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2. TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs

Author

Rajore, Tanmay, Chandran, Nishanth, Sitaram, Sunayana, Gupta, Divya, Sharma, Rahul, Mittal, Kashish, and Swaminathan, Manohar

Subjects
Computer Science - Cryptography and Security, Computer Science - Computation and Language
Abstract

Benchmarking is the de-facto standard for evaluating LLMs, due to its speed, replicability and low cost. However, recent work has pointed out that the majority of the open source benchmarks available today have been contaminated or leaked into LLMs, meaning that LLMs have access to test data during pretraining and/or fine-tuning. This raises serious concerns about the validity of benchmarking studies conducted so far and the future of evaluation using benchmarks. To solve this problem, we propose Private Benchmarking, a solution where test datasets are kept private and models are evaluated without revealing the test data to the model. We describe various scenarios (depending on the trust placed on model owners or dataset owners), and present solutions to avoid data contamination using private benchmarking. For scenarios where the model weights need to be kept private, we describe solutions from confidential computing and cryptography that can aid in private benchmarking. We build an end-to-end system, TRUCE, that enables such private benchmarking showing that the overheads introduced to protect models and benchmark are negligible (in the case of confidential computing) and tractable (when cryptographic security is required). Finally, we also discuss solutions to the problem of benchmark dataset auditing, to ensure that private benchmarks are of sufficiently high quality.

Published
2024

3. TrustRate: A Decentralized Platform for Hijack-Resistant Anonymous Reviews

Author

Dwivedula, Rohit, Sridhar, Sriram, Satija, Sambhav, Sivathanu, Muthian, Chandran, Nishanth, Gupta, Divya, and Lokam, Satya

Subjects
Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Reviews and ratings by users form a central component in several widely used products today (e.g., product reviews, ratings of online content, etc.), but today's platforms for managing such reviews are ad-hoc and vulnerable to various forms of tampering and hijack by fake reviews either by bots or motivated paid workers. We define a new metric called 'hijack-resistance' for such review platforms, and then present TrustRate, an end-to-end decentralized, hijack-resistant platform for authentic, anonymous, tamper-proof reviews. With a prototype implementation and evaluation at the scale of thousands of nodes, we demonstrate the efficacy and performance of our platform, towards a new paradigm for building products based on trusted reviews by end users without having to trust a single organization that manages the reviews., Comment: 23 pages. Poster at The 24th Privacy Enhancing Technologies Symposium, 2024, Bristol, United Kingdom

Published
2024

7. Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains

Author

Mukherjee, Ananta, Kumar, Peeyush, Yang, Boling, Chandran, Nishanth, and Gupta, Divya

Subjects
Computer Science - Artificial Intelligence
Abstract

This paper addresses privacy concerns in multi-agent reinforcement learning (MARL), specifically within the context of supply chains where individual strategic data must remain confidential. Organizations within the supply chain are modeled as agents, each seeking to optimize their own objectives while interacting with others. As each organization's strategy is contingent on neighboring strategies, maintaining privacy of state and action-related information is crucial. To tackle this challenge, we propose a game-theoretic, privacy-preserving mechanism, utilizing a secure multi-party computation (MPC) framework in MARL settings. Our major contribution is the successful implementation of a secure MPC framework, SecFloat on EzPC, to solve this problem. However, simply implementing policy gradient methods such as MADDPG operations using SecFloat, while conceptually feasible, would be programmatically intractable. To overcome this hurdle, we devise a novel approach that breaks down the forward and backward pass of the neural network into elementary operations compatible with SecFloat , creating efficient and secure versions of the MADDPG algorithm. Furthermore, we present a learning mechanism that carries out floating point operations in a privacy-preserving manner, an important feature for successful learning in MARL framework. Experiments reveal that there is on average 68.19% less supply chain wastage in 2 PC compared to no data share, while also giving on average 42.27% better average cumulative revenue for each player. This work paves the way for practical, privacy-preserving MARL, promising significant improvements in secure computation within supply chain contexts and broadly.

