1. Review the conference topics: Before submitting a proposal, it’s important to review the conference topics listed on the IKSEA website to ensure that your proposal aligns with the conference’s focus.
2. Develop your proposal: Your proposal should include a brief overview of your topic, including the main points you plan to cover, the relevance of your topic to the conference theme, and any unique perspectives or insights you can offer. Be sure to highlight your expertise in the field and include any relevant credentials or publications.
3. Submit your proposal: You can submit your proposal using the online submission system found on the IKSEA website. Be sure to follow any specific instructions or guidelines provided by the conference organizers, such as submission deadlines or required formats.
4. Wait for a response: After submitting your proposal, you should receive a confirmation of receipt from the conference organizers. If your proposal is accepted, you will receive further instructions on the next steps, such as preparing your presentation or materials.
5. Prepare for your presentation: If your proposal is accepted, you should start preparing for your presentation well in advance of the conference. This may include creating visual aids, practicing your delivery, and researching the audience to ensure that your presentation is engaging and relevant.

Name and Affiliation:
UTHAYAKUMAR G S
Department of Electronics and Communication Engineering
Associate Professor
St. Joseph’s Institute of Technology
St. Joseph’s University
OMR, Chennai-600119, India
IEEE Senior Member
Member of IEEE-EMBS, APS, ITS, BTS, and RAS
Keynote Speaker Title: Advances in Deep Learning for climate change analysis-Farmers Perspective
Abstract: Deep learning models help in creating more accurate climate models by analyzing vast amounts of historical climate data. Techniques like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are used for short-term and long-term weather forecasting. Deep learning algorithms process satellite images to detect changes in land use, forest cover, and urbanization over time. CNNs analyze satellite data to monitor greenhouse gas concentrations and other atmospheric changes. Deep learning models analyze data from various sources to estimate carbon emissions from industries, transportation, and other sectors. Deep learning is used to assess biodiversity impacts and changes in ecosystems due to climate change. Models help predict extreme weather events (like hurricanes and floods) and assess their potential impacts on communities. Deep learning optimizes energy consumption patterns and integrates renewable energy sources effectively into the grid. Predictive models assist in energy demand forecasting, helping in better resource allocation and management. Deep learning analyzes correlations between climate variables and public health issues, such as heatwaves or air quality impacts. Deep learning models help optimize crop yields and monitor soil health, leading to more sustainable agricultural practices. AI models predict agricultural pest outbreaks based on environmental conditions. Analyzing social media data using natural language processing (NLP) can help gauge public sentiment on climate policies and initiatives. Deep learning can model various climate scenarios to inform policy decisions and long-term planning. AI-driven tools assist governments and organizations in allocating resources efficiently to combat climate impacts. Deep learning aids in the discovery of new materials for renewable energy applications, such as better batteries and solar cells. The integration of deep learning in climate change analysis is creating powerful tools for researchers, policymakers, and organizations to better understand, predict, and respond to climate change challenges. Continued advancements in AI will be crucial for developing effective strategies to mitigate and adapt to the impacts of climate change.

 

Name and Affiliation:
Prathik C S
Department of Computer Science and Engineering
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Chennai, India
Keynote Speaker Title:
Abstract:

 

