Reviewerly/exHarmony huggingface dataset:
Real Time Conversion Of Sign Language To Text and Speech(For Marathi and English)

This report offers an examination of the effectiveness of several methods for translating sign language into text and voice. An application created that can translate real-time sign language to text and speech using the best technique after analysis. The model is made for ASL and marathi sign language. CNN model is used for predicting sign language from images captured from frames using OpenCV. GTTS is used for converting the text output to a sound file then presented as a voice version of text output. Accuracy, precision, recall are used to evaluate the model’s performance. The model achieved 98% accuracy.
Contrastive Learning Approach for Text-to Image Synthesis

Natural language processing models have shown an extensive neural network to perform various text generation tasks. It is also possible to generate visual images based on text prompts using transformers by establishing the relationship between the image caption pairs to create novel and plausible photos for a vast variety of prompts which can produce a high level of abstraction in the generated images. The motive of this model is to establish the generation of ideas based on text prompts by harbouring the strength of transformers by learning the text embeddings of already available images to create a model capable of zero-shot age which means that the model knows the context of the sentences to portray visual representation based on the input. This model can be used for exploring the user’s imagination.
Uncertainty-based Active Learning with Ensemble Technique for Enhancing the Performance of Natural Language Classification with Limited Data

Recently, advances in artificial intelligence have been rapidly driven by the development of large-scale language models, such as GPT-4. These models, trained on more extensive datasets, show remarkable performance across diverse natural language tasks. However, leveraging these models to create effective services can be resource-intensive. Particularly, in addition to the cost of refining and preprocessing data, getting a large amount of data and training them is very challenging. In this paper, we propose an uncertainty-based active learning approach with ensemble technique to enhance the performance of a natural language classification model using limited data. We achieve higher performance with less data regardless of data characteristics and the number of classes.
Enhanced Schema for Proof of Stake Mechanism Based on Stake Size and Random Validator Selection

The Proof of Work consensus algorithm is currently used by many cryptocurrencies like Bitcoin. In Proof of Work, nodes compete with each other to solve a cryptographic puzzle. The node that solves the puzzle fastest is selected as the miner to mine the new block. Proof of Work favors nodes with high computation power. Higher the computation power more the probability that the node will be selected as the miner. Thus Proof of Work is energy intensive. To increase their computation power miners come together to form a mining pool that makes the blockchain network centralized. Due to these pitfalls, Proof of Stake was used as a substitute for Proof of Work. Proof of Stake takes into consideration the amount of staked coins and other factors like coin age to select a validator or miner. Thus Proof of Stake saves energy and prevents the generation of mining pools. But Proof of Stake favors the rich nodes. Higher the stake more the probability of becoming the validator. Since rich nodes are favored, the network becomes centralized over time. To overcome this problem the validator selection process must be randomized, but the size of the stake should also be considered. This paper proposes two enhanced versions of the Proof of Stake algorithm that randomizes the validator selection process and also considers the size of the stake.
Proposed Methodology for Real-Time Pistol Detection System

Handheld weapon violence has increased global crime rates. Numerous incidents show that handguns are commonly used as the primary weapon in break-ins, robberies, etc. Such crimes can be reduced by detecting and monitoring suspicious activity so that law enforcement can act quickly. CCTV cameras are critical security measures that aid in the resolution of this issue. CCTV is installed in every public place for safety, crime investigation, and other detection-based security. A manual surveillance system demands the use of a human eye to detect malicious activity, notifies security personnel steadily, and is susceptible to human error. Pistols can be detected using the You Only Look Once (YOLO) Object Detection algorithm. This paper provides a thorough introduction, network architecture, and internal mechanisms of the three versions of the YOLO algorithm, namely YOLO3, YOLOv4, and YOLOv5. In addition, a comparative evaluation of the performance of these models was carried out by analyzing the implementation outputs and researching past research works. Precision and recall are more crucial than accuracy when it comes to object detection, hence the models were assessed in terms of them. Yolov5 outperformed the other two versions, having a higher F1-score and mean average precision. We go over some of the challenges encountered in pistol detection, as well as some potential future opportunities and new perspectives on how to address them and improve the system.
Leveraging BERT for Next-Generation Spoken Language Understanding with Joint Intent Classification and Slot Filling

