Laxmi Srivastava

@mitsgwalior.in

Professor, Electrical Engineering
Madhav Institute of Technology & Science, Gwalior

Laxmi Srivastava


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https://researchid.co/laxmisrivastava

RESEARCH INTERESTS

Power System Operation and Control, ANN, Fuzzy Logic and Evolutionary Computing techniques in power system
38

Scopus Publications

4286

Scholar Citations

33

Scholar h-index

85

Scholar i10-index

Scopus Publications

  • Effective Unconstrained Face Recognition from Images Using RFG: A review
    Ranbeer Tyagi, Geetam Singh Tomar, Laxmi Shrivastava
    2025 IEEE Madhya Pradesh Section Conference Mpcon 2025, 2025
    This paper represents the review of the Effective Unconstrained Face Recognition from Images Using Reference Face Graph an unconstrained face recognition environment. It provides the competencies like discrepancy, illumination, and expression difference strategies. It is probably useful for both verification and identity. At present time, it could locate masses of manner of top view face popularity. In previous couple of years, for pc imaginative and prescient, numerous face recognition techniques were organized. However, actual-international face detection needs a hard works. The interest about unconstrained beneficial face recognition maintains growing utilizing the detonation of on-line press for example network methods. On this evaluation, we appear to handle popularity in the situation of chart assumption. We’re in a position to decide a paranormal revel in using a numerous technique. This examine light out the picks advised for unconstrained face popularity pleasant vicinity and suggesting the solution to be utilized by RFG (Reference Face Graph) targeted face recognition. RFG popularity is utilized is grouping with DCT locality sensitive hashing for green restoration to guarantee scalability. Our objective is RFG targeted unconstrained face recognition to enhance the demonstration quality.
  • Multi-Layer Image Security Framework Using Autoencoder Compression and Chaotic-AES Hybrid Security
    Varnika Sharma, Laxmi Shrivastava, Neeraj Shrivastava
    2025 International Conference on Electrical Communication and Computing Technologies Iconecct 2025, 2025
    In this digital era, securing multimedia content from attackers on the unsecured network is a crucial task for researchers. This work presents a multilayer security framework for securing multimedia content that integrates the Advanced Encryption Standard (AES) algorithm, a deep autoencoder, and a logistic chaotic map. This security framework begins by compressing the image into a low-dimensional latent representation using a deep autoencoder, effectively minimizing redundancy while preserving the essential features of the image. This latent vector is then encrypted by the AES algorithm to provide high cryptographic security. To further enhance security, this work also uses a logistic chaotic map which is used to produce a dynamic key stream and XORed with the AES-encrypted latent space for introducing non-linearity in the proposed security framework. The simulated results demonstrate high information entropy (7.99), high NPCR (0.9964) minimal reconstruction loss (0.0001), effective against differential and statistical attacks, used large key space (2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup>), and passed the NIST randomness tests, which makes this framework highly suitable for lightweight devices.
  • Image Encryption and Decryption using Deep Learning: A Comprehensive Review
    Varnika Sharma, Laxmi Shrivastava, Neeraj Shrivastava
    2025 IEEE Madhya Pradesh Section Conference Mpcon 2025, 2025