Published
2023

12. Text Summarization Techniques for the Bengali Language: Survey

Author

Parul, Garg, Kamal Deep, Gupta, Divya, Rakhra, Manik, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Singh, Yashwant, editor, Singh, Pradeep Kumar, editor, Gonçalves, Paulo J. Sequeira, editor, and Kar, Arpan Kumar, editor

Published
2024
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13. Unleashing the Potential: Study on Artificial Intelligence Effect in Education Sector

Author

Kaur, Jaspreet, Gupta, Divya, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Singh, Yashwant, editor, Singh, Pradeep Kumar, editor, Gonçalves, Paulo J. Sequeira, editor, and Kar, Arpan Kumar, editor

Published
2024
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15. Fog-centric IoT Smart Healthcare: Architecture, Applications, and Case Study

Author

Gupta, Divya, Bansal, Ankit, Wadhwa, Shivani, Shah, Syed Hassan Ahmed, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Marriwala, Nikhil Kumar, editor, Dhingra, Sunil, editor, Jain, Shruti, editor, and Kumar, Dinesh, editor

Published
2024
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16. Leveraging Content Based Image Retrieval Using Data Mining for Efficient Image Exploration

Author

Kaur, Jaspreet, Gupta, Divya, Singh, Amrinder, Shah, Syed Hassan Ahmed, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Marriwala, Nikhil Kumar, editor, Dhingra, Sunil, editor, Jain, Shruti, editor, and Kumar, Dinesh, editor

Published
2024
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18. Real-World Outcomes Following First-Line Treatment in Patients with Advanced Ovarian Cancer with Multiple Risk Factors for Disease Progression who Received Maintenance Therapy or Active Surveillance

Author

Chase, Dana, Perhanidis, Jessica, Gupta, Divya, Kalilani, Linda, Golembesky, Amanda, and González-Martín, Antonio

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Patient Safety, Clinical Research, Clinical Trials and Supportive Activities, Cancer, Electronic health records, Maintenance therapy, Ovarian cancer, Risk factors, Oncology and carcinogenesis, Epidemiology, Public health
Abstract

IntroductionWe evaluated real-world outcomes in patients with advanced ovarian cancer (AOC) based on their cumulative risk profile and maintenance therapy (MT) status following first-line (1L) treatment.MethodsThis retrospective observational study of a nationwide electronic health record-derived de-identified database included adult patients diagnosed with stage III/IV OC from January 1, 2011 to February 28, 2021, who received 1L therapy and had ≥ 12 weeks of follow-up after the index date (end of 1L therapy). Patients were grouped according to whether they received MT or active surveillance (AS) following 1L treatment and by the cumulative number of risk factors (RF) present (stage IV disease; no surgery/treated with neoadjuvant therapy and interval debulking surgery; had postoperative visible residual disease; and had BRCA wild-type disease/unknown BRCA status). Time to next treatment (TTNT) and overall survival (OS) were assessed with a cloning and inverse probability of censoring (IPC)-weighted Kaplan-Meier method.ResultsAmong 1920 patients, 22.2% received MT and 77.8% received AS. Median IPC-weighted TTNT and OS were 13.3 months (95% CI 11.7-15.8) and 39.1 months (95% CI 32.5-48.6) in the MT cohort, respectively, and 8.6 months (95% CI 8.0-9.5) and 38.4 months (95% CI 36.4-41.0) in the AS cohort, respectively. Almost all patients had ≥ 1 RF (MT 95.3%; AS 96.7%). Median IPC-weighted TTNT was shorter among patients with more RF in both cohorts (MT: 1 RF, 19.3 months, 95% CI 13.5-37.8; 2 RF, 17.2 months, 95% CI 12.8-20.2; 3 RF, 11.0 months, 95% CI 8.2-13.8; 4 RF, 7.0 months, 95% CI 6.2-8.8; AS: 1 RF, 17.7 months, 95% CI 13.5-22.3; 2 RF, 10.2 months, 95% CI 9.1-11.5; 3 RF, 6.5 months, 95% CI 5.8-7.4; 4 RF, 4.1 months, 95% CI 3.5-4.5).ConclusionRegardless of RF number, MT was associated with longer TTNT in real-world patients with AOC.

