11th International Conference on ICT for Sustainable Development: Full Schedule

Authors - Aakanksha Jain, Tejaskumar Bhatt, Darshita kalyani, Jatin Modh, Abhishek Jain
Abstract - The effectiveness of the deep learning models ResNet50, MobileNetV2, VGG16, InceptionV3, and EfficientNetB0 in identifying the freshness of food is evaluated in this study using visual analysis. In resource-constrained situations, these designs are ideal for automated food quality inspection and real-time freshness monitoring as they offer higher accuracy or computational efficiency. Transform learning are used to develop binary classifiers, which were then trained on a dataset of annotated food photos and assessed for efficiency and accuracy. After undergoing standardized preprocessing, the models' capacity to differentiate between fresh and stale food in a variety of test photos was evaluated. The results show the advantages and disadvantages of each model. The current stream of research frequently concentrates on generic picture classification tasks instead of the particular difficulties of distinguishing subtle visual differences in food freshness, leaving a gap in knowledge of model robustness under real-world conditions. The study advances deep learning applications in food quality evaluation by offering useful insights for model selection based on operational requirements.

Tuesday August 25, 2026 9:30am - 11:30am IST
Virtual Room A GOA, India

Virtual Room 4A, Virtual Room A

9:30am IST

A Hardware Security Review of RISC-V

Tuesday August 25, 2026 9:30am - 11:30am IST

Virtual Room A

Authors - Jyotiprakash Mishra, Sanjay K. Sahay, Aman Pathak
Abstract - RISC-V is an open-source Instruction Set Architecture (ISA) designed with a modular and extensible structure, allowing for customizable implementations. Its simplified base ISA, combined with optional standard and custom extensions, provides flexibility for a wide range of computing applications, from embedded systems to high-performance computing. Its open design accelerates innovation and customization but also introduces security challenges by exposing the architecture to potential attacks. While RISC-V offers significant advantages, its lack of standardized security features compared to proprietary ISAs like ARM and x86 highlights persistent risks, particularly in security-critical applications. For this reason, scrutiny of RISC-V’s security is crucial due to its widespread use in academia and its adoption by countries like India and China, who are looking to benefit from its open nature. We review the current security challenges in RISC-V, examining key vulnerabilities that exist in areas such as the microarchitecture, trusted execution environments, secure enclaves, secure boot, cryptographic instruction set architectures, memory encryption, and electromagnetic fault injection attacks. This review aims to cater to the needs of modern researchers for the development and implementation of the RISC-V ISA in a secure manner.

Paper Presenter

Jyotiprakash Mishra

India

Tuesday August 25, 2026 9:30am - 11:30am IST
Virtual Room A GOA, India

Virtual Room 4A, Virtual Room A

9:30am IST

AI-Driven Load Balancer for Cloud Computing Environments

Tuesday August 25, 2026 9:30am - 11:30am IST

Virtual Room A

Authors - Nithin Kandi, Murari Nallamalli, Dorai Sai Charan M, Vijay G, Beena B. M.
Abstract - Dynamic load balancing in distributed computing environments, especially with heterogeneous nodes, remains a significant challenge due to the fluctuating nature of workloads and resource availability. This paper presents a novel approach leveraging Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm, for optimal workload allocation in real-time systems. The system aims to minimize latency and maximize resource utilization by dynamically adapting to varying node metrics, including CPU usage, memory load, and latency. The DDPG model is trained on simulated state data, and real-time inference is performed through an API Gateway, enabling seamless integration with a five-node cluster. Results demonstrate that the proposed system outperforms traditional static and heuristic approaches in balancing workloads, optimizing resource utilization, and reducing latency. The approach is scalable, robust, and easily adaptable for edge and hybrid cloud architectures, providing a cost-effective solution for dynamic load balancing in distributed systems. This work bridges the gap between traditional cloud infrastructure and edge computing, ensuring efficient resource management in real-time systems.

Paper Presenter

Murari Nallamalli

India

Tuesday August 25, 2026 9:30am - 11:30am IST
Virtual Room A GOA, India

Virtual Room 4A, Virtual Room A

9:30am IST

Cache and Speculative Side Channel Attacks: A Comprehensive Review

Tuesday August 25, 2026 9:30am - 11:30am IST

Virtual Room A

Authors - Jyotiprakash Mishra, Sanjay K. Sahay, Aman Pathak
Abstract - Modern processors have achieved significant performance enhancements through the implementation of speculative execution. These enhancements stem from hardware optimizations that not only improve performance but also introduce side channels, which are exploited to undermine the system’s security model. Following the discovery of Spectre, which revealed that speculative execution pipelines could bypass security boundaries, nearly all microarchitectural structures and hardware optimizations have become targets for exploitation. In the wake of these attacks, the immediate response from organizations releasing mitigation patches led to noticeable performance degradation overnight, without fully addressing the underlying issues. This paper provides a comprehensive review of architecture-agnostic attacks on modern computing systems, tracing their evolution from the initial emergence of Spectre to contemporary attacks targeting Apple Silicon. We detail the mechanisms of these attacks, the environments in which they are exploited, and their broader security implications. Furthermore, we analyze various mitigation strategies that have been proposed, acknowledging that these strategies often fail to fully resolve the issues and typically incur a performance cost. These mitigation patterns include proposed changes in operating systems, hardware, and compilers. This review aims to provide researchers and architects with the foundational knowledge needed to develop more effective mitigation strategies that address the vulnerabilities while minimizing performance overhead.

Paper Presenter

Jyotiprakash Mishra

India

Tuesday August 25, 2026 9:30am - 11:30am IST
Virtual Room A GOA, India

Virtual Room 4A, Virtual Room A

9:30am IST

Classification of SSVEP Brain Computer Interface using CCA-CWT CNN

Tuesday August 25, 2026 9:30am - 11:30am IST

Virtual Room A

Parameter-Efficient Folk Art Generation: Fine-Tuning SDXL with LoRA for Madhubani Art Generation

Tuesday August 25, 2026 9:30am - 11:30am IST

Virtual Room D

Authors - Devashree Kute, Anjali Naik
Abstract - This study introduces a resource-efficient method for generating artwork in the traditional Madhubani style using Stable Diffusion XL (SDXL). Using the low rank adaptation (LoRA) technique, the model is fine-tuned with culturally relevant prompts and stylistic guidance to emulate the distinct characteristics of this Indian folk art form. To improve inclusivity and accessibility, prompt multilingual support is incorporated, covering Hindi, Bengali, Telugu, and English, through an automated translation mechanism, which retains the keyword to ensure consistency between languages. The training process is optimized for standard consumer-grade GPUs utilizing FP16 precision, CPU memory offloading, and a fixed Variational Autoencoder (VAE), enabling stable 1024×1024 image generation. For evaluation, a Contrastive Language Image Pretraining (CLIP) based scoring method is employed to assess the semantic alignment between prompts and generated images. The findings indicate that the style remains remarkably consistent across languages, demonstrating that even minimal training can enable AI to effectively capture and preserve traditional art forms within digital media.

