All articles in this proceedings volume have been presented at the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA-2026) on 23rd and 24th March 2026 at Vivekananda Global University, Jaipur, India. These articles have been peer-reviewed by the members...

Lung cancer is the largest cause of cancer-related death globally, necessitating the urgent development of reliable and rapid diagnostic tools to aid clinical decision-making. Although deep learning models like DenseNet121 and ResNet-50 perform well in image-based classification tasks, their high tuning...

Detection of tumors in brain from Magnetic Resonance Imaging (MRI) plays a vital role in both planning and diagnosis for the treatment. Examining these MRI images manually takes a lot of time and it depends upon the experience of the observer. This drawback motivated the significance of automated detection...

PDAC is inherently lethal, which can be mostly explained by late detection and low sensitivity of visual inspection during contrast-enhanced CT. Minor changes in the textures, isoattenuation lesions, and large inter-observers’ variability still remain obstacles to consistent detection. AI-based methods...

Multimodal artificial intelligence has emerged as an effective approach in medical diagnostics by integrating heterogeneous data sources such as clinical symptoms and medical images, thereby addressing the limitations of unimodal diagnostic systems [10], [15], [12]. The creation of the modular multimodal...

Many elderly people require constant monitoring of their vital signs. Every day, going to hospitals for checkups or being admitted for monitoring of vital signs is tiring; therefore, there is a high demand for smart health monitoring systems. To support elderly healthcare, this paper introduces the SmartLife...

The paper proposes a web-based implementation for a bone age classification system that assists clinicians in interpreting skeletal maturation in children using pediatric hand X-ray images. The system is implemented using the Django framework and incorporates a convolutional neural network (CNN) architecture...

There is still the difficult task of accurate delineation of the COVID-19 infection regions in the CT-based scan of the chest, since the lesions have blurred edges, the contrast between infected and noninfected tissues is low, and the lesion appearance is highly variable. Though encoder-decoder-based...

User-generated reviews are written by people who have used medications to describe their experience. Drug reviews include descriptions of the effectiveness of the drug, side effects experienced, and the patient’s overall level of satisfaction. Although reviews of medications provide valuable real-world...

The global medical supply chain, particularly for critical items such as vaccines and pharmaceuticals, faces challenges including low transparency, counterfeit products, slow recall processes, and inadequate tracking of usage and waste. Centralised systems struggle to give an immutable, auditable trail...

An electroencephalogram (EEG) is a non-invasive method used to measure brain activity and is used in various applications, such as medical, security, marketing, gaming, and brain-computer interface (BCI). EEG signals are high-dimensional and redundant in nature, which significantly complicates the channel...

This paper focuses on more accurately and efficiently detecting the presence of brain tumors from their Magnetic Resonance Imaging (MRI) scans, leading to improved clinical decision-making and treatment outcomes. Medical image classification frequently uses deep learning models that must deal with complications...

An automated clinical decision support based on cardiac arrhythmia detection from electrocardiogram (ECG) signals is still a burning issue, especially where high sensitivity, robustness under realistic signal conditions, and post-hoc interpretability are required. This paper proposes a Hybrid Convolutional...

Cardiovascular diseases are one of the major components of the global health budget. Phonocardiography (PCG), the recording of heart sounds, offers a non-invasive method for cardiac assessment, with potential for automated analysis to aid diagnosis. This study compares the effectiveness of five distinct...

In this research, a fundamental method for deep learning-based emotion identification from facial photographs is presented. Happiness, sorrow, surprise, rage, and other basic emotions are automatically extracted and classified using a Convolutional Neural Network (CNN) model. The model's performance...

Android applications are regularly involved with sensitive user information in the contemporary mobile ecosystem and are often regulated by privacy policies that are too long, vague, and incomprehensible to the end-user. Such ambiguity may result in the accidental consent, unauthorized data gathering,...

The rapid growth of IoT has significantly increased network traffic, making modern systems more vulnerable to DDoS attacks. Traditional security mechanisms struggle to detect such attacks effectively due to their dynamic and large-scale nature. To address this challenge, this research shows an exhaustive...

The growth of cryptocurrency and decentralized financial systems has been very rapid, which has also increased the risk of fraudulent transactions. Traditional fraud detection techniques are usually based on centralized data collection, which raises serious concerns about data privacy, data security,...

This paper deals with the major and growing threat of ransomware, which causes significant financial and business losses by using developed multi-stage attacks. The modern threat environment has changed to Ransomware-as-a-Service (RaaS) format and polymorphic code, making past security models that used...