    Image encryption and decryption is one of the sensitive components in modern data security by safeguarding the integrity, confidentiality, and privacy of sensitive visual information. In this work, a comprehensive review of various image encryption and decryption approaches using deep learning is presented. It begins with an overview of traditional image encryption schemes, followed by an in-depth discussion of various deep learning-based image encryption schemes. The review explores the application of recurrent neural networks (RNNs), auto-encoders, long Short Term Memory (LSTMs), conventional neural networks (CNNs), and generative adversarial networks (GANs) in the field of image cryptography. Additionally, a comparative analysis of these schemes is provided, highlighting their strengths and weaknesses. This comparative analysis demonstrates that auto-encoder based schemes are more suitable for image encryption and decryption in terms of time and space complexity, especially for lightweight or smart devices that requires high speed communication and low space requirement.
  • Analysis of Channel Congestion for Vehicular Network in High Vehicle Density Environment
    Bhupendra Dhakad, Sadhana Mishra, Shailendra Singh Ojha, Laxmi Shrivastava
    2025 International Conference on Electrical Communication and Computing Technologies Iconecct 2025, 2025
    Vehicular Ad-Hoc Network (VANET) is a collection of lots of mobile vehicles. These mobile vehicles are communicating with one another to perform diverse functions to avoid accidents, reduced carbon emissions, and gain information about the surrounding environment. VANET could also notify bicyclist in the street and a pedestrian in the crosswalk and reduces pedestrian-related accidents. Paper constrains the performance parameter metrics and calculates the data congestion level on channel by considering the message transmission rate. The road map is designed with the help of SUMO NETEDIT 1.12.0 software and the performance parameter matrices are calculated by NS2 by considering the IEEE802.11p standard. The performance parameter matrices include throughput, end-to-end delay, count of drop packets, and count of received packets.
  • Machine Learning-Based Risk-Aware Congestion Control Scheme for Minimization of Information Loss in Dense VANET Environment
    Bhupendra Dhakad, Laxmi Shrivastava
    Journal of Circuits Systems and Computers, 2024
    Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified [Formula: see text] -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique.
  • Multi-objective optimized multi-path and multi-hop routing based on hybrid optimization algorithm in wireless sensor networks
    Madhav Singh, Laxmi Shrivastava
    Wireless Networks, 2024
  • Survey on Analysis of Power Efficient Transmitter and Receiver of WSN
    Richa Sikarwar, Laxmi Shrivastava
    2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2024, 2024
    A more advanced description communication system has improved the research of WSN power-efficient transmitters (PETx) and power-efficient receivers (PERx) implemented. Many low power approaches have also been used to decrease power dissipation and improve power efficiency. The sensors and the PETx are interfaced via a low power microprocessor. To cut down on standby power, the sensors and PETx are power gated. The maximum system power consumption occurs when the SOC is operating in full functioning mode (i.e., the CPU, image processing, and wireless communication are all active at the same time) in a nanoscaled CMOS process. Low power design and approaches are also used to increase battery life. For the advancement of WSN, a low power efficient Tx and Rx has been developed, and 180nm, 130nm, and 90nm CMOS technologies have been compared. Analog and digital signal processing are also supported by these devices. These systems are widely employed in many fields, including industry, science, medicine, and the military, as a result of their characteristics.
  • WSN Power Efficient Transmitter Using DG FINFET Technique
    Richa Sikarwar, Laxmi Shrivastava
    2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies Inspect 2024, 2024
