Kranthi Kumar SIngamaneni

@gitam.edu

Assistant Professor , Computer Science and Engineering
Gandhi Institute of Technology and Management, formerly GITAM University

Kranthi Kumar SIngamaneni


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https://researchid.co/ksingama

RESEARCH INTERESTS

cloud security, cyber security, applied cryptography, data science
35

Scopus Publications

666

Scholar Citations

15

Scholar h-index

19

Scholar i10-index

Scopus Publications

  • Machine learning framework for predicting athletic injuries and optimising performance
    Sathuluri Raju, Kranthi Kumar Singamaneni, Lim Boon Hooi, Kunche Usha Rani, Chandrika B.
    BMC Sports Science Medicine and Rehabilitation, 2026
    This paper describes an interpretable machine learning model to assess the risk of injury among multi-sport college athletes at the university level based on a publicly available collegiate dataset. The variables to be included in the dataset are workload, recovery, performance, and demographic factors of 200 athletes that represent various sports activities. A set of cross-validated models based on supervised learning such as Random Forest, XGBoost, and Artificial Neural Networks were trained with the stratified cross-validation, and the work of the Random Forest has shown the best performance (accuracy = 0.98; ROC-AUC = 0.97). Preprocessing involved scaling of features, categorical encoding, and inspection of outliers and no imputation was needed because all the data is available. Since explainable Artificial Intelligence (XAI) methods, such as SHAP, were incorporated to help understand the model behaviour. The importance of features was shown to be greatest in ACL risk score, load balance score, fatigue score, and training hours, which suggests that the sports injury is multi-factorial in nature. The results point to early-warning indicators bankable on routine workload-recovery balance monitoring as opposed to the use of expensive wearable technology. This structure offers a pragmatic, replicable, and understandable model of injury prediction that could guide coaches and the practitioners in creating decisions based on information. Future directions in line with real-time monitoring and federated learning and external validation of more extensive athletic populations should be studied as future work.
  • Intelligent task offloading for sustainable energy management in industrial IoT edge cloud systems
    Kranthi Kumar Singamaneni, Meghamala Bag, Tanmaya Bhoi, Abhijit Joshi, Soumya Ranjan Bhoi, Debabrata Dansana
    Discover Sustainability, 2026
    Efficient task offloading is critical for the Industrial Internet of Things (IIoT), yet existing strategies often fail to address the complex inter-task dependencies inherent in real-world industrial workflows. Current approaches predominantly treat tasks as independent units, an oversight that leads to execution deadlocks, suboptimal resource scheduling, and system instability. To bridge this gap, this study introduces M-SIEGFENNet-TUA, a Multi-Scale Integrated Edge-Guided Finite Element Neural Network with Tactical Unit Algorithm, designed for sustainable and reliable task offloading in edge–cloud environments. The model integrates multi-scale analysis for feature extraction, edge-guided attention for prioritization, and finite element optimization for balanced resource allocation. Crucially, unlike standard models, M-SIEGFENNet-TUA explicitly manages task dependencies, ensuring that interconnected tasks are executed in an optimal sequence. Experimental results demonstrate reduced latency (120–160 ms), lower delay (cloud 50→7 ms), high task completion rates (up to 94%), and minimal energy consumption (52.7 J). Overall, the framework improves efficiency, reliability, and sustainability, aligning with SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities).
