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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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Comprehensive analysis of various imputation and forecasting models for predicting PM2.5 pollutant in Delhi

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Neural Computing and Applications 2025年第17期37卷 11441-11458页

作者： Karnati, Hemanth Soma, Anuraag Alam, Adnan Kalaavathi, B. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control Board, detailing air pollutant concentrations at the ITO (Income Tax Office), Delhi station, from 2017 to 2023. Our focus on pollutants such as PM2.5, PM10, NO2, NH3, SO2, ozone, and CO highlights the challenges posed by missing data in environmental studies. Through the application of several imputation models, including KNN, linear regression, forward fill + backward fill, Fourier + KNN, linear interpolation, and statistical methods, we identified combination of forward fill and backward fill as the most effective method for addressing data gaps, specifically for ozone measurements. Building on this imputed dataset, we employ various forecasting models to predict PM2.5 levels, a critical pollutant. Our exploration includes time series and deep learning approaches, such as LSTM, Bi-LSTM, CNN-LSTM, GRU, Deep AR, WaveNet, TCN, ARIMA, SARIMA, and Prophet. The performance of these models is evaluated using daily data, with Bi-LSTM and LSTM with attention emerging as the top performers due to their accuracy in predicting PM2.5 concentrations. The implications of our findings extend beyond academic interest, offering practical insights for environmental policy and health advisories in Delhi. By demonstrating the efficacy of specific imputation and forecasting techniques, our research contributes to the broader effort of improving air quality monitoring and prediction. The success of Bi-LSTM and LSTM with attention models, in particular, suggests a promising avenue for future studies aiming to enhance the precision of environmental forecasts. © The Author(s) 2025.

关键词： Time series

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Text steganography: enhanced character-level embedding algorithm using font attribute with increased resilience to statistical attacks

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Multimedia Tools and Applications 2025年第11期84卷 9367-9392页

作者： KL, Shreya Narasimhan R, Bala Krishnan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India

Text steganography, the science of hiding secret messages in innocent-looking text documents ensures the secrecy of the embedded secret. Cryptography, on the other hand, encrypts and converts the secret message into an unintelligible form ensuring confidentiality, integrity, and authentication of the message. It is to be noted that both steganography and cryptography are expected to work together hand-in-hand in order to attain a better security. Hence, a methodology that combines techniques from both steganography and cryptography is an unavoidable one for the complete protection of sensitive data. This paper aims to achieve one such methodology. The best existing method which works in similar direction is observed to use character spacing feature of text documents to embed the secrets. The method is reported to attain an average embedding capacity of ≈2 bits/cover-character and 8-bits/distortion with high imperceptibility and considerable cryptographic security. However, during experimentation, it is noticed that the methodology has the potential vulnerability of leaving clues for attackers with a probability of 0.51% which paves way for possible statistical attacks namely frequency analysis. This vulnerability occurred because of the constant left circular shift operation that is performed after embedding each secret character. This paper aims to address this vulnerability by proposing a novel modification that facilitates the methodology to perform both left and right circular shifts in an unbiased manner, based on the embedded secret, without affecting the notable achievements of the existing methodology. The proposed modification is successfully implemented, and further analysis indicated that the performed modification has in fact embedded the secret characters uniformly thereby reducing the standard deviation by 24%. This increased the resilience of the extended method to potential attacks that rely on statistical techniques. © The Author(s), under exclusive

关键词： Cryptography

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Improving agility in projects using machine learning algorithm

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Multimedia Tools and Applications 2024年第38期83卷 85987-86005页

作者： Varun, Janani Karthika, R.A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the upcoming modules in the Agile era. The functionality being discussed in this paper is to predict the defects using Random Forest Algorithm. Predictive analytics draws on information from the past to create forecasts about the outcomes of future events. Product team always have the difficulty in delivering the product as per schedule. As we are in the agile era, the requirement keeps changing and team is unsure on upcoming releases. Prediction helps the team to focus on the complex and error prone modules in upcoming releases. The Predictive analytics model designed, can predict defects with an accuracy rate of 88% with the help of historical data. By predicting, testers can focus on the module where there are a greater number of defects predicted by the model and left shift the delivery. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Predictive analytics

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Efficient multiverse electro search optimization for multi-cloud task scheduling and resource allocation

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Multimedia Tools and Applications 2024年 1-26页

作者： Ravi, Rupesh Pillai, Manu J. Department of Computer Science and Engineering TKM Institute of Technology Karuvelil Kerala Kollam691505 India Department of Computer Science and Engineering TKM College of Engineering Karicode Kollam India

Effective task scheduling and resource allocation have become major problems as a result of the fast development of cloud computing as well as the rise of multi-cloud systems. To successfully handle these issues, we provide a novel strategy in this study termed Efficient Multiverse Electro Search Optimization (EMESO). This study presents an Efficient Multiverse Electro Search optimization to improve work scheduling behaviour, with significance on electron transport near the nucleus and cosmological principles. The method uses exploration, exploitation, and local search to discover the optimal solution from a huge amount of potential alternatives. EMESO examines a broad search space to uncover optimal solutions for work scheduling and resource allocation by exploiting the concept of a multiverse, which represents a collection of parallel universes. While related to modern work, the proposed approach performs much better in big and extra-big workflow jobs than in smaller and midrange workflow jobs. It may decrease costs by 6%, shorten project timelines by 8%, and increase resource efficiency by 55% while still enabling all activities to be completed by the deadlines. The proposed approach significantly improves efficiency, resource utilization, and overall performance, making it one of the best options for managing complex cloud systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cloud computing

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Complex preference analysis: a score-based evaluation strategy for ranking and comparison of the evolutionary algorithms

