Reviewerly/exHarmony huggingface dataset:
Optimizing Election Result Prediction Through Fine-Tuned Transformer Models

Predicting election results accurately is crucial for understanding political dynamics and making informed decisions. In recent years, Transformer-Based models have shown remarkable performance in various Natural Language Processing tasks. However, applying these models to Election Result Prediction remains a challenging task due to the complexity of political landscapes and the inherent unpredictability of voter behavior. The pervasive use of Social Media Platforms, exemplified by Twitter, has established itself as a significant channel for users to express and disseminate their sentiments and viewpoints. This widespread adoption has created novel opportunities to quantify and evaluate the popularity and public sentiment concerning political leaders. This paper introduces an innovative approach that harnesses Sentiment Analysis techniques to forecast the outcome of the 2022 Mid-Term Elections in the United States. The proposed methodology comprises of approximately 42,000 tweets and employing diverse Sentiment Analysis approaches to assess the sentiment towards each political candidate. By harnessing the copious user-generated content available on platforms like Twitter, this work uncovers valuable insights that can augment the precision of Election Result Predictions. The performance of the proposed approach is evaluated by computing sentiment scores for individual candidates and employing them to prognosticate poll results and it has been found that the proposed approach is optimal approach amongst the competent approaches for predicting election results based on sentiment analysis of social media. The findings of this paper contribute to the domain of political forecasting by demonstrating the efficacy of Sentiment Analysis in leveraging Social Media data for Election Result Prediction.
Deep Learning Based Model for Malaria Disease Detection Using Convolution Neural Network

The parasite plasmodium causes malaria, a blood disease that is spread by the bite of a female Anopheles mosquito. Microscopists frequently compare thick and thin blood smears to determine parasitemia and make medical diagnoses. However, the accuracy of these tests depends on how large the smear is and the level of skill used to identify and count infected & parasitized cells. However, large-scale diagnoses made during such an examination might be of poor quality. Machine learning and deep learning techniques are used in image-analysis-based computer-aided diagnosis methods on microscopic images of the smears to identify the presence of malaria. In this work, convolutional neural networks (CNNs), which is a technique of DL have been used for end-to-end feature extraction and classification with highly scalable and superior results. This model has been trained and tested on a dataset contained from an open-source repository called “Kaggle”. In order to get better disease screening, we evaluate the effectiveness of CNN-based DL models as feature extractors for classifying parasitized and uninfected cells. Furthermore, the model has been tested at a learning rate of 0.00001 and 0.001 resulting in accuracy values of 93.70% and 94.42%, respectively. In addition to the implementation, the model can be further improved by applying augmentation techniques to the collected images for efficient and effective results.
Power Optimization System of Underwater Data Center

In recent years, with the country's emphasis on information work, the data center, as an information infrastructure, has developed rapidly. There are a large number of data centers in China, and the quality of construction is also in the forefront in the world, mainly using the world's better IT equipment, better refrigeration equipment, better precision air conditioning and better control system. However, PUE, an important index to measure the operation level, has not reached the world advanced level, and the gap is not small. Therefore, it is of great significance to carry out research on energy saving and cost reduction in data center to improve PUE index. This paper studies this problem from two aspects: reducing cooling power consumption and optimizing data center resource allocation. It is found that compared with traditional air-cooling technology, cold plate liquid cooling system has advantages of high density, high efficiency and high reliability, which greatly reduces construction costs while reducing energy consumption. In addition, we have realized intelligent optimization and allocation of power resources by building a power consumption simulator. Make the application of data center optimizer more convenient and reliable.
Sketch Captioning Using LSTM and BiLSTM

The process of creating meaningful captions for hand-drawn drawings, known as sketch captioning, presents a distinct problem since it is hindered by the absence of intricate visual information. This research introduces an innovative methodology that integrates a Convolutional Neural Network (CNN) as an encoder and a Long Short-Term Memory (LSTM) or Bidirectional LSTM (BiLSTM) as a decoder in order to generate captions for sketches. The primary function of the CNN encoder is to extract significant features from the sketch pictures, including both global and local visual information. By capitalizing on the hierarchical characteristics of Convolutional Neural Networks (CNNs), the encoder is able to proficiently capture and depict the underlying structure of the drawing, along with its prominent elements. The decoder, which is based on LSTM/BiLSTM architecture, is responsible for receiving the encoded sketch attributes and producing captions that are both coherent and contextually appropriate. LSTM networks have gained recognition for their proficiency in modeling sequential data, rendering them well-suited for capturing the temporal relationships inherent in the process of sketch captioning. The potential of BiLSTM networks is enhanced by integrating forward and backward contexts, thereby facilitating a more comprehensive comprehension of the sketch context. In order to train our model, we use a large-scale dataset consisting of paired sketch-caption instances. These examples consist of human-made captions added to drawings. The model parameters are optimized via a mix of supervised learning and sequence generating approaches. In the training phase, the decoder is conditioned on the encoded sketch characteristics, enabling it to provide captions that are in accordance with the provided drawings. In order to assess the efficacy of our suggested methodology, we conducted a comparative analysis with established sketch captioning techniques, using the well recognized assessment measure BLEU. A user research study was done in order to evaluate the quality and relevancy of the produced captions. This work aims to compare the accuracies of LSTM and BiLSTM models by using the bilingual evaluation understudy (BLEU) score. The assessment is conducted using the flickr8k and mscoco datasets.
Securing of Medical Images Using Quantum Cryptography

