Fuzzy-Based Optimized Cluster Head Selection With Energy Prediction for Unequal Clustering in Wireless Sensor Networks Shivendra Kumar Pandey, Adwitiya Sinha, Buddha Singh International Journal of Distributed Sensor Networks, 2026 The sensor network consists of several low‐powered electronic sensing devices that are strategically deployed in a predefined area to gather important information. The sensors have low computing capacity, constrained battery energy, and limited storage. Therefore, the sensed data should be communicated to the sink efficiently for further processing. In this work, a clustering‐based multihop data transmission method is proposed using fuzzy logic. The proposed method, termed fuzzy‐based cluster head (CH) selection for unequal clustering (FSUC), utilizes four sensor factors: residual energy, distance to the sink node, node density, and the average distance of neighboring nodes to select optimal CHs. The FSUC selects CHs locally through a distributed competition process, enhancing the scalability of the sensor network. The comparative analysis of FSUC is performed with six existing models that involve LEACH, EEUC, FL‐SEP, MOFCA, FMCR‐CT, and FLPSOC in three different scenarios. The performance evaluation metrics are the number of alive nodes, total energy of alive nodes, and energy consumption in each round. The performance is also evaluated for the metrics occurrence of first node dead, half node dead, and last node dead. The energy consumption prediction across all compared models is conducted using exponential weighted mean (EWM), autoregressive integrated moving average (ARIMA), and linear regression (LR). Furthermore, statistical validation through t ‐test is employed to examine the reliability of the proposed model. The computational complexity of the model is also analyzed along with the complexity–performance curve to assess its practical feasibility.
Unveiling Knowledge Insights from Large-Scale Knowledge Graphs for the Research Community Suruchi Sabherwal, Mou Dutta, Adwitiya Sinha Proceeding of International Conference on Computing Communication Control and Cyber Physical Systems I5cps 2026, 2026 With vast growth in the amount of data that keeps on generating over the internet, it becomes a challenge to find useful insights from such massive networked structures. The concept of knowledge graphs has become public due to this development. A knowledge graph presents a common outline for extracting knowledge, which is based on analysis, extraction of entities, and relationships. This paper provides a characterization of knowledge graphs by presenting two case studies, both of which comprise two datasets from academia. The first one is a single-author publication dataset, and the second is an organisational dataset. Our study includes the construction of knowledge graphs, along with an exploratory analysis of the dataset. In addition, the paper showcases the applications of academic knowledge graphs, which include recommendation applications, finding collaborators for research in the domain of interest, and revealing people who have worked on the same publication.
The Evolution of Social Bot Detection: A Comprehensive Survey of Methods, Datasets, and Emerging Challenges Aditi Tiwari, Adwitiya Sinha, Sharad Verma, Shivendra Kumar Pandey Proceedings of the IEEE International Conference on AI Engineering and Innovations Aiei 2026, 2026 Social media platforms are experiencing a surge in bots, intensifying issues such as misinformation, spam, and fake engagement. Identifying these social bots has emerged as a crucial research challenge. Early bot detection approaches highly relied on textual and profile-based features, which proved effective against simple bots. However, as automated accounts started producing human-like language and displaying coordinated behavior, these techniques progressively lost their reliability. In response, recent studies have shifted toward network-aware, multimodal, and representation-based models which exploit social interactions, temporal dynamics, and heterogeneous data sources. This survey reviews social bot detection techniques, ranging from early feature-based methods to recent deep learning, graph-based, and multimodal approaches. The study examines benchmark datasets, evaluation strategies used in prior studies and discusses their respective limitations. The survey also discusses emerging challenges related to scalability, limited annotation, behavioral evolution, and generalization across platforms.
A Hybrid Framework for Detection and Diagnosis of Plant Diseases Using Retrieval Augmented Generative AI Megha Rathi, Harikesh Pratap Singh, Kaustubh Pandey, Harsh Dhanwani, Adwitiya Sinha Generative Artificial Intelligence Technology and Applications, 2025 Agriculture is considered as one of the main sectors worldwide that is involved in large-scale production and mass employment. The agricultural domain is the primary source of income for the rural households. The agricultural sector has emerged as one of the most prevalent industries that cater to people worldwide to secure the food requirement for good health. This poses a colossal challenge for the agriculture sector to improve food security, which may be hampered by infectious diseases. In such cases, crop yields get reduced, sometimes even leading to losses of a major fraction of crop production.
