptible to sentiment analysis. We develop a neural network classifier to implement speech act filtering and apply the sentimental analysis framework to twitter data. Using Twitter API, our speech act classifier and an online sentiment analysis tool, we conduct a pilot study on financial tweets to show that speech act filtering leads to higher quality sentiment analysis results.
PV Power Generation Forecasting Based on XGBoost and LSTM Models

This paper aims to investigate the use of machine learning techniques for PV power forecasting. In this study, a bibliometric review was conducted to establish a baseline, which involved the use of a SARIMA model as a statistical model, XGBoost model for machine learning, and LSTM for deep learning. These models were trained and evaluated using a sliding window technique and a validation set. The study’s development utilized the generation data collected from May 2018 till February 2023. The dataset underwent preprocessing to fill in the gaps in the data and to analyze trends, seasonality, and stationarity. The study compared three models and found that all of them could predict PV power generation. However, the XGBoost model outperformed the other two in training and validation, resulting in an R-squared value of 0.99, an nMAE of 0.35%, and an nRMSE of 0.55%, indicating that its predictions were the closest to actual values. The study’s limitations and potential areas for future research were discussed at the end.
Analyzing the Impact of Code Commenting on Software Quality

One of the most efficient ways to assist developers in grasping the source code is to make use of comments, which can be found throughout the code. When working in fields such as software development, having comments in your code that are of good quality is a fundamental requirement. Tackling software problems while making use of programs that has already been built. It is essential for the intention of the source code to be made crystal apparent in the comments that are added to the code. This assists programmers in better comprehending the programs they are working on and enables them to complete software maintenance jobs in a more timely manner. In spite of the fact that comments and documentation are meant to improve readability and maintainability, the vast majority of programmers place the majority of their focus on the actual code that is being written. This study provides a complete and comprehensive overview of the prior research carried out on the topic of code comments. The study focuses on four main topics, including automated comment production, comment consistency, comment classification, and comment quality rating. One is able to get the knowledge that is more complete for use in following inquiries if they conduct an analysis of the proper approaches that were used in this study issue.
Improved Performance of Punching Machine Using Servo Motor Feeder Mechanism

The basic and important part of motor manufacturing is stamping. This article highlights innovative method in stamping process using Servo Feeder system with the help of Siemens programmable logic controller (PLC) and Human to Machine Interface (HMI). The Servo drives used in this proposed system increases productivity by increasing Strokes per minutes (SPM) and its quality, minimizes maintenance cost and eliminates human errors. Fully-programmable feed & release settings of Servo is user friendly which reduces setting time as well. Most importantly, the novel servo Feeder is an innovative improvement in the machine which controls the process with cost effectiveness.
Advancing Optical Character Recognition for Handwritten Text: Enhancing Efficiency and Streamlining Document Management

This research paper explores the challenge of converting handwritten text into editable digital text, in both native and English languages. We propose optical character recognition (OCR) as a solution, leveraging algorithms and machine learning models to recognize and convert text into various digital formats. The paper highlights the significance of OCR in digitizing paper documents and streamlining document management across diverse industries. Specifically, the study focuses on developing OCR software capable of identifying regional languages, providing a comprehensive assessment of its precision and effectiveness. The findings demonstrate how OCR technology can significantly improve efficiency and cost-effectiveness for businesses dealing with large volumes of paper documents. Our approach involves using image inputs, analyzing them with Keras and OpenCV, and implementing Convolutional Neural Network (CNN) techniques and Natural Language Processing (NLP) libraries.
Shifting Energy Horizons: Utilizing AI-Driven Energy Forecasting for Hydrogen Production in Urban Environments

The depletion of hydrocarbon supplies has spurred a global paradigm shift toward renewable energy sources, generating an urgent need for creative solutions that can satisfy the energy demands of cities in a sustainable manner. In this context, the development of an AI-powered energy forecasting tool is a viable way to addressing the complex difficulties of urban energy management. This research focuses on explaining the technique and critical components of such a tool, which is critical in providing exact projections for energy generation in urban areas. The major focus is on the use of solar and wind energy for hydrogen synthesis. The creation of this AI tool necessitates a careful data processing approach that includes the acquisition and analysis of a full 12-month dataset including all seasons. This information is critical since it serves as the foundation for assuring the accuracy of energy availability projections. It compensates for variations in solar irradiation, which is crucial for solar energy, and wind speed, which is critical for wind energy. The program can offer realistic estimates that account for the intermittent nature of renewable energy sources by including these dynamic characteristics. This enables urban planners to make well-informed judgments about maximizing energy consumption, resulting in a more sustainable and efficient urban energy environment. The synthesis of hydrogen using proton exchange membrane (PEM) electrolyzers is one of the principal uses of the AI-driven instrument. As renewable energy sources such as sun and wind vary, excess energy may be transferred to PEM electrolyzers, where water is electrolyzed to produce hydrogen. The quantity of hydrogen generated by a hybrid solar and wind energy system is expected to be 3.5 kg/day to 21 kg/day. This sustainable hydrogen generation provides a possible method for efficiently storing and using excess renewable energy, hence boosting energy sustainability in urban settings.
Q-SECURE: A Quantum Resistant Security for Resource Constrained IoT Device Encryption