  • Context aware hierarchical alignment for robust multimodal three stream sentiment analysis
    Mudigonda Krishna Siva Prasad, Manoj Pennada, Kranthi Kumar Singamaneni, S. K. Prakalya, Ch Rushitha, G. Bhavana Mallesh
    Discover Artificial Intelligence, 2026
    Lack of progress in sentiment analysis for conversational text because desktop and mobile communications shift emotions irregularly between different channels. Traditional approaches do not achieve an effective understanding of genuine situations when performing healthcare monitoring, providing customer support, and social media evaluations because they inadequately combine visual audio and text data. The research outlines CHARM3S which offers sentiment analysis capabilities by building hierarchical structures that analyze contextual sentiments from all communication channels. Thus, the new proposed system combines temporal dependency maintenance through new context-aware pooling with solutions for modality alignment through orthogonality constraints together with central moment discrepancy regularization. The model includes three specific encoders where DeBERTa-v3 starts the text processing before Data2Vec processes audio and facial expressions using vision transformers with bidirectional cross-modal attention for enhanced multichannel representation learning. The CHARM3S system outperforms comparable methods during the analysis phase of emotional phone calls between agents and customers, telehealth sessions, and interactive conversations between agents and customers. And the new implementation of this system exists through uncertainty-based methodologies, which ensure operational effectiveness with untrustworthy or incomplete modality information. The Codebase and implementation is made available at https://github.com/ManojPennada/CHARM3S.
  • ATM-AM: An Interpretable Attention SHAP Aligned Framework for Text Classification across IMDb, Amazon, and SST-2
    Ramesh Babu Pittala, Medikonda Asha Kiran, Niteesha Sharma, Manyam Thaile, Kranthi Kumar Singamaneni, Lakshmi Prasanna Byrapuneni, Yerraganti Krishna Bhargavi, Peddada Nagamani
    Journal of Intelligent and Fuzzy Systems, 2026
    In text classification tasks with complex models and high-stakes domains the alignment between predictions and explanations tends to be weak because post-hoc explainability methods operate independent of model training. In this paper, we suggest ATM-AM - an approach based on the Gated Recurrent Unit (GRU) that combines Bahdanau attention with a training-time SHAP-backed alignment objective to offer real-time, context-aware interpretability without trade-off in predictive performance. The model is tested over three frequently-used sentiment analysis datasets (IMDbhttps://huggingface.co/datasets/imdb, Amazon Reviews https://www.kaggle.com/datasets/bittlingmayer/amazonreviews, and SST-2. https://huggingface.co/datasets/glue/viewer/sst2) yielding accuracy scores of 91.8%, 89.5%, and 90.0% with respective F1-scores of 0.899, 0.877, and 0.889 respectively, on each dataset. We also average our measurements over three runs for statistical soundness. The additional training latency added by ATM-AM is quite modest (13–18%), and the inference time remains short (3–4 ms per sample), rendering it feasible to be deployed in real-time. A user-centered interpretability study with 30 participants obtained an average rating of 4.6/5 showing that users trust the explanations produced by our proposed model. These observations posit ATM-AM as a feasible and interpretable solution Text Classification in contexts where model behavior needs to be accountable and reliable.
  • A novel lightweight hybrid cryptographic framework for secure smart card operations
    Kranthi Kumar Singamaneni
    Eurasip Journal on Information Security, 2025