Name and Affiliation:
Dr. Kandarpa Kumar Sarma
Professor and Head
Department of Electronics and Communication Engineering
Gauhati University
Guwahati-781014, Assam, India
Keynote Speaker Title: LPI Radar Signal Discrimination using Hybrid SqueezeNet and MobileNet DPTMs
Abstract: Recognizing low probability of intercept (LPI) radar signals is crucial in electronic warfare (EW) for detecting stealthy threats and maintaining situational awareness. It enables effective countermeasures, intelligence gathering, and prevents friendly fire incidents. However, the performance of existing methods significantly deteriorates at low Signal-to-Noise Ratios (SNRs) due to the networks’ inability to extract sufficient effective features when there is high noise interference. In this direction, a feature consolidation strategy is proposed for effective identification of several types of LPI radar signals. Initially, scalograms of the radar signals are generated using Continuous Wavelet Transform (CWT). Thereafter, two hybrid deep learning pre-trained models (DPTM) have been developed by combining SqueezeNet and MobileNet-v2 with Long Short-Term Memory (LSTM) networks for feature extraction and classification. In the first model, SqueezeNet is employed to extract spatial features from the scalograms, while LSTM captures the temporal dynamics, followed by a fully connected layer for classification. The second model utilizes MobileNet-v2 for spatial feature extraction, with LSTM similarly used for temporal feature extraction, and a fully connected layer for final classification. The decision block further refines the classification accuracy by consolidating the strengths of both models, thereby improving resilience to noise and enhancing the reliability of the classification. The proposed methodology demonstrates robust performance in distinguishing between various types of LPI radar signals even in low SNR situations, highlighting its potential in practical applications.

 

Name and Affiliation:
Dr. Pradeepika Verma
Faculty Fellow
Indian Institute of Technology (IIT) Patna
Patna, Bihar, India
Keynote Speaker Title: Document Summarization using Fuzzy Evolutionary and Clustering Algorithms
Abstract:

Name and Affiliation:
Dr. Praveen Ranjan Srivastava
Associate Professor
Indian Institute of Management (IIM) Rohtak
Rohtak, Haryana, India
Keynote Speaker Title: Exploring the impact of key performance factors on energy markets perspective using hybrid Decision Making Process for Greenhouse Gas Emissions
Abstract: Currently, there are limited mechanisms to control harmful greenhouse gas emissions. There is a need to contain these emissions at the source level; understanding the root cause is imperative. This would aid in monitoring and curbing those factors to minimize these harmful emissions and control incidences of energy risk. While there are studies evidencing the role of generic indicators like per capita carbon consumption on greenhouse gas levels, these are also equally influenced by climatic risk factors such as surface temperature. Research suggests that climatic factors significantly impact fluctuating greenhouse gas emissions. However, existing studies have not quantified the precise extent to which these factors drive harmful emissions, which, in turn, also curb energy efficiency and increase the costs of generation of energy alternatives. To address this gap, the outcome variable ‘Total greenhouse gas emissions including land-use change and forestry’ is examined using advanced machine learning algorithms such as Random Forest, Multi-layer perceptron models and Deep Neural Networks. Algorithms are chosen in the hierarchical order of accuracy to capture the differential capabilities of detecting the causation factors of harmful emissions. While the above algorithms see the essential features in terms of absolute value, there is a need to examine how each factor contributes to the emissions relative to the others. The Shapley framework of Explainable AI is therefore employed to scientifically assess the influence of different factors on consumption levels. The outcomes of the Shapley analysis are then validated through regression and further supported by the Fuzzy Analytical Hierarchy Process (AHP). The research also proposes adopting association rule mining to analyze the co-occurrence of specific climatic conditions on energy consumption. The findings of this study offer valuable insights for both society and experts in climate and energy, enabling them to develop specific strategies and targeted climatic policies for effective energy risk management. This would present an opportunity for economic transformation, job creation, technological advancement, and improved environmental and public health outcomes. While initial costs and challenges may be associated with a transition, the long-term benefits would help attain sustainable energy economics.

