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Precision Agriculture System Using Big Data Analytics in Predicting Yield by Controlling Plant Disease and Irrigation Scheduling 1. ***, Research Scholar, department of Data Science and Business System, School of computing, Srm Institute of Science and technology,Ktr

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SSRN 2022年

作者： Vasudevan, N. Karthick, T. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Chennai India

The agricultural sector is one of India's most important and major endeavors, and it is also critical to the country's economic development. Agriculture is one of the most important things that contributes to humanity's pleasure and welfare, as well as serving as a deterministic element in a nation's economic well-being. Smart agriculture, or the application of information technology in agriculture, attempts to provide precise disease management, irrigation and yield prediction in crop production. We have implemented Big Data Analytics and Internet of Things in the agricultural field, which is nothing but Precision Agriculture. This will extensively impact on the crop yield positively. Precision agriculture is utilizing IoT and Big Data to enhance crop quality, therefore increasing total output rates. In this paper we have implemented the N stage – CNN (Convolutional Neural Networks) using the Smart Agriculture dataset, using parameters such as temperature, humidity, rainfall, pH and solar irradiance. In this paper, irrigation scheduling is performed using machine learning techniques and plant disease detection using DoubleGAN method. As we have taken into consideration almost all required parameters for plant growth with irrigation scheduling and plant disease detection, the yield prediction is precise. The principle aim of this work is to come up with N stage CNN model that will increase the yield irrespective of the type of crop and thereby largely contributing to the agricultural sector, which will in turn increase food production in the coming years. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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A 2-level cascaded deep learning with the adaptive novel feature extraction approach for sales forecasting framework

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International Journal of Computers and Applications 2025年第4期47卷 373-387页

作者： Selvaraj, Sivabalan Minu, R.I. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

This study presents a revolutionary deep-learning architecture that focuses on feature extraction, feature selection, and sales forecasting. The technique begins with a pre-processing step using median imputation and min–max. To extract features, the study uses the Word2Vec (CBOW) model to construct contextual word embeddings and TF-IDF to assess term relevance in texts. For numerical data, the Pearson Correlation Coefficient (PCC) detects linear associations, whereas the Adaptive Bhattacharyya Correlation refines characteristics using statistical metrics. The Tent Chaotic-based Puma Optimizer Algorithm (TC-POA) is used to pick important characteristics for the model effectively. The forecasting challenge employs the 2-level Cascaded Deep-Learning (2-LCDL) model. In this model, an Alexnet with Squeeze-and-Excitation is used along with the Bi-LSTM to capture sequential data dependencies and GRU for efficient long-term memory management. The work wraps up with a fully linked layer and SoftMax activation for sales prediction. This framework demonstrates substantial advances in deep-learning approaches, providing better-predicted accuracy and computational efficiency. The proposed model is evaluated for both 70% and 80% of training data for the metrics MSE of 0.0058, 0.0054, MAPE of 1.1968, 0.9979 and also the RMSE, G-Mean, MAE, MMAE, MNB, IoA have superior performance when compared to the other existing methods. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sales

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Utilizing enhanced deep learning methods for residential burglary detection via analysis of human behavior patterns

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Multimedia Tools and Applications 2025年第10期84卷 7627-7646页

作者： Pavithra, S. Muruganantham, B. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Residential burglary is a severe crime that affects millions of residents each year. It is critical to analyze patterns of human behavior in surveillance video data and discover suspicious actions to avoid and deter this crime. However, the complexity and diversity of human behavior make this undertaking challenging. In this research, we provide a unique system that uses advanced deep learning algorithms to detect abnormal actions that signal burglar attempts at time intervals. We use the state-of-the-art object identification method YOLOv7 to detect an object and its trajectories in video data. We additionally extract the time intervals between the object's movements and utilize them as a categorization feature. Based on the COCO dataset and three videos of distinct scenarios: abnormal activity, burglary attempt, and false alarm, we evaluate our system with earlier versions of YOLO and additional detection methods. On the COCO dataset, our method outperforms all other models with a precision of 93.9%, an f1 score of 94.42%, a recall of 94.71, and an accuracy of 95%. We also show that our algorithm can effectively discriminate between suspicious and normal behaviors based on the motion region's length, width, energy, and orientation variance, achieving an accuracy of 92% on average over three videos. Our system can provide practical domestic protection by automatically detecting and preventing burglary attempts and violent scenarios. Our method can also contribute to the advancement of knowledge in the fields of human behavior analysis and object recognition. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crime