    In this digital age, with exponential usage of smart cards for financial transactions are secured with latest and advanced crypto standards which are prone to advanced cyber threats and quantum attacks. Currently, there are many crypto standards techniques that tend to experience challenges regarding computational overhead, energy requirements, and vulnerable towards modern cyber-attacks. Post-quantum cryptography (PQC) is a crucial necessity to secure financial transactions from the future quantum attacks also. The primary objective is to offer strong security, better performance, and energy efficiency while integrating PQC into prevailing financial transactions. In this article, we propose a novel hybrid crypto standard to enhance the security and privacy of the transactions using smart cards, to achieve this we integrate Elliptic Curve Cryptography Curve25519 to generate secure keys, SPECK block cipher for lightweight encipherment with a powerful hash function for message authentication and integrity checks called PHOTON. Our hybrid model is better than the existing crypto models which need more computational overhead in terms of key length, encryption time, decryption time, energy efficiency that are not fit for lightweight smart card transactions. The proposed hybrid crypto standard shows the considerable performance in terms of various key factors as compared to the several existing hybrid crypto standards. Our standard accomplishes the quick key generation time of 8 ms and minimal energy consumption of 0.34 mJ while preserving the encryption speed of 150 Mbps and decryption speed of 160 Mbps with least memory utilization of 164 KB, our standard key generation time is 88% lesser, encryption time is 25% lesser, decryption time is 27% lesser, energy consumption is 43% lesser, memory utilization is 69% lesser as compared to RSA-2048 + AES-128 hybrid model. Likewise, our model performance is superior to ECDH (P-256) + AES-128 hybrid standard in terms key generation time is 47% lesser, encryption time is 23% lesser, decryption time is 25% lesser, energy consumption is 5% lesser. Based on the experimental outcomes, the proposed hybrid crypto standard is well-suited for lightweight and resource constrained environments such as smart card and IoT applications, posing better performance, low energy utilization, and robust post-quantum security.
  • Decoding the future: exploring and comparing ABE standards for cloud, IoT, blockchain security applications
    Kranthi Kumar Singamaneni, Kusum Yadav, Arwa N. Aledaily, Wattana Viriyasitavat, Gaurav Dhiman, Amandeep Kaur
    Multimedia Tools and Applications, 2025
  • Design and Comparative Analysis of an Optimized and Low Power D Flip flop
    K. Narsimha Reddy, Kranthi Kumar Singamaneni
    Proceedings of the 9th International Conference on Electronics Communication and Aerospace Technology Iceca 2025, 2025
    Flip-flops are basic memory elements used to store digital data. The stored data mostly find its usage for shift operations. The design of Shift registers and Digital Counters involves a set of flipflops. So, it is necessary to design a low power flipflop to have an efficient design built based on it. In this paper, a low power 2OT D- flipflop with preset and clear is proposed. The main aim is to achieve a design promising low power and area. Based on the proposed design of D-flipflop a Universal Shift Register and Synchronous Counter are designed. A Universal Shift Register is a type of shift register that performs left shift, right shift, and parallel loading operations. The Synchronous Counter is used to count digital pulses with respect to clock. The entire designs are implemented using cadence virtuoso tool in 90nm technology. The estimated power of the proposed low power 20T flip-flop is 540.4nJ and delay 43.22 ps. The static CMOS designs are also implemented parallelly to compare with designs based on the proposed D-flipflop. The end results show designs based on the proposed D-flipflop are efficient low power designs and have a reduced device count.
