Research in sovereign, agentic intelligence.

Cognitive Attack Topology (CAT): A Topological Framework for Modeling and Detecting Human-Centric Cyber Attacks

Dhadi Sai Praneeth Reddy, M. Jithender Reddy, GNR Prasad et al.

Data models that classify the psychological signals behind digital fraud: human fear, urgency triggers and more.

DOI

10.5281/zenodo.19303021Copy DOI

Published in

Preprints.org

Available on

Preprints.org (opens in a new tab)Europe PMC (opens in a new tab)Zenodo (opens in a new tab)Google Scholar (opens in a new tab)

Agent Ontogeny & Lineage Physics (AOLP): A Developmental Law Framework for Artificial Intelligence

Dhadi Sai Praneeth Reddy

A theoretical framework that treats AI development as irreversible physical phase transitions rather than brute-force scaling.

DOI

10.5281/zenodo.18641867Copy DOI

Published in

Zenodo

Available on

Zenodo (opens in a new tab)ResearchGate (opens in a new tab)

Transforming Agriculture in Solipur Village: The Impact of AI, ML, and IoT

Jalaja Tattari, T. Adilakshmi, Dhadi Sai Praneeth Reddy

The combination of Artificial Intelligence, Machine Learning, and the Internet of Things in farming is changing how farmers work. This paper discusses how Solipur Village uses these technologies to improve its farming methods. With AI predicting outcomes, ML managing crops, and IoT monitoring in real time, the study looks at better crop yield, resource use, and farmer earnings. Data from fieldwork, surveys, and interviews show the benefits and challenges of using these technologies. The results show higher productivity and sustainability, giving suggestions for policymakers and farmers to adopt AI, ML, and IoT in Indian agriculture.

DOI

10.1007/978-981-95-0144-1_16Copy DOI

Published in

Proceedings of the 4th International Conference on Cognitive and Intelligent Computing, Volume 2 (ICCIC)

Available on

Springer (opens in a new tab)ResearchGate (opens in a new tab)

EagleAI: A 3.1B-Parameter Transformer Pretrained for Drug Law Enforcement

Dhadi Sai Praneeth Reddy

A proprietary language model built from scratch for Telangana's EAGLE Force: custom architecture, tokenizer and pretraining pipeline.

Redefining AGI: The First Practical Framework and Working Demo of General Intelligence

Dhadi Sai Praneeth Reddy, Amulya Biradar, Manikanth Reddy Maram et al.

Practical, measurable definitions for AGI: an evaluation suite plus a modular cognitive framework, with working prototypes.

DOI

10.13140/RG.2.2.20594.16324Copy DOI

Published in

Preprints.org

Available on

Preprints.org (opens in a new tab)Sciety (opens in a new tab)ResearchGate (opens in a new tab)

Multi-Modal Deep Learning Framework for AQI Prediction: An Extensive Analysis of 15 Monitoring Stations in Hyderabad Using Historical and Real-Time Data

Dhadi Sai Praneeth Reddy, Soli Dhana Sri, Sreenidhi Pagidimarri et al.

Especially in fast-expanding cities like Hyderabad, where industrial activity, traffic emissions, and weather conditions create difficult air pollution patterns, air quality forecasting is a serious issue in urban areas. Traditional approaches for predicting the Air Quality Index (AQI) sometimes fall short because they do not represent the non-linear relationships and time dependencies found in air quality information. The research proposes a multi-modal deep learning architecture using Long Short-Term Memory (LSTM), Transformer, and Random Forest models to study past air quality data from 15 monitoring stations in Hyderabad. With a Root Mean Square Error (RMSE) of 3.7, the framework beats conventional statistical techniques and one deep learning technique, with 94.65 percent accuracy. Among the most important contributions are the creation of a strong preprocessing pipeline, the identification of important features including PM2.5, temperature, and wind speed, and the validation of real-time processing capacity. Seasonal study shows major fluctuations, with winter months having PM2.5 levels 45% above the yearly average. In addition, the research emphasizes the practical effects of the framework for policy formulation and management of urban air quality by providing a scalable answer for real-time AQI forecasting and long-term trend analysis.

