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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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HybridFlicks: integrating collaborative filtering and content-based methods for enhanced movie recommendations

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Multimedia Tools and Applications 2025年 1-15页

作者： Dakshinamoorthy, Prabakar Murugan, Arvindh Vijayabhaskar, Sohan Thirumal, Bharani Kumar Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

User engagement has been improved by using recommender systems, which are essential for giving user recommendations. Matrix factorization (MF), one of the traditional approaches, has shown a promising work in capturing user preferences, especially in certain areas like movie recommendation with the help of MovieLens, which are datasets. Nevertheless, there is always a need to optimize long-term user engagement to overcome the challenges like large action space in recommender systems and the recent advancements like deep reinforcement learning (DRL) have shown a much greater role in addressing the same. This study aims to explore the effectiveness of hybrid approaches, wherein our innovative HybridFlicks recommender system is proposed, which combines the strengths of MF-based collaborative filtering with content-based filtering using TF-IDF and Cosine similarity, to improve the accuracy of the recommendations provided to the user, by focusing on enhancing the recall value and F1 score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Reinforcement learning

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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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Dimensional-noise-aware battery lifetime prediction via an EM-TLS framework

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Progress in Natural science:Materials International 2025年第1期35卷 146-155页

作者： Chenlong Yu Ting Lu Guohua Liu Xiaoang Zhai Wuyan Deng Jiayu Wan Yang Liu Xin Li School of Computer Science and Technology Donghua University Future Battery Research Center Global Institute of Future Technology Shanghai Jiaotong University Data Science Research Center Duke Kunshan University

Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.

关键词： Battery lifetime prediction Dimension-variate measurement error Energy storage Expectation-maximization Total-least-squares regression

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LesionAid: vision transformers-based skin lesion generation and classification – A practical review

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Multimedia Tools and Applications 2025年 1-22页

作者： K, Mallikharjuna Rao Krishna, Ghanta Sai Supriya, Kundrapu Sorgile, Meetiksha Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specimen collection. Deep convolutional neural networks (CNNs) perform highly segregated and potentially universal tasks against a classified fine-grained object. This study suggests a novel multi-class prediction framework that uses ViT and ViTGAN to categories skin lesions. To address the class disparity, GANs (Generative Adversarial Networks) based on vision transformers are used. The framework comprises four main phases: ViTGANs, Image processing, and explainable AI. Phase 1 consists of generating synthetic images using ViTGAN to balance all the classes in the dataset. To enhance the amount of the data, the second phase involves using various morphological processes and data augmentation techniques. In phases three and four, a ViT model for edge computing systems that can recognize patterns and classify skin lesions from the user's skin that is visible in the picture is developed. In phase 3, after classifying the lesions into the desired class with ViT, we will use explainable AI (XAI) that leads to more explainable results (using activation maps, etc.) while ensuring high predictive accuracy. The results demonstrate that the model used for generating synthetic images has achieved an FID score of 13.32 and the ViT model has achieved 99.2% as its training accuracy and 97.4% as its validation accuracy. The whole framework is compared with the existing frameworks for skin lesion detection. And explainable AI (XAI) is used in the proposed framework in order to increase model transparency and boost trust among users by illustrating the main factors affecting predictions. This interpretability helps physicians in making more informed decisions by providing clear insights into the model's reasoning. © The Author(s), under exclusive licence to Springer science+Bu

关键词： Generative adversarial networks

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Microbial fouling induced by typical extreme environment-resistant bacteria in oligotrophic solutions of simulated condensate of manned spacecrafts

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Chinese Journal of Aeronautics 2025年第2期38卷 597-616页

作者： Yali FENG Junhang CHEN Qianqian LIU Nana CHEN Lin LU Chaofang DONG Kui XIAO Institute of Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China Corrosion and Protection Center University of Science and Technology BeijingBeijing 100083China

