All of the articles in this proceedings volume have been presented at the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI) during December 12-13, 2025 (Hybrid Mode) in Compucom Institute of Technology and Management, Jaipur, India. These articles have been peer reviewed...

The integrated differential ring voltage controlled oscillator (DRVCO), which has long been utilized in a variety of devices, is based on CMOS technology. CMOS device-based systems are the backbone of the modern electronics industry. It enables electronic systems and devices to be compact and portable....

Background: Digital Twins (DT) and Artificial Intelligence (AI) have become new groundbreaking technologies, allowing real-time monitoring, predictive analytics, and data-based optimization to increase energy efficiency, minimize waste, and streamline the work process. Purpose: The study...

Electricity generation is a complex and dynamic process that is sensitive to demand, calls often due to the weather. The present research paper aims to investigate the association of weather variables with electricity generation, which will, in turn, help improve the efficiency of power plants. We apply...

Using smart appliances can help save electricity and money as well as be eco-friendly and sustainable. This paper proposes a system that provides Adaptive Control and Intelligent Power Monitoring. As its name indicates, the system is designed to monitor, track, analyze and control the electrical power....

Extracting actionable insights from large structured datasets is often a significant challenge in data analytics. Common practices for implementing advanced analytical models are to move data around. This limits agility and real-time exploration. This paper proposes a computing infrastructure for on-database...

The fast-rising urbanization and industrialization rates associated with urban agglomeration studies during recent decades have resulted in a considerable reduction in air quality in urban zones, specifically in cities such as Rajasthan, with profound impacts on public health, environment, and urban...

In recent times, many modern techniques have arrived, one most popular Deep learning. This highly use in image processing and data analysis. With the up-coming time, it has even bigger scope and potential use in future. As currently even it has a part of various and spread fields. Here by study-document...

Marine exploration, like underwater archaeology, ocean exploration, industrial inspection, marine research, and rescue operations, faces issues due to inaccurate visual analysis. Underwater images face critical challenge due to light absorption, scattering of light, and color distortion in aquatic environments,...

IoT (Internet of Things) has been widely used in the agriculture industry, ranging from food production and management to irrigation planning, as well as the prediction and prevention of diseases in plants and vegetables. This paper presents a review study on the IoT infrastructure, including its three-level...

The presented research paper investigates applying AI-based cybercrime detection and legal governance frameworks and evaluating them based on open-source datasets provided by Kaggle, that is, including intrusion detection, phishing, and financial fraud data. The study uses machine learning algorithms...

Pneumonia and other lung abnormalities continue to be significant health issues of global concern and any diagnostic support to aid clinical decision-making must be quick and precise. The Chest X-ray imaging has been extensively utilized in respiratory assessment, but the manual interpretation is time-consuming...

The problem of epileptic seizure detection is still a significant issue in clinical neuroscience because the impact of the seizure episode is unpredictable, and the underlying neural structure is complicated. The conventional diagnostic models are very reliant on the experience of the specialists in...

Over to the last two decades, research on ASD (Autism Spectrum Disorder) has increasingly emphasized the importance of identifying early signs to improve developmental outcome. Although studies have shown that the reliable indicators can be detected between 12 and 36 months, many children worldwide still...

This study aims to show how Explainable Artificial Intelligence (XAI) can increase clinician trust, boost hospital adoption of predictive systems, and improve the accuracy and accountability of AI-assisted diagnostics. The system created predicts the status of three diseases - heart disease, breast cancer...

The urban centers are becoming increasingly complex and data rich with the need for integrated technological frameworks for implementation of an efficient and sustainable development. Based on the above observation, present research presents a Federated Cognitive Internet of Things (FCIoT) framework...

Poor waste management is a major environmental issue, especially in cities where production is constantly rising. Traditional manual segregation methods are ineffective and inconsistent. To address this issue a real-time automated waste segregation system that makes use of computer vision and deep learning...

SDN is a centralized traffic management model with the separation between the control and data planes. Dynamic traffic undermines the conventional load-balancing methods and this brings congestion and underutilization of available bands. This paper provides an outline of a framework that uses the power...

The research paper demonstrates the simulation and design of a Cash changing machine using VHDL (Speed Integrated Circuit Hardware Description Language), which goals, the process of dispensing change in vending machines and based on transaction environments. The basic objective of the machine is to allow...

The objective of this paper is to introduce Multi-Scale Attention Transformer Network (MSAT-Net) for the purpose of providing accurate and reliable brain tumor segmentation over various MRI datasets. This makes it easier to characterize tumor subregions such as edema, necrotic core and enhancing tissues....

The recurrence of head and neck cancer is a life-threatening issue in the post-treatment management because of the diverse biology of tumors as well as clinical heterogeneity. In this paper, the authors suggest a Dual-Stream Convolutional Neural Network with a Graph Neural Network as an indicator of...