Paper Presenter

OcuXPlain: An Explainable AI Approach for Multi-Class Ocular Disease Detection

Tuesday August 25, 2026 9:30am - 11:30am IST

Virtual Room E

Authors - Manan Parekh, Anmol Aafre, Parth Patel, Vaidehi Lad, Nirali Nanavati
Abstract - The human eye is highly sensitive and vulnerable to various diseases that need timely attention; otherwise, they may lead to vision impairment. According to WHO (World Health Organization), most of these cases could be avoided through regular examinations. Therefore, early detection of eye disease has become a necessity. In this manuscript we propose a novel approach for prediction and classification of ocular diseases like age-related macular degeneration, cataract, diabetic retinopathy, glaucoma, hypertensive retinopathy, myopia, and normal using fundus images collected from multiple public sources. Our study comprises the study of multiple contemporary deep learning models, including EfficientNet-B4, ConvNeXt, DenseNet-201, and ViT-16B. Our proposed solution involves the implementation of the ConvNeXt model on our dataset, which achieves an accuracy of 94.14%. Our purpose behind this study is not only to develop an effective classification model but also to ensure its visual - understanding how and why the model is making certain decisions. To achieve this, we implemented the Gradient-Weighted Class Activation Mapping (GradCAM) to provide an explainable AI (XAI) based solution to help researchers and practitioners in the field.

Tuesday August 25, 2026 9:30am - 11:30am IST
Virtual Room E GOA, India

Virtual Room 4E, Virtual Room E

9:30am IST

Relay-assisted framework for mmWave 5G NR BS sys-tem in V2X Communications

Tuesday August 25, 2026 9:30am - 11:30am IST

Virtual Room E

Authors - Vinodh Kumar Minchula, Supraja Reddy A, Sharathchand Kodam
Abstract - This paper investigates the characteristics of 5G NR-V2X channels and explores the benefits of relay-assisted communication for ensuring reliable and robust connectivity in dynamic vehicular environments. A detailed analysis of mmWave Base Station (BS) deployments utilizing MIMO antenna arrays of varying dimensions is conducted for the 5G NR n258 frequency band at 26 GHz. To evaluate system performance, a simulation framework is developed based on a Hyderabad Open Road Roundabout (ORR) scenario, aligned with 3GPP specifications, capturing vehicle mobility patterns and beam steering mechanisms to identify the optimal beam. To address the limitations of the NLoS, relay nodes are introduced to assist NLoS users, leading to measurable improvements in link quality and throughput. Experimental findings show that relay-assisted links yield up to a 4.1% increase in bit rate over direct NLoS connections. These results confirm the efficacy of relay-based architectures in enhancing signal robustness and extending mmWave communication range in V2X environments. The proposed framework provides a scalable and resilient solution for enabling high-performance vehicular networks in urban deployments. These findings clearly demonstrate that relay-assisted communication significantly enhances signal robustness and extends the effective range of mmWave links, thereby facilitating improved reliability and efficiency in next-generation vehicular communication networks.

Paper Presenter

Vinodh Kumar Minchula

India

Tuesday August 25, 2026 9:30am - 11:30am IST
Virtual Room E GOA, India

Virtual Room 4E, Virtual Room E

10:15am IST

Lighting of the Lamp to Mark the Auspicious Begining

Tuesday August 25, 2026 10:15am - 10:20am IST

Assembleia 1

Tuesday August 25, 2026 10:15am - 10:20am IST
Assembleia 1

Inaugural Session, Assembleia 1

10:20am IST

Presentation of Bouquet/Momento to the Guests

Tuesday August 25, 2026 10:20am - 10:25am IST

Session Chair Concluding Remarks

Tuesday August 25, 2026 11:30am - 11:32am IST

Virtual Room C

Invited Guests/ Session Chairs

Dr. Chaya Jadhav

Professor & HOD, Dr. D. Y. Patil Institute of Technology, Pune, India.

Shri Nitin kunkolienker

Chairman Advisory Council, The Manufactures' Association of Information Technology (MAIT), Board Director, Synegra EMS & EP Bio Composites, Goa, India

Group Photograph

Tuesday August 25, 2026 11:50am - 12:00pm IST

Assembleia 1

Tuesday August 25, 2026 11:50am - 12:00pm IST
Assembleia 1

Inaugural Session, Assembleia 1

12:00pm IST

Networking Tea & Coffee

Tuesday August 25, 2026 12:00pm - 12:30pm IST

MOTORIZED SHAPING MACHINE

Tuesday August 25, 2026 12:30pm - 2:00pm IST

West 1

Authors - Husain Shaikh, Dnyaneshwar Kamblajkar, Sayyad Ruhan Quadri, Shashikant Shirke, Ganesh Kadam
Abstract - A wide variety of reciprocating machines are available to most businesses, allowing them to complete machine operations on relatively minor jobs. For smaller jobs with less material, shapers, broaches, and planners are the tools of choice for machining. These machines are perfect for milling intricate patterns into tiny areas, whether they're flat, angled, grooving, or any combination of these. Only the forward stroke removes the materials from the work. Making the product takes longer due to the increased machining time. A compact twin shaper machine is created to address this issue by simultaneously shaping two work parts. This machine can remove material from two work pieces at once and has two directions of ram action. The result is a boost in output with a decrease in machining time

Paper Presenter

Husain Shaikh

India

Tuesday August 25, 2026 12:30pm - 2:00pm IST
West 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 1A, West 1

12:30pm IST

Virtual Machine Migration Optimization in Cloud Data Centers: A Comprehensive Review

