Deciphering Stem Cell Fate with an Integrative Multi-Omics Examination of Microenvironmental Dynamics
The evidence-based literature on healthcare is akin to a vast universe, continually expanding with new discoveries and insights. In this dynamic landscape, the integration oftools and multi-omics approaches a beacon of hope, illuminating the ...
ImmuneChain: A Blockchain-Based Secure and Transparent Vaccine Supply Chain
Blockchain technology has the capability to handle information in a decentralized, permanent, transparent, and secure manner, which makes it unique. The data of the vaccine supply chain is maintained either manually or kept on a central server, ...
An Efficient Real Time Anomaly Detection in Surveillance Videos Using PRU-DPCN Classifier
Detecting the behavior of the crowd that is regarded as anomalous is the need for surveillance.Hence, several machine learning (ML) systems were built for the automatic detection of an anomaly in the crowd video. Still, certain limitations like ...
Evaluating Testing and Use of Photometric Stereo Applications for Riblet Inspection
Computer vision is vital for various applications like object tracking for autonomous driving or quality assurance. Hence, assuring that computer vision fulfills given quality criteria is essential and requires sufficient testing. In previous work,...
Intelligent Models for Diabetic Prediction Using Conventional Machine Learning Techniques and Ensemble Learning Algorithms
The discovery of knowledge from medical database using machine learning approach is always beneficial as well as challenging task for diagnosis. Diabetes if left undiagnosed can affect many other organs (e.g., kidney and liver) of human body and ...
Centrality-Based Approach for Identifying Essential Cancer Proteins in PPI Networks
Protein–protein interaction (PPI) networks serve as invaluable repositories, shedding light on the intricate web of protein interactions within living organisms. Traditional methods for identifying essential proteins fail to capture the complex ...
Seeding Contradiction: a Fast Method for Generating Full-Coverage Test Suites
The regression test suite, a key resource for managing program evolution, needs to achieve 100% branch coverage, or very close, to be useful. Devising a test suite manually is unacceptably tedious. Although many automated methods exist to improve ...
Features Responsible for Spread of Zoonotic Disease Brucella: A Study on Central India Population
- Nishant Namdev,
- Richa Makhijani,
- Kirti Dorshetwar,
- Mayur Parate,
- Jayshree L. Shukla,
- Aliabbas A. Husain,
- Rajpal S. Kashyap
Zoonotic diseases pose a growing health risk in developing nations because of the ongoing animal-human interaction. India has experienced significant urbanization in recent years, with a large population migration from rural to urban areas. One of ...
AI-Inspired Algorithm for the Automated Recognition of the World’s Oldest Script “Brahmi”
One of the internationally known oldest script is Brahmi whose digitisation may be helpful for the archaeologists as well as it may help in the digitisation of other languages. Optical character recognition techniques with genetic algorithms will ...
Hazardous Object Detection for Visually Impaired People Using Edge Device
The motivation for this research stems from the need to improve the safety and independence of visually impaired individuals in their daily lives. These individuals face significant challenges in navigating their environments, particularly when it ...
Heart Disease Prediction Using a Stacked Ensemble Learning Approach
This study introduces a stacked ensemble machine learning approach to enhance the accuracy of heart disease prediction. The approach begins with a variational autoencoder (VA) for unsupervised learning to identify key patterns in the data. For ...
Multi-task Learning for Lung Sound and Lung Disease Classification
Recent advances in deep learning techniques have significantly increased the accuracy and efficacy of medical diagnosis. In this work, we propose a novel multitask learning (MTL) approach for concurrently classifying lung sounds and diseases. Our ...
2-Phase Multi-trait Biometric Authentication Model Against Spoofing Attack Using Deep Hash Model
Multimodal biometric systems offer numerous advantages over unimodal systems, such as reduced error rates, enhanced accuracy, and broader population coverage. However, multimodal systems face an increased demand for security and anonymity since ...
An Empirical Study on Small-Sized Datasets Based on Eubank’s Optimal Spacing Theorem
Conventional machine learning methods for software effort estimation (SEE) have seen an increase in research interest. Conversely, there are few research that try to evaluate how well deep learning techniques work in SEE. This can be attributed to ...
ElementaryCQT: A New Dataset and its Deep Learning Analysis for 2D Geometric Shape Recognition
Geometry problems at the elementary school level typically include a figure and input text, which requires interpreting both and finding a solution. Figures may include shape, side values, angles, perpendicular symbols, and other information that ...
