Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship ...BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship with personality traits has not been previously investigated in the Lebanese population.AIM To examine the prevalence of preoperative anxiety,pain perception,and personality traits among Lebanese surgical patients,and to assess the associations between these factors.METHODS A descriptive cross-sectional study was conducted between April 2024 and January 2025 across Lebanese hospitals.A total of 392 adult patients were recruited through convenience sampling.Data were collected using a questionnaire that included sociodemographic,clinical,and surgical variables,the Amsterdam Preoperative Anxiety and Information Scale for anxiety,the Visual Analog Scale and Numerical Pain Rating Scale for preoperative pain,and the Ten-Item Personality Inventory for personality traits.Ethical approval was obtained from the Institutional Review Boards of Makassed General Hospital and Hammoud University Medical Center.RESULTS Overall,25%of participants experienced preoperative anxiety,and 34.5%reported moderate pain.Personality assessment showed that the majority of participants had moderate extraversion(84.1%),moderate emotional stability(65.1%),high conscientiousness(61%),high agreeableness(54.1%),and moderate openness(49.2%).High conscientiousness was significantly associated with higher pain perception(P<0.05),while high emotional stability was associated with lower levels of anxiety(P<0.05).No significant association was found between preoperative anxiety and pain(P>0.05).CONCLUSION This study challenges the assumption that preoperative anxiety and pain are directly correlated and highlights the role of personality traits in shaping patient experience.These findings support the potential value of integrating psychological profiling into preoperative care and lay the groundwork for developing personalized interventions to improve patient-centered surgical outcomes.展开更多
The current study examined the roles of collective self-esteem and personal self-esteem in the relationship between national identity and subjective well-being.Participants were 583 Chinese college students(females=49...The current study examined the roles of collective self-esteem and personal self-esteem in the relationship between national identity and subjective well-being.Participants were 583 Chinese college students(females=49%;mean age=19.25±1.85 years).They completed measures of national identity,collective self-esteem,personal self-esteem,and subjective well-being.Path analysis findings result indicated national identity to influence the students’subjective wellbeing through three pathways:(1)national identity→collective self-esteem→subjective well-being,meaning higher subjective wellbeing with collective self-esteem.(2)national identity→personal self-esteem→subjective well-being,to suggest higher personal self-esteem was associated with subjective wellbeing;(3)national identity→collective selfesteem→personal self-esteem→subjective well-being.Compared to simple mediation models constructed with only personal self-esteem or collective self-esteem as a single mediating variable,the chain mediation model better explains the mediating mechanism of national identity on subjective well-being(the variance explained by the mediating variables increased by 65.38%and 59.26%,respectively).The collective self-esteem and personal self-esteem mediation is consistent with social identity theory,whereby national identity enhances collective self-evaluation,which in turn bolsters personal self-worth and subjective well-being.These findings of the current study offer new insights into how national identity affects subjective well-being in collectivistic culture.展开更多
Person recognition in photo collections is a critical yet challenging task in computer vision.Previous studies have used social relationships within photo collections to address this issue.However,these methods often ...Person recognition in photo collections is a critical yet challenging task in computer vision.Previous studies have used social relationships within photo collections to address this issue.However,these methods often fail when performing single-person-in-photos recognition in photo collections,as they cannot rely on social connections for recognition.In this work,we discard social relationships and instead measure the relationships between photos to solve this problem.We designed a new model that includes a multi-parameter attention network for adaptively fusing visual features and a unified formula for measuring photo intimacy.This model effectively recognizes individuals in single photo within the collection.Due to outdated annotations and missing photos in the existing PIPA(Person in Photo Album)dataset,wemanually re-annotated it and added approximately ten thousand photos of Asian individuals to address the underrepresentation issue.Our results on the re-annotated PIPA dataset are superior to previous studies in most cases,and experiments on the supplemented dataset further demonstrate the effectiveness of our method.We have made the PIPA dataset publicly available on Zenodo,with the DOI:10.5281/zenodo.12508096(accessed on 15 October 2025).展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Met...Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Methods:A total of 90 elderly pregnant women with gestational diabetes who were delivered in our hospital from January 2023 to January 2024 were selected as the research objects.They were randomly divided into an observation group and a control group,with 45 cases in each group.The control group only received routine pregnancy care and basic nutrition guidance,while the observation group received personalized nutrition support on this basis.Compare the blood glucose control,incidence of pregnancy complications,pregnancy outcomes,and neonatal outcomes between two groups of parturient.Result:After intervention,the fasting blood glucose(FPG),2-hour postprandial blood glucose(2hPG),and glycated hemoglobin(HbA1c)of the observation group were significantly lower than those of the control group,and the differences were statistically significant(p<0.05);The incidence of complications such as gestational hypertension syndrome,polyhydramnios,premature rupture of membranes,and postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the difference was statistically significant(p<0.05);The cesarean section rate in the observation group was significantly lower than that in the control group,and the incidence of adverse neonatal outcomes such as fetal distress,macrosomia,neonatal asphyxia,and neonatal hypoglycemia in the observation group was significantly lower than that in the control group,with statistical significance(p<0.05).Conclusion:Individualized nutritional support for elderly women with gestational diabetes can effectively improve the level of maternal blood sugar control,reduce the incidence of complications during pregnancy,and improve the outcome of pregnancy and neonatal outcomes,which is of high clinical value.展开更多
Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,espe...Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.展开更多
Therapy discontinuation in inflammatory bowel disease,particularly involving immunomodulators,biologics,and small molecules,remains a controversial and evolving topic.This letter reflects on developments following the...Therapy discontinuation in inflammatory bowel disease,particularly involving immunomodulators,biologics,and small molecules,remains a controversial and evolving topic.This letter reflects on developments following the publication by Meštrovićet al,emphasizing the complex balance between risks of relapse,antidrug antibody formation,and potential complications of long-term immunosuppression.Recent evidence underscores high relapse rates following withdrawal-especially of anti-tumor necrosis factor agents-and highlights the lack of robust data for newer biologics.Updated guidelines from European Crohn’s and Colitis Organization,British Society of Gastroenterology,and American College of Gastroenterology all support cautious and individualized approaches,with strict criteria and close follow-up,particularly in Crohn’s disease.For ulcerative colitis,therapeutic cycling remains insufficiently addressed.We proposed a flowchart to support clinical decision-making and stress the importance of shared decisionmaking in the era of personalized medicine since,despite new drug classes and evolving strategies,the therapeutic ceiling in inflammatory bowel disease has yet to be fully overcome.展开更多
With the rapid development of molecular biology and related disciplines, animal breeding has moved from conventional breeding to molecular breeding. Marker-assisted selection and genomic selection have become mainstre...With the rapid development of molecular biology and related disciplines, animal breeding has moved from conventional breeding to molecular breeding. Marker-assisted selection and genomic selection have become mainstream practices in molecular breeding of livestock. However, these techniques only use information from genomic variation but not multi-omics information, thus do not fully explain the molecular basis of phenotypic variations in complex traits. In addition, the accuracy of breeding value estimation based on these techniques is occasionally controversial in different populations or varieties. Given the rapid development of high-throughput sequencing techniques and functional genome and dramatic reductions in the overall cost of sequencing, it is possible to clarify the interactions between genes and formation of phenotypes using massive sets of omic-level data from studies of the transcriptome, proteome, epigenome, and metabolome. During livestock breeding, multi-omics information regarding breeding populations and individuals should be taken into account. The interactive regulatory networks governing gene regulation and phenotype formation in diverse livestock population, varieties and species should be analyzed. In addition, a multi-omics regulatory breeding model should be constructed. Precision, population-personalized breeding is expected to become a crucial practice in future livestock breeding. Precision breeding of individuals can be achieved by combining population genomic information at multi-omics levels together with genomic selection and genome editing techniques.展开更多
Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor fault...Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.展开更多
Personalized products and services in e-commerce bring consumers many new experiences, but also trigger a series of information security issues. Considering the bounded rationality of the game participants, in this pa...Personalized products and services in e-commerce bring consumers many new experiences, but also trigger a series of information security issues. Considering the bounded rationality of the game participants, in this paper, we propose an evolutionary game model of privacy protection between firms and consumers based on e-commerce personalization. Evolutionary stable strategies（ESSs） are obtained from the equilibrium points according to the model analysis, and then simulation experiments are launched to validate the decision-making results and the influencing mechanism of various factors. The results show that the model can eventually evolve toward a win-win situation by wisely varying its various factors, such as ratios of initial strategies, cost of privacy protection, commodity prices, and other related factors. Further, we find that reducing the possibility of the privacy breach under the premise of privacy protection can help promote the e-commerce personalization.展开更多
Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of...Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of the product to users’ needs. Product innovation needs a cognitive design method based on needs of variant users for the product personalization. In this paper, an open concept is introduced to provide ways to meet user’s individual need in product lifespan. It is for industries to propose product concepts based on open sources, develop and support the product on the public capability. Using the open concept in the product architecture, called open?architecture product(OAP), can improve the product personalization leading to massive product innovation. To promote this promise of the OAP, effective methods are discussed for the OAP development. This paper introduces research on OAPs using adaptable design methods to meet product personalization. Adaptable design is based on the modular structure for product adaptability using function modules and adaptable interfaces. The proposed method provides solutions for planning modules and implementation of OAPs. Methods of OAP module planning, detail and interface design are described for transformation of product concepts into physical structures. A multiple?purpose electrical car is developed in a case study to show effectiveness of the proposed method.展开更多