DOI

10.55248/gengpi.06.1125.3858Copy DOI

Published in

International Journal of Research Publication and Reviews (IJRPR)

Available on

DOI (opens in a new tab)

Understanding the Decline of Blockchain Popularity: An Analysis of Market Trends, Technological Challenges, and Regulatory Barriers

Dhadi Sai Praneeth Reddy, Kasireddy Manideep Reddy

Blockchain technology, once considered revolutionary, has experienced a significant downturn in popularity over the past several months. The primary reasons behind this decline include technical challenges, regulatory uncertainty, and diminishing trust in the scalability and security of existing blockchain systems. This paper examines the multifaceted causes of blockchain's downward trajectory and presents a critical analysis of the current state of blockchain technologies, addressing specific technical bottlenecks such as energy inefficiency, governance issues, and lack of interoperability. The analysis is supplemented with real-time data, tables, graphs, and an exploration of emerging trends that could shape blockchain's future.

DOI

10.36227/techrxiv.175372475.58978266/v1Copy DOI

Published in

TechRxiv

Available on

TechRxiv (opens in a new tab)

The Upcoming Era of 5G & Beyond: European Policy, Implementation, and Governance

Jalaja Tattari, T. Adilakshmi, Dhadi Sai Praneeth Reddy et al.

The emphasis of this paper is on the need to update regulations to promote the advancement of 5G and 6G technology in Europe, which is important for the future of the digital economy. The European Union needs a more effective strategy to lead the way in 5G technology, given its current rollout and significance. The focus is on devising a blueprint for the industry that can contribute to the advancement of the telecommunications sector in Europe. This plan would involve all countries in the European Union, rather than focusing on each country separately.

DOI

10.1007/978-981-97-9266-5_11Copy DOI

Published in

Proceedings of the Third International Conference on Cognitive and Intelligent Computing, Volume 2 (ICCIC 2023)

Available on

Springer (opens in a new tab)Google Books (opens in a new tab)Google Scholar (opens in a new tab)

Optimizing Multi-Tenancy FPGA Cloud Systems for Efficient Resource Utilization

Busa Revathi, Dhadi Sai Praneeth Reddy, Kasireddy Manideep Reddy

Multi-tenant FPGA cloud systems represent a new frontier in high-performance computing (HPC), blending flexibility, processing power, and cost efficiency. FPGA-based systems, integrated into cloud environments, hold immense potential for industries requiring real-time data processing, such as artificial intelligence (AI), finance, and healthcare. These systems enable dynamic resource allocation, offering better performance while addressing cost concerns. This paper presents a detailed exploration of the system architecture, challenges, implementation, and the impact of such systems, along with an evaluation of their suitability in line with India's government initiatives. Potential future research directions such as quantum-FPGA integration are also discussed.

Published in

International Journal for Research Trends and Innovation (IJRTI)

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Investigation of the Sources of Microseism Noise in the Indian Ocean Caused by Movements Between the Ocean and the Atmosphere

Dhadi Sai Praneeth Reddy, Kasireddy Manideep Reddy, Sahasra Baggari et al.

