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Enhancing coordinated target tracking: innovative particle filters with meta-heuristic integration and advanced model validation

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Soft Computing 2025年第7期29卷 1-32页

作者： Das, Nitish Kulkarni, Nilima Department of Computer Science and Engineering MIT Art Design and Technology University Loni Kalbhor Pune412201 India

Coordinated target tracking and its augmented variant represent significant challenges in modern surveillance. These tasks are essential for accurately localizing and predicting the movement of dynamic targets. This article introduces an advanced particle filter algorithm with a packet dropout mechanism to enhance tracking accuracy. Additionally, it proposes three meta-heuristic-driven particle filters: the Particle Filter integrated with the Chaotic Gravitational Search Algorithm (PF-CGSA), the Particle Filter integrated with the Improved Grey Wolf Optimizer (PF-IGWO), and the Particle Filter integrated with the Imperialist Competitive Algorithm with Regional Domination Policy (PF-ICARD). These methods are tested using three sophisticated models: the Coordinated Turn Target Tracking Model (CT model), the Augmented Coordinated Turn Target Tracking Model with Cartesian Velocity (ACT1 model), and the Augmented Coordinated Turn Target Tracking Model with Polar Velocity (ACT2 model). Experimental results demonstrate that PF-CGSA, PF-IGWO, and PF-ICARD achieve higher convergence speeds and accuracy. They also effectively mitigate premature convergence phenomena. Comparative analysis based on Root Mean Squared Error (RMSE) metrics underscores their superiority in real-world target tracking challenges, highlighting the potential of these advanced particle filtering techniques in enhancing precision and efficiency in target tracking. Particularly, PF-ICARD achieves the highest accuracy, reducing RMSE by up to 18.64% compared to PF-CGSA and PF-IGWO, making it ideal for high-precision applications. PF-CGSA excels in specific scenarios, notably improving X-coordinate tracking in the ACT2 model by up to 11.39%. Meanwhile, PF-IGWO offers a balanced solution with moderate accuracy and enhanced computational efficiency, suitable for real-time applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Aircraft detection

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Eco-friendly Smart Sensor Adjustment using Deep Reinforcement Learning Approach 2

Eco-friendly Smart Sensor Adjustment using Deep Reinforcemen...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Reethika, A. Lavanya, S. Sheetal, Attar Mahay Vanitha, C.N. Priyadharsini, S. Jenolin rex, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

ISBN: (纸本)9798331541217

Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well. © 2024 IEEE.

关键词： Telecommunication industry

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Spam Sentinel: Revolutionizing Email Protection Using Machine Learning Techniques 1

Spam Sentinel: Revolutionizing Email Protection Using Machin...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Prathyakshini Shetty, Jyothi Department of Information Science and Engineering India Department of Computer Science and Engineering India

ISBN: (纸本)9798350306637

The rapid growth of email usage has brought about an unprecedented increase in spam emails, posing serious challenges to users and businesses alike. To tackle this issue, this study presents an enhanced spam email detection system leveraging the power of machine learning techniques. The objective is to create a robust and efficient model capable of accurately differentiating between legitimate emails and spam messages. Various features are extracted from the email content, including text-based characteristics, header information, and metadata, to create comprehensive representations of the emails. Machine learning algorithms, such as KNeighbors, Support Vector Classifier, Multinomial NB, Logistic Regression, Random Forest, Extra Trees Classifier, AdaBoost, Bagging, XGB Classifier, Decision Tree, Gradient Boosting, Gaussian NB (Naïve Bayes), Multinomial NB, Bernoulli NB (Naïve Bayes) and ensemble model are employed to develop and compare different spam detection models. The experimental results demonstrate the efficacy of the proposed enhanced spam email detection system. The combination of machine learning techniques leads to significant improvements in accuracy and efficiency, enabling effective identification and classification of spam emails. Bernoulli achieved better accuracy of 99%. © 2023 IEEE.