Network analysis of ground-level ozone: Implications for environmental policy and air quality management Harshit Gujral, Somya Jain, Adwitiya Sinha Environmental Modelling and Software, 2025 As network science emerges as a transformative tool in the ’Big Science’ era, this study harnesses this tool to model ground-level ozone distribution dynamics across US states under different regulatory frameworks from 1980 to 2017. The evolution of these regulations provides a unique natural experiment to analyze how network-driven models evolve amidst varied environmental policies. By constructing a network from ozone monitoring sites connected based on Pearson correlation coefficients, we analyzed the structural evolution of air quality networks. Techniques like community detection highlighted localized and temporal variations in ozone levels, influenced by meteorological and energy consumption data. Our findings reveal that geographical and regulatory factors significantly shape the network structure. This research demonstrates how network science can elucidate the complex interdependencies in environmental systems and suggests that integrating these insights could refine air quality regulations, promoting more effective management strategies in line with advanced environmental modeling needs. • Ozone network exhibits ultra-small-world traits with high density and connectivity. • Ozone networks accurately identify ozone attainment and nonattainment areas. • Temporal analysis shows areas with strict standards have stronger ozone connections. • States with strict standards demonstrate distinct evolution of the ozone network. • Network science offers insights for dynamic and effective air quality management.
Modeling Course-Skill Relationship with Association Mining and Weighted Knowledge Graphs Bhavya Saini, Adwitiya Sinha 2025 17th International Conference on Contemporary Computing Ic3 2025, 2025 The increasing availability of online courses has necessitated the requirement of an effective systems for guiding learners toward meaningful skill development. This study introduces a data-driven approach for identifying course-skill relationships using association rule mining and weighted knowledge graphs derived from learner-declared outcomes. By analyzing a curated dataset of over 100 online courses, associated 418 skills and 24,91,922 reviews, we construct a bipartite graph that visually and semantically encodes course-skill associations based on aggregated learner feedback. Association rules are generated through the Apriori algorithm to uncover frequent patterns and reliable sequences of skill acquisition. The experiment reveals key transitions in learning paths and highlight foundational skills that emerge across diverse course clusters. Rather than relying on predefined curricular models, the method surfaces organically validated learning trajectories, with high-lift and high-confidence rules guiding course recommendations. We have also applied weighted knowledge graph that can assist the stakeholders to filter, interpret, and optimize domain-specific course clusters, making it a valuable asset for learners, educators, and policymakers. Unlike traditional recommendation systems, which often prioritize engagement, our framework emphasizes skill relevance and explainability. Our work will further help in scalable, interpretable, and skill-centric educational planning.
Hybrid Link Prediction Model with Explainable AI for Online Social Networks Suruchi Sabherwal, Adwitiya Sinha Proceedings of 2025 International Conference on Computing for Sustainability and Intelligent Future Comp Sif 2025, 2025 Online social network systems are inherently complex due to the various types of connections that can form between any two nodes within a single network. These intricate structures enable users to engage in diverse interactions, resulting in constantly evolving behaviors. This results in cross dimensional evolution of complex networks with links forming between social entities with variable tie strength. This makes prediction of links in online networks quite significant for several higher tasks, like knowledge extraction, network learning, signed behavior analysis, etc. Though there exist several link prediction scores, however, the existing literature lacks hybrid link prediction score to compute the possible formation of strong or weak ties in a social network. In this paper, a hybrid prediction mechanism, namely GML-hybrid is proposed to estimate the possible links using network theory and machine learning. The proposed hybrid link prediction algorithm combines already existing distance metrics and extracts different thresholds across three different levels which comprise of first, second and third quantile used for dataset labelling. This proposed algorithm is applied on Ego Facebook dataset considering 15 classifiers for model building Further, the results obtained from 5 highest performing models are further analyzed using explainable AI using SHAP.