Published
2023

22. List of contributors

Author

Abere, Benjamin Olusola, primary, Adetunji, Charles Oluwaseun, additional, Adetunji, Juliana Bunmi, additional, Adetuyi, Babatunde Oluwafemi, additional, Adeyanju, Adeyemi Ayotunde, additional, Ajayi, Olulope Olufemi, additional, Ajenifujah-Solebo, Oloruntoyin, additional, Akinbo, Olalekan, additional, Akram, Muhammad, additional, Aruwa, Christiana Eleojo, additional, Attanda, M.L., additional, Daramola, Fisayo Yemisi, additional, Erinle, Christianah Oluwakemi, additional, Godwin, Yovwin D., additional, Gupta, Divya, additional, Imah-Harry, Joan, additional, Inobeme, Abel, additional, Kolawole, Olatunji Matthew, additional, Kumar, Subodh, additional, Lewu, Francis Bayo, additional, Masoodi, Mubashir Hussain, additional, Mishra, Pragya, additional, Mishra, Raghvendra Raman, additional, Mushtaq, Wajahat, additional, Nathaniel, Majolagbe Olusola, additional, Noor, Aneeza, additional, Odelade, Kehinde Abraham, additional, Odetokun, Ismail Ayoade, additional, Odeyemi, A.T., additional, Ogundolie, Frank Abimbola, additional, Ogunlana, Olubanke Olujoke, additional, Okonkwo, Clinton Emeka, additional, Olajide, Peace Abiodun, additional, Olaniran, Abiola Folakemi, additional, Olaniran, Olubukola David, additional, Olaniyan, Olugbemi T., additional, Omo-Omorodion, Blessing Itohan, additional, Omowumi, Oluwakemi Semiloore, additional, Oni, Peter Gbenga, additional, Osemwegie, Osarenkhoe Omorefosa, additional, Owoseni, Mojisola Christiana, additional, Popoola, Oluwabukola Atinuke, additional, Qadir, Insha, additional, Rotimi, Damilare Emmanuel, additional, Taiwo, Abiola Ezekiel, additional, Taiwo, Anjolaoluwa Maryham, additional, Toloyai, Pere-Ebi Yabrade, additional, Wike, Nyejirime Young, additional, Yerima, Mohammed Bello, additional, and Zahid, Rabia, additional

Published
2024
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23. Efficient ML Models for Practical Secure Inference

Author

Ganesan, Vinod, Bhattacharya, Anwesh, Kumar, Pratyush, Gupta, Divya, Sharma, Rahul, and Chandran, Nishanth

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives allow inference without revealing users' inputs to a model provider or model's weights to a user. For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital. While secure inference is in principle feasible for this setting, there are no existing techniques that make it practical at scale. The CrypTFlow2 framework provides a potential solution with its ability to automatically and correctly translate clear-text inference to secure inference for arbitrary models. However, the resultant secure inference from CrypTFlow2 is impractically expensive: Almost 3TB of communication is required to interpret a single X-ray on DenseNet-121. In this paper, we address this outstanding challenge of inefficiency of secure inference with three contributions. First, we show that the primary bottlenecks in secure inference are large linear layers which can be optimized with the choice of network backbone and the use of operators developed for efficient clear-text inference. This finding and emphasis deviates from many recent works which focus on optimizing non-linear activation layers when performing secure inference of smaller networks. Second, based on analysis of a bottle-necked convolution layer, we design a X-operator which is a more efficient drop-in replacement. Third, we show that the fast Winograd convolution algorithm further improves efficiency of secure inference. In combination, these three optimizations prove to be highly effective for the problem of X-ray interpretation trained on the CheXpert dataset., Comment: 10 pages include references, 4 figures

