All of the articles in this proceedings volume have been presented at the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025) during 27th and 28th June, 2025 in New Panvel, India. These articles have been peer reviewed by the members...

Cloud computing has transformed IT infrastructure by offering scalable, flexible, and affordable data storage and computational capability. However, the exponential expansion of cloud services has made resource management difficult. AI in cloud computing improves productivity via intelligent automation,...

The growing number of emerging businesses has increased the risk of cyber threats and financial fraud. Common methods employed by cybercriminals for malicious purposes are phishing emails, compromised credentials, and DDoS attacks. Unlike conventional single-threat detection systems, AEGIS offers complete...

Extracting tabular data from scanned documents remains a challenge in CPU-limited environments, despite advances in deep learning. Table structure recognition (TSR) faces challenges due to geometric distortions such as skewness, curvature, and noise from crumbled pages or poor printing. Although deep...

Web The dark web has become significantly used for anonymous communication and commerce but has developed as a cyber-based threat environment. Because of the complex architecture, layers of encryption, and facade that onion sites provide, it is challenging to assess the security model of sites hosted...

In recent years, sentiment analysis has emerged as a crucial instrument within the financial industry, allowing stakeholders to gauge market sentiments and make well-informed choices. Nonetheless, accurately capturing sentiment from financial texts poses significant challenges due to specialized terminology...

The detection of emotions in textual content is crucial for comprehending human behavior, improving user experience, and facilitating intelligent decision-making within artificial intelligence frameworks. This article offers an extensive overview of deep learning methodologies, emphasizing models such...

Emotion recognition from speech improves human computer interaction by allowing machines to recognize and react to human feelings. Emotion classification using Kaggle’s TESS dataset is performed with RF, SVM, and KNN classifiers. To enhance audio quality and consistency, noise reduction, removal of silence,...

This study presents a comparative analysis of two widely used transformer-based models, GPT-2 and BART, in the context of interactive storytelling. The evaluation emphasizes model performance in generating coherent, creative, and contextually aligned narratives. We utilize a Reddit-based dataset of writing...

Breast cancer remains a leading cause of mortality among women worldwide, with early detection being critical for improving survival rates. However, mammogram interpretation is often hindered by image blurring, pectoral muscle interference, and limited 2D visualization. This paper presents MammoCare,...

In order to manage huge amounts of medical data and provide personalized treatment for patients, the healthcare industry in the current digital era faces a growing need for intelligent technologies. The ground-breaking approach outlined in this paper aims to enhance medical decision-making by utilizing...

Web applications face constant threats from cyber attackers exploiting vulnerabilities like Command Injection, and Outdated Components. There are several popular scanners for finding vulnerabilities, but they do not often find these two OWASP TO 10 vulnerabilities. In this research work, our proposed...

Law enforcement faces many difficulties during large gatherings, especially when trying to find lost objects and missing people. The goal of this study is to improve public safety at Simhastha Ujjain, one of the biggest religious congregations in the world, by integrating an AI-driven Face Recognition...

Consumers struggle to interpret food labels, leading to uninformed dietary choices that may impact long-term health. Our study addresses this issue by providing a user-friendly solution that analyses the nutritional quality of packaged foods and delivers clear, actionable insights. Using the Nutri-Score...

The necessity of combining computer vision and artificial intelligence (AI) technologies to improve crop health monitoring and productivity has been brought to light by the quick development of smart agricultural systems. Manual inspection is a labor-intensive and time-consuming process that is frequently...

Diabetes is classified as a noncommunicable disease with a high-risk profile and includes high prevalence of cardiovascular diseases, kidney failure, or nervous system disorders. As the global prevalence of Type 2 diabetes increases, there is a critical need to identify measures that will remove or reduce...

Autism spectrum disorder (ASD) is a neurodevelopmental condition that requires early intervention. However, current diagnostic methods are often subjective and time-consuming. This project aims to develop an automated model that detects ASD by integrating image features using machine learning and deep...

Glaucoma is a chronic eye disease that causes irreversible blindness, necessitating early and precise detection. The lack of symptoms in the early stages makes detection particularly challenging. This study introduces a deep learning-based approach leveraging Transfer Learning and Ensemble Learning to...

