All of the articles in this proceedings volume have been presented at the ICETTACS 2026 during 9th – 10th July 2026 in BIT, Mesra - Lalpur Unit, Ranchi. These articles have been peer reviewed by the members of the Review Committee and approved by the Editor-in-Chief, who affirms that this document is...

An efficient segmentation of the liver in the abdomen computed tomography (CT) images plays an important role in the field of computer-assisted diagnostics, tumor detection, volume measurement, and radiotherapy planning. However, the complexity of the organ’s shape and the low quality of the image make...

This paper presents a deep learning framework that is used to perform automatic multi-classification of stuttering on the Sep28k dataset. This approach extracts Mel-Frequency Cepstral Coefficients (MFCCs), which help represent the spectral information of the stuttered speech audio signal, whereas a convolutional...

The current paper proposes the development of an artificial intelligence-based smart service robot that includes voice interaction, wireless mobility, and real-time health monitoring capabilities. In this regard, the current paper proposes an architecture that includes an artificial intelligence module,...

Breast cancer has been one of the major causes of cancer induced death among women across the globe, with early and proper diagnosis by the use of imaging tests such as mammography, ultrasound and magnetic resonance imaging (MRI), being fundamental in enhancing survival. Over the last few years, deep...

Among various neurological diseases, brain tumors pose a significant challenge due to their complexity and the need for prompt and reliable diagnosis to support effective therapeutic interventions and improve patient prognosis. Magnetic Resonance Imaging (MRI) has become the preferred technique for examining...

This paper examines Vision Transformer (ViT) models for classifying leaf diseases in cinnamon, rice, potato, and maize images. We compare a ViT initialized from ImageNet-21k weights with a custom ViT trained from random initialization. The custom model has approximately 399 million parameters, making...

Accurate identification of potato leaf diseases is very important for reducing potato crop loss. Deep learning models get excellent accuracy on lab controlled datasets but they do not achieve the same accuracy on real-world datasets, because they have complex background. Most existing methods use information...

Contaminating crop diseases are serious issues affecting agricultural output. So, the dynamic identification of these problems accurately in a rapid manner is a potential. Traditional disease observation relies on hands-on visual inspection of plant tissue, an approach that is slow and relies heavily...

Email attachments are a common way for cybercriminals to distribute malware. These attachments can include ransomware, spyware, viruses or other malicious software. Traditional antivirus systems are primarily signature-based, and do not recognize new or unknown malware. It has become increasingly difficult...

When a Salesforce deployment fails, the cleanup is rarely quick. Somebody has to roll it back by hand, the data fixes crawl along, and pinning down which single piece of a 90-component package knocked the org over can swallow the better part of a day. Underneath all of that sits a design choice most...

Everyday bank gives loan to many people. Many people pay the loan whereas there are many people who don’t pay the loan. So, it is very difficult for bank to understand whom to give the loan. Because they don’t know the borrower. They don’t know which borrower will be able to pay the loan and which will...

Quantum image processing (QIP) is still a promising area as the noise of qubit, decoherence and limitations of encoding make it difficult for the current quantum hardware. This paper presents a combined Intelligent Quantum Image Denoising Framework, which uses FRQI and deep learning-based restoration,...

Recent work in sequential recommendation has focused on Transformer-based architectures for their ability to capture long-range dependencies. However, these models face two critical bottlenecks in real-world deployment: quadratic computational complexity O(n2) and an inability to explicitly filter session-irrelevant...

Electric Vehicle (EV) sales are increasing globally. This has created need for accurate forecasting models for formulating policies and implementing industry decisions. This paper presents a comparative study of machine learning (XGBoost) and deep learning models (LSTM and Transformer) for EV sales forecasting...

This study offers a Global Terrorism Prediction System that uses the Global Terrorism Database (GTD) to identify trends in terrorist activity using XGBoost, a sophisticated ensemble machine learning method. In order to determine terrorist risk levels based on past characteristics including attack type,...