This special session aims to bring together researchers and practitioners working at the intersection of AI and Software Engineering to present recent advances, innovative methodologies, and real-world applications of AI in the software development lifecycle. The session will focus on emerging research trends, challenges, and opportunities in applying machine learning, deep learning, natural language processing, and other AI techniques to improve software engineering processes and tools.

This special session focuses on the role of Artificial Intelligence (AI) and Big Data Analytics in promoting environmentally sustainable computing practices. It aims to explore innovative techniques, models, and data-driven solutions that improve energy efficiency, reduce carbon footprints, and optimize resource utilization in computing systems. The session encourages research on intelligent algorithms, sustainable data centers, green cloud computing, and analytics-driven environmental monitoring to support sustainable development and eco-friendly technological advancements.

This special session focuses on the role of Artificial Intelligence (AI) and Deep Learning in advanced time series analysis and forecasting. It aims to explore innovative models and data-driven techniques for extracting meaningful patterns, improving prediction accuracy, and enabling real-time analytics from complex temporal data. The session encourages research on scalable, interpretable, and robust solutions, including transformer-based models, anomaly detection, and spatio-temporal analysis, to address challenges such as noise, high dimensionality, and non-stationarity across domains like healthcare, finance, IoT, and smart systems.

This special track highlights the transformative role of Artificial Intelligence (AI) in the semiconductor industry across multiple stages, including materials discovery, device modelling, process optimization, circuit design, and manufacturing automation. AI-driven approaches have significantly reduced development time and enabled the design of next-generation semiconductor devices and systems. The track focuses on emerging research areas such as AI-assisted materials engineering, predictive modelling of semiconductor devices, intelligent VLSI design automation, fault detection in fabrication processes, and data-driven optimization of semiconductor manufacturing. It emphasizes bridging the gap between fundamental materials research and real-world technology deployment in next-generation electronics. This session aims to provide a comprehensive understanding of cutting-edge AI methodologies shaping the future of semiconductor technologies, encouraging interdisciplinary collaboration and fostering innovative solutions for advanced electronic systems.

The rapid proliferation of Artificial Intelligence (AI) across cloud, edge, and embedded platforms has significantly expanded the attack surface of modern computing systems, making hardware-level trust and security a critical concern. As AI workloads increasingly depend on specialized hardware accelerators, heterogeneous architectures, and distributed infrastructures, vulnerabilities such as hardware Trojans, counterfeiting, side-channel attacks, and supply-chain threats pose serious risks to system integrity and confidentiality. Additionally, the emergence of large-scale quantum computing threatens widely used public-key cryptographic schemes, necessitating the development of quantum-resilient security mechanisms. This special track focuses on advancing AI-driven approaches for hardware security, trusted computing, and quantum-resilient system design. It explores how machine learning and intelligent analytics can be applied to automated vulnerability detection, anomaly detection in hardware behavior, secure system verification, and adaptive defense mechanisms. The track also emphasizes trusted execution environments, confidential computing, and hardware roots of trust for AI-enabled infrastructures. Furthermore, contributions in post-quantum cryptography, secure hardware implementations of quantum-safe algorithms, and AI-assisted cryptanalysis are encouraged. The goal is to promote interdisciplinary research integrating AI, hardware security, and cryptography to build trustworthy and resilient computing systems for the emerging AI-driven and post-quantum era.

This special track focuses on how Artificial Intelligence (AI) can be designed and deployed to genuinely benefit individuals and society. As AI becomes increasingly integrated into everyday domains such as healthcare, education, governance, and environmental management, ensuring fairness, transparency, and alignment with human values is essential. Moving beyond a purely technical perspective, this track emphasizes responsibility, inclusiveness, and accountability in real-world AI applications. The session also explores the role of AI in addressing major societal challenges, including climate change, public health, and social equity. It encourages interdisciplinary contributions that combine technological advancements with social, legal, and ethical perspectives. The objective is to foster meaningful discussions on developing and applying AI systems that are not only innovative but also responsible, trustworthy, and beneficial for society at large.

The special track on AI in Electronics, Sensors, and Communication Systems focuses on the integration of Artificial Intelligence (AI) techniques with modern electronic systems to enable smarter, more efficient, and adaptive technologies. With the rapid growth of intelligent devices and interconnected networks, AI plays a crucial role in enhancing device performance, optimizing signal processing, and improving system reliability. This track explores innovative research areas such as smart sensor systems, AI-driven signal processing, intelligent communication networks, VLSI and hardware design, IoT and embedded systems, and next-generation wireless technologies. It also promotes interdisciplinary approaches combining AI with photonics, automation, and emerging applications like smart cities and healthcare. The objective is to provide a platform for researchers and practitioners to present advancements that leverage AI to address real-world challenges in electronics and communication systems, leading to scalable, energy-efficient, and intelligent solutions for future technologies.