The Internet of Things (IoT) plays a significant role in shaping different aspects of our lives. IoT devices have become increasingly important due to their ability to connect, collect, and analyze data, automate processes, improve safety and efficiency, and deliver personalized experiences. However, the advancement in quantum computer development poses a significant threat to resource-constrained IoT devices. This new generation of computers can break the classic public-key cryptographic schemes and digital signatures implemented in these IoT devices. While protecting IoT devices from quantum computer attacks poses many challenges, researchers are continuously making significant progress in developing lightweight post-quantum cryptographic algorithms for efficient key exchange mechanisms and digital signature algorithms tailored to IoT devices to overcome this issue. This paper proposes Q-SECURE, a post-Quantum resistant Security Enhancing Cryptography for Unified Resource-constrained device Encryption. A novel scheme that enables any IoT system to leverage the assistance of other devices in the network to gain the capability to generate any proposed post-quantum cryptographic key of a given size using distributed and parallel computing.
Generating Relevant Question Answer from Video

Online video lectures are currently a widely used form of instruction as online education has grown significantly. The process of creating question-and-answer pairs from these videos could be laborious, time-consuming, and exhausting. In this study, we offer a cutting-edge methodology for creating questions with meaningful answers, which would simplify the laborious process of question development. To extract text from videos, we employ speech recognition software. By sending a synchronous request to Google Speech Recognition, the video is converted to text. Two times, first for extractive summarization and then to perfect it for question creation, we employed the T5_base transformer. The condensed text is utilised to produce keywords, which are then supplied to the T5_base transformer with SQuAD dataset tuning to produce questions, along with context. In order to examine the calibre of the queries, we also compare our model to a few previously developed models using the same input. We discover that when compared to other models, our model produces superior questions and responses.
LTSum: Legal Text Summarizer

With the abundance of legal cases and law-related documents in countries with a large population such as India, it is becoming increasingly difficult for law professionals to assess cases by going through these large documents. Therefore, there is a growing need for text summarization models that specialize in legal tasks. In this paper, we propose a Legal Text Summarizer (LTSum) for the summarization of legal text documents. We evaluated the performance of the proposed LTSum using the ROUGE metric. Our LTSum incorporates a legal judgment prediction model to improve the accuracy of the generated summaries. We illustrate the effectiveness of our approach on the large set of legal documents. The experimental analysis shows that the proposed LTSum outperforms existing methods in legal text summarization. The results show that the incorporation of a legal judgment prediction model improves legal text memorisation significantly.
Recent ViT based models for Breast Cancer Histopathology Image Classification

Breast cancer is one of the most common type of cancer affecting women worldwide, and a leading cause of cancer-related deaths. Histopathology image analysis plays a vital role in breast cancer diagnosis. Early diagnosis and accurate classification of breast cancer histopathology images can help for effective treatment. In recent years, deep learning-based image classification methods have shown promising results in automated breast cancer diagnosis. Earlier, Convolutional Neural Network(CNN) had a huge attention in image classification tasks, and also obtained a good performances in histopathology image classification. Recently, Vision Transformer (ViT) based on self-attention mechanism has shown promising results in computer vision tasks, including image classification. In this paper, we review recent ViT-based model for breast cancer histopathology image classification. Furthermore, we summarize the results obtained from our experiments with both CNN and ViT-based models in breast cancer histopathology image classification.
Deepfake Detection System Using a Hybrid Model

There has been a rapid advancement in deep fake technology in recent years, which makes it increasingly difficult to detect fake images, especially those involving human faces, which have become difficult to detect. The traditional methods of detecting deep fake images are usually based on a human-crafted feature or a rule-based system, which can be easily beaten with simple cheating techniques. Intending to detect deepfake face images, we present a novel method that uses CapsNet and EfficientNet as feature extractors to detect deepfake face images. They are well suited to detect deepfake images that have undergone geometric transformations or variations as they can learn the spatial relationship between those features. Using EfficientNet, a powerful and efficient deep learning architecture, as a feature extractor, we extract high-level characteristics from an input image to create a more accurate image analysis.Based on our experiments,it was found that this method demonstrated the capability to detect various types of deep fake images with an accuracy rate of 99.64%, including those fabricated through face swapping and re-enacting. This method could be applied to a wide range of real-world applications, including in media forensics, law enforcement, and online media verification, so that it could be applied in many different contexts.
Fin-STance: A Novel Deep Learning-Based Multi-Task Model for Detecting Financial Stance and Sentiment

Stance detection has been gaining popularity in text mining and information retrieval-based research. Recognition of sentiment also plays an important role in the analysis of text whether the underline stances are in favor or against for a particular domain. However, there is extensive research in this direction, but considering its impact on the financial text has not been explored yet. This paper presents a new deep learning based multi-task approach for financial stance detection and sentiment detection. A new model called Fin-STance is introduced which performs two-classification tasks as stance detection and sentiment detection based on the financial data. It constitutes of an input, embedding, followed by shared and task-specific layers. The empirical evaluation of this study is conducted on a newly created dataset and it shows impressive results. Also, this study receives better results with relevant compared methods.
Ensemble Model of U-Net EfficientNet-B3, U-Net EfficientNet B6, CoaT, SegFormer for Segmenting Functional Tissue Units in Various Human Organs