Published
2022

30. Niraparib Population Pharmacokinetics and Exposure-Response Relationships in Patients With Newly Diagnosed Advanced Ovarian Cancer

Author

Monk, Bradley J., Romero, Ignacio, Graybill, Whitney, Churruca, Cristina, O'Malley, David M., Knudsen, Anja Ør, Yap, Oi Wah Stephanie, Baurain, Jean-François, Rose, Peter G., Denys, Hannelore, Ghamande, Sharad, Pisano, Carmela, Fabbro, Michel, Braicu, Elena Ioana, Calvert, Paula M., Amit, Amnon, Prendergast, Emily, Taylor, Adekemi, Kheibarshekan, Leila, Zhang, Zhi-Yi, Zajic, Stefan, Jewell, Roxanne C., Gupta, Divya, and González-Martín, Antonio

Published
2024
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31. Efficacy and safety of niraparib in patients aged 65 years and older with advanced ovarian cancer: Results from the PRIMA/ENGOT-OV26/GOG-3012 trial

Author

Valabrega, Giorgio, Pothuri, Bhavana, Oaknin, Ana, Graybill, Whitney S., Sánchez, Ana Beatriz, McCormick, Colleen, Baurain, Jean-François, Tinker, Anna V., Denys, Hannelore, O’Cearbhaill, Roisin E., Hietanen, Sakari, Moore, Richard G., Knudsen, Anja Ør, de La Motte Rouge, Thibault, Heitz, Florian, Levy, Tally, York, Whitney, Gupta, Divya, Monk, Bradley J., and González-Martín, Antonio

Published
2024
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34. Multi-institution encrypted medical imaging AI validation without data sharing

Author

Soin, Arjun, Bhatu, Pratik, Takhar, Rohit, Chandran, Nishanth, Gupta, Divya, Alvarez-Valle, Javier, Sharma, Rahul, Mahajan, Vidur, and Lungren, Matthew P

Subjects
Computer Science - Cryptography and Security
Abstract

Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 chest x-ray diagnosis model was evaluated on multi-institutional chest radiographic imaging datasets both with and without CrypTFlow2 on two test sets spanning seven sites across the US and India, and comprising 1,149 chest x-ray images. We measure comparative AUROC performance between secure and insecure inference in multiple pathology classification tasks, and explore model output distributional shifts and resource constraints introduced by secure model inference. Secure inference with CrypTFlow2 demonstrated no significant difference in AUROC for all diagnoses, and model outputs from secure and insecure inference methods were distributionally equivalent. The use of CrypTFlow2 may allow off-the-shelf secure 2PC between healthcare systems and AI model vendors for medical imaging, without changes in performance, and can facilitate scalable pre-deployment infrastructure for real-world secure model evaluation without exposure to patient data or model IP.

Published
2021

36. Health-related quality of life in patients with newly diagnosed advanced ovarian cancer treated with niraparib vs placebo: Results from the phase 3 randomized PRIMA/ENGOT-OV26/GOG-3012 trial

Author

Pothuri, Bhavana, Han, Sileny, Chase, Dana M., Heitz, Florian, Burger, Robert A., Gaba, Lydia, Van Le, Linda, Guerra, Eva, Bender, David, Korach, Jacob, Cloven, Noelle, Churruca, Cristina, Follana, Philippe, DiSilvestro, Paul, Baurain, Jean-François, Jardon, Kris, Pisano, Carmela, Peen, Ulla, Mäenpää, Johanna, Gupta, Divya, Bacqué, Emeline, Li, Yong, Compton, Natalie, Antonova, Jenya, Monk, Bradley J., and González-Martín, Antonio

Published
2024
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38. SIRNN: A Math Library for Secure RNN Inference

