Performance evaluation, scale-up design, and economic analysis for remediation of iron-containing wastewater using cow manure biocarbon Ashish Kapoor, Muthamilselvi Ponnuchamy, P. Senthil Kumar, Dan Bahadur Pal, Anjali Awasthi, Meenu Mariam Jacob, Balamurugan Pakkirisamy, Manjula Rajagopal, Gayathri Rangasamy Canadian Journal of Chemical Engineering, 2026 Excessive iron in wastewater poses a significant threat to aquatic ecosystems due to its toxic effects on aquatic life and its contribution to oxygen depletion. When applied to cropland, iron‐containing wastewater leads to soil acidification and reduces phosphorus availability, thereby impacting agricultural productivity. Addressing this issue requires techno‐economically viable remediation strategies. This study investigates cow manure biocarbon as a sustainable adsorbent for iron sequestration from wastewater. Batch experiments using synthetic solutions with 10–50 mg L −1 iron examined the influence of adsorbent dose, pH, and contact duration on iron removal efficiency. The cow manure biocarbon was characterized for evaluation of its physicochemical attributes. Adsorption achieved nearly 84% removal efficiency under optimal conditions of 120 min contact time, pH 9, and 0.25 g adsorbent dosage at 30°C. Adsorption isotherm data were modelled using the Langmuir and Freundlich models, with the Langmuir isotherm providing the best fit, as indicated by a low SSE (0.7056), RMSE (0.84), χ 2 (0.0044), and a high R 2 value of 0.995. Kinetic data were evaluated using pseudo first order and pseudo second order models, revealing that the process followed pseudo first order kinetics, evidenced by a low SSE (0.0144), RMSE (0.12), χ 2 (0.0044), and a high R 2 value of 0.9888. The adsorbent demonstrated good reusability and stability over five regeneration cycles. To assess the real‐world application of this process, a theoretical scale‐up design for wastewater remediation was developed. Furthermore, an economic and preliminary life cycle assessment was conducted to evaluate cost‐effectiveness and sustainability aspects.
Artificial intelligence-based neural network modeling of adsorptive removal of phenol from aquatic environment Bello Abdu Isah, Muthamilselvi Ponnuchamy, B.Senthil Rathi, P. Senthil Kumar, Ashish Kapoor, Manjula Rajagopal, Anjali Awasthi, Gayathri Rangasamy Desalination and Water Treatment, 2024 In this study, adsorptive uptake of phenol from aqueous system using Arachis hypogaea (groundnut) shell-based adsorbent was experimentally investigated and modelled using artificial intelligence-based neural network approach. Artificial neural networks with different number of neurons were designed using Levenberg-Marquardt algorithm to find the best model for phenol adsorption. The feedforward back propagation neural network comprising TRAINLM, LARNGDM and TANSIG as training, adaptation learning and transfer functions, respectively, with ten neurons in the hidden layer exhibited the optimal architecture with the strongest correlation R2=0.9901 and the smallest mean square error MSE=0.045. The studies indicated a maximum adsorptive uptake of phenol to be 37.31 mg/g onto the activated shell powder. The kinetic analysis favored pseudo second order R2=0.9999and the equilibrium data was best represented by Freundlich isotherm modelR2=0.9976. Phenolic remediation phenomenon ensued in a spontaneous manner, was exothermic ∆H0=−34.25kJ/moland involved physisorption. The experimental results are agreement with model.
Advanced Pedestrian Detection in Low Visibility Scenarios Using UltraLytics YOLOv8 and Kalman Filtering Sanjay Kumar Reddy Pattem, Manjula Rajagopal, Nandan Nirujogi 5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024 Nighttime detection of pedestrians brings important problems such as low light conditions, limitations of sensors, and environmental noise. To tackle this problem, in this study, we propose a novel visual-radar data fusion to enhance a pedestrian detection system using the UltraLytics YOLOv8 deep learning model together with Kalman Filtering technique. The system is based on a Low-Light Vision Infrared Paired (LLVIP) dataset that includes infrared vision data to attenuate the performance decrement of a pedestrian detector in the dark. The combination of infrared cameras and millimeter-wave radar achieves a detection rate of 97.6<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, in which UltraLytics YOLOv8 exhibits excellent object recognition performance by generating accurate spatial & category information from the infrared image; complementary distance and velocity data are obtained by millimeter-wave radar. It brings Kalman Filtering to track the radar-detected pedestrian targets, making the system more robust by predicting target and noise reduction. It was capable of fusing infrared and radar data at decision-level to make the system provide very precise position accurately in even nighttime situations. The experimental results show that the proposed model has improved capability of detection accuracy, robustness, and response time in comparison with the single-sensor system, which is suitable for practical autonomous driving scenarios.