Within a span of 2 to 20 seconds, microseism noise occurs as a distinctive sound. It is the loudest band of sound in the earth's background noise. In this study, we looked at the size and direction of the small movements in the Indian Ocean. Data is gathered from a network of ten land stations located at various points across the Indian Ocean. The description of microseism was achieved by utilizing the probability power spectral density. To understand the microseism, we use a method that looks at how the vibration changes with different frequencies. This method uses a mathematical calculation called Eigenvalue decomposition to analyze the way the vibrations spread in three different directions. We studied how microseism changes over space and time in the Indian Ocean area. The Indian Ocean is considered a major contributor to microseism worldwide. In certain stations, we can see that the double frequency microseism breaks down into short periods of 2 to 5 seconds and long periods of 6 to 10 seconds. The study shows that the main causes of the continuous low-frequency seismic vibrations come from the Southern Ocean. In addition, we conducted a comparison between the alterations in wave heights and the intensity of waves at each particular site. It was evident that sound wave intensity and wave height within a particular frequency range were closely linked. This connection was observed both in different places and at different times. We additionally include an overview of the prevalent wave types detected in the microseism band on the Earth's surface. Over an extended duration, Rayleigh waves are the prevalent ones, whereas Love waves tend to be more prominent in shorter intervals.

DOI

10.5281/zenodo.14102511Copy DOI

Published in

Zenodo

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Zenodo (opens in a new tab)ResearchGate (opens in a new tab)

Generative Data Augmentation: Enhancing Imbalanced Data Sets with Synthetic Samples Using Conditional GANs

Revathi Busa, Dhadi Sai Praneeth Reddy, Kasireddy Manideep Reddy et al.

Imbalanced datasets present a significant challenge in machine learning, particularly in tasks such as fraud detection, medical diagnosis, and sentiment analysis. This paper proposes a novel approach using Conditional Generative Adversarial Networks (CGANs) to generate synthetic samples that enhance the performance of models trained on imbalanced datasets. By conditioning the generation process on class labels, CGANs produce realistic and diverse samples of the underrepresented classes. The proposed method is empirically evaluated on various benchmark datasets, demonstrating superior performance in comparison to traditional oversampling methods like SMOTE (Synthetic Minority Over-sampling Technique). Our results show that CGANs significantly improve the predictive accuracy, precision, recall, and F1-score, leading to more balanced and reliable machine learning models.

DOI

10.5281/zenodo.14163827Copy DOI

Published in

Zenodo

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Zenodo (opens in a new tab)ResearchGate (opens in a new tab)

Discovering Cybersecurity Insights through World Religions

Dhadi Sai Praneeth Reddy

To secure firm information, organizations develop and communicate security guidelines for employees, yet employees frequently don't follow them because of human characteristics. Experts advise taking lessons from other disciplines that deal with irrational human behavior, such as religions, in order to overcome this problem. These disciplines have dealt with human errors and flaws for thousands of years. Cybersecurity experts may gain significant insight from faiths, which have evolved strategies for assisting the weak and safeguarding their adherents. A great amount of research was done to develop a cybersecurity plan, including examining religious literature and speaking with religious experts. The objective is to start conversations so that individuals can collaboratively think and find better ways to implement cybersecurity. This strategy can help cybersecurity experts create more efficient plans.

DOI

10.5281/zenodo.14061606Copy DOI

Published in

Zenodo

Available on

Zenodo (opens in a new tab)ResearchGate (opens in a new tab)

NeuroGenesis: A Self-Optimizing Compiler with Transformer IR Encoding and Reinforcement Learning

Dhadi Sai Praneeth Reddy

A compiler that discovers its own optimization rules; 0.28 execution-cost reduction against a 0.00 LLVM baseline, outperforming MLGO and AutoPhase.

KiranaChain: A Multi-Agent Negotiation Benchmark for India's Informal Retail Economy

Dhadi Sai Praneeth Reddy

A multi-agent reinforcement learning benchmark modelled on India's 12–14M kirana stores: informal credit, supply chains and multilingual commerce.

DOI

10.57967/hf/9015Copy DOI

Code & data

GitHub (opens in a new tab)Hugging Face (opens in a new tab)

GCT-100K: A Cyber-Manipulation Benchmark with a Cognitive Manipulation Score

Dhadi Sai Praneeth Reddy

100,000 samples of phishing, OTP fraud and social engineering, scored with a novel Cognitive Manipulation Score (CMS). Apache 2.0.