Name and Affiliation:
Dr. Kavindra Kesari
Department of Electrical Engineering
Aalto University
Espoo, Finland
Keynote Speaker Title: Nanomaterials computing: An emerging approach for bioengineering solutions
Abstract:

Name and Affiliation:
Dr. Nidhi Chauhan  
Professor 
School of Health Sciences & Technology (SoHST), University of Petroleum and Energy Studies (UPES), Bidholi, Dehradun 248007, India,  
Keynote Speaker Title:
Abstract:

Name and Affiliation:
Rajat K. De
Indian Statistical Institute (ISI), Kolkata
Kolkata, West Bengal, India
Keynote Speaker Title: Visualization of Single Cell Omics Profiles under Deep Learning Framework
Abstract:

Name and Affiliation:
Dr. Surbhi Gupta
Assistant Professor
Department of Computer Science and Engineering
Model Institute of Engineering and Technology (MIET)
Jammu, Jammu and Kashmir, India
Keynote Speaker Title:  Harnessing Artificial Intelligence for Medical Diagnosis: Advances in Medical Imaging and Interpretability
Abstract:

Name and Affiliation:
Dr. Manju L. Joshi
Associate Professor
Indian School of Information Management (ISIM)
Indian Institute of Information Management (IIIM)
Jaipur, Rajasthan, India
Keynote Speaker Title:
Abstract:

Name and Affiliation:
Prof. Dr. D. Nirmal, M.E., Ph.D., SMIEEE, LMIETE, MISTE, C.Eng (I)
Professor, Electronics and Communication Engineering
Associate Dean, Engineering and Technology
Karunya Institute of Technology and Sciences (Deemed to be University)
Coimbatore – 641114, Tamil Nadu, India
IEEE EDS R10 SRC – Vice Chair
IEEE Madras Section ExCom Member
Keynote Speaker Title:
Abstract:

Name and Affiliation:
Dr. Saurav Dixit
Khalifa University
Abu Dhabi, UAE
Keynote Speaker Title:
Abstract:

Name and Affiliation:
Dr. Keshav Singh Rawat
Associate Professor & Head of Department
Department of Computer Science & Information Technology
School of Basic Science
Central University of Haryana
Jant-Pali, Mahendragarh, Haryana – 123031
India
Keynote Speaker Title:  Empowering Smart healthcare with intelligent IoT-Driven monitoring
Abstract:

Name and Affiliation:
Prof. Ashish Dwivedi
Jindal Global Business School
OP Jindal Global University
Sonipat – 131001
India
Keynote Speaker Title:  Digital twin readiness: A study to analyze sustainability and transparency in supply chains
Abstract: Production systems consist of occupied dispersed organizations with limited visibility and transparency. This can lead to operational inefficiencies across the Supply Chains. The literature reveals that Digital Twin Technology can assist to improve supply chain operations. There is a need to provide comprehensive theoretical knowledge and framework to help stakeholders understand the adoption of Digital Twin Technology. This study objectives to fulfill the research gap by empirically investigating Digital Twin Technology readiness to enable sustainability and transparency in Supply Chains.

Name and Affiliation:
Prof. Ravi Shankar, FNAE, FIIIE, Ph.D.
Professor of Operations & Supply Chain Management and Decision Sciences
Department of Management Studies
Indian Institute of Technology Delhi
Hauz Khas, New Delhi – 110016
India
Keynote Speaker Title:  Intelligent Knowledge System for Supply Chain Management
Abstract:

Name and Affiliation:
Prof. Satish Kumar
Professor, Finance and Accounting Area
Indian Institute of Management Nagpur
Plot No. 1, Sector 20, MIHAN
Nagpur – 441108, Maharashtra
India
Keynote Speaker Title:
Abstract:

Name and Affiliation:
Vivek Chavda (Gold Medalist)
Assistant Professor (Selection Grade)
Department of Pharmaceutics and Pharmaceutical Technology
L. M. College of Pharmacy
Ahmedabad – Gujarat – 380009
India
Keynote Speaker Title:
Abstract:

 

Name and Affiliation:
Dr. (Prof.) Shalli Rani
Director of Research
Chitkara University
University Campus
Chandigarh-Patiala National Highway (NH-64)
Tehsil Rajpura, Dist. Patiala
Punjab – 140401
India
Keynote Speaker Title:  Integration of WSN and IoT in the era of AI
Abstract: In the age of artificial intelligence (AI), the combination of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) represents a revolutionary breakthrough in the collection, processing, and application of data. Together, the Internet of Things (IoT) and WSNs, with their ability to collect data in real time from dispersed sensor nodes and their large network of linked devices, provide a strong foundation for creating large datasets. This synergy is strengthened by the introduction of AI, which makes sophisticated data analytics, predictive modelling, and thoughtful decision-making possible.

