A Suresh

@sietk.org

Associate Professor and COMPUTER SCIENCE AND ENGINEERING
Siddharth Institute of Engineering & Technology

A Suresh


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https://researchid.co/csesuresh6

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Science Applications, Human-Computer Interaction
33

Scopus Publications

1618

Scholar Citations

23

Scholar h-index

39

Scholar i10-index

Scopus Publications

  • Federated hyper LSTM model for storage optimization and collision prediction in an intelligent IoVT
    A. Balajee, R. Vinoth, A. Suresh, Mudassir Khan, T.R. Mahesh, Anu Sayal
    Egyptian Informatics Journal, 2026
    The Internet of Vehicles (IoV) supports the combination of various techno insights to provide safe and comfortable transportation. These vehicles can share information to facilitate the current status of the location that the vehicle is about to travel with. A collision occurs when the abundant information fails to reach the target IoV within a stipulated time limit. The term collision in an IoT environment is always annexed with storage since the sparse storage system could lead to loss of information. Thus, there is a pressing need for collision avoidance annotated with storage optimization for IoV technologies. In this article, we propose an innovative federated hyper-LSTM model that initially handles the storage environment by incorporating federated learners to optimize it. The collision is predicted simultaneously by the proposed hyper-LSTM model. The entire model is equipped with reinforcement learners to keep track of the current status of storage and collision, achieving a benchmark accuracy of 97% for the proposed model.
  • A Generalized Deep Learning Approach for Cross-Crop Plant Disease Detection Using the Plant Village Dataset
    Roopa R, Rajesh Lingam, Santosh Kumar Ravva, Suresh A, Penubaka Balaji, Avanija J
    Journal of Machine and Computing, 2025
    Plant diseases continue to be one of the leading causes of reduced agricultural productivity worldwide, directly threatening food supply chains and the economic stability of farming communities. With the global population steadily increasing, the demand for intelligent, scalable, and highly accurate plant disease detection systems has never been more critical. Deep learning methods have shown promising results in this field; however, numerous conventional models cannot often generalize well across different crop species and unseen disease types. These limitations hinder their practical deployment in dynamic real-world agricultural environments. In this study, we propose a robust and generalized deep learning-based approach for cross-crop plant disease detection, using the comprehensive and diverse Plant Village dataset. Our model is built upon a custom-designed Convolutional Neural Network (CNN) architecture that incorporates a small Inception module. Unlike traditional CNNs, which primarily focus on the global features of a leaf. Our model detects and analyzes localized disease spread patterns, enhancing detection across diverse crops and adapting to novel conditions. The small Inception module plays a vital role in enabling multi-scale feature extraction from small disease-affected patches without adding excessive computational complexity. This architectural refinement allows the model to learn more discriminative features, resulting in faster convergence and higher classification accuracy. When trained and validated on the Plant Village dataset, our model achieved an impressive accuracy of 98.45%, outperforming many traditional approaches. Additionally, it demonstrated consistently high precision, recall, and F1-score, confirming its reliability and robustness. By addressing the challenges of overfitting and poor generalization, common pitfalls in many deep learning models, our method provides a scalable and effective solution for real-time agricultural disease monitoring. This work contributes to the growing field of precision agriculture by offering a model that is not only accurate but also generally efficient and practical for deployment in diverse agricultural settings. Ultimately, our research aims to support the development of smart farming technologies that ensure healthier crops and contribute to long-term global food security.
  • Leveraging Multi-modal Datasets to Enhance Diagnostic Accuracy and Reliability in MRI Images for Brain Tumor Classification
    D. Ramya Dorai, R. Vinoth, A. Suresh, T. R. Mahesh
    Biomedical Materials and Devices, 2025
  • Fine-Tuned K-Nearest Neighbor for Hybrid Beamforming Algorithm in Massive MIMO Systems
    Jayapal Lande, A. Suresh, K. S. Shashidhara, Alampally Sreedevi, T. S. Ghouse Basha
    Lecture Notes in Electrical Engineering, 2025
  • Real-Time Traffic Congestion Detection Using ResNet50 with Multi-Dimensional Feature Fusion Residual Block in YOLOv8
    Raami Riadhusin, Anupama Sindgi, A. Suresh, S. D. Govardhan, Indu. B
    2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2025, 2025
