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Reinforcing internet of things security measures in smart agriculture with feature extraction-based supervised machine learning for intrusion detection

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Multimedia Tools and Applications 2025年 1-28页

作者： Manna, Sarbajit Sarkar, Arindam Mondal, Atanu Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal711202 India

Most of the countries around the world depends on agriculture for their growth and development. There are different types of agricultural methods, irrigated agriculture is one of them. In irrigated agriculture fresh water acts as the main component. With the progress of time, the scarcity of fresh water is noticed around the globe and it will become worse in upcoming days. Now-a-days to get rid off such an alarming issue, the only workable alternatives are precision agriculture and intelligence-based irrigation. Intelligent-based irrigation and precision agriculture have only recently become financially feasible with rise of the Machine Learning (ML) and Internet-of-Things (IoT). IoT has several advantages such as improved productivity, reduced costs, gain in energy, prediction of events, and enhancement of comfort in different fields of human society. Being emerged with different technologies and information handling aids, IoT becomes susceptible to be hampered from security and confidentiality perspective. In this paper, a methodology is discussed for identifying and categorizing different attacks into IoT-based agricultural paradigm. In all IoT areas, including those linked to agriculture, security and confidentiality are top priorities. The NSL-KDD dataset is utilized in this work, which is initially preprocessed by converting symbolized attributes into numerical ones. Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA) and T-Distributed Stochastic Neighbour Embedding (T-SNE) are applied to extract features. Subsequently, the preprocessed dataset undergoes classification utilizing ML algorithms like Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Stochastic Gradient Descent (SGD). The suggested approach outperforms existing methods, as evidenced by precision, accuracy, recall, F1-Score, Area Under the ROC Curve (AUC), False Discovery Rate

关键词： Nearest neighbor search

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An Improved Energy-Efficient MAC Protocol for WSNs

An Improved Energy-Efficient MAC Protocol for WSNs

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International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2024

作者： Bengheni, Abdelmalek Computer Science Department Math/CS Faculty LRAIS Laboratory University of Ibn Khaldoun Tiaret Algeria

ISBN: (纸本)9783031803000

Wireless Sensor Networks (WSNs) operating on constrained energy resources present a substantial challenge to researchers. Actually, a lot of ideas have been made to either equip a WSN's sensor nodes with technologies that harvest energy or to preserve the energy that it uses efficiently. This paper presents an Improved Energy-Efficient MAC Protocol for WSNs (IEE-MAC). The functioning of IEE-MAC relies on using transmitter-initiated schemes and low-power listening (LPL) with small preamble messages and based on the value of the collected energy rate, each sensor node in the WSN may decide when to sleep next. In doing so, it lowers the duty cycle more adaptively by utilizing the stored remaining energy, which particularly aids in minimizing latency and energy consumption. According to the simulation findings, our suggested protocol performs better in terms of lowering average latency and power consumption while boosting packet delivery rates as well as extending the lifetime of wireless network compared to the PS-EHWSN scheme. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensor nodes

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Quantum-Resistant Lattice-Based Cryptography for Secure UAV Communications

Quantum-Resistant Lattice-Based Cryptography for Secure UAV...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Baidya, Bodhisattwa Mondal, Atanu Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math Howrah India

ISBN: (纸本)9789819613472

Quantum-Resistant Lattice-Based Cryptography, designed especially for secure communication in networks of Unmanned Aerial Vehicles (UAVs). The integrity and security of data must be guaranteed since UAVs are becoming increasingly prevalent in both military and civilian applications. Quantum computing poses an immediate challenge to traditional cryptography. The proposed Quantum-Resistant Lattice-Based Cryptography architecture guarantees secure communication with little computational expense over previous works by using the resilience of lattice-based encryption against quantum assaults. The proposed work also provides the groundwork for trustworthy communication among UAVs via the use of encryption and lattice-based key exchange protocols by optimizing cryptographic processes while addressing UAV network restrictions. Finally, thorough assessments confirm the present work’s effectiveness, guaranteeing stakeholders safe and essential UAV operations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum cryptography

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Enhanced UAV Tracking through Multi-Sensor Fusion and Extended Kalman Filtering 6

Enhanced UAV Tracking through Multi-Sensor Fusion and Extend...

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6th Doctoral Symposium on Intelligence Enabled Research, DoSIER 2024

作者： Baidya, Bodhisattwa Mondal, Atanu Manna, Sarbajit Das, Gourab Santra, Anirban Chakraborty, Arkaprava Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah711202 India

This research introduces a sophisticated method for UAV tracking utilizing multi-sensor fusion and Extended Kalman Filter (EKF) techniques. We model a sophisticated 3D UAV trajectory alongside diverse sensor information, encompassing RF signal strength, radar range and velocity, audio signals, and optical location data. The EKF technique is employed to estimate the drone's position and velocity, exhibiting enhanced tracking precision relative to individual sensor observations. The simulation integrates variable noise levels and adaptive estimation of measurement noise covariance to improve the resilience of the EKF. Real-time visualization of sensor data and EKF estimates facilitates instant evaluation of the algorithm's efficacy. The results indicate that the EKF efficiently eliminates sensor noise, yielding a more refined and precise trajectory estimation. The analysis of sensor data indicates the average signal strengths and standard deviations for each sensor category. The EKF exhibits a 12.5% enhancement in tracking accuracy relative to unprocessed optical sensor data, with mean tracking errors of 0.07 m for the EKF compared to 0.08 m for the optical sensor independently. This study emphasizes the efficacy of multi-sensor fusion and EKF implementation in improving drone tracking capabilities across diverse applications, such as surveillance, search and rescue, and air traffic management. © 2025 Copyright for this paper by its authors.