This paper introduces a framework for chaos-based quantum encryption of healthcare images. It enables secure transmission of cipher images from one location to another via the cloud, where healthcare staff can decrypt and assist users securely. The system combines MATLAB and Verilog, utilizing MATLAB for image preprocessing and Verilog for implementing a quantum cryptography algorithm. Images are preprocessed in MATLAB, converted to an encrypted format using Verilog with a quantum cryptography algorithm like BB84, and decrypted on the receiving end. The proposed approach also incorporates a novel quantum image encryption algorithm using gray code and a chaotic map. This integration enables the development of a secure image processing system for healthcare media using quantum cryptography techniques.
Advancements in Image Compression Techniques

This paper evaluates the impact of various image transforms on compression quality using PSNR. It explores different image compression techniques, formats, and procedures for efficiency. The study analyzes Lossy and Lossless Compression, spectral transforms (IWT, DCT, DWT, POT, HAAR, DPCM), and Hyperspectral image compression. The findings provide insights on effective image compression methods for maintaining image quality.
Empowering Multilingual Insensitive Language Detection: Leveraging Transformers for Code-Mixed Text Analysis

Social media usage was made easier by the internet's expanding accessibility, which also encouraged people to share their thoughts openly. However, it also gives content polluters a platform to spread objectionable postings or contents. Most of these inflammatory postings are written in many languages and are therefore simple to dodge internet monitoring programmes. This study serves as an example of our work towards the EACL 2021 joint task on the identification of offensive language in Dravidian languages. The identification of offensive language in the major social media sites was previously discovered. The necessity to identify offensive language in multilingual messages that are substantially code-mixed or written in a non-native script has arisen as a result of the increase in user diversity. The dataset consists of local languages such as Kannada, Malayalam, Tamil with English code-matching. To acquire the requirements used machine learning method (LR, SVM), deep learning technique-LSTM, and transformer-m-BERT. [RESULT The suggested models received a weighted f1 score of 0.75 (for Tamil), 0.95 (for Malayalam), and 0.71 (for Kannada).]
Research on Clinical Trial Data of New Drugs Based on Mathematical Statistics and Stacking Algorithm

Sedative drugs are one of the commonly used drugs, but adverse reactions may cause problems such as slow recovery and prolonged hospital stay for patients. It is of great significance for patients' health recovery to accurately predict intraoperative and postoperative adverse reactions and vital signs data, and explore the influencing factors of postoperative satisfaction. Firstly, the Stacking ensemble learning algorithm classification model is used to predict the occurrence of adverse reactions. Then, taking patient information and medication information as features, random forest was used to fit the vital characteristics data, and the change of Gini index was used to obtain the importance score of each feature, and the main factors affecting vital characteristics were obtained. Then, a multi-classification model was designed to predict the IPI data within 3 minutes after drug administration. Finally, factor analysis was used to explore the correlation between postoperative satisfaction of patients.
Enhancement of Satellite Images Using Edge Detection Techniques

The image captured by satellites in outer space is known as satellite imaging. Through remote sensing, it captures how the earth's surface appears. Because many items are obscured by cloud shadows in the sky, satellite picture objects are typically difficult to spot. The goal of this research is to assist in resolving the following problems with satellite images: low image resolution, image noise, light changes, image shadows, and excessive over-capturing of inconsequential edges. Kernels are used to smooth over the images in low-resolution cases to make discrepancies less noticeable. Applying median blurs to eliminate minute image irregularities that shouldn't be detected by the edge detector will reduce image noise Consistent results will be achieved by being able to distinguish between these different image kinds and how they are preprocessed. Finally, canny edge detection generates results for less significant edges. The results achieved through the developed methodology were compared to results produced using a different method-using edge detection algorithms for certain photos. Utilizing the right edge detector for the selected image material is made easier by this process.
An Offline Tool to Verify Signature for Indian Languages in Digital Forensics

