All of the articles in this proceedings volume have been presented at the International Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML-2026) during March 20–21, 2026 in Amritsar, India. These articles have been peer reviewed by the members of the Technical Committee...

Structural health monitoring systems, which operate as SHM systems, need to function to maintain sustainable and secure operations for civil infrastructure. The SHM damage detection systems use two different methods to identify structural damage. The first approach assesses the overall state of a structure...

As the telecommunication technologies continue to expand, they have brought about many opportunities in terms of global connectivity, yet they have equally enhanced the chances of other frauds like phishing, SMiShing, voice spam and revenue share fraud. The old systems of detection, which rely on fixed...

This research work explores the application of a novel, cutting edge additive manufacturing technology known as 3D printing within the construction sector. Concrete 3D printing creates highly precise, geometrically detailed, and intricate structures. Parameters such as workability, extrudability, buildability,...

Plant diseases create major problems for agricultural production which threatens food security throughout the world [1], [11]. Current disease identification methods based on human interference face challenges because they consume time while depending on human decision and lack the ability to handle...

With the increasing development of the telecommunication networks, the Internet Protocol Detail Records (IPDRs) have grown exponentially, and the detection of the fraud and anomalies has become more and more complicated. The conventional rule-based systems are not effective to identify changing and nuanced...

Artificial Intelligence (AI) has become an important technology in different sectors such as finance, healthcare, education, transportation and public administration. Various industries are achieving production efficiency, accuracy and innovation with AI’s power of analyzing data, making predictions...

The construction industry worldwide is undergoing a significant shift, known as Construction 4.0. This change involves moving away from old methods that rely on manual labour and construction practices toward automated processes that use 3D printing and robotic arms. A key part of this change is Three-Dimensional...

Hydroponic farming is increasingly being adopted as a sustainable agricultural technique due to its efficient use of water and ability to support controlled environment agriculture. However, maintaining optimal nutrient solution conditions and detecting plant diseases remain critical challenges that...

The recent trend of employing digital productivity tools in software development has unintentionally led development workflows to become more fragmented and cognitively taxing. While AI tools are increasingly being leveraged to support development activities, project management tools are still largely...

The creation of deepfakes with the help of state-of-the-art deep learning methods has become a significant challenge to the authenticity of digital media, cybersecurity and trust in the society. As the Generative Adversarial Networks (GANs) and diffusion models of synthesis continue to progress quickly,...

Agriculture forms the foundation of human civilization and plays a critical role in ensuring global security of food. However, diseases in crops are caused by pathogens, fungi, and bacteria significantly reduce yield and quality [1], [6], [11]. Traditional visual inspection–based diagnosis is subjective,...

The process of determining the ideal mixture of alkaline activators enables scientists to achieve higher compressive strength results for Geo-Polymer mortar with the used of Ground Granulated Ballast Furnace Slag (GGBS) that undergoes ambient temperature curing. The research used a combined methodology...

Federated Learning (FL), which allows the joint model training on distributed agricultural data sources without compromising data privacy, is a promising technology. However, the performance of FL is negatively impacted by high non-IID data heterogeneity, which is caused by the uneven distribution of...

Unmanned aerial vehicles, or UAVs, are becoming more and more significant in a number of applications, such as delivery, search and rescue, and surveillance. UAV path planning in intricate 3D landscapes with numerous obstacles is still a difficult multi-objective optimization problem. Four multi-objective...

Wire electro discharge machining i.e. WEDM of shape memory alloys (SMA) involves complex experimental processes, making optimization time-consuming and expensive. To address this, machine learning-based models trained using limited experimental data are employed as surrogate systems to predict machining...

Sentiment analysis of mobile app reviews presents a challenging natural language processing problem owing to the short, noisy, and linguistically diverse nature of user-generated content. In this paper, we present a large-scale comparative study on approximately 1.35 million English-language reviews...

In the age of digital revolution, higher education institutions are increasingly accepting data-driven approaches to enhance strategic decision-making. This study explores the application of machine learning (ML) classification and prediction models to support institutional planning, student performance...

Detecting crop maturity plays a vital role in modern agriculture as it directly affects harvest timing, yield quality, and global food availability. However, traditional maturity assessment methods mainly rely on farmers’ manual observations, which are time-consuming and prone to human error. Proper...