  • An Efficient Quantum Blockchain Framework With Edge Computing for Privacy-Preserving 6G Networks
    Kranthi Kumar Singamaneni, Budati Anil Kumar, Raenu A. L. Kolandaisamy, Vijaya Saradhi Dommeti, Swetha Katragadda
    IEEE Access, 2025
    The advent of 6G networks places very high demands on ultra-low latency, high throughput, and quantum-secure communication to power Industry 5.0 use cases. Traditional blockchain architectures, given their decentralized and secure nature, often fall short in meeting the performance and security requirements of such an ecosystem. In this paper, we present a post-quantum blockchain architecture that employs CRYSTALS-Dilithium and SPHINCS+ for digital signatures and block and transaction verification, respectively, along with zk-STARKs to facilitate scalable zero-knowledge proof-based privacy, and a DPoS+VDFs consensus protocol to satisfy fairness and efficiency. We prototyped and evaluated the proposed framework with a benchmarking setup composed of Python, PQClean, liboqs, and Google Benchmark tools. Experimental results demonstrate that the system achieves a 40% reduction in latency, a 35% increase in transaction throughput, and a 25% reduction in computational overhead due to the integration of zk-STARK. Furthermore, finality time for consensus was reduced by 30% by using the hybrid DPoS-VDF consensus approach. Comparative studies with various lattice-, hash-, and code-based quantum cryptographic primitives have shown that CRYSTALS-Dilithium and SPHINCS+ outperform others in key generation, signing, and verification performance indicators, and thus qualify as optimal solutions for edge-centric 6G infrastructures. Conversely, zk-Starks showed near-optimal timeliness and verification effectiveness among the several examined zero-knowledge proof schemes. These findings validate the proposed framework as an efficient, scalable, and performance-enhanced blockchain solution for securing industrial ecosystems with latency sensitivity in a 6G-enabled environment.
  • A Novel Hybrid Quantum-Crypto Standard to Enhance Security and Resilience in 6G-Enabled IoT Networks
    Kranthi Kumar Singamaneni, Anil Kumar Budati, Shayla Islam, Raenu Kolandaisamy, Ghulam Muhammad
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025
    One of the most significant threats that hinder the effective functioning of existing models and technologies in 6G networks, particularly within the realms of Industry 5.0, is the lack of privacy and security. Traditional cryptographic models that were once considered secure have proven to be vulnerable to an expanding range of threats, such as quantum attacks. This vulnerability results in the exposure of sensitive data to unauthorized breaches and compromises the integrity and overall functionality of the network. Access control mechanisms implemented up to this point could not ensure that security compromises are prevented as secure data sharing is facilitated effortlessly. As a result, attempts to access sensitive data without authorization are often made, leaving the networks vulnerable to distributed denial of service (DDoS) attacks that have the potential to disrupt their operation. Considering these pervasive challenges, our article proposes an innovative hybrid protocol suite explicitly designed for 6G networks in Industry 5.0. Specifically, our approach integrates leading-edge technologies, such as nth-degree truncated polynomial ring unit encryption with supersingular isogeny Diffie–Hellman (NTRUEncrypt-SIDH) and key-policy attribute-based encryption (KP-ABE). It aims to address the vulnerabilities associated with traditional cryptographic models to ensure high confidentiality and authorized data sharing. Moreover, we implement modern DDoS mitigation techniques, such as rate limiting, traffic filtering, and adaptive