Author

Rathee, Deevashwer, Rathee, Mayank, Goli, Rahul Kranti Kiran, Gupta, Divya, Sharma, Rahul, Chandran, Nishanth, and Rastogi, Aseem

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Mathematical Software
Abstract

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs) use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal of square root. Although prior work on secure 2-party inference provides specialized protocols for convolutional neural networks (CNNs), existing secure implementations of these math operators rely on generic 2-party computation (2PC) protocols that suffer from high communication. We provide new specialized 2PC protocols for math functions that crucially rely on lookup-tables and mixed-bitwidths to address this performance overhead; our protocols for math functions communicate up to 423x less data than prior work. Some of the mixed bitwidth operations used by our math implementations are (zero and signed) extensions, different forms of truncations, multiplication of operands of mixed-bitwidths, and digit decomposition (a generalization of bit decomposition to larger digits). For each of these primitive operations, we construct specialized 2PC protocols that are more communication efficient than generic 2PC, and can be of independent interest. Furthermore, our math implementations are numerically precise, which ensures that the secure implementations preserve model accuracy of cleartext. We build on top of our novel protocols to build SIRNN, a library for end-to-end secure 2-party DNN inference, that provides the first secure implementations of an RNN operating on time series sensor data, an RNN operating on speech data, and a state-of-the-art ML architecture that combines CNNs and RNNs for identifying all heads present in images. Our evaluation shows that SIRNN achieves up to three orders of magnitude of performance improvement when compared to inference of these models using an existing state-of-the-art 2PC framework., Comment: IEEE Security and Privacy 2021

Published
2021

39. Fog-Based Smart Healthcare Architecture in IoT Environment

Author

Gupta, Divya, Bansal, Ankit, Wadhwa, Shivani, Garg, Kamal Deep, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jain, Shruti, editor, Marriwala, Nikhil, editor, Tripathi, C. C., editor, and Kumar, Dinesh, editor

Published
2023
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40. Introduction and History of Insect Biotechnology

Author

Kumar, Dhiraj, Singh, Shubham, Kundapur, Rajesh R., Gupta, Divya, Shukla, Sangeeta, Bosserhoff, Anja K., Series Editor, Berenjian, Aydin, Series Editor, Carbonell, Pablo, Series Editor, Levite, Mia, Series Editor, Roig, Joan, Series Editor, Turksen, Kursad, Series Editor, Kumar, Dhiraj, editor, and Shukla, Sangeeta, editor

Published
2023
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41. Effective Communication in NDN via Transient Popular Content Caching at Edge

Author

Gupta, Divya, Bansal, Ankit, Wadhwa, Shivani, Garg, Kamal Deep, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Marriwala, Nikhil, editor, Tripathi, C.C., editor, Jain, Shruti, editor, and Kumar, Dinesh, editor

Published
2023
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43. Performance Assessment and Evaluation of a 52-kW Solar PV Plant in India

Author

Divya Navamani, J., Lavanya, A., Bhargava, Yash Vardhan, Gupta, Divya Kumar, Singh, Gaurav, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Gupta, Om Hari, editor, Singh, S. N., editor, and Malik, Om P., editor

Published
2023
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44. Parameterization of a Solar Cell Using Multivariable Newton Raphson and Particle Swarm Optimization Based on Single Diode Model

Author

Gupta, Divya, Namrata, Kumari, Samadhiya, Akshit, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Namrata, Kumari, editor, Priyadarshi, Neeraj, editor, Bansal, Ramesh C., editor, and Kumar, Jitendra, editor

Published
2023
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46. Delay Efficient Caching in Fog Computing

Author

Gupta, Divya, Wadhwa, Shivani, Rani, Shalli, Sharma, Parth, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Goyal, Dinesh, editor, Kumar, Anil, editor, Piuri, Vincenzo, editor, and Paprzycki, Marcin, editor

Published
2023
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