Browsing by Author "Jaidhar, C.D."

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1D convolutional neural networks-based soil fertility classification and fertilizer prescription
(Elsevier B.V., 2023) Sujatha, M.; Jaidhar, C.D.; Lingappa, M.
Sustainable agriculture is essential to meet the demands of the global population. An adequate application of fertilizers is essential for sustainable agricultural productivity. This research aims to determine soil fertility and provide precise fertilizer to improve crop yield. Many researchers have proposed soil fertility classification using deep learning-based approaches, such as extreme learning machines (ELMs) and multilayer perceptrons (MLPs). Although both ELM and MLP have the highest performance, insufficient training data prevent them from being useful. To address this limitation, this research proposes a 1D convolution neural networks (1D-CNN)-based soil fertility classification method that is straightforward, compact, and supports scalar additions and multiplications. To classify soil fertility, the classifier employs laboratory-measured soil data that encompasses electrical conductivity, pH, organic carbon, potassium, phosphorus, sulfur, boron, copper, iron, manganese, and zinc. The proposed approach employs MinMax normalization and the synthetic minority oversampling technique (SMOTE) to improve the classifier performance. The results of soil classification are used to recommend fertilizers. An experimental study using a laboratory-measured soil dataset showed that the proposed technique outperformed ELM and MLP classifiers. The proposed approach outperformed ELM and MLP with a classification accuracy of 97.9%, while ELM and MLP achieved classification accuracies of 69.80% and 87.06%, respectively. The proposed method can help farmers manage soil fertility sustainably to increase crop production. © 2023 Elsevier B.V.
A novel bio-inspired hybrid metaheuristic for unsolicited bulk email detection
(Springer Science and Business Media Deutschland GmbH, 2020) Gangavarapu, T.; Jaidhar, C.D.
With the recent influx of technology, Unsolicited Bulk Emails (UBEs) have become a potential problem, leaving computer users and organizations at the risk of brand, data, and financial loss. In this paper, we present a novel bio-inspired hybrid parallel optimization algorithm (Cuckoo-Firefly-GR), which combines Genetic Replacement (GR) of low fitness individuals with a hybrid of Cuckoo Search (CS) and Firefly (FA) optimizations. Cuckoo-Firefly-GR not only employs the random walk in CS, but also uses mechanisms in FA to generate and select fitter individuals. The content- and behavior-based features of emails used in the existing works, along with Doc2Vec features of the email body are employed to extract the syntactic and semantic information in the emails. By establishing an optimal balance between intensification and diversification, and reaching global optimization using two metaheuristics, we argue that the proposed algorithm significantly improves the performance of UBE detection, by selecting the most discriminative feature subspace. This study presents significant observations from the extensive evaluations on UBE corpora of 3,Â 844 emails, that underline the efficiency and superiority of our proposed Cuckoo-Firefly-GR over the base optimizations (Cuckoo-GR and Firefly-GR), dense autoencoders, recurrent neural autoencoders, and several state-of-the-art methods. Furthermore, the instructive feature subset obtained using the proposed Cuckoo-Firefly-GR, when classified using a dense neural model, achieved an accuracy ofÂ $$99\%$$. Â© Springer Nature Switzerland AG 2020.
A novel Multi-Layer Attention Framework for visual description prediction using bidirectional LSTM
(Springer Science and Business Media Deutschland GmbH, 2022) Naik, D.; Jaidhar, C.D.
The massive influx of text, images, and videos to the internet has recently increased the challenge of computer vision-based tasks in big data. Integrating visual data with natural language to generate video explanations has been a challenge for decades. However, recent experiments on image/video captioning that employ Long-Short-Term-Memory (LSTM) have piqued the interest of researchers studying its possible application in video captioning. The proposed video captioning architecture combines the bidirectional multilayer LSTM (BiLSTM) encoder and unidirectional decoder. The innovative architecture also considers temporal relations when creating superior global video representations. In contrast to the majority of prior work, the most relevant features of a video are selected and utilized specifically for captioning purposes. Existing methods utilize a single-layer attention mechanism for linking visual input with phrase meaning. This approach employs LSTMs and a multilayer attention mechanism to extract characteristics from movies, construct links between multi-modal (words and visual material) representations, and generate sentences with rich semantic coherence. In addition, we evaluated the performance of the suggested system using a benchmark dataset for video captioning. The obtained results reveal superior performance relative to state-of-the-art works in METEOR and promising performance relative to the BLEU score. In terms of quantitative performance, the proposed approach outperforms most existing methodologies. © 2022, The Author(s).