Analysing Political Bias in Social Media Vidish Sharma, Aditya Bendapudi, Tarun Trehan, Ashutosh Sharma, Adwitiya Sinha Proceedings of International Conference on Research Innovation Knowledge Management and Technology Application for Business Sustainability Inbush 2020, 2020
Deep Learning Model for Brain Tumor Segmentation Analysis Ekam Singh Chahal, Ankur Haritosh, Ayush Gupta, Kanav Gupta, Adwitiya Sinha 2019 3rd International Conference on Recent Developments in Control Automation and Power Engineering Rdcape 2019, 2019
Fuel Economy Analysis for Vehicular Efficiency Rahul Agrawal, Deepak Gupta, Adwitiya Sinha, Megha Rathi 2019 International Conference on Signal Processing and Communication ICSC 2019, 2019
Discovering Social Influence for Media Dynamics 12th Indiacom 5th International Conference on Computing for Sustainable Global Development Indiacom 2018, 2018
Web Traffic Analysis of Real Time Event of Socially Interacting Network 12th Indiacom 5th International Conference on Computing for Sustainable Global Development Indiacom 2018, 2018
Near and Far Field Effect of Radio Waves in Seawater and Path Loss Models 12th Indiacom 5th International Conference on Computing for Sustainable Global Development Indiacom 2018, 2018
Modified ATP for lossy links in mobile adhoc network Buddha Singh, Adwitiya Sinha, Silky Makker Proceedings 2010 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering Sibircon 2010, 2010
RECENT SCHOLAR PUBLICATIONS
SCGAT: A self-supervised contrastive graph attention framework for community detection in attributed networks P Joshi, A Saxena, MK Singh, A Sinha Chaos, Solitons & Fractals 208, 118293 , 2026 2026
Discovering centrality clusters in social and interaction networks using AI-driven association analysis B Saxena, A Sinha, HS Pattanayak, A Kishore, D Paul Applied Network Science , 2026 2026
The Evolution of Social Bot Detection: A Comprehensive Survey of Methods, Datasets, and Emerging Challenges A Tiwari, A Sinha, S Verma, SK Pandey 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), 1-7 , 2026 2026
Unveiling Knowledge Insights from Large-Scale Knowledge Graphs for the Research Community S Sabherwal, M Dutta, A Sinha 2026 International Conference on Computing, Communication, Control and Cyber … , 2026 2026
Malicious bot detection in Twitter/X social media platform with interpretable machine intelligence S Gupta, A Sinha, B Saxena Journal of Ambient Intelligence and Humanized Computing, 1-16 , 2026 2026
Fuzzy‐Based Optimized Cluster Head Selection With Energy Prediction for Unequal Clustering in Wireless Sensor Networks SK Pandey, A Sinha, B Singh International Journal of Distributed Sensor Networks 2026 (1), 4635198 , 2026 2026 Citations: 1
Multi-Layer Stacking Ensemble AutoML Architecture for Social Media Bot Detection using AutoGluon Framework A Sinha, B Saxena, S Sabherwa 2025 6th International Conference on Communication, Computing & Industry 6.0 … , 2025 2025
Modeling Course-Skill Relationship with Association Mining and Weighted Knowledge Graphs B Saini, A Sinha 2025 Seventeenth International Conference on Contemporary Computing (IC3), 1-6 , 2025 2025
Prediction of Crime Trends and Dynamics with Geospatial and Network-based Analysis A Saxena, A Sinha, B Saxena 2025 Seventeenth International Conference on Contemporary Computing (IC3), 1-6 , 2025 2025 Citations: 1
AI-Based Predictive Models in Healthcare B Saxena, M Jain, A Sinha Advancing Biotechnology: From Science to Therapeutics and Informatics … , 2025 2025 Citations: 2
High-Precision Traffic Accident Detection Using YOLOv11 Model and Image Processing with Deep Learning Techniques GGS Nair, B Ankith Chengappa, A Santosh, J Ayush, S Sabherwal, ... National Conference on Computer Vision, Pattern Recognition, Image … , 2025 2025
Network analysis of ground-level ozone: Implications for environmental policy and air quality management H Gujral, S Jain, A Sinha Environmental Modelling & Software 191, 106502 , 2025 2025 Citations: 1
Hybrid Link Prediction Model with Explainable AI for Online Social Networks S Sabherwal, A Sinha 2025 International Conference on Computing for Sustainability and … , 2025 2025 Citations: 2
Nature inspired recommendation with path optimization for online social network communities M Goel, A Sinha International Journal of Information Technology 16 (8), 5325-5330 , 2024 2024 Citations: 9
Black marketed collusive users primary dataset from twitter/x online social media S Sabherwal, B Saxena, A Sinha Social Network Analysis and Mining 14 (1), 215 , 2024 2024 Citations: 4
