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LiDAR Image-Based Earth Carbon Emission Analysis and Its Impact on Public Health: A Machine Learning Model

Remote Sensing in Earth Systems Sciences 2025年第2期8卷 455-464页

作者： Meghana, S. Padmaja, Dhyaram Lakshmi Gundu, Krishna Sriharsha Kudari, Rajeev Somasekar, J. Chandran, Likha Department of Computer Science Engineering Neil Gogte Institute of Technology Hyderabad India Information Technology Anurag University Hyderabad India Enterprise Technology Arizona State University Tempe AZ United States Department of CSE Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Guntur 522502 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru 562112 India Department of Chemistry VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

Afforestation is a practical way to absorb carbon dioxide in contemporary context of carbon neutrality. In the assessment of forest emission reduction projects, stock can be utilized as a crucial indicator of biomass and carbon storage estimation in addition to serving as an economic value index for forest wood resources. We suggest a framework and technique for data-driven machine learning that uses satellite remote sensing and aerial LiDAR to forecast plantation stock. This research proposes a novel method in LiDAR image-based carbon emission analysis with their health impact using a machine learning method. Here, the input is collected as LiDAR image-based healthcare data for processing and smoothening. Then, this image has been segmented and classified using convolutional contour graph cut equalization with VGG-net transfer encoder Gaussian neural networks. The experimental analysis for LiDAR image dataset has been carried out for analysis of carbon emission, and healthcare data analysis has been carried out in terms of training accuracy, average precision, recall, F-1 score, and AUC. The proposed technique attained a training accuracy of 98%, an average precision of 93%, a recall of 96%, an F-1 score of 92%, and an AUC of 94%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Carbon emission analysis Health impact LiDAR image Machine learning model Transfer encoder

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches 4

Early Detection of Childhood Malnutrition Using Survey Data ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Zumma, Thoufiq Rahaman, Anikur Islam Prova, Nuzhat Noor Haque, Tasriful Joy Bose, Julias Caeser Youki, Ravina Akter Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Washington University Science and Technology Department of Computer Science New York United States Pace University Department of Seidenberg School of Csis New York United States Victorian Institute of Technology Master of Information Technology and Systems Australia University of Wollongong Faculty of Artificial Intelligence and Science Australia University of Technology Sydney Faculty of Engineering and It Australia

ISBN: (纸本)9798331523923

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing Data Aggregation in Cluster-Based Wireless Sensor Networks with LCSS: Longest Common Subsequence Empowerment 8th

Enhancing Data Aggregation in Cluster-Based Wireless Sensor ...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Jamil, Muhammad Farooq, Umer Tariq, Noshina Alwakid, Ghadah Humayun, Mamoona Khan, Nadia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Avionics Engineering Air University Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9789819793266

A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a plethora of approaches. Despite these mechanisms’ best efforts, most current systems still need help with difficulties like noise, redundancy, or application specificity. Thus, we proposed an algorithm to overcome all the problems with redundant and highly correlated data. The longest common subsequence (LCSS)-enabled sliding window algorithm can resolve issues in constrained networks related to energy conservation and congestion control. The proposed method exhibits remarkable performance on multiple benchmark datasets. According to the result analysis, the suggested technique can outperform its equivalents for a range of data sets by about 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

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Improved Quaternion Discriminant Analysis for Feature Extraction and Classification of Hyperspectral Image

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12925-12945页

作者： Wang, Xinpeng Ling, Bingo Wing-Kuen Yu, Long Dai, Qingyun Liu, Shaopeng Zhao, Jiandong Kuang, Weichao Guangdong Polytechnic Normal University Faculty of Computer Science Guangzhou China Guangdong University of Technology School of Information Engineering Guangzhou China Sun Yat-sen University School of Geography and Planning Guangzhou510275 China Dongguan University of Technology Department of Electronic Engineering and Intellectualization Dongguan China