Microbial fouling is an important challenge in water recovery system of manned spacecrafts for longer term *** fouling of 5A06 aluminium alloy induced by typical extreme environment-resistant bacteria in oligotrophic solutions of simulated condensate of manned spacecraft was *** cereus showed poor survival ability to oligotrophic environments,and a small amount of remaining live *** cells mainly existed in the form of spores without forming *** when *** was mixed cultured with Cupriavidus metallidurans,the system was mainly affected by *** biofilms rather than *** *** could promote the thickness of passive films of aluminum alloy,so *** posed a minor threat to the corrosion of 5A06 aluminum ***,*** showed strong adaptability to oligotrophic environments and formed a large number of *** the contamination threat of *** still dominated even cultured with *** when cultured with ***,the threat of contamination from *** still ***,*** would pose a threat of microbial fouling to the oligotrophic water recovery system of manned spacecrafts.

关键词： Microbial corrosion Fouling Biofilms Passivation Oligotrophic environment

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Blockchain-based trust management for data exchange in internet of vehicle network

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Multimedia Tools and Applications 2025年第8期84卷 4837-4855页

作者： Srivastava, Sandeep Agarwal, Deepshikha Chaurasia, Brijesh Kumar Adhikari, Mainak Computer Science Indian Institute of Information Technology UP Lucknow226002 India Information Technology Indian Institute of Information Technology UP Lucknow226002 India Computer Science Pranveer Singh Institute of Technology UP Kanpur208029 India School of Data Science Indian Institute of Science Education & Research Thiruvananthapuram India

Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services required during inter-vehicular communication which makes it essential to use a trust management mechanism. Existing trust management systems are insufficient to address the emerging IoV needs of transparency, mobility, immutability, and scalability. Motivated by the need to address the outlined issues, Proposed additive increase and multiplicative decrease (AIMD) trust management model, to improve the quality of communication, as well as vehicle trustworthiness based on multiple inter-vehicular interactions. The proposed layered architecture helps to reduce overhead in terms of latency and average execution time compared to other trust management mechanism and avoid risk of single point of failure. The Proposed AIMD trust management is implemented on permissioned blockchain, where transactions in the IoV network are only managed by minor nodes which accumulate interactions and uses PBFT and AIMD mechanism to derive immutable reputation score securely and transparently. Simulation results show the AIMD trust mechanism using permissioned blockchain can manage the trust of vehicles efficiently by reducing the latency by 0.29 sec, increasing packet delivery ratio from 0.8 to 0.95, and significantly reduce transaction finality time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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A data representation method using distance correlation

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Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN Computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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Scalable and High-Performance Large-Scale Dynamic Graph Storage and Processing System

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ACM Transactions on Storage 2025年第2期21卷 1-34页

作者： Wang, Rui Zong, Weixu He, Shuibing Li, Yongkun Xu, Yinlong Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China University of Science and Technology of China Hefei China Computer Science University of Science and Technology of China Heifei China

Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these issues through its larger capacity and non-volatile characteristics. However, simply adapting existing DRAM-based graph storage systems to PMEM would result in inefficient PMEM stores and accesses, including high read and write amplification to PMEM, imbalanced work division for PMEM accesses, and costly remote PMEM access across NUMA nodes. These issues severely limit the performance of large graph processing. In this article, we aim at achieving scalable and high-performance graph processing in PMEM. We first propose an XPLine-friendly graph storage model that uses vertex-centric graph buffering, hierarchical vertex buffer managing, and in-place vertex block merging to optimize PMEM graph storage. Furthermore, we develop a scalable graph processing model that leverages multi-threaded work dividing and NUMA-friendly graph accessing to optimize PMEM graph accesses. Based on these techniques, we implement XPGraph, a PMEM-based graph storage system for large-scale evolving graphs, and several variants for different system settings. Our experiments demonstrate that XPGraph surpasses the state-of-the-art in-memory graph storage system on a PMEM-based system by 3.07× to 4.99× in update performance and up to 5.87× in query performance, and performs much better in highly parallel multi-threaded scenarios. © 2025 Copyright held by the owner/author(s).

关键词： Dynamic random access storage

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