Accurately identifying and segmenting functional tissue units is critical for comprehending the structure and function of human organs. This work presents a pioneering method for identifying and semantically segmenting functional tissue units in diverse human organs using an ensemble model. The proposed approach initially extracts features from medical images using a pre-trained U-Net with a backbone as EfficientNet-B3, U-Net with a backbone as EfficientNet-B6, SegFormer MiT-B3, CoaT lite small, and CoaT lite medium, which is then ensembled and utilized to forecast the pixel-wise segmentation mask. Additionally, attention mechanisms were integrated to capture the contextual dependencies of tissue units within the images. To assess the performance of the proposed ensemble model, images from data sources Human Bio-Molecular Atlas Program (HuBMAP) and Human Protein Atlas (HPA) consisting of biopsy slides from various organs were considered. Experiments indicate that the proposed ensemble model outperforms individual models when evaluated using the Dice Co-efficient metric. This innovative approach has the potential to be utilized in other medical imaging tasks that involve functional tissue units for disease diagnosis and treatment planning.
Classification of Software Bugs using Support Vector Machine

Support Vector Machine (SVM) is a most widely used classification technique in machine learning as it gives a better accuracy than most of the existing techniques. These days, users frequently run across glitches and malfunctions when using software or applications. A lot more understanding is required by the developer to identify the type of bug, which takes time. In this system, to find the type of a bug a machine learning classification technique SVM. Here, bug report is fed as an input to a natural language processing algorithm. In this method, a dataset containing various types of bug reports is used to analyse the description in the given bug report using text processing and keyword extraction in natural language processing. This model can identify three types of bugs: memory bugs, concurrency bugs and semantic bugs using SVM. This system proposes a SVM model that gives better accuracy than the existing models which use several other techniques like decision tree, random forest and naive bayes.
Enhancing Quality of Service (QoS) In Cloud Computing

Cloud computing is a paradigm shift in the way computing resources are delivered and consumed.The demand for cloud computing services has led to an increase in the complexity of cloud systems, making load balancing an essential component of their performance and reliability. In this paper, we propose an enhanced approach to cloud computing load balancing that not only improves the performance and reliability of cloud systems, but also finds correlations between resources and applications. Algorithms in Artificial Intelligence and Machine Learning aid in the dynamic and real-time resource allocation, taking into account multiple factors such as traffic patterns, resource utilization, and service level agreements. Our approach goes beyond traditional load balancing methods by also discovering correlations between resources and applications, providing valuable insights into the relationships between different components of the system. The interpretation proves to be helpful in optimizing resource allocation which in turn corresponds to the prevention of performance degradation. Our evaluation results in the enhanced performance with respect to latency and resource utilization, as well as a clear understanding of the correlations between resources and applications. This work highlights the potential for AI and ML to not only enhance cloud computing load balancing, but also provide valuable insights for optimizing resource allocation and improving the overall performance of cloud systems.
Malicious Spam Detection to Avoid Vicious Attack

Online spam leads to malicious attacks such as hacking, phishing, and other problems that are more disruptive to Internet users. Online spam is currently known to be combated using detection and prevention techniques. Although these techniques can effectively curb these spreading messages/mails, it can be admitted that these attackers will persist in sending malicious links via unsolicited emails that can access and damage our system. Spammers easily construct phoney profiles and email addresses to target people unaware of these schemes. Spams always pose to be sent from a verified point by an authorized person; this makes it difficult to categorize as an attack or genuine mail. Consequently, it’s essential to detect fraudulent spam messages/mails. The paper discusses machine-learning techniques and the BERT model for text-based spam filter. The analysis was obtained over live data, extracted from a social media platform Reddit using PRAW. Comparison between different techniques was analysed to find the best fraudulent mail detection framework. It was observed that naive bayes had accuracy rate of 95% whereas accuracy of BERT framework came out to be 97% having rate of predicted spam was 86% and of ham was 99%
Sentiment Analysis of Covid Vaccine Tweet with Vader, and Implementation of Different Machine Learning Models

Sentiment analysis, referred to as opinion mining, is a natural language processing (NLP) technique that indicates a document's emotional tone. Since Covid-19 epidemic, there has been a lot of talk on social media and news sites about the necessity for a COVID-19 vaccine. However, the general public has expressed their concerns of vaccines security and efficacy on social media websites like Twitter. Using the Twitter API, these tweets are gathered from Twitter. For this research, we are using the Natural Language Toolkit (NLTK) for analyzing opinions expressed in tweets associated to covid vaccine utilizing the vocabulary- and rule-based sentiment analysis tool VADER (Valence Aware Dictionary for Sentiment Reasoning), which has been specially designed to analyze sentiments from social media. The dataset is converted into TF-IDF vectorization and the model was trained using different classifiers. We compared different ML applications and got our highest accuracy with Linear SVC with L1-based feature selection.