A Performance Evaluation of Location Prediction Position-Based Routing Using Real GPS Traces for VANET
(Springer New York LLC barbara.b.bertram@gsk.com, 2018) Jaiswal, R.K.; Jaidhar, C.D.
Vehicular ad-hoc network (VANET) is an emerging paradigm for road transportation which minimizes traffic, accidents and improves fuel efficiency. VANET uses the position of the vehicle obtained from satellite system such as global positioning system (GPS), global navigation satellite system, Compass and Galileo as a location id in position-based routing protocol. The position obtained from the satellite system is likely to have an error due to environmental and technical issues which effect the routing performance. Thus, this paper proposes a position-based routing protocol which uses Kalman filter based location prediction technique to improve routing performance by minimizing location error. The routing protocol performance is evaluated on NS-3.23 simulator with real time GPS traces and simulator generated mobility on Two-ray ground and Winner-II propagation model for 500 m transmission range. Further, performance is compared with other prediction-based routing protocol on the metrics of packet delivery ratio, average delay and throughput. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
A single program multiple data algorithm for feature selection
(Springer Verlag service@springer.de, 2020) Chanduka, B.; Gangavarapu, T.; Jaidhar, C.D.
Feature selection is a critical component in data science and has been the topic of research for many years. Advances in hardware and the availability of better multiprocessing platforms have enabled parallel computing to reach very high levels of performance. Minimum Redundancy Maximum Relevance (mRMR) is a powerful feature selection technique used in many applications. In this paper, we present a novel optimized Single Program Multiple Data (SPMD) approach to implement the mRMR algorithm with synchronous computation, optimum load balancing and greater speedup than task-parallel approaches. The experimental results presented using multiple synthesized datasets prove the efficiency and scalability of the proposed technique over original mRMR. Â© Springer Nature Switzerland AG 2020.
An applicability of AODV and OLSR protocols on IEEE 802. 11p for city road in VANET
(Springer Verlag service@springer.de, 2015) Jaiswal, R.K.; Jaidhar, C.D.
Vehicular Ad-hoc Network (VANET) improves, makes more safe and comfortable road transportation by using vehicular communication and the Internet. VANET is the subset of Mobile Ad-hoc Network (MANET). Thus, due to their similar characteristics, MANET routing protocols may also be applicable into VANET. Hence, the performance of MANET routing protocols should be evaluated only on IEEE 802. 11p communication standard, which is specifically designed for VANET communication, with urban and non-urban vehicular traffic. This work compares the performance of Ad-hoc On-Demand Distance Vector (AODV) routing protocol with Optimized Link State Routing protocol (OLSR) on two different road network scenarios, particularly a complex road network, which represents the city road network, having multiple crossroad and an intersection of two roads. We used two distinct simulators such as Vehicular Ad-hoc Networks Mobility Simulator (VANETMOBISIM), to simulate the city road network and vehicular traffic in an area of 700mx700m and NS-2. 35 network simulator to simulate the communication network. AODV and OLSR performances are assessed on different transmission range, i. e. 250m and 500m with four different data generation rate of 512, 1024, 1536 and 2048 Kbps. The primary goal of this work is to do an assessment to scrutinize the applicability of AODV and OLSR protocols in VANET with different traffic scenario and transmission ranges of IEEE 802. 11p standard. Â© Springer International Publishing Switzerland 2015.
An Approach to Speed Invariant Gait Analysis for Human Recognition using Mutual Information
(Institute of Electrical and Electronics Engineers Inc., 2019) Anusha, R.; Jaidhar, C.D.
Gait is a biometric characteristic that facilitates the identification of individuals with low-resolution images. This aspect intensifies its utility in many human detection applications. However, there are many challenges that adversely affect the gait recognition performance. They are caused by the impact of various covariate aspects such as, changes in clothing and carrying conditions, walking speed, walking surface conditions, view variations, and so on. This paper proposes an effective approach for speed-invariant gait recognition system. This approach uses the Region of Interest (ROI) extracted from Gait Energy Image (GEI) to classify a probe sample into a gallery sample. The mutual information obtained from a probe and gallery sample, followed by their classification capture the spatial dynamics of GEI efficiently to improve the gait recognition performance. Further, the proposed method is evaluated on CASIA C and OU-ISIR Treadmill A gait databases. Experimental results demonstrate the capability of the proposed approach in comparison with the existing gait recognition methods. Â© 2019 IEEE.