The timely diagnosis of chronic and life-threatening diseases is crucial to enhancing patient outcomes and lowering the rate of burden in the healthcare systems. The paper introduces DiagniQ, which is an artificial intelligence (AI) multi-disease prediction algorithm that helps to identify breast cancer,...

Early disease detection reduces mortality and healthcare costs. This study proposes an AI-powered early warning system using age, blood pressure, glucose, and BMI to predict risks for diabetes and heart disease. Random Forest with SHAP-based explainability was selected based on a systematic benchmark...

For automated screening and clinical decision support, accu-rate segmentation of diabetic retinopathy (DR) lesions is crucial. How-ever, this is still difficult because of lesion variability, low contrast struc-tures, and semantic discrepancies between encoder and decoder represen-tations in convolutional...

The high rate of Internet of Things (IoT) device growth has led to massive amounts of distributed data being created at the network edge, much of which is highly sensitive data about users. Traditional centralized machine learning models rely on aggregation of the raw data in a cloud server, which raises...

In a world increasingly under siege from would-be cyber saboteurs, the safe storage of very high value data means not only powerful encryption but also fool-proof ways to ensure that its integrity has remained uncompromised. In this article, we describe a lightweight block chain framework with FPGA implementation...

In modern industrial manufacturing, minimizing machining time is essential to enhance productivity and reduce overall production costs. This study presents an artificial intelligence (AI) approach for optimizing face milling parameters to increase the material removal rate (MRR) of EN8 steel. Experiments...

Fake news is the creation of intentional and deceptive information to shape the opinion of people, and the rate at which it has gone viral on social media has caused severe consequences: Social, Political and Economic issues. Consequently, fake news detection has been automatically made a significant...

The accurate and timely detection of brain tumors is vital in enhancing the survival rate of patients suffering from these diseases. Manual analysis of brain Magnetic Resonance Imaging (MRI) data is tedious and may result in bias, particularly in resource-constrained environments. The current paper introduces...

As a result of the rapid progress achieved in integrated circuit technology, modern VLSI systems are becoming highly complex. One of the major challenges faced during digital circuit design is the early estimation of power consumption and silicon area. Early and accurate estimation of these design parameters...

El Niño–Southern Oscillation (ENSO) is a major driver of global climate variability that influences extreme weather, agriculture, water resources, and socio-economic systems. Predicting ENSO is challenging because of the complex interactions between the ocean and atmosphere. This study assesses the ENSO...

The occurrence of cloudburst-like heavy rainfall events in the Himalayan region is very rare but highly destructive on the other hand predicting them is even more difficult, These event initiates from very complex atmospheric crossover like change in pressure, temperature, Wind etc. In this paper, we...

Precast modular housing has progressed from early mechanised formwork, basic conveyor systems, and manual quality inspection to advanced robotic manufacturing environments. Initial practices relied on semi-automated casting and simple material handling. In contrast, modern construction increasingly adopts...

This paper presents the seismic analysis of an 88.90 m high Roller Compacted Concrete (RCC) gravity dam non-overflow block considering a massless foundation model in accordance with EM 1110-2-6051 provisions. A three-dimensional finite element analysis of the dam monolith has been developed to study...

The online food ordering industry in India has shown growth at an impeccable pace, led by platforms like Swiggy and Zomato, that let users explore multiple restaurants, order their desired dishes, and have them delivered to their homes. Users can also post ratings and detailed comments about their experiences...

Predicting short-term stock price movements remains a formidable challenge due to the non-stationary, noisy, and high-dimensional nature of financial time series. While large language models (LLMs) have shown strong capabilities in financial sentiment analysis, most existing hybrid methods compress their...

The conventional approaches to job hiring suffered from biased, inaccurate, and time-consuming due to the dramatic growth of resumes. In general, current Auto-Matcher systems are based on single learning model to extract shallow linguistic features, which cannot represent multidimension information of...

It has been clearly observed that Type 2 Diabetes Mellitus, which is also known as T2DM, is rising tremendously on a global scale, which not only needs accuracy to detect at an early stage, but also requires sound ethical practices. As per traditional statistical methods, it seems difficult to handle...