Tuesday August 25, 2026 12:30pm - 2:00pm IST

West 1

Authors - Vaibhav Sawalkar, Nitin More
Abstract - Virtualization of the cloud data center (CDC) is achieved by operating multiple virtual machines (VM) in parallel processing on a single cloud server, as cloud users increase rapidly. Using dynamic virtual machine migration can help meet the growing demand for resources like compute, storage, and connectivity. The majority of resource management strategies are employed for single or multiple virtual machine migrations in order to optimize the cloud data center's performance and efficiency. The workload that comes with a rapid rise in cloud users also causes a corresponding reduction in QoS (service quality). Only greater computing facilities, managing huge numbers of cloud users, and cutting down on time and energy spent in cloud data centers can be achieved with the help of the potent live VM migration technique. A modern cloud computing system's main component is live virtual machine migration. This document examines all the most recent live virtual machine migration approaches, including their benefits and drawbacks. There has been a brief mention of their future range study and deployment. Through simulation, the pre-copy and post-copy VM migration approaches have been compared with respect to CPU usage, memory usage, and network characteristics. This study examines the important components of virtual machine migration strategies and evaluates a variety of them while conducting a thorough examination of the current schemes. Key objectives guide the exploration of current systems. Some open research issues are also discussed in the domain of virtual machine migration.

Paper Presenter

Vaibhav Sawalkar

India

Tuesday August 25, 2026 12:30pm - 2:00pm IST
West 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 1A, West 1

12:30pm IST

Wavelet-Integrated Framework for Large-Scale Underwater Image Enhancement

Tuesday August 25, 2026 12:30pm - 2:00pm IST

West 1

Authors - Yashas Vishwanathan, Viraat Sai Palamanda, Shreekara R Dandina, Rashmi Ugarakhod
Abstract - Seafloor photographs generally experience severe degradation due to optical properties of light, like absorption and scattering, leading to color anomalies, reduction of contrast, and loss of details. Such vision loss conditions create severe issues for marine science and computer vision-based ocean exploration missions. Thus, an underwater image restoration method is proposed using a wavelet-based U-Net architecture. By incorporating wavelet decomposition into a deep learning framework, the proposed model can extract multiscale spatial information efficiently, preserving the image clarity with structural and textural details. Wavelet-based model is trained on the Large-Scale Underwater Image (LSUI) and Enhancing Underwater Visual Perception (EUVP) datasets, both of which offer diverse underwater scenes captured under various lighting conditions and water types. The model’s performance is evaluated quantitatively in terms of qualitative analysis metrics. For the LSUI dataset, the proposed method achieved a peak signal-to-noise ratio (PSNR) of 23.47 dB and a structural similarity index (SSIM) of 0.9127. Evaluation on the EUVP dataset yielded a PSNR of 23.59 dB and an SSIM of 0.8572.

Paper Presenter

Shreekara R Dandina

India

Authors - Janvi S. Bhoyar, Shantanu A. Lohi, Umaima Syeda Fatima, Sharvari R. Sonukale, Devesh M. Patil, Krishna Borakhade, Archana W. Bhade, Dilip R. Uike, Rajesh M. Metkar
Abstract - Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive functions, affecting millions of individuals worldwide. Patients require continuous assistance to perform daily activities, placing a heavy burden on caregivers. This paper presents Smrutipankh, an AI-powered automated assistance device that utilizes real-time camera tracking and activity recognition to monitor Alzheimer’s patients. The system uses deep learning models to detect routine behaviors and abnormal events such as falls or wandering. By integrating cloud- based data storage and mobile notifications, Smrutipankh enhances patient safety and reduces caregiver workload. The experimental results demonstrate high accuracy in activity recognition and anomaly detection, making Smrutipankh a promising solution for Alzheimer’s care.c

Paper Presenter

Rajesh M. Metkar

India

Shantanu A. Lohi

India

Tuesday August 25, 2026 12:30pm - 2:00pm IST
West 2 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 1B, West 2

12:30pm IST

A Data Science Framework for Enhanced Diabetes Prediction: Integrating Mathematical Modeling, Statistical Feature Engineering, and Machine Learning

Tuesday August 25, 2026 12:30pm - 2:00pm IST

South 1

South 2

Authors - Mani.C, Vinayak Nayak, Ujwala Patil
Abstract - In this paper, we present a comparative study of Event processing that has attained much attention in recent years in the field of autonomous driving in adverse conditions. Frame-based camera simulators for different events capture images at prescribed times, and it is often plagued by problems during high-speed events, as motion blur can occur and fail to perform in dynamic environments. Event cameras, on the other hand, are an alternative that can capture asynchronous, sparse, and high-frequency pixel-level events at very low latency, particularly well suited for applications like autonomous driving. However, high-resolution event capture requires high-speed circuitry and accurate timing mechanisms, and makes it technically challenging and expensive to manufacture the event cameras. To overcome these challenges, several open-source simulators have been proposed to test and validate event generations. In this work, we discuss the comparison of different open-source event simulators such as ESIM, V2E, V2CE, and Recurrent Vision Transformer (RVT). We consider diverse driving conditions, a broad spectrum of sensor noise, and realistic environments for this analysis. Relevant quantitative metrics such as accuracy, temporal resolution, latency, and noise handling are also included in the justification of the selection of event simulation in real-time applications. We observe that the RVT-based event simulator is promising for autonomous driving applications.

Paper Presenter

Towards Secure Federated Learning: Understanding Vulnerabilities and Defense Mechanisms

Tuesday August 25, 2026 12:30pm - 2:00pm IST

Board Room 1

Authors - Rahul H. Bhole, Sachin R. Sakhare, Parikshit N. Mahalle, Priyanka More
Abstract - Federated Learning (FL) is a decentralized machine learning approach that enables collaborative model training without sharing raw data, ensuring privacy preservation. Despite its benefits, FL is vulnerable to adversarial threats such as Byzantine attacks, where malicious clients send corrupted updates to disrupt the global model’s performance. This study introduces a simulation framework designed to evaluate the impact of such adversarial behaviors and assess the resilience of various aggregation techniques. The proposed methodology incorporates a mix of honest and malicious clients, simulating their interactions across multiple training rounds. The results highlight significant fluctuations in the global model's performance caused by malicious updates, exposing the limitations of standard aggregation methods like the simple mean. The visualized outputs emphasize the destabilizing effects of Byzantine attacks and underscore the necessity of implementing robust aggregation and anomaly detection strategies. This work provides a systematic foundation for understanding vulnerabilities in FL and guides the development of more secure, reliable aggregation mechanisms.