In today's world, face recognition is widely used in biometric security systems like border control, passports, identity verification, and so on. These systems are vulnerable to face-morphing attacks, where two or more face images are blended together to make a real image that can fool a whole verification...

The rapid proliferation of malware and the sophistication of evasion strategies have diminished the effectiveness of traditional signature-based detection approaches. While static analysis is computationally efficient, its susceptibility to evasion remains a challenge; dynamic analysis offers behavioral...

Recent advances in artificial intelligence have led to the development of systems that can interpret visual agricultural data and support decision-making at various stages of crop growth. This paper presents an integrated plant intelligence framework that combines structured JSON-based crop knowledge...

Proper detection of microorganisms in microbiology, agriculture and medical services is important as it facilitates the prevention of diseases, environmental pollution, and research. In the project, Microbes Prediction and Recommendation is a web application based on machine-learning that was developed...

The use of machine learning algorithms to analyze land use and land cover (LULC) change has become more important for environmental monitoring and urban planning. This paper looks at how well ensemble machine learning models can map and quantify land transformation patterns across Karnataka State over...

The presented paper proposes a hardware-independent, uncertainty-aware crop recommendation system that integrates agronomic, climatic, and economic factors to produce precise, profit-oriented recommendations. The system takes extensive preprocessing and feature engineering to analyze the nutrient profiles...

Because soil, climate, and local environment affect growing conditions to varying degrees, it has always been difficult to determine what crops to plant and how to predict a harvest’s yield. Traditional crop-selection and yield-prediction tools have relied on fixed data and have therefore been limited...

Agriculture is regarded as one of the largest pillars in the global economy, as it has a direct impact on food security, livelihoods in rural areas, and the general development of the nation. But the issue of crop diseases has become a constant bane, and the loss of yield and economic losses to farmers...

Agriculture is a key contributor to the economy of the Telangana region and also provides jobs for a large part of its rural population. However, agriculture traditionally relies on farmers’ years of experience and does not incorporate scientific principles into the decision-making process. As a result,...

The Indian economy relies heavily on agriculture, yet crop diseases, poor disease diagnosis, and inaccurate crop selection continue to cause significant reductions in crop yield and farmers’ income. Early and accurate disease detection, data-driven crop recommendation, and weather-based advisory can...

Control and early intervention with crop diseases is a serious issue in contemporary agriculture, which directly affects food security in the world and livelihoods of farmers. This paper will encapsulate developments in the field of detecting plant diseases and will show that MobileNetV2 is decisively...

Considering the ongoing rapid land transformation across semi-arid sub-Saharan Africa, accurate and up-to-date information on land use and land cover (LULC) is increasingly important for environmental monitoring, agricultural planning, and sustainable land management. No small part of this challenge...

According to IS 10262:2019, designing a concrete mix includes complicated calculations that can easily go wrong, especially for high-strength concrete mixes. This document discusses the creation and testing of an automated framework for proportioning concrete mixes using Microsoft Excel that completely...

With the emergence of 5G networks, network slicing provides a virtualized network on shared infrastructure, increasing the attack surface to various threats, including DDoS attacks. Traditional security systems cannot handle the dynamic characteristics of 5G networks and therefore require a smart, automated...

Large-scale networks are expanding and becoming increasingly heterogeneous and dynamic. Localizing faults quickly and accurately is of the highest importance for network operators. While centralized monitoring systems have been effective in many deployments, they face scalability and latency challenges...

In modern aerospace systems, reliability-centered maintenance promotes the implementation of condition-based maintenance to ensure operational safety and enable precise lifespan estimation of aircraft engines. This study investigates RUL prediction using the FD001(subset of C-MAPSS) of NASA prognostics...

The rapid growth of the IoT has led to the development of highly interconnected smart environments in which secure and reliable communication is crucial. `Trust management has proven to be an effective approach to evaluating the reliability of nodes in IoT environments while mitigating malicious interactions....

Wireless Sensor Networks (WSNs) are a popular area of study, with applications in recent trends such as environmental monitoring, smart agriculture and military surveillance. The Internet of Things is a type of large-scale system where the sensors use the wireless sensor networks for environmental monitoring...

The beam balancing system is a classical example of a nonlinear and inherently unstable control problem widely used in control engineering research. This paper presents the design and implementation of a beam balancing system using a Brushless DC (BLDC) motor integrated with a Proportional–Integral–Derivative...