This paper introduces a new technique to natural language processing using BERT (Bidirectional Encoder Representations from Transformers). The program encodes the input text with BERT to generate contextual representations of words, which can then be utilized for intent classification and slot filling through joint training. With limited labeled training data, rare words may not have enough examples to learn from. The proposed model was evaluated against existing models based on attention-based recurrent neural networks and slot-gated models, and was found to be more accurate in terms of intent classification accuracy, slot filling F1 metric score. The level of accuracy in determining the semantic meaning of an entire sentence, as it relates to the context in which it is used, was evaluated on various benchmark datasets. This technique has been employed in various NLP applications, making it a promising area for further study.
Evolutionary Programming for Dynamic Resource Management and Energy Optimization in Cloud Computing

This research paper investigates the use of Evolutionary Programming (EP) for dynamic resource management and energy optimization in cloud computing. The goal is to reduce energy consumption and costs by dynamically allocating resources based on the current workload and energy prices. The proposed EP approach is evaluated on a real-world dataset collected from a cloud data centre. The results show that the EP approach is effective in optimizing resource allocation and reducing energy consumption compared to a baseline static allocation approach. Specifically, the EP approach achieved an average reduction of 28% in energy consumption while maintaining similar levels of performance. The findings of this research have important implications for the industrial and commercial use of cloud computing as it can lead to significant cost savings and a reduced carbon footprint. The proposed EP approach can be easily integrated into existing cloud infrastructures and can be customized to meet specific requirements and constraints.
Prediction of coronary heart disease risk based on multimodal EHRs

Clinical diagnostic information for cardiovascular-related diseases is often stored in various forms within electronic health records (EHRs). The utilization of EHRs for accurate early prediction and diagnosis of patients facilitates timely interventions, ultimately leading to a reduction in mortality rates. However, in the context of coronary heart disease risk prediction tasks, relying on a single type of data or model restricts the amount of information that can be captured during data mining, thereby resulting in suboptimal prediction accuracy. To address these challenges, this paper proposes the coronary heart disease prediction model, CHD-TSBAL, which leverages the combination of Bio-Bert, cross-attention, and Bi-LSTM. Firstly, EHRs are embedded using Bio-Bert. Subsequently, Bi-LSTM is employed to capture temporal data, incorporating long-term information to extract temporal features. Construction of textual and time-series analysis models for clinical diagnosis, disease risk and health status, respectively, using cross-attention. Finally, these models are fused to complete the overall model construction process. Experimental evaluations conducted on the MIMIC-III dataset show that CHD-TSBAL model significantly enhances the prediction of coronary heart disease risk. Specifically, it achieves 4.25% increase in ROC-AUC compared to Bi-LSTM and 2.98% increase in accuracy compared to DFATS.
Artificial intelligence-based prediction of acute myocardial infarction mortality risk

Acute myocardial infarction, a critical condition in cardiovascular disease, is associated with a high degree of morbidity and mortality. Early identification of the risk of acute myocardial infarction (AMI) can provide treatment to patients as early as possible and effectively reduce the risk of death. In this study, we constructed an AMI mortality risk prediction model based on the MIMIC-IV database and explored the relationship between relevant data characteristics and prediction effects. We included AMI patients in the database into the study sample, and used the SMOTE algorithm to correct the sample for category imbalance after preprocessing the data outliers and missing values. After three rounds of feature extraction we obtained a final dataset that could be used for model construction. We selected three algorithms, XGBoost, LightGBM, and Random Forest, to construct the prediction model, and selected the model with the best effect through a variety of evaluation indexes. Finally, we found that the prediction model constructed based on the XGBoost algorithm has better prediction results, with an accuracy of 91.6% and an AUC value of 97.5%. Subsequently, we interpreted and analyzed the model with the Shapley value, and found that the three features of hr_min, aniongap_max and respiratory_max had a high impact on the prediction results. This effectively ensures the general applicability and ease of use of the model, which is conducive to the timely detection of disease development in patients, reducing mortality and improving the prognosis of AMI patients.
Design of automatic blood collection scale system and study on measurement method of blood collection velocity

Aiming at the problem that automatic blood collection scale cannot detect blood collection velocity in real time, an automatic blood collection scale is designed and implemented by using DC reduction motor, resistance strain type weighing sensor, serial touch screen and other devices with STM32F407 microcontroller as the core. In this paper, timing difference method, least square method, Kalman filter and moving average filter are used to detect the blood collection velocity based on the blood collection scale. Weighing accuracy test and comparison test were carried out respectively for four blood collection velocity detection methods. The test results show that the system is stable and reliable, reaching 0.2% static weighing accuracy. The least square method and difference method are more suitable for the situation requiring fast response speed of blood collection velocity, while the moving average filter is more suitable for the situation requiring high accuracy of the velocity.
Who Can be Your AI Doctor?: Evaluation for Disease diagnosis on Large Language Models

