Narayan Vyas

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Verified

Narayan verified their affiliation via an institutional email.

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• PhD*
• Professor (Assistant) at Vivekananda Global University

Agricultural land classification is a crucial and demanding task, essential for managing resources and tracking changes in farming activities. Remote sensing (RS) is an excellent technology for monitoring agricultural land and detecting seasonal fluctuations globally. Deep learning models offer promising prospects for crop monitoring. While traditi...

Welcome to the Welcome to the proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA 2025). We are very pleased to provide this collection of papers presenting up-to-date research and developments in Computer Science and Artificial Intelligence on behalf of the orga-nizing commi...

Multimodal Data Fusion for Bioinformatics Artificial Intelligence is a must-have for anyone interested in the intersection of AI and bioinformatics, as it delves into innovative data fusion methods and their applications in ‘omics’ research while addressing the ethical implications and future developments shaping the field today. Multimodal Data F...

Agriculture is crucial for economic growth, rural development, and food security. Remote sensing aids in cost-effective agricultural mapping, but challenges like limited resolution, atmospheric errors, and cloud interference in satellite imagery hinder accurate monitoring. To overcome these challenges, this study introduces an image fusion-based fr...

This book provides in-depth explanations and discussions of the latest applications of Artificial Intelligence (AI), machine learning, and the Internet of Medicine, offering readers the cutting edge on this rapidly growing technology that has the potential to transform healthcare and improve patient outcomes. Over the past five years, there have b...

Image fusion could adopt the optical and microwave images model to obtain more precise agricultural land cover information features by using the optical and microwave remote sensing sensors characteristics. The source of data has decided to take into account Sentinel-2 L2A and Sentinel-1 VH (Vertical-transmit and Horizontal-receive) datasets for te...

This book offers a comprehensive exploration of how advanced technologies are transforming modern agriculture. This book covers the integration of Internet of Things (IoT) and remote sensing technologies, focusing on their applications and benefits for crop monitoring and yield prediction. The chapters build a solid foundation, beginning with an in...

Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum pri...

Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additional...

This chapter delves into the cutting-edge world of advanced real-time simulation frameworks, focusing on how they can revolutionize the dynamics of production lines through the adoption of digital twin paradigms. This chapter provides an in-depth look at the ideas behind digital twins, how they are built, and why they are so crucial to the manufact...

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized m...

The internet of things, security, and blockchain all come together to deal with the influx of sensor data. The autonomous vehicle industry, for example, relies heavily on effective data management. IoT applications in supply chains, healthcare, and smart cities can all benefit from the immutability, decentralization, and transparency offered by blo...

Seizure identification in EEG data is difficult because brain activity is complicated and constantly changing. Conventional methods often fall short when recording complex patterns during seizures. On the other hand, deep learning methods have shown impressive results in learning and feature extraction from complex data. This study uses CNNs, RNNs,...

The convergence of quantum technologies and biomedical intelligence is a frontier of boundless potential. The quantum advancements revolutionize disease detection, personalized medicine, and health monitoring frameworks while confronting the pressing challenge of accountability in machine learning systems within the biomedical domain. How do quantu...

Breast cancer remains a pressing global health challenge, emphasizing the need for reliable diagnostic methods. This research explores the imperative of crafting dependable diagnostic models for breast cancer through mammogram images, bridging an existing knowledge gap. We introduce an innovative Deep Learning model centered on Deep Belief Networks...

In the realm of multi-modal medical image segmentation, this investigation centers its focus on brain and liver tumors. Its core mission is to birth a pioneering Hybrid Convolutional-Recurrent Neural Network (CRNN) architecture, turbocharged with Attention Mechanisms, in a quest to heighten the precision of tumor delineation. In the intricate lands...

This research pivots around the intricate domain of personalized drug interaction analysis, harnessing the power of Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL). Emphasizing the need for personalized medicine, the study bridges the gap between static drug interaction data and dynamic patient-specific requirements. The principa...