Metaheuristics and Reinforcement Techniques for Smart Sensor Applications A Sinha, S Singh Chapman and Hall/CRC , 2024 2024 Citations: 1
Geological Time Series Analysis for Spatial Seismic Forecasting in Japan P Sharma, B Saxena, R Khandelwal, A Sinha 2024 International Conference on Artificial Intelligence and Emerging … , 2024 2024
An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization M Niranjan, A Sinha, B Singh Sādhanā 49 (3), 248 , 2024 2024 Citations: 4
Path loss assessment of electromagnetic signal on air–sea and air–soil boundary in sensor networks P Saini, RP Singh, A Sinha International Journal of System Assurance Engineering and Management 15 (6 … , 2024 2024 Citations: 5
Smart Aviation with Customized Route Discovery Using Urban Transportation Analytics A Agarwal, Shreeji, R Jain, M Chaudhry, A Sinha International Journal of Intelligent Transportation Systems Research 22 (1 … , 2024 2024 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Performance evaluation of data aggregation for cluster-based wireless sensor network A Sinha, DK Lobiyal Human-Centric Computing and Information Sciences 3 (1), 13 , 2013 2013 Citations: 127
Information diffusion modeling and analysis for socially interacting networks P Kumar, A Sinha Social Network Analysis and Mining 11 (1), 11 , 2021 2021 Citations: 125
Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 S Jain, A Sinha Chaos, solitons & fractals 139, 110037 , 2020 2020 Citations: 73
COVID-19 prediction using AI analytics for South Korea: COVID-19 prediction using AI analytics for South Korea A Sinha, M Rathi Applied Intelligence 51 (12), 8579-8597 , 2021 2021 Citations: 41
Enhanced-AES encryption mechanism with S-box splitting for wireless sensor networks M Gupta, A Sinha International Journal of Information Technology 13 (3), 933-941 , 2021 2021 Citations: 39
Path loss analysis of RF waves for underwater wireless sensor networks P Saini, RP Singh, A Sinha 2017 International Conference on Computing and Communication Technologies … , 2017 2017 Citations: 38
Association between exposure to airborne pollutants and COVID-19 in Los Angeles, United States with ensemble-based dynamic emission model H Gujral, A Sinha Environmental research 194, 110704 , 2021 2021 Citations: 36
T-Bot: AI-based social media bot detection model for trend-centric twitter network S Gera, A Sinha Social Network Analysis and Mining 12 (1), 76 , 2022 2022 Citations: 31
Prediction model for automated leaf disease detection & analysis N Goel, D Jain, A Sinha 2018 IEEE 8th International Advance Computing Conference (IACC), 360-365 , 2018 2018 Citations: 30
Prediction models for energy efficient data aggregation in wireless sensor network A Sinha, DK Lobiyal Wireless Personal Communications 84 (2), 1325-1343 , 2015 2015 Citations: 29
TweezBot : An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks R Shukla, A Sinha, A Chaudhary Electronics 11 (5), 743 , 2022 2022 Citations: 28
A multi-level strategy for energy efficient data aggregation in wireless sensor networks A Sinha, DK Lobiyal Wireless personal communications 72 (2), 1513-1531 , 2013 2013 Citations: 25
A statistical approach for reducing misinformation propagation on twitter social media N Saxena, A Sinha, T Bansal, A Wadhwa Information Processing & Management 60 (4), 103360 , 2023 2023 Citations: 24
Social network sustainability for transport planning with complex interconnections S Jain, A Sinha Sustainable Computing: Informatics and Systems 24, 100351 , 2019 2019 Citations: 19
Deep learning model for brain tumor segmentation & analysis ES Chahal, A Haritosh, A Gupta, K Gupta, A Sinha 2019 3rd International conference on recent developments in control … , 2019 2019 Citations: 19
Multi-attribute identity resolution for online social network S Yadav, A Sinha, P Kumar SN Applied Sciences 1 (12), 1653 , 2019 2019 Citations: 16
Clickstream & behavioral analysis with context awareness for e-commercial applications S Bansal, C Gupta, A Sinha 2017 Tenth International Conference on Contemporary Computing (IC3), 1-6 , 2017 2017 Citations: 16
Sustainable time series model for vehicular traffic trends prediction in metropolitan network A Sinha, R Puri, U Balyan, R Gupta, A Verma 2020 6th international conference on signal processing and communication … , 2020 2020 Citations: 14
Social network analysis: tools, techniques, and technologies S Jain, A Sinha Social network analytics for contemporary business organizations, 1-18 , 2018 2018 Citations: 14
Learning Model for Phishing Website Detection. A Suryan, C Kumar, M Mehta, R Juneja, A Sinha EAI Endorsed Transactions on Scalable Information Systems 7 (27) , 2020 2020 Citations: 12