Large Language Models (LLMs) have demonstrated outstanding performance in general domain knowledge inference such as arithmetic reasoning, commonsense reasoning, and open-domain question answering. Traditional LLMs were mainly developed and serviced by big tech companies as commercial, making it nearly impossible for researchers to access parameters. However, the recent emergence of non-commercial LLMs has led to ongoing research efforts to surpass commercial LLMs in specific areas using task-specific or domain-specific LLMs. In this study, among many tasks or domains, we focus on the high-level knowledge-demanding medical domain, particularly Automatic Diagnosis System (ADS). We evaluated whether current representative LLMs can perform disease diagnosis effectively and whether they have the potential to assist doctors in clinical situations. Moreover, we also observe the overall quality of responses to whether each LLM can accurately diagnose based on understanding the patient’s basic information such as age, sex, underlying diseases, and family history, and explore whether non-commercial LLMs are likely to outperform commercial LLMs in ADS.
Use of Attention Mechanism for Decoder in Deep Learning-based Image Super Resolution

One of major issues in training deep neural network for image super resolution is checkerboard artifact. It degrades the quality and resolution of output images. It usually appears at diagonal edges and curved edges of input images. It can also occur when too much information is compressed and lost during encoding process. To address this issues, we propose U-Net based structure with its decoder reinforced with attention modules for image super resolution task. U-Net structure is used to increase the feature reusability and reduce the loss of information during the encoding process. Attention modules are implemented in decoding layers in order to enhance the capability to produce high resolution output at diagonal edges and curved edges. Our proposed method shows improvements in PSNR and SSIM compared to the existing methods.
Indian Sign Language recognition using LSTM and mediapipe

Recognition of Indian Sign Language (ISL) is critical in overcoming communication gaps among the hearing-impaired and the hearing world. To comprehend the nuanced motions of ISL, multiple approaches such as Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used in this difficult endeavor. Hand recognition algorithms recognise sign gestures and use convex hull techniques for precise tracking by utilising technologies such as MediaPipe. Furthermore, preprocessing techniques such as unsharp mask filtering improve image quality, which aids in correct recognition. The system uses Pyttsx3 for text-to-speech functionality to enable low latency communication.The incorporation of these cutting-edge technologies enables the system to transform ISL gestures into textual form and vice versa, encouraging inclusion and enabling the hearing-impaired community to fully participate in discussions and information exchange.
Video Categorization using Feature and Context Extraction

Video classification has received a great deal of attention in machine learning because of its wide use in a number of crucial applications, including human activity recognition and dynamic scene layout. However, video arrangements show great vacillation as a result of large size changes and camera movement, posing serious difficulties for both video representations and classification. Video information must be processed significantly more thoroughly than other types of data, which is its main disadvantage when used for classification, also in terms of storage it is to be noted that these videos do not come in small sizes. Deep learning is one of the most fitting solutions to this problem. Models like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are one of the most significant approaches to video level categorization alongside Spectrogram for audio level categorization. This paper proposes an ensemble solution to the problem put forth by the Department of Space, Indian Space Research Organization (ISRO) in Smart India Hackathon 2022 to categorize videos and short clips of ISRO dataset. A multi modal approach has been put forth in this paper, which involves feature extraction and context extraction using a variety of datasets (Audio dataset, ISRO video dataset and YouTube 8M dataset).
Anomalous Human Trajectory Detection Using Clustering Methods

Anomalous human trajectory detection is a critical task in security surveillance in working places. Many studies have been proposed and achieved specific results in abnormal trajectory detection over recent years. In this paper, a framework is proposed for detecting anomalies in human trajectories using clustering methods. In particular, we propose two anomaly detection methods using two different clustering algorithms: spectral clustering-based anomalous trajectory detection (SC-ATD) and DBSCAN-based anomalous trajectory detection (D-ATD). Firstly, clustering methods are used to find normal trajectory clusters in the dataset. Then input trajectory is detected whether it is an anomaly using found clusters. Besides, determining the input parameters of clustering methods is investigated in this work. With spectral clustering, we choose the number of clusters using the WB-index. With DBSCAN, a new cluster quality index (CQI) is proposed to find an appropriate value of the Eps parameter, directly affecting DBSCAN’s quality. The proposed methods are evaluated on a real trajectory dataset: MIT Badge. The results show that both proposed methods detect anomaly trajectories with 77.89 % for spectral clustering and 80.83 % for DBSCAN in terms of F1-score.
A Deep Learning-Driven Disease Classification Method Using MIMIC-III Database and Natural Language Processing