Paper Presenter

Rahul H. Bhole

India

Tuesday August 25, 2026 12:30pm - 2:00pm IST
Board Room 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 1E, Board Room 1

12:30pm IST

AI-Powered Mobile Applications for Early Detection of Chronic Diseases: A Federated Learning Approach

Tuesday August 25, 2026 12:30pm - 2:00pm IST

Board Room 2

Authors - Yogita Yadav, Shruti Tajne, Vimmi Malhotra
Abstract - AI-powered early detection systems are revolutionizing healthcare by facilitating timely diagnoses, customized treatments, and improved patient outcomes. Utilizing machine learning algorithms, these systems analyze vast medical datasets to identify risk factors for chronic illnesses such as diabetes, cardiovascular diseases, and neurodegenerative conditions—often before symptoms emerge. AI continuously evolves by incorporating new data, enhancing both accuracy and efficiency. While AI offers significant benefits, including greater diagnostic precision, realtime monitoring, and cost efficiency, its adoption comes with challenges. Key concerns include data privacy risks, integration complexities, algorithmic bias, and the need for clear regulatory guidelines. Additionally, AI-driven chatbots are becoming increasingly popular for managing chronic diseases, enhancing patient engagement and self-care. However, further research is required to assess their safety, technical functionality, and long-term effectiveness. AI has vast potential in preventive healthcare, drug discovery, and personalized medicine. However, addressing existing limitations is essential to developing ethical, effective, and accessible AI-driven healthcare solutions.

Paper Presenter

Yogita Yadav

India

Tuesday August 25, 2026 12:30pm - 2:00pm IST
Board Room 2 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 1F, Board Room 2

12:30pm IST

Big Data Analytics: Trends, Challenges, and Applications

Tuesday August 25, 2026 12:30pm - 2:00pm IST

Board Room 2

Online Payment Fraud Detection Using Machine Learning

Tuesday August 25, 2026 12:30pm - 2:30pm IST

Virtual Room B

Deep Tune Network: An Approach Towards Music Classification and Recommendations

Tuesday August 25, 2026 12:30pm - 2:30pm IST

Virtual Room C

PCOS Detection in Ultrasound Images Using Transfer Learning with InceptionV3 and ResNet50

Tuesday August 25, 2026 12:30pm - 2:30pm IST

Virtual Room D

India

Priya Dharshini R

India

Tuesday August 25, 2026 2:00pm - 3:30pm IST
West 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 2A, West 1

2:00pm IST

AI Revolution in CAD: Pioneering the Future of Design

Tuesday August 25, 2026 2:00pm - 3:30pm IST

West 1

Tuesday August 25, 2026 2:00pm - 3:30pm IST
West 2 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 2B, West 2

2:00pm IST

A Review on Mobile Forensic Needs, Challenges, Approaches, Process Model, Tools and Standard

Tuesday August 25, 2026 2:00pm - 3:30pm IST

South 1

Authors - Chitrashri K, Karuna C Gull, SushilaDevi V., Sansktruti V.B., Pavan K. Korlahalli
Abstract - Cybersecurity is a critical field focused on safeguarding digital systems from cyber threats. Within this domain, digital forensics plays a key role in investigating cyber offenses by analyzing evidence from various digital platforms. With the rapid growth of mobile technology and the widespread use of smartphones for communication and transactions, mobile forensics has become an essential subfield. It involves the acquisition, analysis, and preservation of mobile device data to support legal investigations. This paper presents a comprehensive review of mobile forensics, examining its evolution, methodologies, forensic process models, tools, challenges, and the application of machine learning and deep learning techniques. It also references established standards from NIST and SWGDE to recommend best practices, address research gaps, and suggest future directions in mobile forensic investigations.

Paper Presenter

Chitrashri K

India

Tuesday August 25, 2026 2:00pm - 3:30pm IST
South 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 2C, South 1

2:00pm IST

Automated OT Sterilization using UV Radiation and Hydrogen Peroxide Vapor

Tuesday August 25, 2026 2:00pm - 3:30pm IST

South 1

Authors - Trissa Rose, Rithukesh Pillai, Manya Murali, K.Neethu Sathyan, Vidya G S
Abstract - Ensuring a sterile environment in operation theatres is paramount for infection prevention and patient safety. This paper presents an automated sterilization system combining UV-C radiation and hydrogen peroxide vapor to achieve efficient and thorough disinfection. The proposed system integrates germicidal UV-C lamps to neutralize airborne microorganisms and an ultrasonic mist generator to disperse hydrogen peroxide vapor, reaching inaccessible surfaces and enhancing sterilization coverage. Safety is prioritized through real-time motion detection sensors that halt the process upon human presence, preventing accidental exposure. A programmable microcontroller supervises the entire cycle, offering reliability and ease of use. Future advancements include IoT-enabled features for remote monitoring, data logging, and predictive maintenance. This solution addresses limitations of traditional methods, streamlining hospital sterilization protocols while ensuring safety, efficiency, and adaptability to modern healthcare demands.

Paper Presenter

Authors - Kalyanshetti Praneet Vijay, Aarushi, Kartik Sadanand Naik, Krishan V Naikmasur, Saritha
Abstract - The global skin care industry has been evolving at an exponential rate. Established or even the broad skincare industry has been shifting with high acceleration due to the incorporation of artificial intelligence and deep learning technologies. All these innovations are geared towards filling essential voids in skin condition assessment, prescription of products, and the representation of the skin of color. As this survey paper aims, the relevant studies on various algorithms to analyze facial images, skin condition identification, as well as ingredient recommendations are explored. Further, it lists the limitations such as dataset biasness, real time performance re-optimization, data privacy issues along with the prospectus of AI based skincare solutions. This survey paper also focuses on things like why skincare is necessary and how are different chemicals or their compositions useful in fighting multiple facial skin defects. It elaborates on the existing solution shortcomings and suggests possible categories for further research; the development of comprehensive skincare management that incorporates life changes, the extension of the datasets with regards to the minor skin diseases for improved precision of the model. Alleviating these challenges will enable AI-driven skincare systems to transform the beauty market and make customized skincare accessible, understandable, and available to a worldwide population.