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Soft Computing 2025年第4期29卷 1967-1980页

作者： Sarkar, Debojyoti Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar788010 India

In this paper, an evaluation strategy is proposed for evaluation of optimization algorithms, called the Complex Preference Analysis, that assesses the efficiency of different evolutionary algorithms by considering multiple performance metrics concurrently across diverse dimensions and generating a single score for ranking. The proposed strategy allows for a thorough evaluation by assigning distinct priorities in the form of weights, to various performance parameters. The algorithm’s performances on each performance parameter are captured and the best and worst performances in each performance category are identified. The deviation of each algorithm from the best and worst performances in the respective performance category is formulated to generate a single score for each of the algorithms for comparison. The flexibility to adjust weights provides a valuable tool for customizing evaluations according to individual needs and preferences. The proposed evaluation strategy is studied by running evaluations on nine evolutionary algorithms based on their performance over five performance indicators on six datasets of the Travelling Salesman Problem. Conducting Complex Preference Analysis on the different evolutionary algorithms reveals distinctive patterns in their performance, with some algorithms such as Simulated Annealing, Tabu Search, Bat Algorithm, Genetic Algorithm, etc. consistently demonstrating superior results across multiple Travelling Salesman Problem instances, while other algorithms such as Ant Colony Optimization, Teaching Learning Based Optimization, etc. rank lower. The algorithms’ performances on individual performance parameters, when reflected on the final rank of the algorithms, generated based on the collective impact of all the parameters, elucidate the efficiency of the proposed evaluation strategy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Optimization algorithms

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A brief review on Quantum Key Distribution Protocols

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Multimedia Tools and Applications 2025年 1-40页

作者： Kumar, Mandeep Mondal, Bhaskar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna800005 India

Development in Quantum computing paves the path to Quantum key distribution (QKD) by using the principles of quantum physics. QKD enables two remote parties to produce and share secure keys while removing all computing constraints on an adversary. The basic physics laws are used to identify any outside parties eavesdropping on the key exchange. In recent years many revolutionary developments in the field of QKD have been developed to overcome security and networking constraints. This survey provides an overview of the QKD protocol’s evolution and quantum network architecture. The paper also demonstrates QKD deployment techniques and elements of the QKD network. It also highlights ongoing design challenges by considering security and error estimation and correction, in contrast to studies concentrating on optical channels and equipment. Finally, this paper examines the possible directions for future research and offers design principles to guide the development of QKD and its related area. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Quantum computers

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Feedback-based trust module for IoT networks using machine learning

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 78-91页

作者： Iqbal, Sania Qureshi, Shaima Department of Computer Science and Engineering National Institute of Technology Jammu & Kashmir Srinagar India

The increasing emergence of IoT in healthcare, industrial automation, manufacturing, infrastructure, business and the home undoubtedly provides more conveniences in different aspects of human life. Any IoT security and trust solution must consider the heterogeneity and mobility of IoT devices, and the features of other devices connected to the network. Traditional security measures face computation, storage and communication challenges in securing IoT, and the increasing reliance on IoT services and applications has exacerbated this issue. It is crucial to identify a security management model to handle these constraints. Trust as a security regime in the IoT has far-reaching implications. This paper proposes a novel trust-based security mechanism by combining machine learning and traditional summations to determine a node’s trustworthiness in an IoT environment in milliseconds. Based on this, a computational trust management model is designed by aggregating direct and feedback-based trust information. The model parameters like the trust breach window and trust re-initiation point are selected based on extensive experimentation to dynamically calculate each node’s trust value to reach the proposed model’s peak performance. The model’s effectiveness is verified through simulation. In 500 iterations, the proposed model achieved 97% accuracy with a run time of 0.37 seconds. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Network security

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Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

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Inteligencia Artificial 2024年第74期27卷 133-151页

作者： Ikechukwu, Agughasi Victor Department of Computer Science & Engineering Maharaja Institute of Technology Karnataka Mysore571477 India

Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for innovative and widely accessible diagnostic tools has never been more paramount. While spirometry tests serve as conventional diagnostic benchmarks, their reach remains limited, especially in regions with constrained medical resources. The presented research harnesses deep learning algorithms to facilitate early-stage COPD detection, specifically targeting Chest X-rays (CXRs). The clinically annotated VinDR-CXR dataset provides the primary foundation for model training, complemented by incorporating the ChestX-ray14 dataset for initial model pre-training. Such a dualdataset strategy augments model generalization and adaptability. Among several explored Convolutional Neural Network (CNN) architectures, the Xception model emerges as a frontrunner. Through transfer learning methodologies, this model produces a noteworthy recall rate of 98.2%, markedly surpassing the metrics of the ResNet50 model. Recognizing the imperative for transparency in AI applications in medical imaging, the research integrates essential explainability approaches viz: Gradient Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). These techniques elucidate the AI’s decision-making process, offering invaluable visual and analytical insights for fostering trust among medical professionals. In essence, this study not only underscores the potential of integrating AI with medical imaging for COPD detection but also accentuates the pivotal role of transparency in AI-driven medical interventions. © IBERAMIA and the authors.

关键词： Decision making

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Model-to-Circuit Cross-Approximation for Printed Machine Learning Classifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 3532-3544页

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jorg Karlsruhe Institute of Technology Karlsruhe76131 Germany National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece University of Patras Computer Engineering and Informatics Department Patras26504 Greece Karlsruhe Institute of Technology Department of Computer Science Karlsruhe76131 Germany

Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as multilayer perceptrons (MLPs) and support vector machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage overscaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way toward smart complex printed applications. © 1982-2012 IEEE.

关键词： Timing circuits

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