    Analyzing vehicles in a city is important to better understand traffic jams and to assess mobility for environmental and sustainability goals. However, traditional traffic management systems struggle to perform on under dynamic traffic conditions such as accidents, road closures and weather-related disruptions due to significant congestion, or delays. To overcome this problem, this research proposed Deep Learning (DL) based ResNet50 with Multi-dimensional Feature fusion Residual Block (Res-MFRB) in You Only Look Once version 8 (YOLOv8) for vehicle detection. The ResNet50 used to extract deep visual features and YOLOv8 used for identifying and localizing vehicles within each frame. Light, Medium, or Heavy objectives are used to classify traffic congestion levels based on spatial patterns and vehicle density captured in road surveillance images. MFRB continuously captures spatial and sequential features from traffic surveillance images to enhance the classification performance under various congestion levels. The Res-MFRB is evaluated using four publicly available datasets such as UCSD, NGSIM, BDD100K and KITTI. The experimental results of the proposed Res-MFRB achieved a high accuracy of 99.40% on UCSD dataset, which is more accurate than the existing methods like MobileNetV2 with spatial attention.
  • Self-Attention Recurrent Reinforcement Learning Based Anomaly Detection for Dynamic Spectrum Access in Cognitive Radio Networks
    Sachinkumar, A. Suresh, Rajeshwari Patil, Alampally Sreedevi, Yerrolla Chanti
    Lecture Notes in Electrical Engineering, 2025
  • Adaptive Multi-Path Energy Optimization in WSN Using Machine Learning and Meta heuristic Techniques
    M. Giri, A. Suresh, N. Babu, K. Arun Kumar, B. Anandan, M. Srinivasa Rao
    2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025, 2025
    WSN more commonly network adopted in many critical real time applications like smart agriculture, industrial automation, environmental monitoring, and healthcare systems. However, the energy limitation of battery-powered sensor nodes severely impacts network longevity and reliability. Traditional clustering and routing protocols often fail to adapt to dynamic network conditions, resulting in inefficient energy usage and network partitioning. In this research paper proposed computational intelligence approaches like meta heuristic techniques, Deep learning (DL), and machine learning (ML) for improving energy efficiency in WSNs. The study critically analyzes existing approaches like BiLSTM (Bi directional Long Short Term Memory), CNN (Convolution Neural Networks), PSO (Particle Swarm Optimization), and GA (Genetic Algorithms). A novel protocol is used to identify multiple paths using Computational Intelligence Optimized Algorithm (AMPRP-CIOA), is proposed and simulated in MATLAB under various network scales. Experimental results demonstrate that AMPRP-CIOA outperforms, then existing similar kind of methods in terms of time complexity, throughput, lifetime of network, and power utilization. The research identifies limitations such as training overhead, scalability challenges, and interpretability concerns.
  • Efficient and Accurate Traffic Sign Detection Leveraging YOLOv8: A Cutting-Edge Deep Learning Framework
    Gunji Sreenivasulu, Lakshmi H N, Muni Kumari T, Anjaiah P, Suresh A, Avanija J
    Journal of Machine and Computing, 2025
    The timely and precise identification of traffic signs is essential for maintaining the effectiveness and safety of contemporary roads, particularly in light of the increasing number of self-driving cars. Conventional image processing methods have faced challenges because to the intricate and fluctuating variables present in real-world settings, including various signage, erratic weather, and inconsistent illumination. This study utilizes recent breakthroughs in deep learning, particularly the YOLOv8 (You Only Look Once version 8) model, to tackle these difficulties. YOLOv8 incorporates cutting-edge neural network architectural advancements, such as an anchor-free detection methodology, adaptive spatial feature pooling, and dynamic neural configurations. In order to further increase detection efficiency and accuracy, this study presents two innovative models, YOLOv8-DH and YOLOv8-TDHSA. These models make use of improvements such decoupled heads and transformer-based self-attention mechanisms. Experimental results indicate that the suggested models substantially surpass current deep learning models, attaining enhanced performance across multiple measures, including accuracy, recall, F-score, and mean average precision (mAP). This research enhances traffic sign detecting technology, facilitating the development of safer and more intelligent transportation systems.
  • Brain Stroke Prognosis: A Fusion of Machine Learning and Deep Learning
    A. Suresh, V Sambasiva, P. Somya, G. Yashmitha, K. Soma Sagar, N. Tenil Sai
    2025 International Conference on Data Science and Business Systems Icdsbs 2025, 2025
    Since brain stroke is one of the world's major causes of disability and death, accurate prediction models are essential for prompt diagnosis and treatment. The research analyzes combined approaches between machine learning (ML), deep learning (DL), and ensemble-based techniques for improving brain stroke outcome prediction. The research utilized a dataset containing demographics alongside lifestyle factors and medical information processed through normalization and imputation as well as the Synthetic Minority Oversampling Technique (SMOTE) which addressed class imbalance issues and solved missing values. The research examines four popular modeling approaches starting with Support Vector Machines (SVM) and Random Forest (RF) followed by CatBoost then Convolutional Neural Networks (CNN) and an improved ensemble combination. The ensemble model performed exceptionally well across comprehensive performance metrics which demonstrated accuracy at 96 % and precision at 97 % with recall at 96 %. Support Vector Machines showed outstanding recall performance which makes the model an ideal choice for medical applications that need successful diagnosis detection. The author stresses how ensemble approaches use different models to generate precise predictions for stroke outcomes. Advanced predictive systems in healthcare receive implementation guidance through these findings that will lead to enhanced patient results in critical medical conditions.