Paper Presenter

Kalyanshetti Praneet Vijay

India

Integrating Social Media’s Role in Elections with the Theory of Planned Behavior: A Comprehensive Exami-nation of Voter Intention

Tuesday August 25, 2026 2:00pm - 3:30pm IST

Board Room 2

India

Tuesday August 25, 2026 2:00pm - 3:30pm IST
Board Room 2 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 2F, Board Room 2

2:30pm IST

Session Chair Concluding Remarks

Tuesday August 25, 2026 2:30pm - 2:32pm IST

Virtual Room A

Invited Guests/ Session Chairs

Dr. Anuradha Yenkikar

Assistant Professor, Vishwakarma Institute of Information Technology, Pune, India.

Tuesday August 25, 2026 2:30pm - 2:32pm IST
Virtual Room A GOA, India

Virtual Room 5A, Virtual Room A

2:30pm IST

Session Chair Concluding Remarks

Tuesday August 25, 2026 2:30pm - 2:32pm IST

Virtual Room B

Invited Guests/ Session Chairs

Prof. Nilesh Popat Sable

Associate Professor and Head, Department of Computer Science & Engineering (Artificial Intelligence), Vishwakarma Institute of Information Technology, Pune, India

Tuesday August 25, 2026 2:30pm - 2:32pm IST
Virtual Room B GOA, India

Virtual Room 5B, Virtual Room B

2:30pm IST

Session Chair Concluding Remarks

Tuesday August 25, 2026 2:30pm - 2:32pm IST

Virtual Room C

Invited Guests/ Session Chairs

Dr. Shailesh Pramod Bendale

Head and Associate Professor, NBN Sinhgad School Of Engineering, India

Tuesday August 25, 2026 2:30pm - 2:32pm IST
Virtual Room C GOA, India

Virtual Room 5C, Virtual Room C

2:30pm IST

Session Chair Concluding Remarks

Tuesday August 25, 2026 2:30pm - 2:32pm IST

Virtual Room D

Invited Guests/ Session Chairs

Dr. Priyanka More

Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India

Tuesday August 25, 2026 2:30pm - 2:32pm IST
Virtual Room D GOA, India

Virtual Room 5D, Virtual Room D

2:30pm IST

Session Chair Concluding Remarks

Tuesday August 25, 2026 2:30pm - 2:32pm IST

Virtual Room E

Invited Guests/ Session Chairs

Prof. Amit Sharma

Associate Professor, Vivekananda Global University, Jaipur, India

Sentiment-Enhanced Natural Language Processing for Fictional Character Analysis: Classifying Moral Alignments

Tuesday August 25, 2026 3:30pm - 5:00pm IST

West 1

Authors - K Yashita Varsha, K Chinmay Naag, Divyansh Maurya, Rashmi Ugarakhod
Abstract - A deep learning approach to classify fictional and comic characters into four alignment categories (Good, Bad, Neutral and Antihero) based on their back-stories using natural language processing (NLP) has been presented. Our methodology incorporates comprehensive text preprocessing, including data cleaning, WordNet-based augmentation and dataset balancing techniques. The provided classification model employs a neural network architecture with vectorizers, embedding layers, global max pooling, and regularized fully connected layers, optimized using Adam with early stopping along with it to enhance the user experience, a cosine similarity retrieval system is integrated which identifies the most similar character in our database based on input descriptions and provides that along with the alignment. The model achieves approximately 91% validation accuracy which is better compared to all previous state of art models, demonstrating its effectiveness for text-based classification tasks. Beyond entertainment applications, this approach shows potential for adaptation to criminal profiling and behavioral analysis which can be helpful for analyzing class of a criminal profile using a similar NLP model, where textual descriptions of backgrounds and psychological factors could predict behavioral patterns

Paper Presenter

Divyansh Maurya

India

Authors - Kavitha A. R, Ramya M, Charanya T. N, Lita Pansy. P, E. BHUVANESWARI
Abstract - The chronic brain disease known as Alzheimer’s disease (AD) primarily affects short-term memory while advancing through its neurodegenerative stages. The initial symptoms of the disease develop gradually before the condition deteriorates thus early detection becomes vital. A Machine learning approach powers the Disease Diagnostic System (DDS) that analyzes T2 weighted Magnetic Resonance Imaging (MRI) scans from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The paper examines the Hippocampus and amygdala located in the left hemisphere of the human brain as Region of Interest (ROI) which is extracted from small cohort MRI scans. The μ±3σ normalization method applies to segmented ROI while first-order histogram features extract Skewness and Kurtosis values. The Region of Interest receives two-dimensional wavelet features which include the Max norm of the original image and the Diagonal Detail Coefficient. The extracted textural markers serve as inputs to build a Mamdani FIS which defines minimum rules for AD stage diagnosis. The proposed classification method delivers accuracy levels of 96.13 percent for AD vs NC diagnosis and 94.73 percent for MCI vs NC diagnosis and 93.11 percent for AD vs MCI diagnosis. Multiple feature extraction through biomarker texture analysis enables better decision generation within the expert system framework.

Paper Presenter

Kavitha A. R

India

Ramya M.

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
West 2 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3B, West 2

3:30pm IST

NeuroInsight: Automated EEG Pattern Analysis for Critical Care

Tuesday August 25, 2026 3:30pm - 5:00pm IST

West 2

Authors - Roopa Ravish, Priyadarshi Sivakumaran, Darshana Vedavalli, Sai Sooraj Ramagiri, Nitish R
Abstract - The complexity of the analysis of electroencephalography (EEG) is a difficulty for application in clinical practice, it has historically relied on expert interpretation in an effort to identify discrete patterns of signals, and that makes scalability and efficiency difficult. In response to this challenge, a method of EEG signal automation classification using deep learning has been developed to help in the diagnosis of neurological disease through the EEG signal classification of six types: seizure, lateralized periodic discharges (LPD), generalized periodic discharges (GPD), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), and other. This approach uses a multi-class EEG classifier based on ResNet blocks, 1D convolution, and GRU layers to learn EEG signal multi-scale and temporal features.The data used are EEG samples divided into training, validation, and test sets, and the architecture is optimized for processing sequences efficiently and hence ideal for pattern detection specific to every neurological disorder. Performance measures reflect the model's strength, with an accuracy of 83.28%, a macro F1 measure of approximately 79.85%, and a Kullback-Leibler (KL) divergence loss of 0.26 during training. The high F1 score also reflects the model's strength for clinical application, with the potential to simplify diagnostic procedures. The results reveal the strength of convolutional networks to classify EEG and enable large-scale automated neurological evaluation solutions of the future.

