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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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Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

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Computer Modeling in Engineering & sciences 2025年第1期142卷 185-208页

作者： Kaviyaraj Ravichandran Uma Mohan Department of Computational Intelligence School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different *** increased use of mobile devices has an even greater *** the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in ***,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational ***,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)*** 3D objects for augmented reality education applications can be challenging and time-consuming for the *** address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)*** for augmented and virtual reality can then utilize these created 3D *** evaluated the proposed pipeline based on the usability of the 3D object and performance ***,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry *** analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above *** study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

关键词： Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion

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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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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 pelican optimization with optimized mask RCNN for automatic lung cancer detection

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Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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A Review of Cyber Security Challenges and Solutions in Unmanned Aerial Vehicles (UAVs)

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Inteligencia Artificial 2025年第75期28卷 199-219页

作者： Renu, Yuvaraj Sarveshwaran, Velliangiri Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu India

The widespread use of unmanned aerial vehicles in a variety of sectors poses major challenges to these issues of cyber security and data protection. In this paper, a detailed study of the security vulnerabilities in UAV-based systems was conducted and analytically categorized into three types: Software, hardware and communication links. These vulnerabilities include access rights without authentication, control channel tampering, data breaches and spoofing of the global positioning system. These vulnerabilities jeopardize the security, integrity of the data and privacy of not only the drone but also the users. The study analyzes some of the security protocols that aim to mitigate these risks. They range from advanced encryption techniques to authentication mechanisms and intrusion detection systems that can use classification models based on machine learning. The study also provides a context in which emerging technologies such as blockchain, machine learning and deep learning can make a greater contribution to securing drones. The detailed and in-depth discussion on the development of the security solution that the work provides emphasizes the importance of securing UAV operations and their data integrity aspects alongside securing public trust for autonomous systems. © IBERAMIA and the authors.

关键词： Cybersecurity

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Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-22页

作者： Dennyson, W. Beniel Jothikumar, C. Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Intelligently connected automobiles have come a long way thanks to the deep integration of cutting-edge networked gadgets and advancements in automotive technology. The increasing connectivity of vehicles has introduced significant cybersecurity concerns, particularly in controller area network (CAN) bus systems, which are crucial for vehicle communication. For this, the research presents a novel hybrid model combining and integrating a long short-term memory (LSTM) neural network and bacterial foraging optimization (BFO) technique to address challenges like identifying fuzzy and denial of service (DoS) attacks on the CAN bus system. The model’s efficacy is demonstrated by evaluating various cyber-attacks from the Car-Hacking dataset. An adaptive feature selection method using BFO to identify optimal CAN bus characteristics for accurate attack detection. The LSTM-based temporal pattern recognition system detects anomalous message sequences and real-time countermeasures for mitigating DoS and fuzzy attacks. Reducing attack detection time to 0.0838 s, an enhancement over LSTM-AE, suggests improving detection speed. With a higher precision of 94.6% and F1-scores of 95.8%, LSTM-BFO outperforms other models based on accuracy, precision, and F1-score under the current experimental setup. © The Author(s) 2025.

关键词： Automotive network Bacterial foraging optimization Controller area network Deep learning Denial-of-service Fuzzy attack

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