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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Chiral exceptional point enhanced active tuning and non reciprocity in micro-resonators

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光：科学与应用（英文版） 2025年第2期14卷 401-410页

作者： Hwaseob Lee Lorry Chang Ali Kecebas Dun Mao Yahui Xiao Tiantian Li Andrea Alù Sahin K.Özdemir Tingyi Gu Department of Electrical and Computer Engineering University of DelawareNewarkDelaware 19716USA Department of Engineering Science and Mechanics Pennsylvania State UniversityUniversity ParkPA 16802USA Photonics Initiative Advanced Science Research CenterCity University of New YorkNew YorkNY 10031USA Physics Program Graduate CenterCity University of New YorkNew YorkNY 10016USA Department of Engineering Science and Mechanics Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Computer Engineering Saint Louis UniversitySaint LouisMO 63103USA

Exceptional points(EPs)have been extensively explored in mechanical,acoustic,plasmonic,and photonic ***,little is known about the role of EPs in tailoring the dynamic tunability of optical devices.A specific type of EPs known as chiral EPs has recently attracted much attention for controlling the flow of light and for building sensors with better responsivity.A recently demonstrated route to chiral EPs via lithographically defined symmetric Mie scatterers on the rim of resonators has not only provided the much-needed mechanical stability for studying chiral EPs,but also helped reduce losses originating from nanofabrication imperfections,facilitating the in-situ study of chiral EPs and their contribution to the dynamics and tunability of ***,we use asymmetric Mie scatterers to break the rotational symmetry of a microresonator,to demonstrate deterministic thermal tuning across a chiral EP,and to demonstrate EP-mediated chiral optical nonlinear response and efficient electro-optic *** results indicate asymmetric electro-optic modulation with up to 17 dB contrast at GHz and CMOS-compatible voltage *** wafer-scale nano-manufacturing of chiral electro-optic modulators and the chiral EP-tailored tunning may facilitate new micro-resonator functionalities in quantum information processing,electromagnetic wave control,and optical interconnects.

关键词： active point micro chiral enhanced exceptional reciprocity resonators tuning

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Robust Frame-level Speaker Localization in Reverberant and Noisy Environments by Exploiting Phase Difference Losses

Robust Frame-level Speaker Localization in Reverberant and N...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shanmukha Srinivas Battula Hassan Taherian Ashutosh Pandey Daniel Wong Buye Xu DeLiang Wang Department of Computer Science and Engineering The Ohio State University USA Meta Reality Labs USA Center for Cognitive and Brain Sciences The Ohio State University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper investigates robust speaker localization at the frame level on the basis of complex spectral mapping, which is capable of learning both the magnitude and phase of the target signal. Unlike prevailing deep learning methods for speaker localization, we perform MIMO (multi-input multi-output) based multi-channel speech enhancement first and then localize the enhanced speaker using weighted generalized cross correlation. In addition, we propose new multi-channel loss functions that incorporate phase differences in order to preserve inter-channel phase relations, which is key to accurate sound localization. Systematic evaluations using simulated and recorded room impulse responses demonstrate that the proposed model yields excellent frame-level speaker localization results in reverberant and noisy environments and outperforms related methods by a large margin, even surpassing their utterance-level results.

关键词： Location awareness Deep learning Accuracy Systematics Correlation Speech enhancement Signal processing Acoustics Noise measurement

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The Wilhelm Tell Dataset of Affordance Demonstrations 20

The Wilhelm Tell Dataset of Affordance Demonstrations

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Ringe, Rachel Pomarlan, Mihai Tsiogkas, Nikolaos De Giorgis, Stefano Hedblom, Maria Malaka, Rainer University of Bremen Digital Media Lab Bremen Germany University of Bremen Bremen Germany Ku Leuven Department of Computer Science Leuven Belgium Institute of Cognitive Sciences and Technologies National Research Council Catania Italy Jönköping School of Engineering Department of Computing Jönköping Sweden

ISBN: (纸本)9798350378931

Affordances - i.e. possibilities for action that an environment or objects in it provide - are important for robots operating in human environments to perceive. Existing approaches train such capabilities on annotated static images or shapes. This work presents a novel dataset for affordance learning of common household tasks. Unlike previous approaches, our dataset consists of video sequences demonstrating the tasks from first- and third-person perspectives, along with metadata about the affordances that are manifested in the task, and is aimed towards training perception systems to recognize affordance manifestations. The demonstrations were collected from several participants and in total record about seven hours of human activity. The variety of task performances also allows studying preparatory maneuvers that people may perform for a task, such as how they arrange their task space, which is also relevant for collaborative service robots. © 2025 IEEE.

关键词： Industrial robots

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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

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IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Material informatics-driven insights into brain cancer nanocarriers: A bibliometric comparison of PLGA vs. liposomes

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OpenNano 2025年 21卷

作者： Makalew, Brilly Andro Abrori, Syauqi Abdurrahman Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta 11480 Indonesia