    The study of WSN power-efficient transmitters (PETx) has been enhanced by the implementation of a more sophisticated description communication system. Double Gate FINFET (DGF) (Fin Shaped Field Effect Transistor) is used in WSN power-efficient transmitters (PETx), and it has been shown to be the mainly proficient in basis of transmitter frequency, low power consumption, and circuit development speed. A low power CPU interfaces the sensors and the PETx. The sensors and PETx are power gated to reduce standby power consumption. In deep submicron technology, circuit approaches to decrease leakage have been identified, and double gate FINFET has converse & functional on WSN power-efficient transmitters. Low power of PETx at 90nm technology has been calculated. In order to lower the power consumption of the WSN power-efficient transmitters, DG FINFET methods have been used. We look at how the DGF system, which uses transistors with the highest speed and lowest threshold voltage for PETx, provides low power and high presentation function.
  • Deep Learning-Based Automatic Segmentation of Spinal Magnetic Resonance Images
    Shaeba Khan, Laxmi Shrivastava, Sarita Singh Bhadauria
    Lecture Notes in Networks and Systems, 2024
  • Comprehensive Analysis of Deep Learning in Spine Image Segmentation
    Shaeba Khan, Laxmi Shrivastava, Sarita Singh Bhadauria
    2024 International Conference on Advances in Computing Research on Science Engineering and Technology Acroset 2024, 2024
    A clinical diagnosis and treatment plan for spinal disorders must start with an imaging assessment. The complicated structure of the spine is encircled by nerve tissue, blood vessels, and muscles. Spine morphology can be easily altered by pathological alterations. Precise division of spine images can be useful. Physicians are able to precisely identify lesions, assess lesions quickly, and direct surgical intervention. Because of its strong feature learning capabilities, deep learning has been applied extensively in the field of spine segmentation in recent years. We compare the traditional deep learning frameworks, provide the widely used data sets and evaluation indicators in spine image segmentation, and provide an overview of CNN, FCN, U-Net, and GAN in order to examine the state of research and development of deep learning in spine image segmentation tasks. The most recent developments in the use of various network models for spine segmentation are presented, along with an analysis of the models' characteristics, a detailed discussion of the difficulties and issues that deep learning is currently facing, and a prospect for future development.
  • Impact of Vehicle Density on Channel Congestion for Vehicular Ad-Hoc Network (VANET)
    Bhupendra Dhakad, Laxmi Shrivastva
    Communications in Computer and Information Science, 2023
  • Performance Analysis of IOT based Wireless Sensor Network and its Graphical User Interface for Air Quality Monitoring
    Madhav Singh, Laxmi Shrivastava
    Proceedings 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2023, 2023
  • Hybrid ANT Colony Optimization Routing Algorithm for AODV Protocol Improvement in FANET
    Tripti Gupta, Ajay Kumar Dadoria, Laxmi Shrivastava
    Lecture Notes in Networks and Systems, 2023
  • Unconstrained Face Recognition from Image Sequence
    Ranbeer Tyagi, Geetam Singh Tomar, Laxmi Shrivastava
    Proceedings 2023 IEEE World Conference on Applied Intelligence and Computing Aic 2023, 2023
  • A Survey on Lifetime Maximization in MANET
    Manoj Singh Tomar, Laxmi Shrivastava
    Proceedings of 2022 IEEE International Conference on Current Development in Engineering and Technology Ccet 2022, 2022
  • Unconstrained Face Detection of Multiple Humans Present in the Video
    Ranbeer Tyagi, Geetam Singh Tomar, Laxmi Shrivastava
    Wireless Personal Communications, 2021
  • IOT based wireless sensor network for air pollution monitoring
    Ajay Chaturvedi, Laxmi Shrivastava
    Proceedings 2020 IEEE 9th International Conference on Communication Systems and Network Technologies Csnt 2020, 2020
  • Side searching and object improving algorithms for Images
    Ranbeer Tyagi, Geetam Singh Tomar, Laxmi Shrivastava
    Proceedings 2020 IEEE 9th International Conference on Communication Systems and Network Technologies Csnt 2020, 2020
  • A wideband microstrip patch antenna with integrated circular slot for RF energy harvesting
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Improvement in K-medoids using shortest path in wireless sensor network
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Connectivity analysis of mobile ad hoc network using fuzzy logic controller
    Poonam Rathore, Laxmi Shrivastava
    Communications in Computer and Information Science, 2019
  • Performance analysis of fuzzy based RED for congestion control in MANET
    Richa Rai, Laxmi Shrivastava, Sarita Singh Bhadauria
    International Journal of Smart Home, 2016
  • Load balanced congestion adaptive routing for randomly distributed mobile adhoc networks
    Geetam Singh Tomar, Laxmi Shrivastava, Sarita Singh Bhadauria
    Wireless Personal Communications, 2014
  • Load balanced congestion adaptive routing for mobile ad hoc networks
    Jung-Yoon Kim, Geetam S. Tomar, Laxmi Shrivastava, Sarita Singh Bhadauria, Won-Hyoung Lee
    International Journal of Distributed Sensor Networks, 2014
  • Corrective action planning for power system load frequency control
    M. Sharma, L. Shrivastava, M. Pandit
    Proceedings of 2013 International Conference on Power Energy and Control Icpec 2013, 2013
  • Secure congestion adaptive routing using group signature scheme
    Laxmi Shrivastava, Sarita S. Bhadauraia, Geetam Singh Tomar, Brijesh Kumar Chaurasia
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
  • Effect of number of CBR connections on the performance of a load balanced congestion adaptive routing for MANET
    Laxmi Shrivastava, G.S. Tomar, Sarita Singh Bhadoria, Brijesh Kumar Chaurasia
    Proceedings 4th International Conference on Computational Intelligence and Communication Networks Cicn 2012, 2012
  • An advanced congestion adaptive routing mechanism
    Laxmi Shrivastav, Geetam Singh Tomar, Frank Z. Wang
    Proceedings 2012 4th International Conference on Computational Intelligence Communication Systems and Networks Cicsyn 2012, 2012
  • Performance evaluation of reactive routing in mobile grid environment
    L. Shrivastava, G. S. Tomar, S. S. Bhadauria
    Applications and Developments in Grid Cloud and High Performance Computing, 2012
  • A load-balancing approach for congestion adaptivity in MANET
    L. Shrivastava, G. S. Tomar, S. S. Bhadoria
    Proceedings 2011 International Conference on Computational Intelligence and Communication Systems Cicn 2011, 2011
  • Comparative study of three mobile ad-hoc network routing protocols under different traffic source
    Rachit Jain, Naresh B. Khairnar, Laxmi Shrivastava
    Proceedings 2011 International Conference on Communication Systems and Network Technologies Csnt 2011, 2011
  • Performance evaluation of routing protocols in MANET with different traffic loads
    Laxmi Shrivastava, Sarita S. Bhadauria, G.S. Tomar
    Proceedings 2011 International Conference on Communication Systems and Network Technologies Csnt 2011, 2011
  • Performance evaluation of reactive routing in mobile grid environment
    L. Shrivastava, G. S. Tomar, S. S. Bhadauria
    International Journal of Grid and High Performance Computing, 2011
  • Conundrums measurement for market power
    Kirti Pal, Manjaree Pandit, Laxmi Shrivastava
    2010 Joint International Conference on Power Electronics Drives and Energy Systems Pedes 2010 and 2010 Power India, 2010
  • A wireless ad-hoc network: Design and performance evaluation of dynamic routing model
    Sarita Singh Bhadauria, Laxmi Shrivastava
    2008 International Conference of Recent Advances in Microwave Theory and Applications Microwave 2008, 2008
  • On the investigations of a wide band proximity fed bow tie shaped microstrip antenna
    Journal of Microwaves and Optoelectronics, 2004
  • Proximity fed bow-tie shaped micro strip antenna for wide band operation
    L. Shrivastava, P.K. Singhal
    Proceedings of the IEEE International Conference on Industrial Technology, 2002
  • Broad band printed MM-wave antenna with reflector element
    P.K. Singhal, R.N. Baral, L. Shrivastava
    Proceedings of the IEEE International Conference on Industrial Technology, 2002

RECENT SCHOLAR PUBLICATIONS

  • Multi-objective DG placement in radial distribution systems using
    N Saxena¹, M Pandit, L Srivastava
    Integrated Energy System Planning, Optimization, Trading and Benefit … , 2026
    2026
  • Solution of optimal power flow problem using sine-cosine mutation based modified Jaya algorithm: a case study
    S Gupta, N Kumar, L Srivastava
    Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 47 … , 2025
    2025
    Citations: 15
  • Optimal power flow solution using novel optimization technique: A case study
    S Gupta, M Bilal, M Zuhaib, L Srivastava, H Malik, FPG Marquez, ...
    Expert Systems with Applications 287, 128163 , 2025
    2025
    Citations: 5
  • Cascaded deep NN‐based customer participation by considering renewable energy sources for congestion management in deregulated power markets
    A Agrawal, P Walde, SN Pandey, L Srivastava, RK Saket, B Khan
    IET Renewable Power Generation 19 (1), e12678 , 2025
    2025
    Citations: 3
  • Multi-objective DG placement in radial distribution systems using the IbI logic algorithm
    N Saxena, M Pandit, L Srivastava
    Frontiers in Energy Research 12, 1453715 , 2024
    2024
    Citations: 8
  • Multi-objective optimal sizing of hybrid micro-grid system using an integrated intelligent technique
    P Singh, M Pandit, L Srivastava
    Energy 269, 126756 , 2023
    2023
    Citations: 61
  • Improved binary bat algorithm for optimally placing multiple DGs in RDN
    N Saxena, M Pandit, L Srivastava
    2022 IEEE 10th Power India International Conference (PIICON), 1-5 , 2022
    2022
    Citations: 1
  • Rao algorithm based optimal Multi‐term FOPID controller for automatic voltage regulator system
    N Paliwal, L Srivastava, M Pandit
    Optimal Control Applications and Methods 43 (6), 1707-1734 , 2022
    2022
    Citations: 24
  • Techno-socio-economic-environmental estimation of hybrid renewable energy system using two-phase swarm-evolutionary algorithm
    P Singh, M Pandit, L Srivastava
    Sustainable Energy Technologies and Assessments 53, 102483 , 2022
    2022
    Citations: 31
  • Performance Evaluation of Sine Cosine Algorithm-Based Controllers for LFC in an Isolated Hydropower System Integrated with Energy Storage System
    N Paliwal, L Srivastava, M Pandit
    Proceedings of International Conference on Communication and Computational … , 2022
    2022
    Citations: 1
  • Optimal sizing of stand-alone hybrid energy system using black widow optimization technique
    P Singh, M Pandit, L Srivastava
    Proceedings of International Conference on Communication and Computational … , 2022
    2022
    Citations: 3
  • Pareto-frontier differential evolution based financial approach for multi-objective congestion management using customer participation and on-site generation
    A Agrawal, SN Pandey, L Srivastava
    Renewable Energy Focus 42, 253-265 , 2022
    2022
    Citations: 6
  • Minimization of real power losses of transmission lines and improvement of voltage stability in power system using recurring MODE algorithm
    HS Ahirwar, L Srivastava
    Journal of The Institution of Engineers (India): Series B 103 (2), 525-540 , 2022
    2022
    Citations: 5
  • Hybrid deep neural network-based generation rescheduling for congestion mitigation in spot power market
    A Agrawal, SN Pandey, L Srivastava, P Walde, S Singh, B Khan, ...
    IEEE Access 10, 29267-29276 , 2022
    2022
    Citations: 27
  • Application of grey wolf optimization algorithm for load frequency control in multi-source single area power system
    N Paliwal, L Srivastava, M Pandit
    Evolutionary Intelligence 15 (1), 563-584 , 2022
    2022
    Citations: 119
  • Multiobjective salp swarm algorithm approach for transmission congestion management
    A Agrawal, SN Pandey, L Srivastava, P Walde, RK Saket, B Khan
    International Transactions on Electrical Energy Systems 2022 (1), 8256908 , 2022
    2022
    Citations: 7
  • Artificial Intelligence and Sustainable Computing
    HM Dubey, H Mohan, P Dubey, L Srivastava
    Springer Singapore , 2022
    2022
    Citations: 5
  • Enviro-economic sizing of a grid-connected hybrid energy system using tunicate swarm algorithm
    P Singh, M Pandit, L Srivastava
    2021 IEEE 2nd International Conference On Electrical Power and Energy … , 2021
    2021
    Citations: 6
  • A robust optimization approach for optimal power flow solutions using Rao algorithms
    S Gupta, N Kumar, L Srivastava, H Malik, A Anvari-Moghaddam, ...
    Energies 14 (17), 5449 , 2021
    2021
    Citations: 40
  • An efficient Jaya algorithm with Powell’s Pattern Search for optimal power flow incorporating distributed generation
    S Gupta, N Kumar, L Srivastava
    Energy Sources, Part B: Economics, Planning, and Policy 16 (8), 759-786 , 2021
    2021
    Citations: 17

MOST CITED SCHOLAR PUBLICATIONS

  • Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch
    KT Chaturvedi, M Pandit, L Srivastava
    IEEE transactions on power systems 23 (3), 1079-1087 , 2008
    2008
    Citations: 617
  • Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch
    KT Chaturvedi, M Pandit, L Srivastava
    International Journal of Electrical Power & Energy Systems 31 (6), 249-257 , 2009
    2009
    Citations: 351
  • Fast voltage contingency screening using radial basis function neural network
    T Jain, L Srivastava, SN Singh
    IEEE transactions on power systems 18 (4), 1359-1366 , 2003
    2003
    Citations: 143
  • Reserve constrained multi-area economic dispatch employing differential evolution with time-varying mutation
    M Sharma, M Pandit, L Srivastava
    International journal of electrical power & energy systems 33 (3), 753-766 , 2011
    2011
    Citations: 122
  • Application of grey wolf optimization algorithm for load frequency control in multi-source single area power system
    N Paliwal, L Srivastava, M Pandit
    Evolutionary Intelligence 15 (1), 563-584 , 2022
    2022
    Citations: 119
  • Optimal placement of distributed generation: An overview and key issues
    A Yadav, L Srivastava
    2014 International Conference on Power Signals Control and Computations … , 2014
    2014
    Citations: 108
  • Particle swarm optimization with crazy particles for nonconvex economic dispatch
    KT Chaturvedi, M Pandit, L Srivastava
    Applied Soft Computing 9 (3), 962-969 , 2009
    2009
    Citations: 104
  • Hybrid multi‐swarm particle swarm optimisation based multi‐objective reactive power dispatch
    L Srivastava, H Singh
    IET Generation, Transmission & Distribution 9 (8), 727-739 , 2015
    2015
    Citations: 78
  • Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection
    M Pandit, L Srivastava, M Sharma
    Applied Soft Computing 28, 498-510 , 2015
    2015
    Citations: 76
  • Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch
    KT Chaturvedi, M Pandit, L Srivastava
    Applied Soft Computing 8 (4), 1428-1438 , 2008
    2008
    Citations: 75
  • Modified differential evolution algorithm for multi-objective VAR management
    H Singh, L Srivastava
    International Journal of Electrical Power & Energy Systems 55, 731-740 , 2014
    2014
    Citations: 71
  • Bacteria foraging optimization based bidding strategy under transmission congestion
    AK Jain, SC Srivastava, SN Singh, L Srivastava
    IEEE Systems Journal 9 (1), 141-151 , 2013
    2013
    Citations: 69
  • Multi-objective optimal sizing of hybrid micro-grid system using an integrated intelligent technique
    P Singh, M Pandit, L Srivastava
    Energy 269, 126756 , 2023
    2023
    Citations: 61
  • Fast voltage contingency selection using fuzzy parallel self-organizing hierarchical neural network
    M Pandit, L Srivastava, J Sharma
    IEEE Transactions on power systems 18 (2), 657-664 , 2003
    2003
    Citations: 56
  • Generation scheduling and micro-grid energy management using differential evolution algorithm
    N Tiwari, L Srivastava
    2016 International Conference on Circuit, Power and Computing Technologies … , 2016
    2016
    Citations: 53
  • A hybrid neural network model for fast voltage contingency screening and ranking
    L Srivastava, SN Singh, J Sharma
    International Journal of Electrical Power & Energy Systems 22 (1), 35-42 , 2000
    2000
    Citations: 51
  • Neural network-based approach for ATC estimation using distributed computing
    SN Pandey, NK Pandey, S Tapaswi, L Srivastava
    IEEE Transactions on power systems 25 (3), 1291-1300 , 2010
    2010
    Citations: 48
  • Corrective action planning using RBF neural network
    D Ram, L Srivastava, M Pandit, J Sharma
    Applied soft computing 7 (3), 1055-1063 , 2007
    2007
    Citations: 46
  • ANN based integrated security assessment of power system using parallel computing
    S Varshney, L Srivastava, M Pandit
    International Journal of Electrical Power & Energy Systems 42 (1), 49-59 , 2012
    2012
    Citations: 45
  • Nodal congestion price estimation in spot power market using artificial neural network
    SN Pandey, S Tapaswi, L Srivastava
    IET generation, transmission & distribution 2 (2), 280-290 , 2008
    2008
    Citations: 45