关键词： Adaptive boosting

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AMBU-BOT: Effective Accident Preventive System Using IOT

AMBU-BOT: Effective Accident Preventive System Using IOT

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, P. Varalakshmi, I. Priyadharshini, S. Pradeepa, R. Sathvika, A. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331510022

This paper intends to propose a new robot which overcomes the hurdles of an AED(Automated External Defibrillator) at the nearest Ambubot: A robot, ambulance robot designed and developed that carries an AED for assisting lay rescuers to save patients' lives, as well as Time taken before the ambulance is located in the case of a sudden cardiac arrest. The working of Ambubot could go searching for the victims and also obedient to the human instructions. Population is growing daily, and with it, so is the risk of heart disease also increasing. Even though traffic on our roads is normal for people, it is crucial to admit heart attack victims to hospitals in case of emergency because it can be easy for them to receive an automated external defibrillator at such a young age. Its independent purpose is to save the sufferer. © 2024 IEEE.

关键词： Defibrillators

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Potato Leaf Disease Detection Method Based on the YOLO Model 4

Potato Leaf Disease Detection Method Based on the YOLO Model

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Srivastava, Animesh Rawat, Bhupender Singh Bajpai, Prashant Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Coer University College of Smart Computing Roorkee India Sr Institute of Management & Technology Department of Computer Science & Engineering Lucknow India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350393354

Global potato production is at risk because of potato leaf diseases, which cause huge economic losses. To ensure crop productivity and disease management, their efficient and accurate localization is of utmost importance. The YOLOv7 deep learning model is presented in this paper as a means of disease detection and classification in potato leaves. The suggested approach uses YOLOv7's superior detection accuracy to successfully identify and categorize potato leaf diseases. To train the model, we used a wide variety of images showing both healthy and diseased potato leaves. Verified by the test results, the suggested methodology successfully identified and classified diseases affecting potato foliage with a 98.1% accuracy rate. Through early disease detection and prompt control measure implementation, this method holds great promise for enhancing the yield and quality of potato crops in precision agriculture systems. The proposed framework shows great promise for practical implementation, as the experimental results confirm that it offers enhanced accuracy in detecting and predicting potato crop diseases. © 2024 IEEE.

关键词： Losses

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Polycystic Ovary Syndrome (PCOS) diagnostic methods in machine learning: a systematic literature review

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Multimedia Tools and Applications 2025年第16期84卷 16301-16337页

作者： Arora, Shikha Vedpal Chauhan, Naresh J.C. Bose University of Science and Technology YMCA Haryana Faridabad India Department of Computer Applications J.C. Bose University of Science and Technology YMCA Haryana Faridabad India Department of Computer Engineering J.C. Bose University of Science and Technology YMCA Haryana Faridabad India

Polycystic Ovarian Syndrome, also known as PCOS, is a major hormonal imbalance affecting women primarily in their reproductive age. Women with PCOS may have either infrequent or extended menstrual cycles or sometimes excess male hormone i.e. androgen levels. The ovaries may grow with number of slight collections of fluid, called follicles that fail to release eggs every month regularly. It is also seen that PCOS not only affects a woman’s fertility but also indirectly causes various other health issues like type 2 Diabetes, obesity, blood pressure and other metabolic disorders. Recent researches have focussed on the use of different algorithms in Machine Learning to diagnose PCOS using structured or unstructured data. Therefore, in this study, a considerable Literature Review has been carried out to provide a detailed analysis of various algorithms that have been used to detect PCOS with their comparative study. Also some of the work particularly focuses on health issues, the food and dietary patterns and the ways to manage PCOS. Further, these algorithms are critically analysed to understand the framework and limitations that should be considered while putting forward the solutions relevant to the diagnosis of PCOS in an effective way. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood pressure

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Early-Stage Segmentation and Characterization of Brain Tumor

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computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： Syed Nauyan Rashid Muhammad Hanif Usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman Department of Computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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