Paper Presenter

Authors - Rachel Reju John, Eisha M, Safla P, Sreekutty K S, Vidya G S
Abstract - Automated laryngoscopes enhance airway management by enhancing intubation accuracy and safety, particularly in difficult cases. The technology aims to reduce reliance on operator skill, and intubation is made safer and more reliable in emergency situations. Automated laryngoscopes distinguish from conventional devices in employing AI and machine learning for real-time feedback and enhanced airway visualization. Image detection and recognition from the mouth to the epiglottis is the first step using python. Motion integration to automate tube insertion along the lungs is a possible future research direction.

Paper Presenter

Eisha M

India

Rachel Reju John

India

Safla P

India

Sreekutty K S

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
South 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3C, South 1

3:30pm IST

Availability Evaluation and Performance Analysis of Steam Generating System in Thermal power plant Through RAMD Approach

Tuesday August 25, 2026 3:30pm - 5:00pm IST

South 1

Authors - Jagriti Singh Chundawat, Ashish Kumar, Monika Saini
Abstract - Thermal power plants are the backbone of the electricity generation industries. The steam generating unit is the core element of the thermal power plants. The performance and maintenance of this system faces a lot of challenges over time. RAMD analysis plays a crucial role in evaluating the performance of any system. The main purpose of this study is to evaluate the RAMD measures such as reliability, availability, maintainability, dependability, mean time between failure (MTBF) and mean time to repair (MTTR). For this purpose, for each subsystem the diagram of transition between state to state are drawn and using Markov chain process their corresponding Chapman Kolmogorov differential equations derived. An Exponential distribution follows by Both the failure and repair rates for all subsystems. This RAMD analysis shows that the boiler subsystems perform best with highest availability i.e. 0.99912 among all the subsystems. The overall availability of a steam generating system is 0.98008. The main findings of these studies are helpful for the thermal power plant designers, containing comprehensive knowledge that will promote productivity and efficiency.

Paper Presenter

Jagriti Singh Chundawat

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
South 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3C, South 1

3:30pm IST

Bridging Linguistic Scripts: A Comprehensive Survey of Transliteration Techniques Across Languages

Tuesday August 25, 2026 3:30pm - 5:00pm IST

South 1

Authors - Devika Deshpande, Pranjali Deshpande
Abstract - Transliteration is the process of converting text from one script to another while keeping phonetic and orthographic accuracy. It’s a key technique for enhancing interlingual communication and fostering accessibility in multilingual settings. The review encompasses a thorough examination of transliteration systems considering several languages and scripts. Examining their benefits, drawbacks and applications, the review classifies transliteration methods into grapheme-based, phoneme-based and hybrid systems. While phoneme-based models ensure better phonetic alignment but require significant processing power, grapheme-based approaches provide simplicity but lack in phonetic correctness. Hybrid models use statistical and deep learning techniques to integrate contextual and linguistic data, resulting in enhanced adaptability and accuracy as compared to the traditional approaches. The survey focuses on the importance of transliteration systems for Natural Language Processing (NLP), Cross-Lingual Information Retrieval (CLIR), and multilingual communication. It also addresses the challenges like inconsistency, limited data, and phonetic ambiguity. It also explores the possibilities in enhancement of current transliteration systems that will help create strong, flexible, and context-sensitive solutions to linguistic ambiguities and improve access to digital resources in multilingual setting.

Paper Presenter

Devika Deshpande

India

Pranjali Deshpande

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
South 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3C, South 1

3:30pm IST

COMPARATIVE ANALYSIS OF RAINFALL USING MACHINE LEARNING AND DEEP LEARNING

Tuesday August 25, 2026 3:30pm - 5:00pm IST

South 1

India

EchoSight: An Assistive Device for the Visually Impaired

Tuesday August 25, 2026 3:30pm - 5:00pm IST

Board Room 1

Authors - Komal Papanwar, Selina Shrivastava, Anjali Askhedkar
Abstract - This paper presents an intelligent assistive navigation system designed to enhance the mobility and spatial awareness of visually impaired individuals. The system is built on a compact and low-power Raspberry Pi platform, enabling portability and affordability. It integrates real-time object detection using the YOLOv8 deep learning model with AprilTag-based localization to estimate the user’s position within a mapped environment. A calibrated monocular camera captures visual input to detect both objects and tags, while geometrical calculations estimate distances and directional angles to guide navigation. Users can input their desired destination via speech, and the system provides auditory instructions using a text-to-speech engine. By combining computer vision, lightweight edge computation, spatial localization, and audio feedback, the system transforms visual data into actionable navigation commands. Experimental evaluations in controlled indoor environments demonstrate the system’s ability to deliver accurate, real-time guidance. This work advances the development of portable, intelligent, and cost-effective assistive technologies for independent mobility among the visually impaired.

Paper Presenter

Komal Papanwar

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
Board Room 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3E, Board Room 1

3:30pm IST

Empowering Healthcare Decisions: The Impact of Big Data and Predictive Modeling

Tuesday August 25, 2026 3:30pm - 5:00pm IST

Board Room 1

Authors - Priyanka Pawar, Anagha Kulkarni, Prajakta Pawar, Bhavana Pansare, Manisha Bhende, Harshal Raje
Abstract - The healthcare industry is undergoing a change thanks to the quick development of Big Data Analytics (BDA), which makes data-driven decisions that improve patient outcomes possible. Predictive modeling offers previously unheard-of possibilities for predicting health outcomes and identifying at-risk groups due to the massive volumes of data generated by the healthcare industry, including genomic information, wearable technology, and electronic health rec- ords (EHRs). This study examines how predictive analytics is being used in the healthcare industry, outlining strategies that use statistical and machine learning methods to better allocate resources, lower readmission rates, and foresee disease outbreaks. Even with its revolutionary potential, integrating BDA is fraught with difficulties, such as the requirement for standardized data formats, interoperabil- ity problems, and data protection issues. For predictive models to be successfully adopted, these obstacles must be removed. The paper explores the potential of predictive analytics in transforming healthcare from reactive to proactive, enhancing patient care and operational efficiency. It proposes the AAI-CDS frame- work, a structured approach to enhance clinical decision-making through AI, aiming for adaptability and integration into the healthcare landscape.