routing, to enhance the resilience of the network and ensure that system operation remains uninterrupted. Experimental evaluations demonstrate the superiority of the proposed model over existing standards. Notably, the hybrid protocol achieves encryption and decryption times of 1.8 s and 1.667 s per gigabyte (GB), respectively, with a key generation time of 2.857 s per GB, ensuring efficient performance even in resource-constrained environments. The system achieves a total computational overhead of 11.7 ms for KP-ABE combined with NTRUEncrypt-SIDH, outperforming other cryptographic combinations. In addition, the model exhibits enhanced scalability, flexibility, and security, offering robust defenses against quantum and classical threats while ensuring seamless user policy-based access control. These results underscore the proposed hybrid cryptosystem's potential as a transformative standard for the secure and efficient operation of 6G networks in Industry 5.0.
  • Enhancing Breast Cancer Prediction with Xplainable AI: Application of LIME and SHAP Techniques
    Rakesh Salakapuri, Kranthi Kumar Singamaneni, Panduranga Vital Terlapu, Balakrishna Peesala, Siva Naga Raju B, Ravikiran Reddy Kandadi
    2025 International Conference on Artificial Intelligence and Machine Vision Aimv 2025, 2025
    With a primary focus on elucidating the opaque decision-making processes inherent in complex black-box machine learning systems such as DNN-deep neural networks, Explainable Artificial Intelligence (XAI) has garnered significant attention from the scientific community in recent years. The widespread application of black-box approaches, particularly in vital domains like fraud detection and healthcare, is the cause of this spike in interest and highlights the pressing need to fully understand and assess their decision-making processes. In order to raise the standard of decision-making, this work systematically addresses this significant issue with DL-deep learning and ML-machine learning models, highlighting the critical role that XAI plays in promoting model transparency. Furthermore, the accuracy of these explanations can be guaranteed using popular XAI approaches such as LIME-Local Interpretable Model-Agnostic Explanations and SHAP-Shapley Additive exPlanations. The proposed methodology consists of two primary steps: first, ML models are used to train black-box models on the Wisconsin dataset; second, the models are interpreted and their internal decision-making processes are visualized using LIME and SHAP techniques.
  • A novel integrated quantum-resistant cryptography for secure scientific data exchange in ad hoc networks
    Kranthi Kumar Singamaneni, Ghulam Muhammad
    Ad Hoc Networks, 2024
  • A Novel Multi-Qubit Quantum Key Distribution Ciphertext-Policy Attribute-Based Encryption Model to Improve Cloud Security for Consumers
    Kranthi Kumar Singamaneni, Ghulam Muhammad, Zulfiqar Ali
    IEEE Transactions on Consumer Electronics, 2024
  • A Novel Quantum Hash-Based Attribute-Based Encryption Approach for Secure Data Integrity and Access Control in Mobile Edge Computing-Enabled Customer Behavior Analysis
    Kranthi Kumar Singamaneni, Ghulam Muhammad, Zulfiqar Ali
    IEEE Access, 2024
  • An Efficient Q-KPABE Framework to Enhance Cloud-Based IoT Security and Privacy
    Kranthi Kumar Singamaneni, Anil Kumar Budati, Thulasi Bikku
    Wireless Personal Communications, 2024
  • Binary Image Classification on Fashion-MNIST Using TensorFlow-Quantum and CIRQ
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Unleashing the Power of a Novel Lightweight Lattice-based CP-ABE for Robust IoT Data Transmission
    Ad Hoc and Sensor Wireless Networks, 2024
  • Intelligent Agribusiness System-An Echo Friendly IOT based Approach
    Venkateswarlu Gundu, Kranthi Kumar Singamaneni, Ramesh G., Prabhakar Kandukuri, Deepti Sharma, Madhavi Karanam
    E3s Web of Conferences, 2023