关键词： Drones

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Prediction of Software Cost Estimation Using Stacking Ensemble Learning method

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Procedia computer Science 2025年 258卷 727-736页

作者： Nedaa Thamer Qassem Ibrahim Ahmed Saleh Software Department College of Computer & Math University of Mosul Iraq

The process of accurate cost estimation is one of the important factors for the success of software projects, so it is necessary to understand the process of estimating the cost and planning in advance to manage the project positively and effectively, which in turn helps the project manager and the client to determine the work budget. This paper introduced Ensemble learning method of stacking. This method works in two stages: the first is to train many individual models called models with basic learners, and the second is to collect model expectations from the first stage as an introduction to an intermediate model called the meta-model that works on the final prediction. The following models (Random Forest (RF), Linear Regression (LR), AdaBoost, XGBoost, Gradient Boost, and K_Nearest Neighbor (K_NN)) are basic learners for the stacking method. The learners were trained on the ISBSG dataset from the ISBSG repository. At the beginning of the work, the dataset was processed to obtain high prediction accuracy, and the characteristics that suit the cost estimation process were determined. The proposed method was applied to it, and the model performance was evaluated using the following accuracy measures (MAE, RMSE, R-squared, MMRE). The results of implementing the stacking model gave a prediction accuracy of 98% and a lower error rate compared to the individual models by 0.0927, thus proving its superiority over the individual models by increasing the estimation accuracy. The results of implementing the algorithm for the accuracy measures, respectively (207,533,0.983,0.0927). The proposed method contributes to producing a software development cost estimation tool with an accuracy of 98%, which in turn helps estimators in pre-estimating the cost and effort with high accuracy, which leads to the success of the work.

关键词： software Engineering cost estimation ensemble learning stacking Decision tree Random forest boosting

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A Homogeneous Federated Learning Approach Toward Prediction of Diabetes

A Homogeneous Federated Learning Approach Toward Prediction ...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Karmakar, Rahul Sarkar, Arindam Malik, Debraj Sain, Priyabrata The University of Burdwan West Bengal Bardhaman India Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math Howrah India

ISBN: (纸本)9789819613472

Federated learning (FL), which provides a decentralized method of model training without jeopardizing data privacy, has become a paradigm-shifting breakthrough in artificial intelligence (AI). This work addresses homogeneous federated learning in healthcare. The homogeneous federated learning (HFL) aims to train models across several devices or nodes while maintaining data security and privacy. The same data distribution and model architecture are usually shared by all nodes involved in HFL. In this paper, we propose a HFL model for diabetes prediction case study. The dataset is divided into five segments and assign one segment to each client, then apply four machine learning classification algorithms to find some metrics such as accuracy, precision, recall, and f1 score. The healthcare field has been experiencing a major impact from federated learning. FL has several other uses outside of the medical field, like IoT and Android, Blockchains, among others. This paper primarily addresses federated learning about data privacy in the medical field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Digit Detection: Localizing and Convoluting 44th

Digit Detection: Localizing and Convoluting

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44th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, SGAI 2024

作者： Martens, Tyrell Zhang, John Z. Department of Math and Computer Science University of Lethbridge LethbridgeABT1K 3M4 Canada

ISBN: (纸本)9783031779176

We explore the development of a set of algorithms for accurately localizing and classifying handwritten digits, addressing the challenges posed by variations in individual writing styles, digit sizes, and the presence of multiple digits in an image. Our work is structured into three tasks, each building upon the previous ones. Task A, which is trivial, focuses on classifying individual handwritten digits with 28 × 28 pixel resolution using a convolutional neural network. Task B extends the CNN in Task A, as a black box, by developing algorithms to recognize multiple digits with identical dimension sizes, placed on a large image. Task C further complicates Task B by considering digits with varying dimension sizes in a large image. Our proposed approach involves the use of convolution operations for digit localization, and takes advantage of inspirations from existing algorithms for handling varying digit dimension sizes. Experimental results demonstrate the effectiveness of our approach in achieving high accuracy rates. Our work contributes to the advancement of robust algorithms capable of accurately classifying handwritten digits in different scenarios and is expected to be applicable to classifying other handwritten objects, such as handwritten letters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolution

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in Engineering & Sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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Quantum Algorithms for Optimal Power Flow

Quantum Algorithms for Optimal Power Flow

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Power, Energy and Electrical Engineering (CPEEE), International Conference on

作者： S. Fathi Hafshejani Md Mohsin Uddin David Neufeld Robert Benkoczi Daya Gaur Department of Math and Computer Science University of Lethbridge Lethbridge AB Canada

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

This paper explores the use of quantum computing, specifically the use of HHL and VQLS algorithms, to solve optimal power flow problem in electrical grids. We investigate the effectiveness of these quantum algorithms in comparison to classical methods. The simulation results presented here which substantially improve the results in [1] indicate that quantum approaches yield similar solutions and optimal costs compared to classical methods, suggesting the potential use case of quantum computing for power system optimization.

关键词： Electrical engineering Electric potential Quantum algorithm Costs Simulation Power systems Optimization Load flow

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