The automated encoding of medical diagnoses from clinical records is an indispensable task in healthcare management. This study proposes a deep learning-driven method for International Classification of Diseases (ICD) coding, derived from clinical narratives using the MIMIC-III database. The proposed approach leverages a Label Attention Model for ICD coding from clinical text (LAAT), and explores the effectiveness of various loss functions, notably focal loss. ICD codes are classified according to their frequency, and model hyperparameters are optimized separately for each category. Empirical results demonstrate that the integration of focal loss significantly bolsters the performance of LAAT in ICD coding, achieving an Area Under the Curve (AUC) of 0.92131 and a micro-F1 score of 0.98658 for the entire MIMIC-III dataset. Additionally, the data suggests that fine-tuning hyperparameters for specific label groups, based on their frequency, fosters further performance enhancements. The developed approach can facilitate precise and efficient ICD coding from clinical notes, providing invaluable insights for healthcare practitioners and investigators.
Extracting Visual Semantic Information for Solving Visual Question Answering

Varies of language models exist, such as LLaMA, Chat-GPT3, and PaLM. These models display a striking resemblance to human cognition, exhibiting a robust capability to analyze images or videos, fully understanding and assessing the overall content and significance of visuals. This holds true for the VQA (Visual Question Answering) model as well, which heavily relies on language models to process and interpret visuals. GRiT, a new language model which can transform the image into text, can use some simple sentences or words to describe each part of an image, and can also provide an appropriate description of the image as a whole. This paper illustrates how to utilize the GRiT framework to extract localized information from an image (using a dataset derived from VQA), and provides a method for utilizing the Generate Region to Text converter to enhance object understanding in the VQA 2.0 dataset.
An Optimal Re-parametrization Scheme for Generalization in Reinforcement Learning

The use of Reinforcement learning (RL) in addressing a variety of engineering tasks has seen a lot of increase lately. However, because RL policies are trained under specific environmental conditions, their performance degrades when deployed in environments with different environmental conditions. As a result, several researchers have focused on developing schemes aimed at minimizing this performance degradation and consequently realizing RL agents that are capable of generalizing to different environmental conditions. In this paper, we propose an optimal re-parameterization scheme based on weighted averaging to facilitate generalization in RL. In the proposed method, two RL agents are trained on simulated environments with different environmental or model parameters, and then covariance matrix adaptation evolution strategies (CMA-ES) is used to determine the optimal averaging weights to combine the two agents for a test environment. We evaluate the performance of our method on a set of popular RL locomotion environments and show that it can significantly improve the generalization performance of RL policies.
BlockCrate: A Blockchain-Based Asset Sharing and Management Solution

A decentralized platform is one that allows users to securely and transparently share documents and cryptocurrency with one another. The platform utilizes smart contracts to facilitate the transfer of files and coins between users, with the terms of the agreement written directly into the code. Users can also earn cryptocurrency by providing storage and processing power to the network. The platform utilizes a decentralized storage system, such as IPFS, to store and distribute files among the network’s nodes, providing increased security and fault tolerance. The platform also utilizes a blockchain to record all transactions and to ensure the immutability of the stored files. The platform can provide a user-friendly interface for easy access to stored files and also can integrate with other external services for the possibility to provide more functionality such as document signing, encryption, and decryption. This platform can be especially useful for people who want to share sensitive information and want to ensure that it is kept private and secure, as well as for people who want to monetize their unused storage space, and for businesses or organizations that want to share files in a secure and decentralized way.
Optimization of manipulated cement mill variables using AI models

Reducing energy consumption without sacrificing the quality of the products is a modern research topic. The cement industry is considered as an industrial sector of high pollution. The operators of a cement plant can only influence a few parameters of the system named manipulated variables. In this work Artificial Intelligence models which predict the non-manipulated variables are incorporated into an optimization process using the Differential Evolution method in order to extract optimal values for the manipulated variables in the TITAN cement plant in Greece. The optimization depends on the mill feed, the cement particles, the mill and the separator energy consumption and the mill vibrations. The results have shown that the system can be switched to an optimized state of low energy consumption while respecting the parameters set on the cement particles.