Smart contracts, widely utilized in blockchain applications, are vulnerable to security threats that can lead to severe financial and operational consequences. This study introduces the Hierarchical Contextual Embeddi...
详细信息
Advent of GAN networks has enabled several tasks such as text to face generation easier. It helps in synthesizing several instances of data from the actual data. It gives an idea on new possibilities for existing data...
详细信息
In the context of modern technology, Internet of Things (IoT) has garnered significant academic interest as a crucial tool for enhancing the efficiency of daily life management. IoT is vital for environmental monitori...
详细信息
In the three-dimensional modeling technology, it is very difficult to draw all plants accurately and carefully. The current level of computer software and hardware is far from meeting the requirements. In addition, ma...
详细信息
Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machi...
详细信息
ISBN:
(纸本)9798350379136
Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machine learning (ML) and artificial intelligence (AI). In order to overcome these obstacles, the proposed Face Generation and Recognition in Forensic science will make use of sophisticated recognition algorithms and AI-based face generation models. Fully trained Stable Diffusion model is applied to generate high-quality face images from textual descriptions. Image Generation, Text Guided Image Manipulation using Denoising Diffusion Probabilistic Models (DDPMs), and Dataset Matching are the three primary components of the process. Using a stable diffusion model, Image Generation quickly creates high-resolution images from word prompts by combining an autoencoder (VAE), U-Net, and text encoder. With the introduction of an alternate noise space for DDPMs, Text Guided picture Manipulation makes it possible to do meaningful picture altering tasks in response to text prompts. VGG-16 , a convolutional neural network architecture is used in dataset matching to extract features and calculate similarity, which makes dataset alignment and comparison easier. The suggested methodology gives law enforcement authorities effective tools for identifying suspects, which represents a substantial development in forensic investigations. The project intends to increase the efficiency of criminal investigations, accelerate the matching process with large datasets, and enhance the accuracy of facial sketches by utilizing AI and ML approaches. The approach's ability to produce coherent and contextually relevant face images is validated by experimental results, which also show the approach's potential for speeding up the conclusion of criminal cases, particularly unsolved cold cases. All things considered, Face Generation and Recognition in Forensic science is a promising step in st
The analysis of Integrated Circuit (IC) Scanning Electron Microscopy (SEM) images plays a crucial role in the reliability and authenticity investigation of modern ICs. It pertains to the task of predicting masks, whic...
详细信息
Indian Sign Language (ISL) is a standardized and commonly used medium of communication for the deaf and dumb community in India. Despite an enormous deaf and dumb community, there are only about 300 certified ISL inte...
详细信息
Wearable devices for arrhythmia diagnosis are battery-powered and require improved power efficiency. In our previous study, we applied approximate computing, which is effective for power reduction, to QRS identificati...
详细信息
Infant mortality is a significant public health challenge in developing nations, particularly in India, where effective predictive models are critical for targeted interventions. This study utilizes the XGBoost algori...
详细信息
Background:The global impact of the highly contagious COVID-19 virus has created unprecedented challenges,significantly impacting public health and economies *** research article conducts a time series analysis of COV...
详细信息
Background:The global impact of the highly contagious COVID-19 virus has created unprecedented challenges,significantly impacting public health and economies *** research article conducts a time series analysis of COVID-19 data across various countries,including India,Brazil,Russia,and the United States,with a particular emphasis on total confirmed ***:The proposed approach combines auto-regressive integrated moving average(ARIMA)'s ability to capture linear trends and seasonality with long short-term memory(LSTM)networks,which are designed to learn complex nonlinear dependencies in the *** hybrid approach surpasses both individual models and existing ARIMA-artificial neural network(ANN)hybrids,which often struggle with highly nonlinear time series like COVID-19 *** integrating ARIMA and LSTM,the model aims to achieve superior forecasting accuracy compared to baseline models,including ARIMA,Gated Recurrent Unit(GRU),LSTM,and ***:The hybrid ARIMA-LSTM model outperformed the benchmark models,achieving a mean absolute percentage error(MAPE)score of 2.4%.Among the benchmark models,GRU performed the best with a MAPE score of 2.9%,followed by LSTM with a score of 3.6%.Conclusions:The proposed ARIMA-LSTM hybrid model outperforms ARIMA,GRU,LSTM,Prophet,and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE,symmetric mean absolute percentage error,and median absolute percentage error across all countries *** findings have the potential to significantly improve preparedness and response efforts by public health authorities,allowing for more efficient resource allocation and targeted interventions.
暂无评论