Microbial population and enzyme activities are the significant indicators of soil strength.Soil microbial dynamics characterize microbial population and enzyme activities.The present study explores the development of ...Microbial population and enzyme activities are the significant indicators of soil strength.Soil microbial dynamics characterize microbial population and enzyme activities.The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics,like rock phosphate solubilization,bacterial population,and ACC-deaminase activity.More specifically,optimized subtractive clustering(SC)and Wang and Mendel's(WM)fuzzy inference systems(FIS)have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics.Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected strains.Three experimental parameters,including temperature,pH,and incubation period have been used as inputs SC-FIS and WM-FIS.The better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination(0.9988)2 R=in the estimation of previous microbial dynamics.展开更多
Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwat...Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwater wet- lands within the lake and at the mouths of neighboring rivers, due to disturbance, primarily from human activities. The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them. In the paper, an integrated method, which combined per-pixel and per-field classifi- cation, was used for mapping wetlands of Hongze Lake and their neighboring upland types. Firstly, Landsat ETM＋ imagery was segmented and classified by using spectral and textural features. Secondly, ETM＋ spectral bands, textural features derived from ETM＋ Pan imagery, relative relations between neighboring classes, shape fea^xes, and elevation were used in a decision tree classification. Thirdly, per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context. The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies, but also has im- proved the accuracy of identification by nearly 5%.展开更多
The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in ...The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.展开更多
This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous m...This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.展开更多
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
文摘BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship with personality traits has not been previously investigated in the Lebanese population.AIM To examine the prevalence of preoperative anxiety,pain perception,and personality traits among Lebanese surgical patients,and to assess the associations between these factors.METHODS A descriptive cross-sectional study was conducted between April 2024 and January 2025 across Lebanese hospitals.A total of 392 adult patients were recruited through convenience sampling.Data were collected using a questionnaire that included sociodemographic,clinical,and surgical variables,the Amsterdam Preoperative Anxiety and Information Scale for anxiety,the Visual Analog Scale and Numerical Pain Rating Scale for preoperative pain,and the Ten-Item Personality Inventory for personality traits.Ethical approval was obtained from the Institutional Review Boards of Makassed General Hospital and Hammoud University Medical Center.RESULTS Overall,25%of participants experienced preoperative anxiety,and 34.5%reported moderate pain.Personality assessment showed that the majority of participants had moderate extraversion(84.1%),moderate emotional stability(65.1%),high conscientiousness(61%),high agreeableness(54.1%),and moderate openness(49.2%).High conscientiousness was significantly associated with higher pain perception(P<0.05),while high emotional stability was associated with lower levels of anxiety(P<0.05).No significant association was found between preoperative anxiety and pain(P>0.05).CONCLUSION This study challenges the assumption that preoperative anxiety and pain are directly correlated and highlights the role of personality traits in shaping patient experience.These findings support the potential value of integrating psychological profiling into preoperative care and lay the groundwork for developing personalized interventions to improve patient-centered surgical outcomes.
文摘The current study examined the roles of collective self-esteem and personal self-esteem in the relationship between national identity and subjective well-being.Participants were 583 Chinese college students(females=49%;mean age=19.25±1.85 years).They completed measures of national identity,collective self-esteem,personal self-esteem,and subjective well-being.Path analysis findings result indicated national identity to influence the students’subjective wellbeing through three pathways:(1)national identity→collective self-esteem→subjective well-being,meaning higher subjective wellbeing with collective self-esteem.(2)national identity→personal self-esteem→subjective well-being,to suggest higher personal self-esteem was associated with subjective wellbeing;(3)national identity→collective selfesteem→personal self-esteem→subjective well-being.Compared to simple mediation models constructed with only personal self-esteem or collective self-esteem as a single mediating variable,the chain mediation model better explains the mediating mechanism of national identity on subjective well-being(the variance explained by the mediating variables increased by 65.38%and 59.26%,respectively).The collective self-esteem and personal self-esteem mediation is consistent with social identity theory,whereby national identity enhances collective self-evaluation,which in turn bolsters personal self-worth and subjective well-being.These findings of the current study offer new insights into how national identity affects subjective well-being in collectivistic culture.