Signature verification is a crucial aspect of digital forensics as it helps identify an individual's identity when validating various documents such as bank cheques. Since signatures are language-dependent, different states in India have different languages, making it essential to consider native languages and combinations of signatures for research purposes. However, publicly available datasets for Kannada, Hindi, and English language signatures are limited. Consequently, the creation of a database and techniques to identify signatures in these languages becomes necessary. This paper employs a series of processes including pre-processing (binarization, noise removal, and thinning), feature extraction (global and grid-based features), and verification of test signatures using Self Organized Maps (SOM). The system yields verification results for combinations of Kannada, English, and Hindi languages. With an overall identification rate of 94.3% accuracy, the system demonstrates successful signature detection across all three languages. Moreover, individual language accuracies for Kannada, English, and Hindi are at 99.5%, 84.5%, and 98.5% respectively. Additionally, the system's performance can be further enhanced by employing different classification and feature extraction techniques.
Image Labeling Using Convolutional Neural Network

Due to the rapid rise in the identification of digital materials, automatic image classification has emerged as the most difficult topic of computer vision. In comparison to human vision, automatic visual understanding and analysis by a system is challenging. The findings of many experiments that were done to fix problems of automatic picture classification with the current categorization method were simple picture primitives. However, those methods don't accurately classify photos. In this research work, a system has been created that makes use of deep learning to deliver the necessary results in the area of computer vision. Our technology automatically categorizes photographs using Convolutional Neural Network, a popular machine learning method. Our algorithm classifies grayscale photos using the Digit of MNIST dataset as a benchmark. The dataset that is employed for the training of the system contains grayscale images, which demand higher processing resources for image categorization. By employing Convolution Neural Network to train the photos, we were able to attain higher accuracy results in the experimental section, demonstrating that our model is highly accurate at classifying images.
AI Tool as a Fitness Trainer Using Human Pose Estimation

The COVID-19 pandemic situation where trainers are not accessible, has forced many people to work out at home, resulting in difficulties in accessing professional trainers to validate exercise postures. Therefore, there is a need for an automated training facility. To address this challenge, a deep learning model is developed in this research work which uses Mediapipe, a machine learning and computer vision solution, and BlazePose, a real-time pose estimation model, to analyze exercise movements and provide real-time feedback to users. Our model enables safer and more effective home workout routines by validating posture and offering corrective suggestions, while increasing accessibility and reducing costs associated with professional physical trainers. This paper presents our proposed model, which has the potential to revolutionize the way people exercise at home. The model has been evaluated on an exercise video dataset and its performance is satisfactory.
Robot Automatic Control System Based on Genetic Algorithm

With the fast growth of Tech, robot Tech has become an important research direction in various fields. Among them, robot automatic control system is one of the key technologies to realize robot autonomous movement and task execution. At present, how to optimize the performance of robot automation control system has become the focus of researchers. An optimization method of robot automatic control system based on GA is proposed. This method relies on GA to optimize the parameters of the control system, which improves the accuracy and stability of the robot control system.Through the optimization experiment of genetic algorithm, it is found that the robot has been significantly improved in the automatic control system. It is particularly noteworthy that the success rate, one of the key indicators of the robot's ability, was significantly improved in the optimized robot. This shows the effectiveness of genetic algorithm in the optimization of robot control system and provides an important reference for the further improvement and application of automatic control system.
A Study on Technology-Enhanced Mulsemedia Learning for Enhancing Learner's Experience in E-Learning

Multimedia makes e-learning more interactive, which helps learners to remember information for a long time. The role of multimedia in education is to create content in an interactive format using videos, images, audio, and animation that enhance the interests of the learners and the learning process. However, the human sense covers more than two media senses. Mulsemedia denotes Multiple sensorial media that combines more than one sense (i.e., visual, hearing, haptic, gustatory, air flow, water jet, and olfactory), which helps to provide a more interactive and immersive learning experience instead of traditional multimedia learning styles in e-learning. In this paper, a study has been conducted on the possibility of including mulsemedia effects such as olfactory, airflow, and haptic effects in e-learning, and analyzing the learner's satisfaction through Quality of Experience (QoE) with and without Mulsemedia effects. For this preliminary study, two pieces of audio content have been created with multimedia and mulsemedia effects. This work initially experimented with 10 participants who are under and post-graduate students with the help of a self-recorded QoE form. Moreover, learners' stress level has been analyzed with Galvanic Skin Response (GSR) when using Mulsemedia in e-learning. The results show that the usage of Mulsemedia in e-learning enhanced the learner experience and had a significant impact on learners' performance compared to traditional learning methods and participants recommend this kind of learning in future e-learning environments.
Real Time Oral Cavity Detection Leading to Oral Cancer using CNN