In this study the Genetic Algorithm-Driven Hyperparameter Optimization of Capsule Networks (GA-Capsule) and two prominent algorithms, Genetic Algorithms (GAs) and Capsule Networks (CapsNets), converge to revolutionize drug interaction analysis. Employing a comprehensive dataset tailored to the predictive analysis of genetic mutations, this research...

Numerous studies have connected air pollution to various health problems, including asthma, COPD, and reduced lung function. Particulate Matter (PM), Nitrogen Dioxide (NO2), and Ozone are three of the most notable pollutants that contribute significantly to worsening chronic diseases. Recent research has revealed that patients with respiratory diso...

Water, our planet's life force, faces multiple challenges in the 21st century, including surging global demand, shifting climate patterns, and the urgent need for sustainable management. Guidance, knowledge, and hope is sharply needed in academia and technology industries, and Innovations in Machine Learning and IoT for Water Management is a formid...

Sign language is an essential means of communication for people with hearing disabilities. However, there is often a communication gap between hearing and non-hearing individuals, which can lead to social exclusion and discrimination. To bridge this gap, They proposed a deep learning-based sign language interpreter using TensorFlow. The system can...

The impact of AI on the workplace is wide-ranging and significant. While AI can increase productivity and create new job opportunities, it also raises concerns about the displacement of human labor and the potential for unintentional bias and discrimination. The development of new skills, the assurance of transparency and accountability in the deci...

Critical data and systems are now more frequently kept and analyzed in the cloud as a result of the cloud computing industry's quick development. As enterprises trust valuable information to third-party service providers, this presents significant security risks. This article's goal is to outline the security issues that cloud computing raises and...

Precision agriculture relies on the early detection and isolation of crop diseases, and this research details how the You Only Look Once, Version 8 (YOLOv8) algorithm was used for the PlantVillage dataset. This research looks at how Deep Learning (DL) and Computer Vision (CV) could streamline and improve the diagnostic process, a problem with conve...

📢 Call For Chapters 📚 Publisher: Scrivener Publishing - Wiley, USA #Scopus Chapter Proposal Submission - https://lnkd.in/dZsZCMiC Final Chapter Submission - https://lnkd.in/dNXJC_XY We're excited to announce an upcoming edited book titled "Secure Energy Optimization: Leveraging IoT and AI for Enhanced Efficiency ". Explore the dynamic fusion of...

Speech data can help Machine Learning (ML) systems diagnose Parkinson's disease (PD). This study predicted PD progression using the Parkinson's Progression Markers Initiative (PPMI) dataset and Deep Belief Networks (DBN). PD and healthy control voice inputs formed a Convolutional Neural Network (CNN) model. This DBN model was compared to others usi...

The degenerative condition known as Chronic Kidney Disease (CKD) impairs kidney function and causes waste products to build up in the body. Early CKD prediction and identification can stop or slow the disease's progression. This study uses demographic, clinical, and lab data to provide a machine learning-based method for CKD prediction. The suggest...

A cancer treatment drug delivery system based on nanotechnology has been created and tested. Characterization, formulation, and optimization of a nanoscale drug delivery system were the objectives. The effectiveness and safety of the system were evaluated using cancer cell lines and animal models. The findings demonstrate how pharmaceutical deliver...

Significant threats to property, human life, and the environment arise from fire events. For fires to have as minimal of an impact as possible, quick discovery and effective action are essential. AI-based fire and smoke detection and security control systems use cutting-edge technology and algorithms to accomplish early and precise fire event detec...

The research proposes improving night-time driving safety by handling high-resolution car projector lights. Internet of Things (IoT) and Computer Vision technology are employed in this strategy. The technology constantly monitors traffic data to adjust the light output and leave room for incoming traffic to prevent accidents. The proposed approach...