Paper Presenter

Priyanka Pawar

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
Board Room 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3E, Board Room 1

3:30pm IST

Experimental Study of Statis Analysis Frameworks for Incremental Data Flow Analysis

Tuesday August 25, 2026 3:30pm - 5:00pm IST

Board Room 1

Authors - Smakshi Alhat, Aayushee Gujarathi, Bhakti Chougule, Shreya Mokalikar, Chhaya Gosavi
Abstract - Incremental data flow analysis is a crucial technique aimed at efficiently updating analysis results when modifications are made to a program, reducing the need for a complete reanalysis. In this study, we conducted an in-depth evaluation of various static analysis tools to assess their capabilities for incremental data flow analysis. The tools explored include Soot and SootUp, known for their flexibility in static analysis; Heros, a framework for interprocedural data flow analysis based on the IFDS/IDE framework; and Boomerang, SparseBoomerang, and SPDS, which focus on demand-driven approaches. Additionally, we examined Vasco and Reviser, tools specifically tailored for incremental analysis. While Reviser appeared promising for its dedicated focus on incremental analysis, its non-functional state, as confirmed by its creators, rendered it impractical for use. This paper offers a comparative assessment of these tools, highlighting their theoretical support, practical limitations, and potential for enabling incremental data flow analysis in future tool development. Furthermore, we implemented a program using SootUp that, while not achieving true incremental analysis, efficiently analyzes only the modified portions of code across different program versions, significantly reducing analysis time. Our findings offer a critical perspective on the current capabilities of analysis tools and suggest directions for future research and tool development.

Paper Presenter

Shreya Santosh Mokalikar

India

Smakshi Alhat

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
Board Room 1 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3E, Board Room 1

3:30pm IST

Leveraging AI and Blockchain Technology for Enhancing Healthcare Data Management and Patient Care

Tuesday August 25, 2026 3:30pm - 5:00pm IST

Board Room 1

LiDAR-GPS Integrated System for Real-Time Pothole Detection and Visualization Using Google Earth

Tuesday August 25, 2026 3:30pm - 5:00pm IST

Board Room 2

Authors - Vishal B Pattanashetty, Prakash Pawar
Abstract - Potholes pose a significant threat to road safety, leading to accidents, infrastructure degradation, and increased maintenance costs. This paper presents an efficient approach for detecting and visualizing potholes by integrating LiDAR point cloud data with Google Earth through GPS georeferencing. A YDLIDAR G2 sensor is employed to collect spatial data, which is transformed from polar to Cartesian coordinates and mapped onto GPS locations using Python-based processing. The methodology follows a structured workflow: data acquisition, coordinate transformation, georeferencing, and visualization in Google Earth, facilitating precise pothole localization. The proposed technique is compared with Martin Isenburg’s streaming pipeline method for large-scale LiDAR visualization, highlighting distinctions in computational efficiency and real-time application. The results demonstrate accurate mapping of road anomalies, enabling municipalities to optimize maintenance strategies. This study underscores the potential of LiDAR-based Road surface analysis for infrastructure monitoring, with future applications in real-time assessment and autonomous navigation.

Paper Presenter

Vishal B Pattanashetty

India

Tuesday August 25, 2026 3:30pm - 5:00pm IST
Board Room 2 Taj Cidade de Goa Horizon, Goa, India

Physical Technical Session 3F, Board Room 2

3:30pm IST

SCALING A DISTRIBUTED SERVICE MESH SYSTEM

Tuesday August 25, 2026 3:30pm - 5:00pm IST

Board Room 2

India

Tuesday August 25, 2026 3:30pm - 5:30pm IST
Virtual Room C GOA, India

Virtual Room 6C, Virtual Room C

3:30pm IST

Multimodal Media Creation: Integrating LLMs for High-Quality Video Generation

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room C

Design and Implementation of a Secure QR Payment System Using Visual Cryptography

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room D

Authors - Sharim Iqbal, Purnima Ahirao, Deepti Patole
Abstract - QR code payment systems have become increasingly popular thanks to their speed, simplicity, and convenience. However, as their usage grows, so do concerns around security—issues like tampering, spoofing, and man-in-the-middle attacks are becoming more common. To address these vulnerabilities, this paper introduces a novel approach to securing QR-based transactions using visual cryptography. Visual cryptography works by splitting an image into multiple shares, which individually reveal nothing but can reconstruct the original image when overlaid—without the need for complex decryption algorithms. This research proposes a secure QR payment system that leverages visual cryptography to enhance data integrity, prevent fraud, and strengthen the overall security of mobile payments. The study covers current security challenges, outlines the proposed system architecture, implementation details, and evaluates its performance.

Paper Presenter

Sharim Iqbal

India

Tuesday August 25, 2026 3:30pm - 5:30pm IST
Virtual Room D GOA, India

Virtual Room 6D, Virtual Room D

3:30pm IST

Enhancing Sentiment Analysis of Movie Reviews

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room D

Authors - A.Akshaya, G.Veera yasaswini, P.Akshith, K.Mahimanusha, M.TanviSahasra, Sushmarani
Abstract - Sentiment analysis is among the primary natural language processing (NLP) tasks and is widely utilized for extracting emotions and sentiments from text corpora. This paper proposes a comprehensive sentiment analysis approach for movie reviews based on Word2Vec, TextBlob, VADER, and Gated Recurrent Units (GRU). Word2Vec is employed for word embeddings to extract semantic word relationships for improved feature representation. TextBlob and VADER are implemented as lexicon-based sentiment analysis tools, for which TextBlob is interested in polarity and subjectivity and VADER is engineered for short texts with clear-cut sentiment indications. Besides, deep learning architecture in the form of GRU is employed for extracting long dependencies and context associations between words of text corpora for enhanced sentiment classification. Methods are experimented and contrasted on the basis of a benchmark IMDB dataset with reference to accuracy, precision, recall, and F1 score. Experimental findings substantiate that sentiment handling by deep learning-based approaches, i.e., GRU via Word2Vec embeddings, is better than traditional lexicon based approaches. The work provides insights to NLP-based opinion mining researchers and practitioners regarding the merit of utilizing hybrid approaches towards sentiment classification.