An Efficient Infectious Disease Detection in Plants Using Deep Learning
(Springer Science and Business Media Deutschland GmbH, 2024) Sunil, C.K.; Jaidhar, C.D.
Over the past decade, agriculture has suffered reduced productivity from climate change and improper water, fertilizer, and pesticide use, fueling plant diseases. Pathogens pose the main threat, impacting crop yield and quality. Early detection and targeted treatments are crucial to improve both yield and quality. To address this, we have carried out deep learning-based approaches and published ours works in conferences and journSal. Those works are briefly discussed in the paper as follows: (i) Empirical work on different plant datasets is conducted to analyze the hyperparameters of the neural network. (ii) The research minimizes misclassifications by leveraging an ensemble-based strategy with AlexNet, ResNet, and VGGNet across seven plant leaf image datasets. The complexity of plant disease diagnosis in diverse conditions is tackled through a hybrid deep learning strategy, exemplified in the cardamom plant disease detection approach. (iii) An innovative deep learning-based approach is introduced to precise plant disease detection, crucial in the face of similar symptoms and imbalanced data. The proposed Multilevel Feature Fusion Network (MFFN) incorporates adaptive attention mechanisms, enhancing robustness by considering diverse network features. (iv) With cardamom plant disease classification utilizing U2-Net for background removal and EfficientNetV2 for classification, the network excels the performance on images with complex background, with this generated benchmark dataset with a complex background. This research work produced good results by achieving 99% accuracy on the tomato plant and 98.28% accuracy on the cardamom leaf dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
An Empirical Study to Detect the Collision Rate in Similarity Hashing Algorithm Using MD5
(Institute of Electrical and Electronics Engineers Inc., 2019) Gangavarapu, T.; Jaidhar, C.D.
Similarity Hashing (SimHash) is a widely used locality-sensitive hashing algorithm employed in the detection of similarity, in large-scale data processing, including plagiarism detection and near-duplicate web document detection. Collision resistance is a crucial property of cryptographic hash algorithms that are used to verify the message integrity in internet security applications. A hash function is said to be collision-resistant if it is hard to find two different inputs that hash to the same output. In this paper, we present an empirical study to facilitate the detection of collision rate when SimHash is employed to check the integrity of the message. The analysis was performed using bit sequences with length varying from 2 to 32 and Message Digest 5 (MD5) as the internal hash function. Furthermore, to enable faster collision detection with more significant speedup and efficient space utilization, we parallelized the process using a distributed data-parallel approach with synchronous computation and optimum load balancing. Collision detection is desirable, owing to its applicability in digital signature systems, proof-of-work systems, and distributed content systems. Our empirical study revealed a collision rate of 0% to 0.048% in SimHash (with MD5) with the variation in the length of the bit sequence. Â© 2019 IEEE.
An empirical study to estimate the stability of random forest classifier on the hybrid features recommended by filter based feature selection technique
(Springer, 2020) Shiva Darshan, S.L.S.; Jaidhar, C.D.
The emergence of advanced malware is a serious threat to information security. A prominent technique that identifies sophisticated malware should consider the runtime behaviour of the source file to detect malicious intent. Although the behaviour-based malware detection technique is a substantial improvement over the traditional signature-based detection technique, current malware employs code obfuscation techniques to elude detection. This paper presents the Hybrid Features-based malware detection system (HFMDS) that integrates static and dynamic features of the portable executable (PE) files to discern malware. The HFMDS is trained with prominent features advised by the filter-based feature selection technique (FST). The detection ability of the proposed HFMDS has evaluated with the random forest (RF) classifier by considering two different datasets that consist of real-world Windows malware samples. In-depth analysis is carried out to determine the optimal number of decision trees (DTs) required by the RF classifier to achieve consistent accuracy. Besides, four popular FSTs performance is also analyzed to determine which FST recommends the best features. From the experimental analysis, we can infer that increasing the number of DTs after 160 within the RF classifier does not make a significant difference in attaining better detection accuracy. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
Analysis of Tweets for Cyberbullying Detection
(Institute of Electrical and Electronics Engineers Inc., 2023) Mathur, S.A.; Isarka, S.; Dharmasivam, B.; Jaidhar, C.D.