The increasing integration of Artificial Intelligence (AI) into critical societal systems such as smart infrastructure, healthcare, transportation, communication networks, and governance demands robust, adaptive, and trustworthy solutions. This special track explores the role of AI in designing resilient, secure, and intelligent systems capable of operating under uncertainty, dynamic environments, and large-scale complexity. The track focuses on the convergence of AI and machine learning techniques, optimization methods, statistical inference, and system modelling to address challenges related to reliability, decision-making, and performance in real-world applications. Special emphasis is placed on systems where failures or inefficiencies have direct societal consequences, including smart cities, disaster response, cyber-physical systems, and digital public infrastructure. The objective is to bring together researchers and practitioners from diverse domains to present innovative methodologies, interdisciplinary approaches, and scalable solutions that enhance system robustness, efficiency, and trustworthiness. It encourages contributions that bridge theory and practice, aligning with the broader vision of leveraging AI for sustainable, inclusive, and socially impactful development.

This special track focuses on the advancements in Artificial Intelligence (AI) for audio signal processing, enabling intelligent systems capable of perceiving, interpreting, and responding to audio signals in complex and real-world environments. With rapid developments in AI, audio technologies are becoming more adaptive, efficient, and applicable across diverse domains such as healthcare, communication, and multimedia systems. The track emphasizes research tailored to real-world challenges, particularly in resource-constrained and multilingual settings such as India. Key focus areas include automatic speech recognition (ASR), music information retrieval (MIR), healthcare audio analytics, embedded systems, and speech technologies. Special attention is given to low-resource and multilingual scenarios, noisy environments, culturally contextualized AI solutions, and efficient edge deployment. The objective is to bring together researchers, practitioners, and industry experts to present innovative AI-driven methodologies and applications that address practical challenges and contribute to the development of scalable, inclusive, and intelligent audio processing systems.

Physical AI is an interdisciplinary field combining machine perception, control systems, mechanical design, machine learning, and human–machine interaction to develop intelligent robotic systems. It emphasizes safety, robustness, adaptability, and interpretability in dynamic environments. Key areas include AI-driven robotics, soft robotics, autonomous systems, and wearable sensing. The field promotes collaboration across disciplines and focuses on scalable, real-world solutions addressing societal challenges through deployable, human-centered, AI-powered physical systems.

Artificial intelligence is playing a vital role in transforming agriculture to address challenges such as climate change, resource scarcity, and food security. This track focuses on AI-driven solutions for precision farming, crop monitoring, disease detection, yield prediction, and supply chain optimization. It highlights the integration of machine learning, computer vision, and IoT technologies to improve efficiency and sustainability. The session encourages interdisciplinary research to develop scalable, environmentally responsible, and intelligent agricultural systems that support global food security and sustainable farming practices.

Artificial intelligence is transforming healthcare through intelligent, data-driven solutions for diagnosis, prediction, and personalized treatment. This track focuses on advancements in medical imaging, disease detection, healthcare informatics, and clinical decision support systems. It emphasizes the use of machine learning, deep learning, and emerging technologies such as explainable AI and federated learning to ensure privacy and trust. The session encourages interdisciplinary research addressing challenges like data scarcity, interpretability, and ethics to develop scalable and reliable AI-driven healthcare solutions.

Federated learning is transforming artificial intelligence by enabling decentralized model training while preserving data privacy and security. This track focuses on scalable and robust privacy-preserving AI techniques, including differential privacy, homomorphic encryption, and secure multi-party computation. It highlights applications across IoT, cloud, and distributed systems while addressing challenges such as data heterogeneity, communication efficiency, and adversarial threats. The session encourages research on trustworthy, fair, and explainable AI solutions that support secure and collaborative intelligent systems in real-world environments.

Artificial intelligence is increasingly vital in strengthening cybersecurity by enabling automated threat detection, prediction, and response in complex digital environments. This track focuses on applying machine learning and deep learning techniques to address challenges such as intrusion detection, malware analysis, anomaly detection, and risk assessment. It highlights the role of AI in improving security efficiency and resilience against evolving cyber threats. The session encourages collaboration among researchers and practitioners to develop scalable, intelligent, and secure cybersecurity solutions for modern systems.

The objective of this special track is to bring together researchers, academicians, and industry practitioners working at the intersection of AI and intelligent cyber-physical systems to present innovative methodologies, interdisciplinary approaches, and scalable solutions for next-generation smart and sustainable applications. This track emphasizes emerging research areas including edge AI, federated learning, explainable and trustworthy AI, intelligent digital twins, AI-enabled cybersecurity, autonomous cyber-physical systems, and sustainable intelligent infrastructures. It also encourages research addressing challenges related to interoperability, data privacy, energy efficiency, robustness, resilience, and real-time decision-making in large-scale distributed environments.