  • A Machine Learning Approach To Predict Autism Spectrum Disorder
    U. Sivaji, B. Rupa Devi, A. Suresh, M. Reddi Durgasree, K. Reddy Madhavi, J. Avanija
    Lecture Notes in Networks and Systems, 2025
  • Advancing Corporate Finance: A Multigranularity Approach to Bankruptcy Prediction
    A Suresh, Durairaj K, B Anandan, K. D. Mohana Sundaram, B. Ravi Babu, Agan Prabu S
    2025 IEEE International Conference on Advanced Computing Technologies Icact 2025, 2025
  • Predictor: Critical Illness from Chronic Problems and Discovered Features using ML
    M. Giri, Dr A Suresh, N. Babu, B Anandan, S. Vanathi, N Kamal
    Proceedings 2025 5th International Conference on Internet of Things Smart Innovation and Usage Iot Siu 2025, 2025
  • Empowering Fake News Detection Through Innovative Hybrid Deep Learning-Based Approach
    A Suresh, R M Mallika, S Gireesh, G Hari Kiran Singh, E S Jeevanandham, A Hemalatha
    Proceedings of 2025 International Conference on Computing for Sustainability and Intelligent Future Comp Sif 2025, 2025
  • A Predictive Model for Cardio Stroke Risk Using Hybrid Machine Learning
    N. Babu, M. Giri, A. Suresh, M.L.M. Prasad, B. Anandan, M. Srinivasa Rao
    2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025, 2025
  • ML Based Framework for Predicting Red Wine Quality
    A. Suresh, M. Giri, K. Ammulu, N. Babu, B. Anandan, M. Srinivasa Rao
    2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025, 2025
  • Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems
    A. Suresh, Erasappa Murali, V. Anjali Sri, E Sumana, Gopaluni Venkata Sai Sathwik, P. Devendra Reddy
    3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025
  • Non-Invasive Sensor-Based Health Monitoring and Prediction in an Iot Environment
    A. Suresh, M. Giri, M. Murali Mohana Kumara Varma, N. Babu, B. Anandan, M. Srinivasa Rao
    2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025, 2025
  • Evolutionary Algorithms and Quantum Computing—Pioneering the Next Era of Intelligence and Innovation
    Quantum Computing the Past the Present and the Future, 2025
  • A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model
    A. Suresh, B. Geetha Vani, M. Lavanya, R G Kumar
    2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024
  • Enhance the Context-Based Online Recommendation System using Deep Reccurrent Neural Network with Enhaned Pigeon Search Optimization
    A Suresh, J Sridhar, R M Mallika, D Nagaraju, G Indiravathi
    IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024
  • Hybrid Filtering-Based Product Recommendation System Integrating GRU and BFGS Optimization
    A. Suresh, R. G. Kumar, D. Nagaraju, K D Mohana Sundaram, B Anandan
    IEEE International Conference on Electronic Systems and Intelligent Computing Icesic 2024 Proceedings, 2024
  • IoT-Enabled Smart Parking System using Machine Learning for Real-Time Parking Prediction
    A. Suresh, P.M.S.S. Chandu, S. Subhash Chandra Bose, V. Dhamini, P. Adi Narayana, K. Dilli Balaji
    4th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2024, 2024
  • Adaptive Health Records Protection Using Modular Encryption Standard In Cloud Computing
    R.G. Kumar, A. Suresh, G. Amrutha, S. Himaja, T. Bhanu, C. Dinesh
    2024 IEEE International Conference on Smart Power Control and Renewable Energy Icspcre 2024, 2024
  • Advanced Cardiovascular Disease Prediction: A Comparative Analysis of Ensemble Stacking and Deep Neural Networks
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Enhancing Crypto Transaction Security: A Machine Learning Approach
    T.M.S. Mekala Rani, A. Suresh, S. Bhargavi, M. Harsha Vardhan Reddy, K. Sai Nikhil, G. Chandu Priya
    10th International Conference on Electrical Energy Systems Icees 2024, 2024
  • Enhancing Public Safety through Real-time Weapon Detection: A Deep Learning Approach
    J. Maria Arockia Dass, A. Suresh, K. Swathi, Paidimuddala Shalini, N A Yaswanth Sinha, Kotholla Uday Kiran U
    2024 3rd International Conference for Advancement in Technology Iconat 2024, 2024
  • Cloud Computing’s Effect on Video Games Streaming
    A. Komathi, J. Lenin, S. Asha, A. Suresh, M. Suguna, C. Srinivasan
    2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
  • A FRAMEWORK FOR TWEET CLASSIFICATION AND ANALYSIS ON SOCIAL MEDIA PLATFORM USING FEDERATED LEARNING
    Voruganti Naresh Kumar, U. Sivaji, Gunipati Kanishka, B. Rupa Devi, A. Suresh, et al.
    Malaysian Journal of Computer Science, 2023
  • Online product recommendation system using gated recurrent unit with Broyden Fletcher Goldfarb Shanno algorithm
    A. Suresh, M. J. Carmel Mary Belinda
    Evolutionary Intelligence, 2022
  • Accelerated - Generic gradient descent for e-commerce recommender systems
    Suresh A, M J Carmel Mary Belinda Dr.
    Indian Journal of Computer Science and Engineering, 2021
  • A comprehensive study of hybrid recommendation systems for E-commerce applications
    International Journal of Advanced Science and Technology, 2020
  • The statistical analysis and E-risks of major E-commerce systems in India
    International Journal of Advanced Science and Technology, 2019
  • Enhanced cyber security for big data challenges
    S. Padmapriya, N. Partheeban, N. Kamal, Arjun Suresh, S. Arun
    International Journal of Innovative Technology and Exploring Engineering, 2019