Cyberbullying takes place online on gadgets like smartphones and computers. Cyberbullying can occur through social media platforms. This paper presents a real-time cyber-bullying detection system for Twitter using Natural Language Processing (NLP) and Machine Learning (ML). The system is trained on a dataset of cyberbullying tweets using several ML algorithms and their performance is compared. Random Forest was found to provide the best results after tuning. To achieve real-time analysis, Selenium was used to scrape tweets from a given Twitter account and store the timestamp of the already checked tweets. Additionally, an image captioning model was employed to generate descriptions for images posted on the account and compare them with user-written captions to filter out spam tweets. The proposed work aims to prevent cyberbullying and provides a valuable tool for online platforms to detect and remove harmful content. The results of this study have shown that the selection of appropriate ML algorithms and preprocessing techniques significantly impact the performance of cyberbullying detection on Twitter. Our model sheds light on the appropriateness of different ML algorithms for the detection of cyberbullying. Â© 2023 IEEE.
Android Malware Detection using Function Call Graph with Graph Convolutional Networks
(Institute of Electrical and Electronics Engineers Inc., 2021) Vinayaka, K.V.; Jaidhar, C.D.
As smartphone adoption is happening at a rapid rate, its threat landscape is also widening. Android is a popular smartphone Operating System (OS) which was subject to many malware attacks in recent years, compromising the privacy and security of its users. Although many works are developed to detect Android malware, few use graphs extracted from the Android Package (APK) directly as an input to the deep learning model due to the lack of suitable architectures. Graph Convolutional Networks (GCNs) are becoming a popular architecture in the deep learning community that can directly take a graph as an input. However, their applicability to Android malware detection is less explored. To bridge this gap, this work proposes an Android malware detection model using GCNs based on Function Call Graph (FCG). FCG captures the caller-callee relationships between the methods inside an APK as a directed graph. Every node in FCG is assigned a feature vector that represents its characteristics. To evaluate the performance of the proposed model, a set of experiments is conducted by varying GCN algorithms, node features and the number of GCN layers in the model. A recent Android malware dataset is used to conduct experiments. As GCNs consider the node count of the FCG, the dataset is balanced using a new technique to make node count distributions of benign and malware APKs similar. As a result of these experiments, the maximum accuracy of 92.29% with the F1-score of 0.9223 is obtained, suggesting that the GCNs have the potential to detect Android malware. Â© 2021 IEEE.
Anomalous Electrical Power Consumption Detection in Household Appliances via Micro-Moment Classification
(Institute of Electrical and Electronics Engineers Inc., 2025) Nayak, R.; Jaidhar, C.D.
The detection of anomalous power consumption is critical for improving energy efficiency, particularly with the increasing demand in buildings. This study explores Convolutional Neural Network-based models by transforming 1-dimensional micro-moment labeled data into 2-dimensional matrices to capture both temporal and spatial consumption patterns. Three architectural variants are investigated: a conventional Deep Convolutional Neural Network (DCNN), a Depthwise Separable Convolutional Neural Network (DS-CNN), and a Depthwise Separable Residual Convolutional Neural Network (DSR-CNN). Unlike earlier studies, this work incorporates hyperparameter tuning, statistical validation, and cross-validation, resulting in the evaluation of over 450 model configurations. The results indicate that while the DCNN consistently achieves the highest accuracy, the DS-CNN achieves comparable performance with significantly reduced parameters and computational cost, making it suitable for real-time and resource-constrained environments. Model complexity analysis and statistical tests confirm the robustness of the findings. Finally, a systematic model selection strategy is presented, identifying the DS-CNN as the most balanced solution for effective and efficient anomaly detection in smart grid applications. © 2020 IEEE.
An applicability of AODV and OLSR protocols on IEEE 802. 11p for city road in VANET
(2015) Jaiswal, R.K.; Jaidhar, C.D.