Transfer Learning Based Facial Emotion Recognition
The efficiency and accuracy of facial emotion detection imparts on mental health monitoring through Transfer Learning with Facial Emotion Recognition (FER). The Transfer Learning has a resource-efficient and robust solution through FER which has ...
A Hybrid ANN Random Forest Regression Method of Feature Extraction in Time Series Data
A time series is a logical grouping of values about time gleaned from numerous applications. The fundamental properties of the time series data (TSD) include their enormous quantity, high complexity, and traits like trend, cycle, seasonality, and ...
Lightweight Attention Based Deep CNN Framework for Human Facial Emotion Detection from Video Sequences
Emotions with their intensities are associated with the action of humans which decides the behaviour of an individual.The recent research has gained enormous attention in the domain of emotion detection due to automatic facial emotion detection. ...
Optimized Placement of Service Function Chains in Edge Cloud with LSTM and ILP
Edge computing has emerged as a transformative approach for reducing latency and enhancing network performance by placing computing resources closer to data sources and end users via edge nodes. This approach addresses the delays inherent in ...
Energy-Proficient Cluster Enrichment in Wireless Sensor Networks via Categorized Fuzzy Clustering and Multi-Hop Routing Optimization
- Harish Kumar Shakya,
- Amit Kumar Chandanan,
- Chatti Subbalakshmi,
- Geetanjli Khambra,
- Mohd Shahnawaz Ansari,
- Ajay Kushwaha,
- Vandana Roy
The WSN's lifetime can be extended by practicing energy saving. By using the appropriate routing mechanism when executing clustering, it can be done successfully. For improved energy conservation, clustering, and routing work together. Clustering ...
(S2M Semiotics Similarity Measurement and Information Quantification, Lecture Information Weightage Calculation
AI and ML-based smart applications will be the next best way towards essential services, like basic education, and quality optimization. In the educational delivery context, not only summarization but advanced stages of NLP require capabilities to ...
Robust Adversarial Defense: An Analysis on Use of Auto-Inpainting
In recent years, adversarial patch attacks have become a major concern since they can seriously compromise the security and reliability of deep neural networks. These attacks involve modifying a clean image by adding a patch with carefully crafted ...
A Survey on Graph Neural Networks and its Applications in Various Domains
Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT)...
Enhanced Modelling Performance with Boosting Ensemble Meta-Learning and Optuna Optimization
Improving modeling performance on imbalanced multi-class classification problems has continued to attract attention from researchers considering the critical and significant role such models should play in mitigating the prevalent problem. ...
Enhancing Blood Platelet Counting through Deep Learning Models for Advanced Diagnostics
Platelet counting is considered an essential factor for the diagnosis of blood clotting disorders as well as other illnesses as COVID-19 and Leukemia. Hemocytometer-based manual techniques are labor-intensive and prone to errors, especially in ...
A Novel Approach to Detection of COVID-19 and Other Respiratory Diseases Using Autoencoder and LSTM
Innumerable approaches of deep learning-based COVID-19 detection systems have been suggested by researchers in the recent past, due to their ability to process high-dimensional, complex data, leading to more accurate prediction of the COVID-19 ...
Boosting Classification Reliability of NLP Transformer Models in the Long Run—Challenges of Time in Opinion Prediction Regarding COVID-19 Vaccine
Transformer-based machine learning models have become an essential tool for many natural language processing (NLP) tasks since the introduction of the method. A common objective of these projects is to classify text data. Classification models are ...
Intelligent Chat Conversation and Dialogue Management for Gujarati Dialogue
Nowadays, all social media applications have upgraded advanced features such as chat conversation and question-answering bots. However, due to its limited retrieval features, those bots suffered to answer all kinds of dialogue. Hence, this current ...
Reinforcement Learning Driven Trading Algorithm with Optimized Stock Portfolio Management Scheme to Control Financial Risk
In recent years, the application of deep learning techniques in financial markets has achieved significant attention for developing effective investment strategies. Traditional schemes focus on optimizing models to maximize returns but often fail ...
Revolutionizing Rose Grading: Real-Time Detection and Accurate Assessment with YOLOv8 and Deep Learning Models
Yield estimation and identifying the growth stages of roses greatly depend on the automatic detection of roses in orchards. YOLO is well known for its high accuracy and real-time performance among the deep learning-based object detection ...