Real domain discriminant analysis (RDA) can not solve the crosstalk of strong correlation between adjacent spectral bands, and also causes spectral information loss of hyperspectral image (HSI). As a result, the effect of feature extraction is not ideal. Besides, in the case of small samples, using quaternion discriminant analysis (QDA) based on Cos-sin Decomposition, the matrix obtained by the first orthogonal trigonometric decomposition is not a diagonal matrix, that makes the projection axis not optimal. To solve these problems, firstly, a quaternion discriminant analysis method based on stable quaternion Cossin Decomposition (QDASG) is proposed to solve the small sample problem. Secondly, this paper theoretically analyzes that when the number of projection axes in QDASG is smaller than that in traditional DA methods, the discriminant formula can achieve optimal results, and verifies this conclusion through experiments. Then, according to the number of training samples, the implementation process of QDA is discussed in two cases. Finally, spatial filtering is used to extract spatial information, and the distance quotient formula between classes is proposed as the standard for selecting the the optimal number of stacking layers for the filter. Experimental results show that this method is effective compared with deep learning method and and other traditional machine learning methods, especially in the case of small samples. © 2008-2012 IEEE.

关键词： Discriminant analysis

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators 27

Studying of Machine Learning Models for Forecasting Macroeco...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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IALS: Innovative Approach for Lung Segmentation Applying Artificial Intelligence and Deep Learning

IALS: Innovative Approach for Lung Segmentation Applying Art...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Hossain, Mohammad Alamgir Shahi, Fazal Imam Husain, Sadia Suliman, Abdelrazig Kamal, Mohammad Shahid Ahmad, Yasir Talukdar, Mohammad Mazedul Huq Martin, R. John Jazan University College of Engineering & Computer Science Department of computer Science Jazan Saudi Arabia Jazan University ELearning Center Jazan Saudi Arabia University of Bisha Faculty of Computers and Information Technology Department of Information Technology Saudi Arabia Jazan University College of Arts and Humanities Department of Foreign Languages Jazan Saudi Arabia

ISBN: (纸本)9798331513894

The automated segmentation of lungs within chest CT scans remains crucial for many medical applications that require disease diagnosis and treatment design. The presented deep incremental learning (DIL) system addresses fundamental limitations that arise during lung segmentation processes. The exceptional segmentation outcomes result from the neural network structure which includes masked RCNN+UNet for tissue segmentation and GAN+VARMA for classification. Medical literature identifies lung tissue structures as difficult to study because they present irregular forms and complex structures. The task of obtaining precise segmentation proves very challenging. The application of masked RCNN+UNet enables shape control and improves segmentation precision of the lung system. The DIL function helps maintain improved features within segmented images. The applied framework enables improved performance through DSC and MAE and JI metrics evaluation. Experimental evidence shows that the new framework achieves superior performance compared to current top-ranking methods for lung segmentation. The proposed framework exhibits excellent precision and detection efficiency with a DSC value of 0.96 and JI value of 0.93 and a low MAE © 2025 IEEE.

关键词： Medical applications

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical Sciences. All rights reserved.

关键词： Electroencephalography

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Siddiqi, Hafied Nur Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Department of Information Technology PO Box 84428 Riyadh11671 Saudi Arabia Shenzhen University College of Computer Science & Software Engineering Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Prince Sultan University EIAS Data Science and Blockchain Laboratory CCIS Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a security mechanism that exploits the unique, unrepeatable physical characteristics of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The unique identification of IoT devices along the supply chain is implemented by using PUFs as tamper-resistant IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its potential to combat several attack scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing

关键词： Supply chain management

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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An Enhanced Product Recommendation System Using Decision Tree Algorithm 7th

An Enhanced Product Recommendation System Using Decision Tre...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Akinyoola, Oluwatimilehin Moses Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany

ISBN: (纸本)9789819764648

Product recommendation systems are a critical element of e-commerce platforms, as they enable customers to identify the items that best meet their needs. Product recommendation systems are important tools used by organizations to increase customer engagement, satisfaction, and loyalty. Existing product recommendation systems have several weaknesses, such as an inability to incorporate features outside of customer preferences. Therefore, this paper proposes an enhanced product recommendation system using a decision tree algorithm. The main objectives of this system are to improve the accuracy and efficiency of the existing product recommendation systems. To accomplish this, a framework includes a data preprocessing phase, a feature selection phase, and a model training phase. The data preprocessing phase is used to clean the data and eliminate any noise. The feature selection phase is used to identify the most informative features, which are then used to train the decision tree model. The model is trained using CART, a supervised learning algorithm, and is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Finally, the model is tested on a test dataset to compare with existing solutions. The results show that the proposed system outperforms existing recommender systems in terms of all the evaluation metrics discussed. Furthermore, the improved system also provides useful insights into the product recommendation process for future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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