Vehicular Ad-hoc Network (VANET) improves, makes more safe and comfortable road transportation by using vehicular communication and the Internet. VANET is the subset of Mobile Ad-hoc Network (MANET). Thus, due to their similar characteristics, MANET routing protocols may also be applicable into VANET. Hence, the performance of MANET routing protocols should be evaluated only on IEEE 802. 11p communication standard, which is specifically designed for VANET communication, with urban and non-urban vehicular traffic. This work compares the performance of Ad-hoc On-Demand Distance Vector (AODV) routing protocol with Optimized Link State Routing protocol (OLSR) on two different road network scenarios, particularly a complex road network, which represents the city road network, having multiple crossroad and an intersection of two roads. We used two distinct simulators such as Vehicular Ad-hoc Networks Mobility Simulator (VANETMOBISIM), to simulate the city road network and vehicular traffic in an area of 700mx700m and NS-2. 35 network simulator to simulate the communication network. AODV and OLSR performances are assessed on different transmission range, i. e. 250m and 500m with four different data generation rate of 512, 1024, 1536 and 2048 Kbps. The primary goal of this work is to do an assessment to scrutinize the applicability of AODV and OLSR protocols in VANET with different traffic scenario and transmission ranges of IEEE 802. 11p standard. � Springer International Publishing Switzerland 2015.
Applicability of machine learning in spam and phishing email filtering: review and approaches
(Springer Science+Business Media B.V. editorial@springerplus.com, 2020) Gangavarapu, T.; Jaidhar, C.D.; Chanduka, B.
With the influx of technological advancements and the increased simplicity in communication, especially through emails, the upsurge in the volume of unsolicited bulk emails (UBEs) has become a severe threat to global security and economy. Spam emails not only waste users’ time, but also consume a lot of network bandwidth, and may also include malware as executable files. Alternatively, phishing emails falsely claim users’ personal information to facilitate identity theft and are comparatively more dangerous. Thus, there is an intrinsic need for the development of more robust and dependable UBE filters that facilitate automatic detection of such emails. There are several countermeasures to spam and phishing, including blacklisting and content-based filtering. However, in addition to content-based features, behavior-based features are well-suited in the detection of UBEs. Machine learning models are being extensively used by leading internet service providers like Yahoo, Gmail, and Outlook, to filter and classify UBEs successfully. There are far too many options to consider, owing to the need to facilitate UBE detection and the recent advances in this domain. In this paper, we aim at elucidating on the way of extracting email content and behavior-based features, what features are appropriate in the detection of UBEs, and the selection of the most discriminating feature set. Furthermore, to accurately handle the menace of UBEs, we facilitate an exhaustive comparative study using several state-of-the-art machine learning algorithms. Our proposed models resulted in an overall accuracy of 99% in the classification of UBEs. The text is accompanied by snippets of Python code, to enable the reader to implement the approaches elucidated in this paper. © 2020, Springer Nature B.V.
An Approach to Speed Invariant Gait Analysis for Human Recognition using Mutual Information
(2019) Anusha, R.; Jaidhar, C.D.
Gait is a biometric characteristic that facilitates the identification of individuals with low-resolution images. This aspect intensifies its utility in many human detection applications. However, there are many challenges that adversely affect the gait recognition performance. They are caused by the impact of various covariate aspects such as, changes in clothing and carrying conditions, walking speed, walking surface conditions, view variations, and so on. This paper proposes an effective approach for speed-invariant gait recognition system. This approach uses the Region of Interest (ROI) extracted from Gait Energy Image (GEI) to classify a probe sample into a gallery sample. The mutual information obtained from a probe and gallery sample, followed by their classification capture the spatial dynamics of GEI efficiently to improve the gait recognition performance. Further, the proposed method is evaluated on CASIA C and OU-ISIR Treadmill A gait databases. Experimental results demonstrate the capability of the proposed approach in comparison with the existing gait recognition methods. � 2019 IEEE.
Assessment of objective functions under mobility in RPL
(2018) Sanshi, S.; Jaidhar, C.D.
Due to the technological advancement in Low-power and Lossy Network (LLN), the sensor node mobility has become a basic requirement. Routing protocol designed for LLN must ensure certain requirements in a mobile environment such as reliability, flexibility, scalability to name a few. To meet the needs of LLN, Internet Engineering Task Force (IETF) released the standard IPv6 Routing Protocol for LLNs (RPL). RPL depends on Objective Function (OF) to select optimized routes from source to destination. However, the standard did not specify which OF to use. In this study, performance analysis of different OFs such as Objective Function zero (OF0), Energy-based Objective Function (OFE), Delay-Efficient Objective Function (OFDE), and Minimum Rank with Hysteresis Objective Function (MRHOF) is carried out under different mobility models, which makes this study unique. The metrics used to measure the performance are latency, packet delivery ratio (PDR), and power consumption. Simulation results demonstrate that under different mobility models, MRHOF achieved better results in terms of PDR and power consumption, while OFDE shows better results in terms of latency compared to other OFs. � Springer Nature Singapore Pte Ltd. 2018.
Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM
(Elsevier B.V., 2018) M.a, A.K.; Jaidhar, C.D.
In order to fulfill the requirements like stringent timing restraints and demand on resources, Cyber–Physical System (CPS) must deploy on the virtualized environment such as cloud computing. To protect Virtual Machines (VMs) in which CPSs are functioning against malware-based attacks, malware detection and mitigation technique is emerging as a highly crucial concern. The traditional VM-based anti-malware software themselves a potential target for malware-based attack since they are easily subverted by sophisticated malware. Thus, a reliable and robust malware monitoring and detection systems are needed to detect and mitigate rapidly the malware based cyber-attacks in real time particularly for virtualized environment. The Virtual Machine Introspection (VMI) has emerged as a fine-grained out-of-VM security solution to detect malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS) by functioning at the Virtual Machine Monitor (VMM) or hypervisor. However, the reconstructed semantic details by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, extensive manual analysis is required by the existing out-of-VM security solutions. To address the foremost issue, in this paper, we propose an advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS. More specifically, the AMMDS system detects and classifies the actual running malicious executables from the semantically reconstructed process view of the guest OS. The two sub-components of the AMMDS are: Online Malware Detector (OMD) and Offline Malware Classifier (OFMC). The OMD recognizes whether the running processes are benign or malicious using its Local Malware Signature Database (LMSD) and online malware scanner and the OFMC classify unknown malware by adopting machine learning techniques at the hypervisor. The AMMDS has been evaluated by executing large real-world malware and benign executables on to the live guest OSs. The evaluation results achieved 100% of accuracy and zero False Positive Rate (FPR) on the 10-fold cross-validation in classifying unknown malware with maximum performance overhead of 5.8%. © 2017 Elsevier B.V.
Binary class and multi-class plant disease detection using ensemble deep learning-based approach
(Inderscience Publishers, 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.
Providing food for the exponentially growing global population is a highly challenging task. Owing to the demand and supply gap may diminish food production due to diseases in plants, such as bacterial disease, viral disease, and fungal diseases. Early recognition of such diseases and applying an appropriate pesticide or fertiliser can improve crop yield. Accordingly, early plant disease detection necessitates continuous crop monitoring from its initial stages. Recently some research works have been proposed as remedial measures. However, such methodologies utilise costly equipment that is infeasible for small-scale farmers. Thus, there is a need for a cost-effective plant-disease-detection approach. This study embellishes the challenges and opportunities in plant disease detection. Correspondingly, this research proposes an ensemble deep learning-based plant disease diagnosis approach using a combination of AlexNet, ResNet50, and VGG16 deep learning-based models. It effectively ascertains plant diseases by analysing the plant leaf images. A broad set of experiments were conducted using different plant leaf image datasets such as cherry, grape, maize, pepper, potato, strawberry, and cardamom to evaluate the robustness of the proposed approach. Experiential results demonstrated that the proposed approach attained a maximum detection accuracy of 100% for binary and 99.53% for multi-class datasets. © © 2022 Inderscience Enterprises Ltd.
Canopy centre-based fuzzy-C-means clustering for enhancement of soil fertility prediction
(Inderscience Publishers, 2024) Sujatha, M.; Jaidhar, C.D.
For plants to develop, fertile soil is necessary. Estimating soil parameters based on time change is crucial for enhancing soil fertility. Sentinel-2’s remote sensing technology produces images that can be used to gauge soil parameters. In this study, values for soil parameters such as electrical conductivity, pH, organic carbon, and nitrogen are derived using Sentinel-2 data. In order to increase the clustering accuracy, this study suggests using Canopy centre-based fuzzy-C-means clustering and comparing it to manual labelling and other clustering techniques such as Canopy, density-based, expectation-maximisation, farthest-first, k-means, and fuzzy-C-means clustering, its usefulness is demonstrated. The proposed clustering achieved the highest clustering accuracy of 78.42%. Machine learning-based classifiers were applied to classify soil fertility, including Naive Bayes, support vector machine, decision trees, and random forest (RF). Dataset labelled with the proposed RF clustering classifier achieves a high classification accuracy of 99.69% with ten-fold cross-validation. © 2024 Inderscience Enterprises Ltd.. All rights reserved.