In recent years, there have been significant advancements in Remotely Operated Vehicles (ROVs), particularly in air and underwater applications. However, the progress in land-based ROVs has been slow, necessitating innovative approaches to address the existing challenges. This research paper presents a comprehensive analysis of the current state of...

One of the most challenging problems in robotics and autonomous vehicles is autonomous navigation in complex and dynamic environments. Deep Reinforcement Learning (DRL), which enables agents to learn complicated behaviors autonomously through trial and error, has demonstrated that it has the potential to be an effective solution to this problem. By...

A thorough examination of the healthcare industry in the post-pandemic era is necessary, given the Covid-19 epidemic's extraordinary changes to healthcare systems worldwide. An in-depth investigation of Covid-19's effects on the healthcare industry and an exploration of the new standard for healthcare delivery are the goals of this research paper....

Using multimodal neuroimaging data, this study attempted to compare the efficacy of different deep-learning models for the identification of Alzheimer's disease at an early stage. Ten recent studies were selected and compared based on their methodology, results, and limitations. The Generative Adversarial Network (GAN) algorithm was chosen to apply...

One of the most important characteristics that might reveal a person's mental condition is their facial expression. Humans can communicate around 55% of their message nonverbally and about 45% audibly. One of the most challenging topics in computer science is now automatic facial expression detection. Some types of facial expression recognition (FE...

This study investigates the advantages and disadvantages of using a variety of machine learning approaches to estimate an individual person's risk of Cardiovascular Disease (CVD). Not all relevant risk variables specific to an individual may be considered by conventional risk assessment methods because they rely on predetermined risk factors. Algor...

This research aims to develop a Machine Learning model for predicting soil moisture levels, which may be used to construct smart irrigation systems. The model was evaluated and trained using data from the "Smart Irrigation System Dataset" made publicly available by the University of California, Irvine. A transfer-learned ResNet50 model is evaluated...

This research used a wearable sensor to gather photoplethysmography (PPG) signals from 15 healthy subjects. The dataset includes 7,308 PPG segments, each containing 8 seconds of PPG data and corresponding labels indicating the type of physical activity the subject performed. The article proposes a convolutional neural network (CNN) model to classif...

The creation of DeepFakes, which are altered videos, audio, and photographs capable of disseminating false information and fake news and modifying sensitive records, is the result of the rapid advancements in artificial intelligence and machine learning. DeepFakes may also be used for interactive learning and visual effects in entertainment and edu...

Data anomalies are found using anomaly detection. Generative adversarial networks (GANs) can produce synthetic data and learn complex patterns. Temporal dependencies and relevant features are needed to identify time- series anomalies. A GAN is trained to detect anomalies in time- series data. The model performs better with synthetic data. Tradition...

Most machine learning models have had tremendous success in implementing prediction analysis on dePD end diseases such as brain tumors, making it an ambitious goal to apply machine learning to medical research discoveries. In the case of Parkinson's disease, for example, early diagnosis and understanding might allow patients to adopt preventative m...

Health care is playing a major role in designing any kind of prediction model and there is a large amount of data related to different diseases is available over the globe. A major part of machine learning lies in the identification of the data related to different diseases and make them helpfulfor people. The major implementation is in the identif...

In terms of social, psychological, physical, technological, and other elements, the educational system is undergoing significant transformation. Today, education is becoming a joint venture between the state, the market, and the community. Alternative education and training providers that place a greater emphasis on employability provide a problem,...

Monitoring attendance of people became a very important part of any organization. Earlier, we used paper-based systems to monitor attendance records, which is a very tedious task. Nowadays, many new techniques evolved to monitor attendance records, and RFID (Radio Frequency Identification) based systems are one of them. RFID uses radio waves to tra...

Big data introduces the conventional data that can’t be analyzed by using traditionally used applications. When the big data is processed, it starts with the raw data which is not obviously accumulated and very though to store on a single machine. In the current high-stakes business climate, big data organizations - enterprises that separate, outfl...