This study explores a comparative analysis of PLGA nanoparticles and liposomes as potential carriers for brain cancer drug delivery, with a special focus on how material informatics enhances their design, biocompatibility, and drug release profiles to improve treatment efficacy and contribute to sustainable health outcomes. The investigation employed a bibliometric analysis using Scopus and VOSviewer to uncover the role of material informatics in optimizing these nanocarriers. The analysis revealed that material informatics, particularly through the application of machine learning and molecular dynamics simulations, significantly optimizes the performance of both PLGA nanoparticles and liposomes. The results highlighted distinct strengths of each nanocarrier: PLGA nanoparticles excel in biodegradability, while liposomes offer superior drug encapsulation capabilities. However, material informatics techniques bridged these enhancing drug release kinetics, stability, and biocompatibility. These improvements are crucial for effective delivery across the blood-brain barrier, a major challenge in brain cancer treatment. The integration of computational modelling, machine learning, and high-throughput screening enabled by material informatics is shown to be a key factor in advancing the design and optimization of these nanocarriers. By leveraging these tools, researchers can develop more personalized and efficient drug delivery systems tailored to address the specific challenges of glioblastoma therapy, ultimately contributing to sustainable health outcomes © 2024 The Author(s)

关键词： Bibliometric analysis Brain cancer Drug delivery Liposomes Material informatics Nanocarriers PLGA nanoparticles VOSviewer

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RL-Based USV Path Planning under the Marine Multimodal Features Considerations

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IEEE Internet of Things Journal 2025年第11期12卷 15274-15287页

作者： Lin, Quanbao Gou, Huaxing Tian, Peidong Zuo, Tian-Yu Zhang, Hanzhong Wang, Xin Sun, Poly Z. H. Shanghai Jiao Tong University School of Aeronautics and Astronautics Shanghai200240 China Southeast University School of Computer Science and Engineering Nanjing211189 China Shanghai University School of Microelectronics Shanghai200444 China Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China East China Normal University School of Psychology and Cognitive Science Shanghai200062 China Shanghai Jiao Tong University Department of Industrial Engineering Shanghai200240 China

Path planning is an important step in ensuring the safety of Unmanned Surface Vehicle (USV) navigation and executing missions quickly and efficiently. However, current USV path planning methods lack comprehensive consideration of electronic nautical charts and meteorological data, resulting in planned paths being unable to fully utilize marine environmental conditions, which may easily lead to collisions and long navigation times. Based on the above considerations, our study designs a USV path planning system that comprehensively considers the multimodal information from electronic nautical charts and meteorological data. The system consists of three parts: (1) image processing module, (2) meteorological analysis module, and (3) path planning module. In detail, the image processing module obtains the geographical feature information from the electronic chart and constructs a static obstacle environment. The meteorological analysis module obtains the meteorological feature information from meteorological data and constructs a dynamic meteorological vector field environment. The path planning module introduces a designed double deep Q-Network (DQN) structure, a multivariate weighted Dueling network, and a priority sampling mechanism to enhance the DQN algorithm for promising performance in USV path planning. Extensive experiments illustrate the superior performance of the proposed fusion DQN algorithm. Furthermore, the feasibility of the entire path planning system is confirmed. © 2014 IEEE.

关键词： Unmanned surface vehicles

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Autolysosomal Dysfunction in Obesity-induced Metabolic Inflammation and Related Disorders

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Current Obesity Reports 2025年第1期14卷 1-16页

作者： Cheong, Lenny Yi Tong Saipuljumri, Eka Norfaishanty Loi, Gavin Wen Zhao Zeng, Jialiu Lo, Chih Hung Lee Kong Chian School of Medicine Nanyang Technological University Singapore 308232 Singapore Program in Neuroscience & Cognitive Science University of Arizona Tucson 85721 AZ United States School of Biomedical Sciences The University of Queensland St Lucia 4072 QLD Australia Department of Biomedical and Chemical Engineering Syracuse University Syracuse 13244 NY United States Interdisciplinary Neuroscience Program Syracuse University Syracuse 13244 NY United States Department of Biology Syracuse University Syracuse 13244 NY United States

Purpose of Review: Obesity is a global health crisis affecting individuals across all age groups, significantly increasing the risk of metabolic disorders such as type 2 diabetes (T2D), metabolic dysfunction-associated fatty liver disease (MAFLD), and cardiovascular diseases. The World Health Organization reported in 2022 that 2.5 billion adults were overweight, with 890 million classified as obese, emphasizing the urgent need for effective interventions. A critical aspect of obesity’s pathophysiology is meta-inflammation—a chronic, systemic low-grade inflammatory state driven by excess adipose tissue, which disrupts metabolic homeostasis. This review examines the role of autolysosomal dysfunction in obesity-related metabolic disorders, exploring its impact across multiple metabolic organs and evaluating potential therapeutic strategies that target autophagy and lysosomal function. Recent Findings: Emerging research highlights the importance of autophagy in maintaining cellular homeostasis and metabolic balance. Obesity-induced lysosomal dysfunction impairs the autophagic degradation process, contributing to the accumulation of damaged organelles and toxic aggregates, exacerbating insulin resistance, lipotoxicity, and chronic inflammation. Studies have identified autophagic defects in key metabolic tissues, including adipose tissue, skeletal muscle, liver, pancreas, kidney, heart, and brain, linking autophagy dysregulation to the progression of metabolic diseases. Preclinical investigations suggest that pharmacological and nutritional interventions—such as AMPK activation, caloric restriction mimetics, and lysosomal-targeting compounds—can restore autophagic function and improve metabolic outcomes in obesity models. Summary: Autolysosomal dysfunction is a pivotal contributor to obesity-associated metabolic disorders, influencing systemic inflammation and metabolic dysfunction. Restoring autophagy and lysosomal function holds promise as a therapeutic strategy to miti

关键词： Autophagy function Lysosomal acidification Metabolic inflammation Neurodegeneration Neuroinflammation Therapeutic strategies

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