RECENT SCHOLAR PUBLICATIONS

  • Enhancing layout design in aluminum die casting for reduced cycle time and material handling distance
    A Suresh, A Ramesh
    Mechanics of Advanced Materials and Structures 33 (1), 2669359 , 2026
    2026
  • Federated hyper LSTM model for storage optimization and collision prediction in an intelligent IoVT
    A Balajee, R Vinoth, A Suresh, M Khan, TR Mahesh, A Sayal
    Egyptian Informatics Journal 33, 100884 , 2026
    2026
  • Experimental Study on Mechanical, Thermal Conductivity, Wear, and Water Absorption Behaviour of Calotropis gigantea Stem Fiber/Citrus Maxima ZnO Reinforced Epoxy Composites
    A Suresh, L Jayakumar
    Silicon, 1-16 , 2026
    2026
  • Tesla Coil-Powered Wireless Charging for Drone in Supply Chain Optimization
    A Suresh, G Pranesh, ST Anbu, R Sankarasubramani, K Nagarjun
    ADVANCES IN ADDITIVE MANUFACTURING TECHNOLOGIES, 373-378 , 2026
    2026
  • Adaptive Multi-Path Energy Optimization in WSN Using Machine Learning and Meta heuristic Techniques
    M Giri, A Suresh, N Babu, KA Kumar, B Anandan, MS Rao
    2025 IEEE 17th International Conference on Computational Intelligence and … , 2025
    2025
  • ML Based Framework for Predicting Red Wine Quality
    A Suresh, M Giri, K Ammulu, N Babu, B Anandan, MS Rao
    2025 IEEE 17th International Conference on Computational Intelligence and … , 2025
    2025
  • Non-Invasive Sensor-Based Health Monitoring and Prediction in an Iot Environment
    A Suresh, M Giri, MMMK Varma, N Babu, B Anandan, MS Rao
    2025 IEEE 17th International Conference on Computational Intelligence and … , 2025
    2025
    Citations: 1
  • A Predictive Model for Cardio Stroke Risk Using Hybrid Machine Learning
    N Babu, M Giri, A Suresh, MLM Prasad, B Anandan, MS Rao
    2025 IEEE 17th International Conference on Computational Intelligence and … , 2025
    2025
  • Predictor: Critical Illness from Chronic Problems and Discovered Features using ML
    M Giri, A Suresh, N Babu, B Anandan, S Vanathi, N Kamal
    2025 5th International Conference on Internet of Things: Smart Innovation … , 2025
    2025
  • Leveraging Multi-modal Datasets to Enhance Diagnostic Accuracy and Reliability in MRI Images for Brain Tumor Classification
    D Ramya Dorai, R Vinoth, A Suresh, TR Mahesh
    Biomedical Materials & Devices, 1-16 , 2025
    2025
  • Advancing Corporate Finance: A Multigranularity Approach to Bankruptcy Prediction
    A Suresh, K Durairaj, B Anandan, KDM Sundaram, BR Babu
    2025 IEEE International Conference on Advanced Computing Technologies (ICACT … , 2025
    2025
  • Self-Attention Recurrent Reinforcement Learning Based Anomaly Detection for Dynamic Spectrum Access in Cognitive Radio Networks
    Sachinkumar, A Suresh, R Patil, A Sreedevi, Y Chanti
    International Conference on 6G Communications Networking and Signal … , 2025
    2025
  • Fine-Tuned K-Nearest Neighbor for Hybrid Beamforming Algorithm in Massive MIMO Systems
    J Lande, A Suresh, KS Shashidhara, A Sreedevi, TS Ghouse Basha
    International Conference on 6G Communications Networking and Signal … , 2025
    2025
  • Manufacturing of Wind Turbine Blades Using PLA and ABS Materials
    S Rajakumar, A Suresh, VS Greeshma
    Recent Developments in Wind Engineering: Select Proceedings of NCWE 2024, 361 , 2025