基金supported by“the Fundamental Research Funds for the Central Universities”(GrantNos.:3282025045,3282024008)“Science and Technology Project of the State ArchivesAdministration ofChina”(Grant No.:2025-Z-009).
文摘Person recognition in photo collections is a critical yet challenging task in computer vision.Previous studies have used social relationships within photo collections to address this issue.However,these methods often fail when performing single-person-in-photos recognition in photo collections,as they cannot rely on social connections for recognition.In this work,we discard social relationships and instead measure the relationships between photos to solve this problem.We designed a new model that includes a multi-parameter attention network for adaptively fusing visual features and a unified formula for measuring photo intimacy.This model effectively recognizes individuals in single photo within the collection.Due to outdated annotations and missing photos in the existing PIPA(Person in Photo Album)dataset,wemanually re-annotated it and added approximately ten thousand photos of Asian individuals to address the underrepresentation issue.Our results on the re-annotated PIPA dataset are superior to previous studies in most cases,and experiments on the supplemented dataset further demonstrate the effectiveness of our method.We have made the PIPA dataset publicly available on Zenodo,with the DOI:10.5281/zenodo.12508096(accessed on 15 October 2025).
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
文摘Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Methods:A total of 90 elderly pregnant women with gestational diabetes who were delivered in our hospital from January 2023 to January 2024 were selected as the research objects.They were randomly divided into an observation group and a control group,with 45 cases in each group.The control group only received routine pregnancy care and basic nutrition guidance,while the observation group received personalized nutrition support on this basis.Compare the blood glucose control,incidence of pregnancy complications,pregnancy outcomes,and neonatal outcomes between two groups of parturient.Result:After intervention,the fasting blood glucose(FPG),2-hour postprandial blood glucose(2hPG),and glycated hemoglobin(HbA1c)of the observation group were significantly lower than those of the control group,and the differences were statistically significant(p<0.05);The incidence of complications such as gestational hypertension syndrome,polyhydramnios,premature rupture of membranes,and postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the difference was statistically significant(p<0.05);The cesarean section rate in the observation group was significantly lower than that in the control group,and the incidence of adverse neonatal outcomes such as fetal distress,macrosomia,neonatal asphyxia,and neonatal hypoglycemia in the observation group was significantly lower than that in the control group,with statistical significance(p<0.05).Conclusion:Individualized nutritional support for elderly women with gestational diabetes can effectively improve the level of maternal blood sugar control,reduce the incidence of complications during pregnancy,and improve the outcome of pregnancy and neonatal outcomes,which is of high clinical value.
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number DS.C2025-28-06.
文摘Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.
文摘Therapy discontinuation in inflammatory bowel disease,particularly involving immunomodulators,biologics,and small molecules,remains a controversial and evolving topic.This letter reflects on developments following the publication by Meštrovićet al,emphasizing the complex balance between risks of relapse,antidrug antibody formation,and potential complications of long-term immunosuppression.Recent evidence underscores high relapse rates following withdrawal-especially of anti-tumor necrosis factor agents-and highlights the lack of robust data for newer biologics.Updated guidelines from European Crohn’s and Colitis Organization,British Society of Gastroenterology,and American College of Gastroenterology all support cautious and individualized approaches,with strict criteria and close follow-up,particularly in Crohn’s disease.For ulcerative colitis,therapeutic cycling remains insufficiently addressed.We proposed a flowchart to support clinical decision-making and stress the importance of shared decisionmaking in the era of personalized medicine since,despite new drug classes and evolving strategies,the therapeutic ceiling in inflammatory bowel disease has yet to be fully overcome.