An intelligent hybrid model for cyber attack classification with selected feature set G. Geetha, Manjula Rajagopal, K. Purnachand Intelligent Decision Technologies, 2024 Cyber security evolving as a severe problem almost in all sectors of cyberspace, due to the time-to-time increase in the number of security breaches. Numerous Zero-days attacks occur continuously, due to the increase in multiple protocols. Almost all of these attacks are small variants of previously known cyber attacks. Moreover, even the advanced approach like Machine Learning (ML), faces the difficulty in identifying those attack’s small mutants over time. Recently, Deep Learning (DL) has been utilized for multiple applications related to cybersecurity fields. Making use of this DL to identify the cyber attack might be a resilient mechanism for novel attacks or tiny mutations. Thereby, a novel cyber attack classification model named DCNN-Bi-LSTM-ICS is proposed in this work. This proposed DCNN-Bi-LSTM-ICS has five working stages. Firstly, in the data acquisition stage, the input data (considering the datasets) for attack classification has been collected. These raw data are pre-processed in the second stage, where an improved class imbalance balancing processing is conducted which makes use of the Improved Synthetic Minority Oversampling Technique (ISMOTE). In the third stage, along with the conventional mutual information and statistical features, Improved holo-entropy-based features are extracted. To choose the appropriate feature from those retrieved features, an Improved Chi-Square (ICS) processing is developed in the fourth stage. In the final classification stage, a hybrid classification model that combines both the Deep Convolutional Neural Network (DCNN) and Bi-directional Long Short Term Memory (Bi-LSTM) has been developed. The outcomes show that the proposed DCNN-Bi-LSTM-ICS can offer outstanding performance in the cyber attack classification task.
A Survey on Cyber Attack Detection: Techniques, Datasets, and Challenge G Geetha, Manjula Rajagopal, K Purna Chand 2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023 Networking technology is expanded along with cyber threats. Cyber threats are the instances which adversely influences the functional operations of the system or network. It results in various issues such as illegal access, data leakage, data stolen, DDoS attacks, etc. To overcome such issues, several intrusion detection systems are implemented over the past years. Though, these intrusion detection systems exhibit several limitations while trying to achieve excellent efficiency in attack detection. In this survey, totally 50 cyber-attack detection based research works are analysed under several perspectives. The analysis is done on several IDS techniques implemented in existing works. The reviewed learning-based approaches are explained in two major categories as ML and DL approaches. Here, the ML algorithms are further classified into three types: supervised, unsupervised and reinforcement learning approaches. Moreover, the existing works are also analysed in accordance with optimization algorithms and it is classified as metaheuristic and stochastic algorithms. In which, population-based algorithms and swarm-based algorithms are the categories classified under metaheuristic algorithms. In addition, the analyses are also performed on datasets usage and cyber-attack mitigation models. Furthermore, the analysis on existing works is done on performance analysis. And it is analysed under performance of cyber-attack detection models with specific description about maximum and minimum performance attained by recent works on cyber-attack detection models. Finally, the chronological review and challenges involved in existing cyber-attack detection models are discussed.
ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH Jagadeesan S, K. Sujigarasharma, Manjula R, Vetriselvi T 2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023 This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases.
Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People Manjula R, Jagadeesan S, Venkadeshan Ramalingam, Vetriselvi T, Johnpeter T International Conference on Sustainable Communication Networks and Application Icscna 2023 Proceedings, 2023 Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.
An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach Jagadeesan S, Deepakraj E, Venkadeshan Ramalingam, Ilayaraja Venkatachalam, Manojkumar Vivekanandan, Manjula R 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques Easct 2023, 2023 Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics.
DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm S. Raghavendra, Vasudev Parvati, R. Manjula, Ashok Kumar Nanda, Ruby Singh, D. Lakshmi, S. Velmurugan Intelligent Automation and Soft Computing, 2023 In contemporary medicine, cardiovascular disease is a major public health concern. Cardiovascular diseases are one of the leading causes of death worldwide. They are classified as vascular, ischemic, or hypertensive. Clinical information contained in patients’ Electronic Health Records (EHR) enables clinicians to identify and monitor heart illness. Heart failure rates have risen dramatically in recent years as a result of changes in modern lifestyles. Heart diseases are becoming more prevalent in today’s medical setting. Each year, a substantial number of people die as a result of cardiac pain. The primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of diseases. To improve the efficiency of the classification algorithms, we construct a data pre-processing stage using feature selection. Experiments using unidirectional and bidirectional neural network models found that a Deep Learning Modified Neural Network (DLMNN) model combined with the Pet Dog-Smell Sensing (PD-SS) algorithm predicted the highest classification performance on the UCI Machine Learning Heart Disease dataset. The DLMNN-based PDSS achieved an accuracy of 94.21%, an F-score of 92.38%, a recall of 94.62%, and a precision of 93.86%. These results are competitive and promising for a heart disease dataset. We demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease dataset. Our proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.
Energy efficiency in wireless sensor network for risk analysis using RMS algorithm International Journal of Control Theory and Applications, 2016
Extracting templates from web pages R. Manjula, A. Chilambuchelvan Proceedings of the 2013 International Conference on Green Computing Communication and Conservation of Energy Icgce 2013, 2013
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Free cooling feasibility of a typical commercial building in Pune city, India M Thambidurai, N Krishnamohan, M Rajagopal, R Velraj International Journal of Applied Engineering Research 10 (2), 4419-4435 , 2015 2015.0 Citations: 7
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