This paper suggests a deep learning-based system for the automatic detection and classification of respiratory diseases from chest X-rays. This method is intended to facilitate fast and trustworthy diagnosis, especially in resource-poor environments with no access to expert radiologists. Utilizing Convolutional...

Anthropogenic activities have severely deteriorated the air quality and taken it to a critical level of hazard. There is a need to analyse the pattern and trend of pollutants with respect to the influence on AQI. To optimise the repercussions of air pollution it is very essential to quantify the pollutants...

This study presents a comprehensive analysis and prediction of the structural behavior of a tensegrity-based Foot Over Bridge (FOB) using both finite element analysis (FEA) and artificial intelligence (AI) modeling. The bridge design featured a lightweight tensegrity framework with an 8.0-m span and...

This paper explores the dynamic response of a cable-stayed bridge with a 500-m main span, focusing on the role of tuned mass dampers (TMDs) in controlling vibrations caused by wind, traffic, and seismic loads. By conducting a dynamic analysis, including time-history and modal studies, the natural frequencies...

Traffic congestion is one of the most pressing challenges in urban transportation systems, especially in rapidly developing regions. This study focuses on traffic planning and management using Geographic Information System (GIS) technologies, with a case study conducted in the Majiwada area of Thane,...

Undetermined source separation problem using compressed sensing technique is the emerging data reconstruction algorithms. This paper develops the advanced basis vector based Non-negative sparse Bayesian learning (NNSBL) algorithm for speech separation. The manifold matrix that is developed for sparse...

Singular Value Decomposition (SVD) has long been a trusted tool for merging images from different sources into a unified whole. In our work, we set out to reinvent image fusion by pairing the strength of SVD with the intuitive learning abilities of Convolutional Neural Networks (CNNs). Imagine an approach...

Sign language is a means of communication for people with hearing and speech impairments. But without the right translation tools, it’s a challenge to interact smoothly. To address this we use machine learning and natural language processing to convert American Sign Language (ASL) to text and speech...

This integrated smart traffic signal and emergency response optimization system increases the efficiency of urban traffic and also reduces emergency response time. Every signal has two Artificial Intelligence models powered by YOLO for detecting accidents and counting vehicles during the red phases of...

Depression, a widespread and debilitating mental health condition, often remains undiagnosed due to the subjective and time-intensive nature of traditional diagnostic methods. This study presents an artificial intelligence system that combines different modalities to detect depression because the current...

As adoption of UPI for online transactions is on the rise, fraud has also been rising sharply. This project applies machine learning to identify UPI fraud in real time with the help of Random Forest algorithm. The model was trained using a dataset that includes transaction information such as amount,...

Cardiac arrhythmia, a type of heart condition, is responsible for 12% of global deaths. While IoT-based health monitoring has advanced, the manual methods used have several limitations. Therefore, there’s a need for an automatic healthcare approach, specifically for identifying arrhythmia. We propose...

Adoption of Artificial Intelligence (AI) and blockchain-based systems in healthcare supply-chain management offers a promising approach for improving security, transparency, and traceability throughout the system. A robust, AI-blockchain-driven healthcare supply-chain has the potential to increase trust...

It remains among the most common causes of cancer death. The disease therefore has to take its share in the midst. Thus, discovering an effective early detection method and the precise forecasting tools is the crucial need at present. The new developments in machine learning, deep learning, and medical...

Alzheimer’s Disease (AD) is a neurodegenerative disorder that significantly impacts cognitive functions, particularly in the aging population. Early diagnosis of AD remains a critical challenge due to the disease’s subtle initial symptoms and the complexity of available clinical data. In recent years,...

Artificial Intelligence (AI) is rapidly transforming the field of pharmacy by enhancing efficiency, accuracy, and patient-centered care. This review critically examines the multifaceted applications of AI in pharmacy practice and pharmaceutical research, including drug discovery, personalized medicine,...

Heart disease remains a leading cause of mortality globally, and early detection and preventive measures can help reduce the mortality rate. Recent studies suggest a correlation between retinopathy and cardiovascular diseases, highlighting the potential for using retinal images as a diagnostic tool for...