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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

SSRN

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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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Analysis of the SEIR Model for Measles Spread with the Influence of Vaccination

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IAENG International Journal of Applied Mathematics 2025年第3期55卷 626-635页

作者： Hamzah, Joseph Leonardo Marvin Fadhilah, Helisyah Nur Syaifullah, Rindra Department of Data Science School of Computing Telkom University Surabaya Indonesia

In this paper, we analyze the impact of vaccination on the dynamics of measles transmission using the SEIR mathematical model. We demonstrate that high vaccination coverage significantly reduces disease transmission and establishes herd immunity, thereby protecting both vaccinated and unvaccinated populations. We observe that low to moderate vaccination levels result in an increase in the reproduction number, leading to periodic outbreaks, especially in communities with low vaccine uptake. Our analysis identifies both adult and child vaccination rates as critical factors in stabilizing disease dynamics. In the absence of vaccination, we observe uncontrolled outbreaks that pose significant risks to public health. These findings underscore the importance of implementing robust vaccination programs to prevent measles outbreaks and maintain public health stability. © (2025), (International Association of Engineers). All rights reserved.

关键词： Vaccines

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Triple Sensors Integrated Deep Learning-Based Network for Detection of Defects in Medicines

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IEEE Sensors Journal 2025年第10期25卷 18171-18179页

作者： Ganesh, Karthikeyan Prakash, M. SRM Institute of Science and Technology School of Computing Department of Research Scholar Data Science and Business Systems Tamil Nadu Kattankulathur603203 India SRM Institute of Science and Technology Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603203 India

The accurate medicine dispensing is extremely important in all hospitals, as medical errors can cause serious health issues, underscoring the need for improved protocols. Numerous research has been conducted on developing detection techniques for unpackaged pills or drugs with similar packages. However, the existing frameworks face issues, such as dispensing errors and accurate defect prediction. To overcome these challenges, a novel Tri-SID Medi technique has been proposed for detecting the defects in medicines and delivering defect-free medicines using tri sensors, such as ultrasonic sensor, weight sensor, and global positioning system (GPS). Initially, the collected drug images from the hospital/pharmacy are preprocessed using the adaptive unsharp mask-guided filter to enhance the image quality and reduce noise artifacts. Then, the preprocessed images are fed into APM-YOLO to detect the images as defect or nondefect. The nondefect medicines are given into the first similarity group (SG), in which the squeeze and excitation-based bidirectional long short-term memory (SE-BiLSTM) framework is trained for grouping the medicinal products. Finally, the defect-free group medicines are directly delivery to the customers in the delivery phase by using tri sensors and global system for mobile communications (GSMs). The proposed Tri-SID Medi method achieves 99.03% accuracy, which is 9.11%, 2.45%, and 5.07% better than the existing techniques, such as YOLO, deep convolutional neural network (DCNN), and bidirectional encoder representations from transformer (BERT). The experimental results of the proposed techniques highlight the potential for real-time drug defect detection and similar drug groupings with diverse packaging, which can reduce medical errors and are key to ensure patient safety. © 2001-2012 IEEE.

关键词： Adaptive filters

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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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GMFLDA: Improved prediction of IncRNA-disease association via graph convolutional network

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IEEE Access 2025年 13卷 85330-85341页

作者： Kim, Kwangsu Ha, Jihwan Pukyong National University Department of Scientific Computing Busan48513 Korea Republic of Pukyong National University Major of Big Data Convergence Division of Data Information Science Busan48513 Korea Republic of

The rapid expansion of diverse networks has created a growing need to integrate multiple heterogeneous structures to effectively capture both inter- and intra-entity relationships. This integration helps preserve the intrinsic meaning of complex biological interactions. Network-based approaches have been highly effective in predicting node labels and uncovering hidden associations between entities. These methods have been widely applied in areas such as user-item recommendations, gene-gene interactions, and lncRNA-disease association prediction. In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. Specifically, GCNs are leveraged to extract and encode high-fidelity feature representations of lncRNAs and diseases, while deep matrix factorization, implemented via a multi-layer perceptron, facilitates the discovery of potential disease-associated lncRNAs. Our model exhibits outstanding predictive performance, achieving AUCs of 0.9183 and 0.9057 in leave-one-out and five-fold cross-validation experiments, respectively. Extensive comparative evaluations demonstrate that GMFLDA surpasses four state-of-the-art methods, underscoring its superior predictive capability. We anticipate that GMFLDA will serve as a powerful computational tool for biomarker discovery, significantly accelerating the identification of disease-associated lncRNAs while mitigating the time and cost constraints of traditional wet-lab experiments. © 2013 IEEE.

关键词： Matrix factorization

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Large Graphs with Small Burning Numbers

Large Graphs with Small Burning Numbers

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International Conference on Computational Engineering, ICCE 2023

作者： Rajasingh, Angelin Jemima Parthiban, N. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

In this paper, we have determined the burning number of graphs with universal vertices and graphs that are generated using the graph operation namely ‘join of graphs’. © The Author(s), under exclusive license t... 详细信息

ISBN: (纸本)9783031777639

关键词： Burning number Join of graphs Universal vertex

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Adaptive sail fish optimizer based optimized region selection for robust video steganography

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2417-2427页

作者： Babu, J. Suresh Niranjana, G. Ramana, Kadiyala Department of Computing Science & Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology (Autonomous) Hyderabad India

Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometimes fail to identify the best pixels for encoding hidden data, resulting in lowerquality and robustness. To solve this issue, this article proposes an innovative steganography technique that improves the security and quality of data hiding. The proposed method takes cover video and secret images as input. The Structural Similarity Index Measurement (SSIM) first collects key frames from the input video. The adaptive sailfish optimizer (ASFO) is then used to find the best regions within these keyframes to insert the secret data in. To improve security, the discrete wavelet transform (DWT) is applied to the specified region, and the LL band is used for the hiding process. The secret image is encrypted by Adaptive elliptical curve cryptography (AECC) and transformed to binary bits, which then get embedded in the chosen band. This method leads to the construction of a stego-video. The extraction procedure is repeated using the same approach, which ensures secure and robust data transmission. Performance metrics, including PSNR, BER, NAE, TAF, and NC are analyzed to compare each method's efficacy. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive elliptical curve cryptography And stego-video Discrete wavelet transform Structural similarity index measurement Video steganography

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SECOND-ORDER OPTIMALITY CONDITIONS FOR NONSMOOTH MULTIOBJECTIVE OPTIMIZATION SUBJECTED TO MIXED CONSTRAINTS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2025年第1期7卷 39-55页

作者： Tung, Nguyen Minh Cuong, Nguyen Minh Department of Data Science of Business Ho Chi Minh University of Banking Ho Chi Minh City Viet Nam Department of Mathematics and Computing University of Science Vietnam National University Ho Chi Minh City Viet Nam

This paper provides some second-order optimality conditions for local weak and strict efficient solutions to a nonsmooth multiobjective optimization problem subjected to both equality and inequality constraints. We first propose a type of second-order Abadie constraint qualification (CQ) and give a sufficient condition for this CQ with the help of the property of local error bound. Then, we employ it to obtain primal necessary conditions. A primal sufficient condition is also established. We next employ some alternative theorems to obtain the corresponding dual results. Applications to nonsmooth fractional programming problems are given. Finally, some examples are provided to illustrate our results. ©2025 Applied Set-Valued Analysis and Optimization.

关键词： Optimization algorithms

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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