Large volumes of data produced by WSNs and IoT devices can be processed by AI algorithms, which can then be used to find patterns and insights that were previously unattainable. Innovations in a number of fields, such as smart cities, healthcare, agriculture, and environmental monitoring, are made possible by this integration.

But it also brings with it issues with privacy, data security, and system interoperability. To fully realise the benefits of this integration, certain obstacles must be overcome. This abstract examines how WSN and IoT are currently convergent, emphasises how AI may optimise this integration, and talks about the consequences for further technical advancements and applications.

 

 

Name and Affiliation:
Dr. Mohd Javaid
Associate Professor
Department of Mechanical Engineering
Jamia Millia Islamia (JMI)
New Delhi – 110025
India
Keynote Speaker Title:
Abstract:

 

Name and Affiliation:
Dr. Manish Raturi
Associate Professor
Department of [Specify Department]
Swami Rama Himalayan University (SRHU)
Jolly Grant, Dehradun – 248016
Uttarakhand, India
Keynote Speaker Title:
Abstract:

 

Name and Affiliation:
Dr. Vandana Sharma
Post Doctoral Fellow, Ph.D., M.Tech
Senior Member, IEEE (SMIEEE)
ACM Member
Associate Professor
Christ University
Delhi NCR, India
Keynote Speaker Title:  Applications of Generative AI in Industry 5.0
Abstract:

 

Name and Affiliation:
Dr. Sachin Modgil
Associate Professor – Operations and Supply Chain Management
Area Chair – Operations Management
Chairperson – Alumni Relations
International Management Institute, Kolkata
2/4 C, Judges Court Road, Alipore
Kolkata – 700027
West Bengal, India.
Keynote Speaker Title:
Abstract:

 

Name and Affiliation:
Dr. K Kotecha, PhD (IIT Bombay)
Director, Symbiosis Institute of Technology
Dean, Faculty of Engineering
Head, Symbiosis Centre for Applied Artificial Intelligence
Symbiosis International (Deemed University)
Pune 412115, Maharashtra, India
Honorary Professor, Aston University, UK
Keynote Speaker Title:  Applied Generative AI
Abstract:

Name and Affiliation:
Sougato Das
Indian Institute of Technology, Kharagpur
India
Keynote Speaker Title:  Implications of AI in Evaluating Student Performance
Abstract: This study investigates the ethical implications of using AI-driven grading (ADG) systems to evaluate student performance. Employing PLS-SEM, we analyzed how specific factors influence ADG acceptability among 281 respondents, including students, teachers, and parents. Our findings reveal that ethical perception, perceived fairness, and personal innovativeness significantly enhance ADG acceptability. Conversely, trust in AI shows an inverse relationship, indicating concerns about AI’s reliability and credibility in grading. Interestingly, data privacy concerns do not affect ADG acceptability, suggesting a nuanced relationship between privacy issues and acceptance. Self-efficacy also does not significantly impact ADG acceptability. This study underscores the importance of ethical considerations, fairness perceptions, and innovation in shaping attitudes toward AI-based grading systems. These insights are crucial for policymakers, educators, and stakeholders aiming to integrate AI into educational assessment responsibly. The research highlights the need for transparent, fair, and reliable AI systems to foster acceptance and addresses the ethical complexities associated with AI in education.

Name and Affiliation:
Vivek Kumar Gaur
Ulsan National Institute of Science and Technology (UNIST)
South Korea
Keynote Speaker Title:  Integrating Artificial Intelligence for Advancement in Synthetic Biology and Metabolic Engineering Approaches
Abstract: