Packialakshmi S

@sathyabama.ac.in

Associate Professor
sathyabama institute of science and technology

Packialakshmi S


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https://researchid.co/packialakshmi

EDUCATION

B.E M.E Ph.D (Water Resources)

RESEARCH INTERESTS

Water Resources Development and Management, Environmental Aspects, Studies related to Sustainablity
33

Scopus Publications

337

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Study of Bacterial Concrete Corrosion Resistance in Marine Environment
    Prajeesha M.P, Packialakshmi S
    Ssrg International Journal of Civil Engineering, 2026
    Concrete is a major building material. This study looked at Bacterial Concrete (BC), which is created by mixing a bacterial solution with a cell concentration of 10⁷ CFU/ml. This amount is equivalent to 8% of the cement weight and helps to improve the performance in marine environments. Adding bacterial culture significantly enhanced the concrete’s mechanical properties, durability, and self-healing ability. As a result, it showed better compressive strength than regular concrete. The major aim of this study is to see how the bacterial concrete could reduce the harmful effects of environmental stressors on marine structures. It also evaluated the economic feasibility and sustainability of Bacterial Concrete before use. During testing, Bacterial concrete beams were soaked in seawater for 365 days and showed no rebar corrosion, which is a common problem in normal concrete. Durability tests included water absorption, sorptivity, bulk diffusion, and sulphate resistance. Rice husk ash is utilized for the purpose of strengthening the M40-grade concrete, while adding 5 to 10 percent corn starch improved flowability and the setting time without losing strength. Furthermore, 0.5 percent silica fume is included to boost strength and durability. The study wraps up by discussing sustainability challenges and offering insights to promote the use of bacterial concrete in strong and lasting marine applications.
  • Evaluation of Bond Strength on Fiber Reinforced Concrete (FRC) with GFRP Rebars under Marine Environmental Conditions-An Experimental Investigation
    Mohamed Firdows M Z, Packialakshmi S
    Ssrg International Journal of Civil Engineering, 2025
    Researchers examined how Glass Fiber Reinforced Polymer (GFRP) bars bond with standard and Fiber-Reinforced Concrete (FRC) when exposed to marine conditions at 45°C.Thirty-six specimens with 12 mm diameter GFRP rebars featuring twisted and sand-coated surfaces were embedded in 100 mm concrete cubes and subjected to direct tension pullout tests per ASTM D 7913. Results show that surface treatment significantly influences bond-slip relationships and durability. Sand-coated GFRP rebars exhibited superior performance, achieving bond stresses of 11.77 MPa in plain concrete and 13.66 MPa in FRC, compared to 9.89 MPa and 12.39 MPa for twisted rebars, respectively. Durability assessment revealed lower bond strength reductions for sand-coated rebars (7% in plain concrete, 13.5% in FRC) compared to twisted rebars. These findings provide crucial insights for designing corrosion-resistant concrete structures using GFRP reinforcement.
  • Idealizing the Composition of Bacterial Concrete Using PSO Algorithm for Withstanding the Vagaries of Environmental Changes
    M. P. Prajeesha, S. Packialakshmi, B. Anuradha, K. Deepa
    Advances in Civil Engineering, 2025
    The construction sector is a vital component of a nation’s economic framework, playing a significant role in its overall potential. This industry exhibits considerable diversity and encompasses a wide range of clients, including property builders, developers, suppliers of materials, and contractors. Concrete is a crucial construction material, but it can deteriorate over time. Technological advancements have improved the lifespan of structures, relying on self‐healing concrete properties. Researchers are experimenting with algorithms to enhance the quality of bacterial concrete. The study employed particle swarm optimization (PSO) to optimize bacterial concrete composition in real‐time building applications. The results were verified by changing materials and proportions. The PSO algorithm predicts the optimal value of the materials and their proportions. The results obtained from the proposed method are compared with the existing ones and found to be better on two significant factors: durability and robustness. Experimental data were used to train a neural network model enhanced by PSO, enabling the prediction of strength properties in concrete with an optimal bacterial solution. This work aims to determine the optimal mix proportion of bacterial concrete and then predict the relevant mechanical properties of concrete under this optimal mix by using both conventional experimental methods and the use of the PSO algorithm.
  • Evaluation of Strength and Durability Parameters of Geopolymer Concrete
    Ch. Navyatha, S. Packialakshmi, D. Sabitha, N. P. Rajamane, Nagesh R. Iyer
    Lecture Notes in Mechanical Engineering, 2025
  • Artificial Neural Networks-Based Machine learning for Analysis of Sub-surface Water quality
    B.Anuradha, Sheena. A. D, Hemamalini. J, S. Packialakshmi, Nagamani.K, T.UdayaBanu
    2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025
    Ground water is the most important source of water for the entire world. In India, almost 65% of the water needs for various purposes are met by ground water. Improper disposal of several waste, the ground water is being contaminated includes presence of heavy metals and toxic substance. There are other certain technologies adopted to predict the quality of water. The accuracy will not be high due to several factors like climate change, Temperature change, and pollution. Machine Learning technologies can be adapted to predict the quality of water. Different Algorithms are used to train the model and the accuracy of all algorithms is then compared to check which algorithm has the highest accuracy to predict the quality of the water.
  • Land Use Classification with different Machine Learning technique with Landsat MSS Image
    Nagamanai Katukotta, Stephen Jayaseelan, Atchuthan Purushothaman, Baskaran Anuradhha, Shanmugam Packialakshmi, T. UdayaBanu
    2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025
    Land use/land cover changes had accelerated near the turn of the 20th century owing to rapid and unregulated population growth, economic activity, and industrialization, especially in emerging nations. A quantitative assessment evidently is imperative for grasping and controlling land transformation processes. In this study, an assessment is made of the performance of four commonly used machine-learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM), for LULC mapping. Kappa statistics used to assess the accuracy of the study helps to see that all classifiers present accurate results, though some variation is present. The highest accuracy (0.89 Kappa) is attained through the Random Forest algorithm. These results highlight SVM and RF out of all the algorithms used for LULC mapping as the most effective classifiers. However, more tests with the RF algorithm should be conducted in various regions and climate settings to confirm its robustness and potential applicability.
  • Analysis and Automation of Pipe leakage deduction using Artificial intelligence and machine learning
    T.UdayaBanu, B.Anuradha, M. Radha, S. Packialakshmi, Nagamani.K, S.Sujatha
    2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025
    The DNN-based water leakage detection system project has introduced an innovative approach to identify leakages in water distribution networks using Deep Neural Networks (DNNs). Traditional methods for detecting leaks often depend on manual inspection or costly sensor networks, which may not scalable or cost-effective. In contrast, this project applies the power of DNNs to automatically scrutinize audio data captured by microphones and detect leaks in real-time. The system operates by training a DNN model on a dataset of audio recordings containing both normal pipeline sounds and leak-related abnormalities. The trained model is then put in place to incessantly monitor audio data from strategically placed microphones along the pipeline network. By analyzing the audio signals using the trained DNN, the system can accurately identify and localize potential leaks with high precision using DNNs for leak detection, the project presents several advantages, including improved accuracy, scalability, and cost-effectiveness. Further-more, the system’s capacity to work in real-time enables water utilities to proactively detect and address leaks before they amplify into larger issues, thus reducing water loss and minimizing environmental impact. In general, the DNN-based water leakage detection system project represents a noteworthy advancement in the field of leak detection, offering an innovative solution that merges machine learning with audio signal processing to ensure the integrity of water distribution networks.
  • Evaluation of Tensile Strength of Glass Fiber Reinforced Polymer Rebars under the Marine Environment-A Durability Approach
    Mohamed Firdows Mohamed Zakkaria, Packialakshmi Shanmugam
    Ssrg International Journal of Civil Engineering, 2024
    Over the past decade, there has been a significant rise in the use of Glass Fiber-Reinforced Polymer (GFRP) bars as internal reinforcement for concrete structures, primarily owing to their remarkable corrosion resistance. However, a critical concern has arisen regarding their susceptibility to degradation in terms of tensile strength and elastic modulus when exposed to harsh alkaline and saline environments. This study specifically focuses on the impact of such environments on two distinct types of GFRP rebars: sand-coated and twisted. The experiment involved subjecting these rebars to an accelerated temperature of 60 degrees Celsius for 180 days. The primary objective is to assess the extent to which GFRP rebars experience a reduction in tensile strength under the influence of moisture, alkaline solutions, and saline conditions. Preliminary findings reveal that the tensile strength of the GFRP rebars underwent a significant reduction during exposure to alkaline conditions. Specifically, the twisted GFRP rebars experienced a 25-30% reduction, while the sand-coated counterparts exhibited a 20% reduction in tensile strength. These observations highlight the vulnerability of GFRP rebars in alkaline environments. Furthermore, the study has identified a 15% reduction in tensile strength for both types of GFRP rebars under the influence of saline conditions. The implications of these reductions are currently the subject of further investigation as the research delves into understanding the effects of alkaline and saline exposure on the overall performance and durability of GFRP bars in concrete structures.
  • REDEFINING NITROGEN AND PHOSPHORUS SPECIES REMOVAL WITH LANTHANUM-ENHANCED METHODS AND MECHANISM
    Journal of Environmental Protection and Ecology, 2024
  • Utilization of agricultural, industrial waste and nanosilica as replacement for cementitious material and natural aggregates – Mechanical, microstructural and durability characteristics assessment
    M. Siva Chennakesava Rao, Packialakshmi S, Badrinarayan Rath, Sulaiman Ali Alharbi, Saleh Alfarraj, Praveenkumar T R, Beata Gavurová
    Environmental Research, 2023
    This study examines the effect of rice husk ash (RHA) and nanosilica, and ground granular blast furnace slag (GGBS) on concrete mechanical and durability properties. The cement had been partially replaced with nanosilica and RHA having substitution percentages up to 6% and 10% respectively whereas the sand had been partially replaced by GGBS at 20% for all mixes. A water-to-cementitious materials ratio of 0.38 and a sand-to-cementitious materials ratio of 2.04 were used to cast eight different concrete mixes. The nanosilica used in the present research possessed some favorable effects such as rich fineness, higher surface area and greater reactivity which signified one of the best cement replacement materials. Both the durability and strength of concrete specimens possessing nanosilica, RHA and GGBS was evaluated using in-elastic neutron scattering, SEM image, piezoresistive test, split tensile strength, flexural strength and compressive strength test. Concrete specimens were also subjected to chloride penetration and water absorption to examine the impact of replacement materials on the concrete's durability attributes. Concrete performance was increased by the ternary blending of concrete because of the active participation of nanosilica in durability and strength at early ages, both RHA and GGBS played an important role in improving packing density. It was found that as the percentage of cement replaced with nanosilica increases, the durability of concrete also significantly increases. But the optimum strength parameter was found when 4% of cement was replaced by the nanosilica effectively. The proposed ternary mix may be eco-friendly by saving cement and enhancing strength and durability effectively.
  • MTBE adsorption on surface modified adsorbent kaolin-KOH – A study on kinetic equilibrium and surface morphology
    Global Nest Journal, 2023
  • Flexural behavior of fiber-reinforced concrete beams with GFRP rebars under marine environmental conditions
    Mohamed Firdows Mohamed Zakkaria, Packialakshmi Shanmugam
    International Journal of Advanced Manufacturing Technology, 2023
  • Hydrochemical Investigation and Water Quality Mapping in and Around Pallikaranai Marshland Area in Chennai, India
    S. Packialakshmi, K. Nagamani, B. Anuradha
    Impacts of Urbanization on Hydrological Systems in India, 2023
  • Fluoride contamination of groundwater in a coastal region – a growing environmental pollution threat
    A. A. Sambhavi, K. Nagamani, B. Gowtham, S. Packialakshmi, B. Anuradha
    Global Nest Journal, 2023
  • Identification of Groundwater Potential Zones Using Machine Learning Algorithms and Geospatial Techniques
    K Nagamani, Mohammad Suhail Meer, Baskaran Anuradhha, C Bhuvaneswari, S. Packialakshmi
    2023 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2023, 2023
  • Assessing the Wastewater Pollutants Retaining for a Soil Aquifer Treatment using Batch Column Experiments
    V. R. Raji, S. Packialakshmi
    Civil Engineering Journal Iran, 2022
  • Integration of ALOHA- MARPLOT in the Real Study of Monitoring Air Quality Measures
    A D Sheena, B Anuradha, S Packialakshmi
    2022 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2022, 2022
  • An Analytical Study for Assessing Water Productivity in Pre- and Post-Rehabilitation Period of Rural Tank System
    B. Anuradha, S. Packialakshmi, Naveen Sanjay, V. Vivekananthan
    Advances in Civil Engineering, 2022
  • Groundwater Recharge Planning Using Field Survey for Talupula Mandal in Anantapur District, Andhra Pradesh, India
    K. Nagamani, Prabhu Dass Batvari, S. Packialakshmi, C. Sai Kumar Reddy, B. Anuradha
    Nature Environment and Pollution Technology, 2021
  • Performance evaluation of fire resistant characteristics in polypropylene fiber concrete
    S Packialakshmi, Krishnakumar, Solomon Erskine, B Anuradha
    Materials Today Proceedings, 2021
  • Investigation Of The Efficacy Of Water Hyacinth (Aquatic Plant) For The Treatment Of Dairy Effluent
    Indian Journal of Environmental Protection, 2021
  • Treatment of industrial wastewater using coconut shell based activated carbon
    S. Packialakshmi, B. Anuradha, K. Nagamani, J. Sarala Devi, S. Sujatha
    Materials Today Proceedings, 2021
  • Role of irrigation tank rehabilitation and related off-farm activities in achieving sustainable rural development
    Journal of Green Engineering, 2020
  • Statistical study of water users association for sustainable agriculture in rural development
    Journal of Green Engineering, 2020
  • Roughness prediction models based on variable distress parameters using neural network and MLRA for PMGSY roads
    International Journal of Advanced Science and Technology, 2020
  • Treating waste water using electrocoagulation approach
    International Journal of Civil Engineering and Technology, 2018
  • The peri-urban to urban groundwater transfer and its societal implications in chennai, south India - A case study
    S. Packialakshmi, N.K. Ambujam
    Indian Journal of Agricultural Research, 2017
  • Role of aquatic plants in treating wastewater - A sustainable approach
    Pollution Research, 2017
  • Assessment of groundwater quality index in and around Sholinganallur area, Tamil Nadu
    S. Packialakshmi, Meheli Deb, Hrituparna Chakraborty
    Indian Journal of Science and Technology, 2015
  • Application of groundwater balance equation for estimating the stage of groundwater development in the urbanized environment
    International Journal of Earth Sciences and Engineering, 2015
  • Inducing recharge of groundwater by treated waste water - a pilot study in Southern Chennai metropolitan area
    S. Packialakshmi, S. Balaji, T. Kumaresan
    Indian Journal of Science and Technology, 2015
  • A hydrochemical and geological investigation on the Mambakkam mini watershed, Kancheepuram District, Tamil Nadu
    Packialakshmi Shanmugam, Ambujam N. K.
    Environmental Monitoring and Assessment, 2012
  • Groundwater market and its implications on water resources and agriculture in the southern peri-urban interface, Chennai, India
    Shanmugam Packialakshmi, N. K Ambujam, Prakash Nelliyat
    Environment Development and Sustainability, 2011

RECENT SCHOLAR PUBLICATIONS

  • Artificial Neural Networks-Based Machine learning for Analysis of Sub-surface Water quality
    B Anuradha, S Packialakshmi, T UdayaBanu
    2025 International Conference on Data Science, Agents & Artificial … , 2025
    2025
    Citations: 2
  • Land Use Classification with different Machine Learning technique with Landsat MSS Image
    N Katukotta, S Jayaseelan, A Purushothaman, B Anuradhha, ...
    2025 International Conference on Data Science, Agents & Artificial … , 2025
    2025
    Citations: 2
  • Analysis and Automation of Pipe leakage deduction using Artificial intelligence and machine learning
    T UdayaBanu, B Anuradha, M Radha, S Packialakshmi, S Sujatha
    2025 International Conference on Data Science, Agents & Artificial … , 2025
    2025
    Citations: 1
  • Idealizing the Composition of Bacterial Concrete Using PSO Algorithm for Withstanding the Vagaries of Environmental Changes
    MP Prajeesha, S Packialakshmi, B Anuradha, K Deepa
    Advances in Civil Engineering 2025 (1), 2624134 , 2025
    2025
  • Evaluation of Bond Strength on Fiber Reinforced Concrete (FRC) with GFRP Rebars under Marine Environmental Conditions - An Experimental Investigation
    SP M.Z. Mohamed Firdows1
    SSRG International Journal of Civil Engineering 12 (1), 16-29 , 2025
    2025
  • Evaluation of Strength and Durability Parameters of Geopolymer Concrete
    NR C., Navyatha, Ch , P., Shanmugam, Packialakshmi , D.S., Sabitha, D. S ...
    Lecture Notes in Mechanical Engineering, 119-130 , 2024
    2024
    Citations: 1
  • Evaluation of Tensile Strength of Glass Fiber Reinforced Polymer Rebars under the Marine Environment – A Durability Approach
    MFP Shanmugam
    SSRG International Journal of Civil Engineering 11 (3), 1-11 , 2024
    2024
  • Evaluation of Bacterial Concrete Corrosion Resistance in Marine Settings: A Morphological Analysis Perspective
    M Prajeesha, S Packialakshmi
    Int. Res. J. Educ. Technol 6 , 2024
    2024
    Citations: 1
  • Identification of groundwater potential zones using machine learning algorithms and geospatial techniques
    K Nagamani, MS Meer, B Anuradhha, C Bhuvaneswari, S Packialakshmi
    2023 International Conference on Data Science, Agents & Artificial … , 2023
    2023
    Citations: 1
  • Flexural behavior of fiber-reinforced concrete beams with GFRP rebars under marine environmental conditions
    MFM Zakkaria, P Shanmugam
    The International Journal of Advanced Manufacturing Technology, 1-12 , 2023
    2023
    Citations: 4
  • Fluoride contamination of groundwater in a coastal region-a growing environmental pollution threat
    AA Sambhavi, K Nagamani, B Gowtham, S Packialakshmi, B Anuradha
    GLOBAL NEST JOURNAL 25 (9), 41-52 , 2023
    2023
    Citations: 2
  • Utilization of agricultural, industrial waste and nanosilica as replacement for cementitious material and natural aggregates–Mechanical, microstructural and durability …
    MSC Rao, S Packialakshmi, B Rath, SA Alharbi, S Alfarraj, ...
    Environmental Research 231, 116010 , 2023
    2023
    Citations: 29
  • Hydrochemical investigation and water quality mapping in and around Pallikaranai marshland area in Chennai, India
    S Packialakshmi, K Nagamani, B Anuradha
    Impacts of Urbanization on Hydrological Systems in India, 25-42 , 2023
    2023
    Citations: 3
  • MTBE adsorption on surface modified adsorbent kaolin-KOH-A study on kinetic equilibrium and surface morphology
    S Mahendran, S Packialakshmi, N Al-Zaqri, A Boshaala, S Krishnapriya, ...
    GLOBAL NEST JOURNAL 25 (4), 86-94 , 2023
    2023
    Citations: 1
  • Fluoride contamination of groundwater in a coastal region-a growing environmental pollution threat.
    A Amuthini Sambhavi, K Nagamani, B Gowtham, S Packialakshmi, ...
    2023
  • Performance evaluation of fire resistant characteristics in polypropylene fiber concrete
    S Packialakshmi, S Erskine, B Anuradha
    Materials Today: Proceedings 81, 1152-1156 , 2023
    2023
    Citations: 7
  • Treatment of industrial wastewater using coconut shell based activated carbon
    S Packialakshmi, B Anuradha, K Nagamani, JS Devi, S Sujatha
    Materials Today: Proceedings 81, 1167-1171 , 2023
    2023
    Citations: 51
  • Integration of ALOHA-MARPLOT in the Real Study of Monitoring Air Quality Measures
    AD Sheena, B Anuradha, S Packialakshmi
    2022 International Conference on Data Science, Agents & Artificial … , 2022
    2022
  • Assessing the wastewater pollutants retaining for a soil aquifer treatment using batch column experiments
    VR Raji, S Packialakshmi
    Civil Engineering Journal 8 (7), 1482-1491 , 2022
    2022
    Citations: 78
  • Research Article An Analytical Study for Assessing Water Productivity in Pre-and Post-Rehabilitation Period of Rural Tank System
    B Anuradha, S Packialakshmi, N Sanjay, V Vivekananthan
    2022

MOST CITED SCHOLAR PUBLICATIONS

  • Assessing the wastewater pollutants retaining for a soil aquifer treatment using batch column experiments
    VR Raji, S Packialakshmi
    Civil Engineering Journal 8 (7), 1482-1491 , 2022
    2022
    Citations: 78
  • Treatment of industrial wastewater using coconut shell based activated carbon
    S Packialakshmi, B Anuradha, K Nagamani, JS Devi, S Sujatha
    Materials Today: Proceedings 81, 1167-1171 , 2023
    2023
    Citations: 51
  • Groundwater market and its implications on water resources and agriculture in the southern peri-urban interface, Chennai, India
    S Packialakshmi, N K Ambujam, P Nelliyat
    Environment, development and sustainability 13 (2), 423-438 , 2011
    2011
    Citations: 31
  • Utilization of agricultural, industrial waste and nanosilica as replacement for cementitious material and natural aggregates–Mechanical, microstructural and durability …
    MSC Rao, S Packialakshmi, B Rath, SA Alharbi, S Alfarraj, ...
    Environmental Research 231, 116010 , 2023
    2023
    Citations: 29
  • A hydrochemical and geological investigation on the Mambakkam mini watershed, Kancheepuram District, Tamil Nadu
    P Shanmugam, A NK
    Environmental monitoring and assessment 184 (5), 3293-3306 , 2012
    2012
    Citations: 29
  • Assessment of Groundwater Quality Index in and Around Sholinganallur Area, Tamil Nadu
    HC S. Packialakshmi *, Meheli Deb
    Indian Journal of Science and Technology 8 (36), 1-7 , 2015
    2015
    Citations: 28
  • Inducing recharge of groundwater by treated waste water–a pilot study in Southern Chennai Metropolitan Area
    S Packialakshmi, S Balaji, T Kumaresan
    Indian Journal of Science and Technology 8 (11) , 2015
    2015
    Citations: 17
  • Emerging land use changes and their effects on groundwater: a study of the Mambakkam mini watershed, southern suburban area of Chennai, India.
    S Packialakshmi, NK Ambujam, S Mahalingam
    2010
    Citations: 16
  • Roughness prediction models based on variable distress parametrs using neural network and MLRA for PMGSY roads
    P Makendran Vignesh Kumar Mahalingam S
    International Journal of Advanced Science and Technology 27 (7), 1-11 , 2020
    2020
    Citations: 11
  • The peri-urban to urban groundwater transfer and its societal implications in Chennai, south India-A case study.
    S Packialakshmi, NK Ambujam
    Indian Journal of Agricultural Research 51 (2) , 2017
    2017
    Citations: 9
  • Performance evaluation of fire resistant characteristics in polypropylene fiber concrete
    S Packialakshmi, S Erskine, B Anuradha
    Materials Today: Proceedings 81, 1152-1156 , 2023
    2023
    Citations: 7
  • An Analytical Study for Assessing Water Productivity in Pre‐and Post‐Rehabilitation Period of Rural Tank System
    B Anuradha, S Packialakshmi, N Sanjay, V Vivekananthan
    Advances in Civil Engineering 2022 (1), 1119931 , 2022
    2022
    Citations: 6
  • Flexural behavior of fiber-reinforced concrete beams with GFRP rebars under marine environmental conditions
    MFM Zakkaria, P Shanmugam
    The International Journal of Advanced Manufacturing Technology, 1-12 , 2023
    2023
    Citations: 4
  • Comparison of flood discharge calculated by different statistical distribution functions and software
    TE Keskin, E Doğan, O Sönmez, HB Umarusman, P Spor, M Badfar, ...
    Disaster Science and Engineering 6 (2), 1-7 , 2020
    2020
    Citations: 4
  • Hydrochemical investigation and water quality mapping in and around Pallikaranai marshland area in Chennai, India
    S Packialakshmi, K Nagamani, B Anuradha
    Impacts of Urbanization on Hydrological Systems in India, 25-42 , 2023
    2023
    Citations: 3
  • Artificial Neural Networks-Based Machine learning for Analysis of Sub-surface Water quality
    B Anuradha, S Packialakshmi, T UdayaBanu
    2025 International Conference on Data Science, Agents & Artificial … , 2025
    2025
    Citations: 2
  • Land Use Classification with different Machine Learning technique with Landsat MSS Image
    N Katukotta, S Jayaseelan, A Purushothaman, B Anuradhha, ...
    2025 International Conference on Data Science, Agents & Artificial … , 2025
    2025
    Citations: 2
  • Fluoride contamination of groundwater in a coastal region-a growing environmental pollution threat
    AA Sambhavi, K Nagamani, B Gowtham, S Packialakshmi, B Anuradha
    GLOBAL NEST JOURNAL 25 (9), 41-52 , 2023
    2023
    Citations: 2
  • Groundwater Recharge Planning Using Field Survey for Talupula Mandal in Anantapur District, Andhra Pradesh, India
    K Nagamani, PD Batvari, S Packialakshmi, CSK Reddy, B Anuradha
    Nature Environment and Pollution Technologythis link is disabled 20 (5 … , 2021
    2021
    Citations: 2
  • Analysis and Automation of Pipe leakage deduction using Artificial intelligence and machine learning
    T UdayaBanu, B Anuradha, M Radha, S Packialakshmi, S Sujatha
    2025 International Conference on Data Science, Agents & Artificial … , 2025
    2025
    Citations: 1