Oral cancer presents a significant burden to global health, characterized by rising incidence rates and the associated mortality it entails. Timely identification and intervention hold immense potential in enhancing patient prognosis in the face of this disease. Nevertheless, conventional diagnostic approaches often hinge on subjective visual assessments by healthcare professionals, which can lead to potential delays in detection and diagnosis. The realm of medical image analysis has witnessed a transformative shift in recent times with the advent of deep learning methodologies. By harnessing the capabilities of convolutional neural networks (CNNs), scientists have made remarkable strides in employing these models to address diverse medical imaging tasks, including the early detection of cancerous conditions across different anatomical locations. In this paper, we propose a groundbreaking approach for the real-time identification of oral cavity conditions, with a specific emphasis on predicting the occurrence of oral cancer through the utilization of a deep learning framework. Our methodology synergistically integrates patient questionnaires and oral cavity images, culminating in a prediction model that enhances accuracy and reliability. Through this user-friendly system, individuals can undergo oral cancer diagnosis without invasive techniques, making early detection more accessible and potentially life-saving. The integration of communication with healthcare professionals further enhances the overall process, enabling users to seek timely guidance and consultation for better management of their oral health. This streamlined approach empowers individuals to actively participate in their healthcare, bridging the gap between medical technology and patient-centric care.
Super Resolution in Medical Imaging

Medical imaging is a technique and a procedure that involves imaging of the interior portion of a human body for clinical evaluation, medical intervention, and to show how certain organs or tissues are functioning (physiology). MRI scan images of any patient can degrade in quality if they are shared over the internet or in hardcopy. This paper intends to provide an application to obtain high-resolution medical images from lower resolution images. The healthcare sector requires services and data of the best possible quality and this application would help them in better diagnosis and treatment. This paper uses Deep Learning Models and Machine Learning techniques, particularly the Super Resolution Generative Adversarial Networks, to high-resolution Brain MRI images (128 x 128 dimensions) from low-resolution images (32 x 32 dimensions). A web application is developed using the Streamlit library that can also be accessed from a mobile (responsive User Interface) for the end-to-end implementation of the paper. The underlying architecture of the SRGAN model can be replicated to other use cases with suitable customization to achieve high accuracy in generation of super-resolution images. The future scope would include the implementation of a mobile application with real-time processing of generation of high-resolution images from lower resolution images.
Using CNN-LSTMs and Transformer RNNs for COVID19 Impact Prediction

The COVID-19 pandemic has had a significant effect on worldwide healthcare, economic growth, and communities all over the world. Forecasting COVID-19 cases accurately is critical for proactive decision-making, resource allocation, and the implementation of effective control measures. Time series forecasting techniques, particularly those based on deep learning models, have proven useful in predicting and understanding infectious disease spread. Long Short-Term Memory (LSTM) networks are gaining favor as highly effective models for time series forecasting due to their ability to capture long-term reliance and manage sequential data effectively. This study thoroughly investigates using LSTM networks in conjunction with CNNs to accurately forecast global COVID-19 daily case counts, focusing on India and the United States. The study tackles the difficult task of predicting COVID-19 case counts by considering multidimensional and multivariate factors. A suitable sequential dataset for prediction modeling is constructed by leveraging a comprehensive dataset encompassing the early stages of the pandemic. The current study assesses and compares LSTM networks and LSTM-CNN combinations to Transformer models with self-attention mechanisms, conducting a thorough analysis and benchmarking regarding prediction accuracy. The results consistently show that CNN-LSTM-based models outperform other models, with mean square errors ranging from 0.06 to 0.09. These findings highlight CNN-LSTM models' remarkable ability to capture the complex dynamics and spatial correlations inherent in COVID-19 data. By employing LSTM-based models and conducting extensive benchmarking against Transformer models, this study significantly contributes to COVID-19 forecasting and analysis.
Comparitive Analysis of Support Vector Machine and Convolutional Neural Network in Image Classification

This research is aimed at comparing the effectiveness of two AI models in the classification of images of cats and dogs. Based on previous research in this field, two distinct models presented the best results, Support Vector Machine from traditional machine learning methods and Convolutional Neural Networks from deep learning methods. A dataset of 20,000 and a subset of 4000 images were used to train and test the models. The CNN model exhibited better results, with an overall classification accuracy of 89% and 76%, respectively. The SVM model performed poorly, with an accuracy of only 61%. This paper discusses the differences and efficacy of each model classifying pets.