    2025
  • Empowering Fake News Detection Through Innovative Hybrid Deep Learning-Based Approach
    A Suresh, RM Mallika, S Gireesh, GHK Singh, ES Jeevanandham, ...
    2025 International Conference on Computing for Sustainability and … , 2025
    2025
  • A novel approach for predicting net irrigated area in India using hybrid deep learning architectures
    NV Palanichamy, M Kalpana, N Balakrishnan, A Suresh, V Balamurugan, ...
    2025
  • IoT-Enabled Smart Parking System using Machine Learning for Real-Time Parking Prediction
    A Suresh, P Chandu, SSC Bose, V Dhamini, PA Narayana, KD Balaji
    2024 4th International Conference on Mobile Networks and Wireless … , 2024
    2024
    Citations: 2
  • Topology Optimization of Electric Solar Vehicle Brake Pedal
    S Pavithra, A Suresh, RG Moorthy, A Parthiban, D Dinesh, ...
    Advances in Additive Manufacturing Technologies, 414-417 , 2024
    2024
  • LM6 Aluminium Alloy Processing by Die Casting—A State of the Art
    A Parthiban, A Ramesh, A Suresh, D Dinesh, PS Kanishkha
    Advances in Additive Manufacturing Technologies, 326-330 , 2024
    2024
  • Design and Analysis of a Rugged Swing-Arm for Electric Two-Wheelers
    K Swamy, A Suresh, S Ravi, K Niranjan, R Srivardhan, SM Nishanth
    Advances in Additive Manufacturing Technologies, 145-149 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Changes in protein metabolism in hemolymph and fat body of the silkworm, Bombyx mori (Lepidoptera: Bombycidae) in response to organophosphorus insecticides toxicity
    BS Nath, A Suresh, BM Varma, RPS Kumar
    Ecotoxicology and environmental safety 36 (2), 169-173 , 1997
    1997
    Citations: 152
  • Design of small horizontal axis wind turbine for low wind speed rural applications
    A Suresh, S Rajakumar
    Materials Today: Proceedings 23, 16-22 , 2020
    2020
    Citations: 99
  • Comparative study on the inhibition of acetylcholinesterase activity in the freshwater fish Cyprinus carpio by mercury and zinc.
    A Suresh, B Sivaramakrishna, PC Victoriamma, K Radhakrishnaiah
    Biochemistry international 26 (2), 367-375 , 1992
    1992
    Citations: 84
  • Bioaccumulation of nickel in the organs of the freshwater fish, Cyprinus carpio, and the freshwater mussel, Lamellidens marginalis, under lethal and sublethal nickel stress
    P Sreedevi, A Suresh, B Sivaramakrishna, B Prabhavathi, ...
    Chemosphere 24 (1), 29-36 , 1992
    1992
    Citations: 78
  • Microalgal fatty acid methyl ester a new source of bioactive compounds with antimicrobial activity
    A Suresh, R Praveenkumar, R Thangaraj, FL Oscar, E Baldev, ...
    Asian Pacific Journal of Tropical Disease 4, S979-S984 , 2014
    2014
    Citations: 76
  • Bright, low voltage europium doped gallium oxide thin film electroluminescent devices
    P Wellenius, A Suresh, JF Muth
    Applied Physics Letters 92 (2) , 2008
    2008
    Citations: 74
  • Effect of nickel on some aspects of protein metabolism in the gill and kidney of the freshwater fish, Cyprinus carpio L.
    P Sreedevi, B Sivaramakrishna, A Suresh, K Radhakrishnaiah
    Environmental Pollution 77 (1), 59-63 , 1992
    1992
    Citations: 72
  • Patterns of cadmium accumulation in the organs of fry and fingerlings of freshwater fish Cyprinuscarpio following cadmium exposure