The increased development of Large Language Models (LLMs) has presented difficulties in choosing the most appropriate model to use in task-specific applications as they can be different in terms of performance, cost, latency, and safety. This paper will suggest the implementation of a web-based decision-support...

Online reviews strongly influence purchasing choices in today’s e-commerce platforms. However, due to an increase in promotional, spammy, and deceptive reviews, it has often become difficult for a consumer to get a correct idea about the quality of a product. To mitigate this issue, this paper proposes...

Personalized recommendation systems and customer segmentation contribute significantly to enriching user experience and optimizing marketing strategies in the retail and e-commerce sectors. Traditional recommendation approaches usually suffer from issues of limited personalization and scalability. This...

This study presents a deep learning-based Sanskrit to Hindi translation system for Bhagwad Gita verses using a sequence-to-sequence (Seq2Seq) model with an LSTM based encoder-decoder architecture. The model is trained on a parallel corpus containing aligned Sanskrit to Hindi verse pairs. Data preprocessing...

Pretrained transformer-based models have significantly advanced abstractive summarization. However, selecting the right model for a new dataset remains challenging because exhaustively fine-tuning multiple candidate models for each dataset is computationally expensive and limits the scalability of summarization...

Real-time speech emotion analysis is crucial in domains such as call center analytics and human-computer interaction. Although many existing emotion recognition systems achieve high accuracy, they often operate offline and overlook the impact of transcription errors and processing delays in real-time...

Autonomous parking assistance systems are increasingly required to improve the efficiency of vehicle parking in organized environments while reducing the reliance on costly sensor infrastructure. This paper presents a vision-based autonomous parking assistant that detects parking slots and provides navigation...

Convolutional Neural Networks (CNNs) are commonly employed for image classification, but training stability and generalization are difficult due to discretization errors during feature propagation. In this paper, we provide a Modified Runge-Kutta (MRK4) integration approach as a parameter-free post-pooling...

Human Action Recognition (HAR) is a major field of study in both smart surveillance systems and computer vision. Human activities such as falls or fights are of great concern due to the need for immediate contact to avert injuries and preserve public safety. This research proposes a real-time HAR and...

It’s well developed in lane detection techniques for autonomous driving in advanced driver-assistance systems (ADAS). While keeping the vehicle localized in the correct track by detecting lane borders, interference may be caused by the windshield wipers, especially in bad weather conditions. Such annoyance...

Stray dogs represent one of the major problems in India, according to NCDC (National Centre for Disease Control) reports in 2024 & 2025, including road accidents and dog bite cases. To address this issue, we are developing a web platform that allows public users to submit reports to registered NGOs....

In today’s fast-paced society, women’s safety is still a major concern despite the tremendous improvements in technology. In addition to having a physical and psychological impact on victims, the rise in harassment, assault, and gender-based violence also imposes broader socioeconomic difficulties by...

Natural disasters such as floods, earthquakes, and landslides have become a global concern due to their destructive effects on the environment. These events have become more frequent due to massive climate change, which demands the need for an automated system that can analyze these effects and manage...

In the transition from legal tech to online, we are faced with a problem: how to keep all our documents secure whilst ensuring that they are legally and constitutionally accurate. Blockchain is a secure means for creating a permanent record that cannot be tampered with. However, at the same time, it...

Artificial Intelligence (AI) plays a vital role in facilitating an Intelligent Tutoring System. XAI-based personalised Intelligent Tutoring Systems (ITS) for lifelong learning are transforming education by offering adaptive and customised learning experiences. But the black-box nature of many AI learning...

The Student-Alumni Networking and Career Development Platform will serve as a link between students and alumni to foster mutual development. It will not only store students’ and alumni’s details, but it also provides a communication environment between them so that they can share their knowledge. For...

Bug tracking is a time-consuming activity in current software development, which generally involves manual bug reporting, bug triage, and bug propagation. In large and fast-evolving projects, this manual dependency causes slowing down bug resolution, duplicating reports, inconsistency in labeling severity,...

This paper describes the framework HQIAT-ML, the hybrid quantum- inspired adaptive transformer with meta-learning, for predicting and explaining the risk of student dropout for the Open University Learning Analytics Datasets (OULAD). Unlike traditional explainable AI methods based on SHAP and LIME, for...

The study gives a thorough literature review of cloud computing and its impact on four main areas: Healthcare, Cybersecurity, Agriculture, and Education. On the basis of more than 50 peer-reviewed publications published during 2019-2025, This paper explores the trend in technological integrations, challenges,...