  • Paddy Leaf Disease Identification on Infrared Images based on CNN with Auto Encoders
    Prabhakar Kandukuri, B. Sameer Sowrab, G. Ramesh, Kranthi Kumar Singamaneni
    Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2023, 2023
  • An Efficient Hybrid QHCP-ABE Model to Improve Cloud Data Integrity and Confidentiality
    Kranthi Kumar Singamaneni, Ali Nauman, Sapna Juneja, Gaurav Dhiman, Wattana Viriyasitavat, Yasir Hamid, Joseph Henry Anajemba
    Electronics Switzerland, 2022
  • A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks
    Kranthi Kumar Singamaneni, Gaurav Dhiman, Sapna Juneja, Ghulam Muhammad, Salman A. AlQahtani, John Zaki
    Sensors, 2022
  • An efficient quantum hash-based CP-ABE framework on cloud storage data
    Kranthi Kumar Singamaneni, P. Sanyasi Naidu
    International Journal of Advanced Intelligence Paradigms, 2022
  • The Performance Analysis and Security Aspects of MANET
    Kranthi Singamaneni, Abdullah Shawan Alotaibi, Sri Vijaya K, Purnendu Shekhar Pandey
    Ecs Transactions, 2022
  • A Novel Implementation of Linux Based Android Platform for Client and Server
    M. Kiran Kumar, S. Kranthi Kumar, Ella Kalpana, Donapati Srikanth, K. Saikumar
    Intelligent Systems Reference Library, 2022
  • RETRACTED: An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security
    Kranthi Kumar Singamaneni, Abhinav Juneja, Mohammed Abd-Elnaby, Kamal Gulati, Ketan Kotecha, A. P. Senthil Kumar
    Security and Communication Networks, 2022
  • Stock Price Prediction Using Optimal Network Based Twitter Sentiment Analysis
    Singamaneni Kranthi Kumar, Alhassan Alolo Abdul-Rasheed Akeji, Tiruvedula Mithun, M. Ambika, L. Jabasheela, Ranjan Walia, U. Sakthi
    Intelligent Automation and Soft Computing, 2022
  • Big data analytics with oenn based clinical decision support system
    Thejovathi Murari, L. Prathiba, Kranthi Kumar Singamaneni, D. Venu, Vinay Kumar Nassa, Rachna Kohar, Satyajit Sidheshwar Uparkar
    Intelligent Automation and Soft Computing, 2022
  • A novel blockchain and bi‐linear polynomial‐based qcp‐abe framework for privacy and security over the complex cloud data
    Kranthi Kumar Singamaneni, Kadiyala Ramana, Gaurav Dhiman, Saurabh Singh, Byungun Yoon
    Sensors, 2021
  • Executing CNN-LSTM algorithm for recognizable proof of cervical spondylosis infection on spinal cord MRI image: Machine learning image
    Sasank V. V. S., Kranthi Kumar Singamaneni, A. Sampath Dakshina Murthy, S. K. Hasane Ahammad
    Handbook of Research on Innovations and Applications of AI Iot and Cognitive Technologies, 2021
  • Exploration of convolutional neural network with node-centred intrusion detection structure plan for green cloud
    Journal of Green Engineering, 2020
  • A novel integrated approach to predict bitcoin price using LSTM of RNN, GBDT and gated recurrent unit architecture
    International Journal of Advanced Science and Technology, 2020
  • An improved dynamic polynomial integrity based QCP-ABE framework on large cloud data security
    Kranthi Kumar Singamaneni, Sanyasi Naidu Pasala
    International Journal of Knowledge Based and Intelligent Engineering Systems, 2020
  • Image transformation technique using steganography methods using LWT technique
    Singamaneni Kumar, Pallela Reddy, Gajula Ramesh, Venkata Maddumala
    Traitement Du Signal, 2019
  • Iblind quantum computing and HASBE for secure cloud data storage and accessing
    Kranthi Singamaneni, Pasala Naidu
    Revue D Intelligence Artificielle, 2019
  • Secure key management in cloud environment using quantum cryptography
    Kranthi KUMAR SINGAMANENI, Pasala SANYASI NAIDU
    Ingenierie Des Systemes D Information, 2018
  • Efficient quantum cryptography technique for key distribution
    Kranthi KUMAR SINGAMANENI, Pasala SANYASI NAIDU, Pasupuleti VENKATA SIVA KUMAR
    Journal Europeen Des Systemes Automatises, 2018