    A Suresh, B Sivaramakrishna, K Radhakrishnaiah
    Chemosphere 26 (5), 945-953 , 1993
    1993
    Citations: 71
  • A visible transparent electroluminescent europium doped gallium oxide device
    P Wellenius, A Suresh, JV Foreman, HO Everitt, JF Muth
    Materials Science and Engineering: B 146 (1-3), 252-255 , 2008
    2008
    Citations: 69
  • Changes in protein metabolism in haemolymph and fat body of the silkworm, Bombyx mori L., in response to organophosphorus insecticides toxicity
    BS Nath, A Suresh, B Mahendra Varma, RP Kumar
    Ecotoxicol. Environ. Saf 36, 169-173 , 1997
    1997
    Citations: 62
  • Cloud Computing’s Effect on Video Games Streaming
    A Komathi, J Lenin, S Asha, A Suresh, M Suguna, C Srinivasan
    2023 2nd International Conference on Automation, Computing and Renewable … , 2023
    2023
    Citations: 56
  • Biodiversity of microalgae in Western and Eastern Ghats, India.
    A Suresh, RP Kumar, D Dhanasekaran, N Thajuddin
    Pakistan Journal of Biological Sciences: PJBS 15 (19), 919-928 , 2012
    2012
    Citations: 41
  • Evaluation and characterization of the plant growth promoting potentials of two heterocystous cyanobacteria for improving food grains growth
    A Suresh, S Soundararajan, S Elavarasi, FL Oscar, N Thajuddin
    Biocatalysis and Agricultural Biotechnology 17, 647-652 , 2019
    2019
    Citations: 38
  • Investigation of mechanical and wear characteristic of Banana/Jute fiber composite
    A Suresh, L Jayakumar, A Devaraju
    Materials Today: Proceedings 39, 324-330 , 2021
    2021
    Citations: 37
  • Glutathione-S-transferase and catalase activity in different tissues of marine catfish Arius arius on exposure to cadmium
    R Mani, B Meena, K Valivittan, A Suresh
    International Journal of Pharmacy and Pharmaceutical Sciences 6 (1), 326-332 , 2014
    2014
    Citations: 35
  • Cadmium induced changes in ion levels and ATPase activities in the muscle of the fry and fingerlings of the freshwater fish, Cyprinus carpio
    A Suresh, B Sivaramakrishna, K Radhakrishnaiah
    Chemosphere 30 (2), 367-375 , 1995
    1995
    Citations: 35
  • Effects of lethal and sublethal concentrations of copper on glycolysis in liver and muscle of the freshwater teleost, Labeo rohita(Hamilton).
    K Radhakrishnaiah, P Venkataramana, A Suresh, B Sivaramakrishna
    Journal of Environmental Biology 13 (1), 63-68 , 1992
    1992
    Citations: 33
  • Flexural behaviour of reinforced geopolymer concrete incorporated with hazardous heavy metal waste ash and glass powder
    AS Kumar, M Muthukannan, A Irene, KK Arun, AC Ganesh
    Materials science forum 1048, 345-358 , 2022
    2022
    Citations: 32
  • Effect of lethal and sublethal concentrations of Cadmium on energetics in the gills of fry and fingerlings of Cyprinus carpio
    A Suresh, B Sivaramakrishna, K Radhakrishnaiah
    Bulletin of environmental contamination and toxicology 51 (6), 920-926 , 1993
    1993
    Citations: 32
  • Effect of sublethal concentration of mercury and zinc on the energetics of a freshwater fish Cyprinus carpio (Linnaeus).
    K Radhakrishnaiah, A Suresh, B Sivaramakrishna
    Acta Biologica Hungarica 44 (4), 375-385 , 1993
    1993
    Citations: 31