Mammography-based breast cancer screening is constrained by thick tissue characteristics, obscure lesion sizes, and annotations by professionals. The proposed self-supervised attention-based deep learning model is a solution to automated malignant lesion detection. This approach uses contrastive pretraining...

Cardio Vascular Disease (CVD) are the major cause of death worldwide; with increasing numbers of individuals in recent days trust on internet-based health quizzes and social media for self-diagnosis. We prove that clinical examination features (blood pressure, cholesterol, glucose levels) will be significantly...

A number of EEG frequency bands are studied in this work to classify-specific-based emotions using DEAP (Dataset for Emotion Analysis using Physiological Signals). The need for more precise and scalable solutions to recognize human emotional states is increasing, especially when it comes to understanding...

The research paper will come up with a hybrid system that combines the two models, Reinforcement Learning (RL) and Graph-Based Curriculum Sequencing in order to serve the needs of students in higher education institutions by personalizing the learning process. By using two open datasets, Open University...

Most existing models of sentiment analysis degrade in performance significantly when used out of domains they are originally trained on, which is essentially because vocabularies, semantics, and contextual patterns all vary across domains. In this paper, we will introduce a simple, lightweight, and flexible...

The article proposes a framework of CNN and RFC to classify brain tumors by using MRI images, which combines CNN (Convolution Neural Networks) and RFC (Random forest classification). Pre-processing, Feature bring-out, and Categorization are the three phases of the proposed framework. We use the Gaussian...

A proposed methods seeks to improve the processing of Electrocardiogram signals with the intent of reducing the impacts of power-line interference and magnetic-field defects that commonly result in uncertainties of medical diagnosis. The initial step of our proposed approach involves decomposition of...

The objective of this study is to carry out a systematic investigation of risk-optimized portfolio models in digital financial ecosystems, during the 2020 pandemic crisis. This paper uses computational frameworks to estimate digitally available market data. We investigate performance of minimum variance...

The objective of this paper is the optimisation of distribution networks to minimise loss and operational cost for DSO. The presence of untransposed lines in unbalanced voltages and currents in distribution networks requires an active strategy against negative impacts. In this paper, we use various compensation...

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In the age of digital, bank sector campaigns are often challenged by poor customer engagement causing significant waste in re- sources. Marketing dollars are often being wasted because potential sub- scribers aren’t being identified properly. This research aims to deliver a structured prediction model...

The accuracy of variant calling in next-generation sequencing (NGS) is critical for genomic research and clinical applications. Traditional variant callers, using diverse methodologies such as haplotype- based, position-based, and pattern growth approaches, often produce discordant results due to their...

Pelvic fractures are considered some of the worst orthopedic injuries because of the complex nature of the anatomy, the seriousness of the vascular and visceral injuries that can be caused by the injury, and the fact that they are not easily diagnosed using medical imaging. The traditional radiographic...

The research reported on this paper developed an integrated hybrid model using machine learning (ML) and deep neural networks (DNNs) for developing accurate sentiment classification techniques for movie reviews. This hybrid architecture combined classical ML algorithms with Convolutional Neural Networks...

Sentiment Analysis is a significant NLP task that focuses on identifying polarity (sentiment) and intention for an identifiable piece of textual data. Movie reviews are important in determining whether the movie is successful or unsuccessful. To analyze this unstructured data, we employ newly developed...

The growth of video content has increased at a very high rate in digital platforms and there has been a high demand of automated systems that are capable of capturing the brief and meaningful summaries without compromising the underlying information. This paper hypothesizes a multimodal deep learning...

Digital leadership has become a decisive factor towards the efficacy of the workforce in technology-oriented organizations especially in the fast-evolving banking environment in India. As tools of artificial intelligence (AI) and machine learning (ML) are being integrated in human resource analytics,...

The goal of Image Super-Resolution is to obtain high-resolution images from low-resolution images, but it is still difficult to obtain both structure accuracy and perceptual sharpness. CNN models have been shown to restore edges and textures, but results can often be overly smooth. Transformer architectures...

The development of digital information raises the demand for insight extraction from large data in the shortest possible time. Users suffer from inability to keep updated due to growing online news content and time limits. There-fore, this solution combines text extraction using OCR (Tesseract) in newspaper...

One of the most important elements of network traffic cybersecurity is an anomaly detection algorithm that searches for differences in normal trends that may indicate a cyber threat (such as malware, intrusions, or denial-of-service attacks). Machine learning and deep learning methods are needed to better...

Cybersecurity is crucial in today’s digital era. The crucial concern is keeping information secure. Cybercrimes, which are on the rise daily, are the primary concern whenever we consider cybersecurity. In order to stop these cybercrimes, numerous governments and businesses are enacting innumerable precautions....

SQL Injection remains a persistent and evolving threat to the security of web applications, as it allows attackers to change database queries to gain unauthorized access or seize control of systems. Despite numerous mitigation tools, SQLi continues to appear among the top OWASP vulnerabilities due to...