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Ensemble Multimodal Disease Risk Prediction: Integrating Chest X-Ray Images and Medical Data with ERSGB-RSW Method

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 697-711页

作者： Amirtharaj, Shiny Irene David Marudappa, Pushpalatha Sachin, Lade Khalid, Mohammad Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chennai India

The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific diseases. Traditional methods face several challenges such as limited capacity to handle heterogeneous datasets, high computational power, and less efficiency. To solve the drawbacks a novel Ensemble Random Support vector Gradient Bayes Random Seal Whiskers method is proposed. In this study, the Random Forest is utilized to extract chest X-ray images from the dataset, the Support Vector Machine (SVM) is employed to categorize images by integrating various medical data modalities, the gradient boosting is implemented to capture complex relationships in chest X-ray images and Naive Bayes is used to classify image features. Also, a weighted average ensemble is employed to integrate the outcomes of the methods namely Random Forest, SVM, Gradient boosting, and Naive Bayes by assigning different weights based on its predictions in this work, the Harbor seal whiskers optimization algorithm with a random update is employed to optimize the tuned parameters based on the datasets. The experimental results revealed that the proposed method improved the efficiency in the process of multimodal disease risk prediction. © The Institution of Engineers (India) 2024.

关键词： Support vector machines

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EHRT-RWB: A Novel Ensemble Hybrid Recurrent Transformer for Multimodal Heart Disease Risk Prediction

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 475-486页

作者： Irene, D. Shiny Peter, J. Selvin Paul Sankarasubramanian, Nivetha Krishnakanth, S. Praveen Department of Computing Technologies School of Computing Colllege of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chennai India

The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various diagnostic measures for the prediction of heart diseases. However, these techniques consist of some errors, delays, and high-cost consumption. These limitations affect the patients in many ways such as inadequate medication during the right time, affecting their mental health, affecting their physical health, and sometimes death. Hence, there is a need for an effective automatic multimodal disease risk prediction mechanism related to heart diseases. Therefore, this paper proposes the Ensemble hybrid recurrent transformer-based random wolf bird (EHRT-RWB) algorithm. The details of the patient acquired as input by the heart disease dataset are preprocessed initially and classification is performed with the EHRT-RWB methodology. The RNN method extracts reliable features and helps to study the temporal sequence data. Time series classifications are maintained with the help of the transformer method and the ensemble method integrates advanced cutting-edge technologies for performance augmentation. Finally, the RWB algorithm selects and tunes the hyperparameters of the developed classification method. Experiments conducted using certain performance assessment measures and the comparison with existing strategies show the method achieves superior cardiac disease detection performance with the highest accuracy score of 98.7%. © The Institution of Engineers (India) 2024.

关键词： Birds

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Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

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Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Learn to optimize——a brief overview

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National Science Review 2024年第8期11卷 39-47页

作者： Ke Tang Xin Yao Department of Computer Science and Engineering Southern University of Science and Technology Department of Computing and Decision Sciences Lingnan University

Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required,which is very costly and even prohibitive for problems that are already computationally intensive,*** problems associated with machine learning *** the past decades,many studies have been conducted to accelerate the tedious configuration process by learning from a set of training *** article refers to these studies as learn to optimize and reviews the progress achieved.