RECENT SCHOLAR PUBLICATIONS

  • A hybrid framework for pill identification using convolutional neural networks and optical character recognition
    JJ Pujari, T Bikku, KK Singamaneni, AC Priya, NADI Ranjani, S Thota
    Discover Artificial Intelligence , 2026
    2026
  • A hybrid PQC framework for resilient IoT security
    KK Singamaneni, S Kadry
    Discover Internet of Things , 2026
    2026
  • Compact quad-polarisation agile slotted waveguide array antenna with hybrid sequential-corporate feed network for enhanced planar array integration
    M Chakrabarti, A Chatterjee, K Purkait, K Singamaneni
    Journal on Wireless Communications and Networking , 2026
    2026
  • Context aware hierarchical alignment for robust multimodal three stream sentiment analysis
    MKS Prasad, M Pennada, KK Singamaneni, SK Prakalya, C Rushitha, ...
    Discover Artificial Intelligence , 2026
    2026
  • Intelligent task offloading for sustainable energy management in industrial IoT edge cloud systems
    KK Singamaneni, M Bag, T Bhoi, A Joshi, SR Bhoi, D Dansana
    Discover Sustainability , 2026
    2026
  • Machine learning framework for predicting athletic injuries and optimising performance
    S Raju, KK Singamaneni, LB Hooi, KU Rani, C B
    BMC Sports Science, Medicine and Rehabilitation 18 (1), 107 , 2026
    2026
    Citations: 1
  • Enhancing Breast Cancer Prediction with Xplainable AI: Application of LIME and SHAP Techniques
    R Salakapuri, KK Singamaneni, PV Terlapu, B Peesala, RR Kandadi
    2025 International Conference on Artificial Intelligence and Machine Vision … , 2025
    2025
  • An Efficient Quantum Block chain Framework with Edge Computing for Privacy-Preserving 6G Networks
    KK Singamaneni, BA Kumar, A Raenu, L Kolandaisamy, VS Dommeti, ...
    IEEE Access , 2025
    2025
    Citations: 10
  • BioPrint-Blood: ML-Based Blood Group Detection from Fingerprints
    KK Singamaneni, MA Kiran, RB Pittala, M Thaile, VS Pittala, ...
    Networking International Conference on Emerging Trends in Expert … , 2025
    2025
  • Machine Learning Networks System (MLNS) A Multi-objective Machine Learning Framework for Network Structured Data
    KK Singamaneni, B Ramakrishna, M Prasad, RB Pittala, GNV Rao, ...
    Networking International Conference on Emerging Trends in Expert … , 2025
    2025
  • Enhancing Malicious URL Detection Accuracy Using Multi-modal Feature Fusion
    KK Singamaneni, RB Pittala, MA Kiran, M Thaile, P Sambaram, ...
    Networking International Conference on Emerging Trends in Expert … , 2025
    2025
  • A novel lightweight hybrid cryptographic framework for secure smart card operations
    KK Singamaneni
    EURASIP Journal on Information Security 2025 (1), 19 , 2025
    2025
    Citations: 7
  • Decoding the future: Exploring and comparing ABE standards for cloud, IoT, blockchain security applications
    KK Singamaneni, K Yadav, AN Aledaily, W Viriyasitavat, G Dhiman, ...
    Multimedia Tools and Applications 84 (13), 12299-12327 , 2025
    2025
    Citations: 14
  • Quantum Computing Models for Cybersecurity and Wireless Communications
    BA Kumar, SK Kumar, L Xingwang
    John Wiley & Sons , 2025
    2025
    Citations: 2
  • A novel hybrid quantum-crypto standard to enhance security and resilience in 6G-enabled IoT networks
    KK Singamaneni, AK Budati, S Islam, RAL Kolandaisamy, G Muhammad
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote … , 2025
    2025
    Citations: 37
  • Design and Comparative Analysis of an Optimized and Low Power D Flip flop
    KN Reddy, KK Singamaneni
    2025 9th International Conference on Electronics, Communication and … , 2025
    2025
  • A novel integrated quantum-resistant cryptography for secure scientific data exchange in ad hoc networks
    KK Singamaneni, G Muhammad
    Ad Hoc Networks 164, 103607 , 2024
    2024
    Citations: 22
  • A novel QoS-based IoT network security approach with lightweight lattice-based quantum attribute-based encryption
    A Ramakrishna, KK Singamaneni, GJ Reddy, KR Madhavi, ...
    Tsinghua Science and Technology , 2024
    2024
    Citations: 6
  • Unleashing the Power of a Novel Lightweight Lattice-based CP-ABE for Robust IoT Data Transmission.
    KK Singamaneni, E MOHAN
    Adhoc & Sensor Wireless Networks 59 , 2024
    2024
    Citations: 1
  • An efficient Q-KPABE framework to enhance cloud-based IoT security and privacy
    KK Singamaneni, AK Budati, T Bikku
    Wireless Personal Communications, 1-29 , 2024
    2024
    Citations: 41