基金supported by National High Technology Plan of China (2013 AA102502)the National Natural Science Foundation of China (313300453)the National Key Basic Research Program of China (2015CB943101)
文摘With the rapid development of molecular biology and related disciplines, animal breeding has moved from conventional breeding to molecular breeding. Marker-assisted selection and genomic selection have become mainstream practices in molecular breeding of livestock. However, these techniques only use information from genomic variation but not multi-omics information, thus do not fully explain the molecular basis of phenotypic variations in complex traits. In addition, the accuracy of breeding value estimation based on these techniques is occasionally controversial in different populations or varieties. Given the rapid development of high-throughput sequencing techniques and functional genome and dramatic reductions in the overall cost of sequencing, it is possible to clarify the interactions between genes and formation of phenotypes using massive sets of omic-level data from studies of the transcriptome, proteome, epigenome, and metabolome. During livestock breeding, multi-omics information regarding breeding populations and individuals should be taken into account. The interactive regulatory networks governing gene regulation and phenotype formation in diverse livestock population, varieties and species should be analyzed. In addition, a multi-omics regulatory breeding model should be constructed. Precision, population-personalized breeding is expected to become a crucial practice in future livestock breeding. Precision breeding of individuals can be achieved by combining population genomic information at multi-omics levels together with genomic selection and genome editing techniques.
基金supported by National Natural Science Foundation of China(Grant No. 51275264)National Hi-tech Research and Development Program of China(863 Program, Grant No. 2011AA11A269)
文摘Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.
基金Supported by the National Natural Science Foundation of China(71571082,71471073)the Fundamental Research Funds for the Central Universities(CCNU14Z02016,CCNU15A02046)
文摘Personalized products and services in e-commerce bring consumers many new experiences, but also trigger a series of information security issues. Considering the bounded rationality of the game participants, in this paper, we propose an evolutionary game model of privacy protection between firms and consumers based on e-commerce personalization. Evolutionary stable strategies（ESSs） are obtained from the equilibrium points according to the model analysis, and then simulation experiments are launched to validate the decision-making results and the influencing mechanism of various factors. The results show that the model can eventually evolve toward a win-win situation by wisely varying its various factors, such as ratios of initial strategies, cost of privacy protection, commodity prices, and other related factors. Further, we find that reducing the possibility of the privacy breach under the premise of privacy protection can help promote the e-commerce personalization.
基金Supported by Discovery Grants(Grant No.RGPIN-2015-04173)of the Natural Sciences and Engineering Research Council(NSERC)of CanadaNational Natural Science Foundation of China(Grant Nos.51375287,51505269)
文摘Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of the product to users’ needs. Product innovation needs a cognitive design method based on needs of variant users for the product personalization. In this paper, an open concept is introduced to provide ways to meet user’s individual need in product lifespan. It is for industries to propose product concepts based on open sources, develop and support the product on the public capability. Using the open concept in the product architecture, called open?architecture product(OAP), can improve the product personalization leading to massive product innovation. To promote this promise of the OAP, effective methods are discussed for the OAP development. This paper introduces research on OAPs using adaptable design methods to meet product personalization. Adaptable design is based on the modular structure for product adaptability using function modules and adaptable interfaces. The proposed method provides solutions for planning modules and implementation of OAPs. Methods of OAP module planning, detail and interface design are described for transformation of product concepts into physical structures. A multiple?purpose electrical car is developed in a case study to show effectiveness of the proposed method.
文摘Microbial population and enzyme activities are the significant indicators of soil strength.Soil microbial dynamics characterize microbial population and enzyme activities.The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics,like rock phosphate solubilization,bacterial population,and ACC-deaminase activity.More specifically,optimized subtractive clustering(SC)and Wang and Mendel's(WM)fuzzy inference systems(FIS)have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics.Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected strains.Three experimental parameters,including temperature,pH,and incubation period have been used as inputs SC-FIS and WM-FIS.The better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination(0.9988)2 R=in the estimation of previous microbial dynamics.
基金Under the auspices of Natural Science Foundation of Jiangsu Province (No. BK2008360)Foundamental Research Funds for the Central Universities (No. 2009B12714,2009B11714)
文摘Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwater wet- lands within the lake and at the mouths of neighboring rivers, due to disturbance, primarily from human activities. The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them. In the paper, an integrated method, which combined per-pixel and per-field classifi- cation, was used for mapping wetlands of Hongze Lake and their neighboring upland types. Firstly, Landsat ETM＋ imagery was segmented and classified by using spectral and textural features. Secondly, ETM＋ spectral bands, textural features derived from ETM＋ Pan imagery, relative relations between neighboring classes, shape fea^xes, and elevation were used in a decision tree classification. Thirdly, per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context. The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies, but also has im- proved the accuracy of identification by nearly 5%.
文摘The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.
文摘This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.