关键词： optimization data-driven algorithm design automated algorithm configuration machine learning

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Develop a novel, faster mask region-based convolutional neural network model with leave-one-subject-out to predict freezing of gait abnormalities of Parkinson’s disease

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Neural computing and Applications 2025年第7期37卷 5441-5457页

作者： Ezhilarasi, J. Senthil Kumar, T. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu603203 India

A common symptom of severe Parkinson’s disease (PD) is Freezing of Gait (FoG), a gait disorder that causes sudden difficulty in initiating or maintaining walking. FoG frequently leads to falls and has a detrimental impact on a patient’s regular life. Real-time detection algorithms identify FoG occurrences using wearable sensors. Anticipating FoG in advance allows for pre-emptive cueing, which may prevent the episodes or reduce their severity and duration. This research proposes a Faster Mask Region-based Convolutional Neural Network (FMRCNN) signal processing approach for FoG identification. The model captured gyroscope, magnetometer, and tri-axial accelerometer signals using an inertial measurement device on the left side of the abdomen. The experimental results demonstrate a reduction in the equal error rate to 1.9% in the Leave-One-Subject-Out (LOSO) Cross-Validation (CV) with Long Short Term Memory (LSTM) assessment. Additionally, the tenfold CV evaluation enhances specificity and sensitivity by 0.045 and 0.017, respectively, compared to previous best results. It takes only 0.52 ms to detect a 256-data section. The proposed work uses the LOSO-CV-LSTM to evaluate various machine learning (ML) and deep learning (DL) techniques for FoG detection. The proposed system not only detects FoG but also enhances the automation of PD detection and therapy at an earlier stage. The results demonstrate that the proposed system improves performance measures compared to existing systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Adaptive whale optimization based clustering method for K- anonymization in social networks

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Multimedia Tools and Applications 2024年 1-21页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of CSE SRM Institute of Science and Technology Ramapuram Chennai600089 India Department of computing technologies School of computing College of engineering and technology SRM Institute of science and technology Kattankulathur603203 India

The usage of online social networks (OSNs) has been significantly swelling over the years. Data from SNs has been shared publicly for the purpose of deeper understanding user behaviour and data mining tasks. However, securing individual identification from the publicly published data is the primary issue in sharing social network databases. K-anonymization is a well-known technique for preventing individual identification of publically available datasets. According to this method, each record in the anonymized dataset must be identical to at least k-1 other records in terms of a set of privacy-related attributes. Although anonymizing the original dataset to meet the k-anonymity criteria is straightforward, the anonymized dataset must maintain as much information from the original dataset as possible. Recently, clustering methods have been used to apply K-anonymization to the social dataset successfully. This paper applies a swarm-based clustering technique, called adaptive whale optimization algorithm (AWOA) for clustering social data. The WOA algorithm replicates humpback whale behavior and employs fewer internal parameters to support a better balance of exploitation and exploration. The method is tested on three publicly available data sets: Yelp and Facebook dataset. The experimental findings show that the proposed method attained the minimum information loss of 11% for Yelp dataset and 10% for facebook dataset. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anonymity

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Manhattan-based k-member clustering and enhanced rabbit optimization algorithm for k-anonymization in social network

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Multimedia Tools and Applications 2024年第30期83卷 74739-74756页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of computer science and engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of computing technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual identity in publicly accessible datasets is K-anonymization. Existing anonymity techniques' primary drawback is that they're susceptible to similarity and attribute disclosure attacks. Furthermore, a large amount of data has been lost in the given database. To address this limitation, the proposed model integrated an anonymizing algorithm with Manhattan-based k-member clustering and an enhanced rabbit optimization algorithm (MKCAEROA). The Manhattan distance-based k member (MKCA) clustering algorithm is employed in this case to cluster the initial database. The resulting clusters are then further optimized, and the original database is made anonymous, using the enhanced rabbit optimization algorithm (EROA). In the suggested model, EROA is included to satisfy the specified anonymity constraints while concurrently minimizing the clustering error rate and the generated information loss. When compared to existing approaches, the experimental results demonstrate that the suggested method provides the highest clustering accuracy with the least amount of information loss. The MATLAB workbench will be used to implement the suggested model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Database systems

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