MOST CITED SCHOLAR PUBLICATIONS

  • A novel QKD approach to enhance IIOT privacy and computational knacks
    KK Singamaneni, G Dhiman, S Juneja, G Muhammad, SA AlQahtani, ...
    Sensors 22 (18), 6741 , 2022
    2022
    Citations: 78
  • Image Transformation Technique Using Steganography Methods Using LWT Technique
    SK Kumar, PDK Reddy, G Ramesh, VR Maddumala
    Traitement du Signal 36 (3), 233-237 , 2019
    2019
    Citations: 56
  • A novel implementation of Linux based android platform for client and server
    M Kiran Kumar, S Kranthi Kumar, E Kalpana, D Srikanth, K Saikumar
    A Fusion of Artificial Intelligence and Internet of Things for Emerging … , 2021
    2021
    Citations: 51
  • A novel multi-qubit quantum key distribution ciphertext-policy attribute-based encryption model to improve cloud security for consumers
    KK Singamaneni, G Muhammad, Z Ali
    IEEE Transactions on Consumer Electronics 70 (1), 1092-1101 , 2023
    2023
    Citations: 45
  • An Effective Parkinson's Disease Prediction Using Logistic Decision Regression and Machine Learning with Big Data
    DSD K. Singamaneni, Dr. G. Puthilibai, D. Saravanan, Sagaya Aurelia, P. Krishna
    Turkish Journal of Physiotherapy and Rehabilitation 32 (3), 778-786 , 2021
    2021
    Citations: 42
  • An efficient Q-KPABE framework to enhance cloud-based IoT security and privacy
    KK Singamaneni, AK Budati, T Bikku
    Wireless Personal Communications, 1-29 , 2024
    2024
    Citations: 41
  • An efficient hybrid QHCP-ABE model to improve cloud data integrity and confidentiality
    KK Singamaneni, A Nauman, S Juneja, G Dhiman, W Viriyasitavat, ...
    Electronics 11 (21), 3510 , 2022
    2022
    Citations: 38
  • A novel hybrid quantum-crypto standard to enhance security and resilience in 6G-enabled IoT networks
    KK Singamaneni, AK Budati, S Islam, RAL Kolandaisamy, G Muhammad
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote … , 2025
    2025
    Citations: 37
  • Automated speech based evaluation of mild cognitive impairment and Alzheimer’s disease detection using with deep belief network model
    C AI-Atroshi, J Rene Beulah, KK Singamaneni, C Pretty Diana Cyril, ...
    International Journal of Healthcare Management, 1-11 , 2022
    2022
    Citations: 34
  • A novel blockchain and Bi-linear polynomial-based QCP-ABE framework for privacy and security over the complex cloud data
    KK Singamaneni, K Ramana, G Dhiman, S Singh, B Yoon
    Sensors 21 (21), 7300 , 2021
    2021
    Citations: 34
  • A novel quantum hash-based attribute-based encryption approach for secure data integrity and access control in mobile edge computing-enabled customer behavior analysis
    KK Singamaneni, G Muhammad, Z Ali
    IEEE Access 12, 37378-37397 , 2024
    2024
    Citations: 24
  • A novel integrated quantum-resistant cryptography for secure scientific data exchange in ad hoc networks
    KK Singamaneni, G Muhammad
    Ad Hoc Networks 164, 103607 , 2024
    2024
    Citations: 22
  • An efficient quantum hash-based CP-ABE framework on cloud storage data
    KK Singamaneni, PS Naidu
    International Journal of Advanced Intelligence Paradigms 22 (3-4), 336-347 , 2022
    2022
    Citations: 19
  • Stock price prediction using optimal network based twitter sentiment analysis
    SK Kumar, A Akeji, T Mithun, M Ambika, L Jabasheela, R Walia, U Sakthi
    Intelligent Automation and Soft Computing 33 (2), 1217-1227 , 2022
    2022
    Citations: 18
  • An improved dynamic polynomial integrity based QCP-ABE framework on large cloud data security
    KK Singamaneni, SN Pasala
    International journal of knowledge-based and intelligent engineering systems … , 2020
    2020
    Citations: 17
  • Decoding the future: Exploring and comparing ABE standards for cloud, IoT, blockchain security applications
    KK Singamaneni, K Yadav, AN Aledaily, W Viriyasitavat, G Dhiman, ...
    Multimedia Tools and Applications 84 (13), 12299-12327 , 2025
    2025
    Citations: 14
  • An Efficient Quantum Block chain Framework with Edge Computing for Privacy-Preserving 6G Networks
    KK Singamaneni, BA Kumar, A Raenu, L Kolandaisamy, VS Dommeti, ...
    IEEE Access , 2025
    2025
    Citations: 10
  • Secure key management in cloud environment using quantum cryptography.
    KK Singamaneni, P Naidu
    Ingénierie des Systèmes d'Information 23 (5) , 2018
    2018
    Citations: 10
  • Efficient quantum cryptography technique for key distribution
    KK Singamaneni, PS Naidu, PVS Kumar
    J. Eur. Des Syst. Autom 51, 283 , 2018
    2018
    Citations: 10
  • An Enhanced Dynamic Nonlinear Polynomial Integrity‐Based QHCP‐ABE Framework for Big Data Privacy and Security
    KK Singamaneni, A Juneja, M Abd-Elnaby, K Gulati, K Kotecha, ...
    Security and Communication Networks 2022 (1), 4206000 , 2022
    2022
    Citations: 9