Tuesday August 25, 2026 3:30pm - 5:30pm IST
Virtual Room D GOA, India

Virtual Room 6D, Virtual Room D

3:30pm IST

Face Recognition Using Support Vector Machines

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room D

Authors - V M Aparanji, Chaithanya C K, Nanditha N, Poornima H S, Shreya A N
Abstract - Face recognition is a vital biometric technology with growing applications in security, access control, and automation. Despite challenges such as lighting variation, facial expressions, pose angles, and aging effects, Support Vector Machine (SVM) has proven effective in modeling and classifying facial features due to its ability to construct optimal decision boundaries in high-dimensional spaces. In this study, SVM was applied to a face recognition system for smart lock operations, yielding strong performance metrics of 89.9% accuracy, 89.7% precision, 89.5% recall and an F1 score of 89.7%. These results demonstrate the capability of SVM to effectively manage facial variability while maintaining high accuracy in face recognition tasks.

Paper Presenter

Shreya A N

India

Tuesday August 25, 2026 3:30pm - 5:30pm IST
Virtual Room D GOA, India

Virtual Room 6D, Virtual Room D

3:30pm IST

Medicine Recommendation System Using NLP(Natural Language Processing)

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room D

Authors - Ajay Talele, Amruta Mankawade, Aryan Sutar, Nishit Shelar, Urvesh Somwanshi, Anushka sonde, Shiv Sagar Singh, Sharvari Savardekar, Shivanand Satao, Shridhar Sarda, Raj Bapat
Abstract - A Medicine Recommendation System intended to use user-provided symptoms to identify possible diseases and provide personalized suggestions for safety measures, diets, drugs, and exercise regimens. The system predicts symptoms using Natural Language Processing (NLP) and Fuzzy Matching, guaranteeing accurate identification even in the presence of noisy inputs. It makes predictions about likely diseases and obtains overarching information for each by comparing extracted symptoms with a disease- symptom dataset. The system, which was developed with the Flask framework, provides an intuitive online interface for smooth communication. This initiative aims to direct users toward informed medical treatment by showcasing potential in early disease identification. Future research will concentrate on improving accessibility more broadly and integrating healthcare data in real-time.

Tuesday August 25, 2026 3:30pm - 5:30pm IST
Virtual Room D GOA, India

Virtual Room 6D, Virtual Room D

3:30pm IST

Modelling Comprehensive Web framework for Enhancing Administrative Efficiency in An Educational Institute

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room D

Authors - Praniv Warungshe, Siddhi Desale, Sushant Mhatre, Rujata Chaudhari, Saylee Lapalikar
Abstract - After graduation, alumni often face difficulties in managing important academic documents such as Leaving Certificates (LCs), marksheets, Letters of Recommendation (LORs), and convocation updates. Traditional processes are manual, time-consuming, and lack real-time tracking, leading to delays and repeated campus visits. To address these challenges, a cross-platform application was developed using React Native to simplify and digitize post-graduation work-flows. The application enables alumni to request, verify, and correct documents through a single digital platform with real-time status updates. LCs and marksheets are available on-screen, making the verification process faster and more convenient. Convocation details can also be managed easily within the app. Faculty benefit from tools to handle LORs and other academic tasks efficiently, while administrators use a centralized dashboard to track applications, generate reports, and respond to urgent requests quickly. The cross-platform nature ensures seamless access across various devices, enhancing user convenience. By uniting all stakeholders on one platform, the system boosts transparency, reduces manual workload, and modernizes the overall process, offering an efficient and user-friendly solution to manage alumni post-graduation needs.

Paper Presenter

Praniv Warungshe

India

Tuesday August 25, 2026 3:30pm - 5:30pm IST
Virtual Room D GOA, India

Virtual Room 6D, Virtual Room D

3:30pm IST

Oral Disease Detection Using Multimodal Fusion

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room D

Automated Incident Response System for Cybersecurity Threat Mitigation

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room E

SyncVox: Synchronized AI Based Video Dubbing

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room E

Authors - Owais Ansari, Hemangini Patel, Tejas Maroo, Morvi Panchal, Nikita Raichada
Abstract - In recent years, emotional voice conversion and expressive speech synthesis have gained attention due to their applications in areas such as automated dubbing, human-computer interaction, and assistive technologies. Our research proposes an AI-based dubbing system, SyncVox, which presents a seamless voice dubbing custom pipeline designed to provide seamless voice conversion across languages. It addresses low-resource video dubbing using various advanced technologies like speech recognition, translation, and style transfer. The pipeline combines speaker embeddings with advanced techniques to produce natural-sounding, speaker-alike voice synthesis. By employing multitask learning with Text-To-Speech, the pipeline is capable of capturing rich linguistic information while retaining languages; this allows content creators to dub videos without compromising the original speaker’s intent and naturalness. Early results show that this system effectively synthesizes natural-sounding speech with high emotional fidelity.

Paper Presenter

Hemangini Patel

India

Tuesday August 25, 2026 3:30pm - 5:30pm IST
Virtual Room E GOA, India

Virtual Room 6E, Virtual Room E

3:30pm IST

The Transformative Role of AI in the Programming of ICT in the Present Corporate World

Tuesday August 25, 2026 3:30pm - 5:30pm IST

Virtual Room E

Authors - Sunitha Ratnakaram, Venkamaraju Chakravaram, Chakravaram Sri Surya Narayan Raj
Abstract - Artificial Intelligence (AI) has significantly reshaped the landscape of Information and Communication Technology (ICT), particularly in the corporate sector. Integrating AI into programming and ICT development has led to automation, enhanced efficiency, and optimized business operations. AI-driven solutions are not only transforming software development but also impacting key business functions such as cybersecurity, finance, marketing, and decision-making processes. AI-powered automation has enabled businesses to achieve unprecedented productivity levels, making operations faster and more accurate while reducing human intervention. Companies now rely on AI for predictive analytics, customer insights, fraud detection, and even real-time strategic decision-making. This research paper explores the transformative role of AI in ICT programming, highlighting its vast impact on corporate efficiency, software development methodologies, cybersecurity frameworks, and business functions such as marketing and finance. It also delves into the ethical considerations of AI implementation, the challenges posed by AI-driven ICT automation, and the potential of AI to reshape the global economy. This study presents various case studies and empirical findings that illustrate how organizations have successfully incorporated AI into their ICT programming strategies to gain competitive advantages. Furthermore, the research investigates future trends, exploring how AI is expected to evolve within the corporate sector and the potential risks it may pose. The researcher used descriptive exploratory research methodology. The findings of this paper contribute to the ongoing discourse on AI's role in shaping the digital landscape, offering insights into both opportunities and concerns surrounding AI adoption in the modern corporate world.

Paper Presenter