An Infectious Disease Prediction Method Based on K-Nearest Neighbor Improved Algorithm

With the continuous development of medical information construction, the potential value of a large amount of medical information has not been exploited. Excavate a large number of medical records of outpatients, and train to generate disease prediction models to assist doctors in diagnosis and improve work efficiency.This paper proposes a disease prediction method based on k-nearest neighbor improvement algorithm from the perspective of patient similarity analysis. The method draws on the idea of clustering, extracts the samples near the center point generated by the clustering, applies these samples as a new training sample set in the K-nearest neighbor algorithm; based on the maximum entropy The K-nearest neighbor algorithm is improved to overcome the influence of the weight coefficient in the traditional algorithm and improve the accuracy of the algorithm. The real experimental data proves that the proposed k-nearest neighbor improvement algorithm has better accuracy and operational efficiency

When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual benefits, presents a unique opportunity to unlock new possibilities in AI research, and address critical challenges in AI and real-world applications. FL expands the availability of data for FMs and enables computation sharing, distributing the training process and reducing the burden on FL participants. It promotes collaborative FM development, democratizing the process and fostering inclusivity and innovation. On the other hand, FM, with its enormous size, pre-trained knowledge, and exceptional performance, serves as a robust starting point for FL, facilitating faster convergence and better performance under non-iid data. Additionally, leveraging FM to generate synthetic data enriches data diversity, reduces overfitting, and preserves privacy. By examining the interplay between FL and FM, this paper aims to deepen the understanding of their synergistic relationship, highlighting the motivations, challenges, and future directions. Through an exploration of the challenges faced by FL and FM individually and their interconnections, we aim to inspire future research directions that can further enhance both fields, driving advancements and propelling the development of privacy-preserving and scalable AI systems

TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation

This paper focuses on an under-explored yet important problem: Federated Class-Continual Learning (FCCL), where new classes are dynamically added in federated learning. Existing FCCL works suffer from various limitations, such as requiring additional datasets or storing the private data from previous tasks. In response, we first demonstrate that non-IID data exacerbates catastrophic forgetting issue in FL. Then we propose a novel method called TARGET (federat\textbf{T}ed cl\textbf{A}ss-continual lea\textbf{R}nin\textbf{G} via \textbf{E}xemplar-free dis\textbf{T}illation), which alleviates catastrophic forgetting in FCCL while preserving client data privacy. Our proposed method leverages the previously trained global model to transfer knowledge of old tasks to the current task at the model level. Moreover, a generator is trained to produce synthetic data to simulate the global distribution of data on each client at the data level. Compared to previous FCCL methods, TARGET does not require any additional datasets or storing real data from previous tasks, which makes it ideal for data-sensitive scenarios.Comment: ICCV 202

Sequential Pattern Mining with Multidimensional Interval Items

In real sequence pattern mining scenarios, the interval information between two item sets is very important. However, although existing algorithms can effectively mine frequent subsequence sets, the interval information is ignored. This paper aims to mine sequential patterns with multidimensional interval items in sequence databases. In order to address this problem, this paper defines and specifies the interval event problem in the sequential pattern mining task. Then, the interval event items framework is proposed to handle the multidimensional interval event items. Moreover, the MII-Prefixspan algorithm is introduced for the sequential pattern with multidimensional interval event items mining tasks. This algorithm adds the processing of interval event items in the mining process. We can get richer and more in line with actual needs information from mined sequence patterns through these methods. This scheme is applied to the actual website behaviour analysis task to obtain more valuable information for web optimization and provide more valuable sequence pattern information for practical problems. This work also opens a new pathway toward more efficient sequential pattern mining tasks

Peroxisome Proliferator-Activated Receptor-γ (PPAR-γ) Agonists Attenuate the Profibrotic Response Induced by TGF-β1 in Renal Interstitial Fibroblasts

Background. Studies have shown that peroxisome proliferator-activated receptor-γ (PPAR-γ) agonists could ameliorate renal fibrotic lesions in both diabetic nephropathy and nondiabetic chronic kidney diseases. In order to elucidate the antifibrotic mechanism of PPAR-γ agonists, we investigated the effects of PPAR-γ activation on TGF-β1-induced renal interstitial fibroblasts. Methods. In rat renal interstitial fibroblasts (NRK/49F), the mRNA expression of TGF-β1-induced α-smooth muscle actin (α-SMA), connective tissue growth factor (CTGF), fibronectin (FN) and collagen type III (Col III) were observed by reverse transcriptase-polymerase chain reaction (RT-PCR). The protein expressions of FN and Smads were observed by Western blot. Results. In NRK/49F, TGF-β1 enhanced CTGF, FN and Col III mRNA expression in a dose- and time-dependent manner. α-SMA, CTGF, FN and Col III mRNA and FN protein expression in 15-deoxy-Δ12,14-prostaglandin J2 (15d-PGJ2)-troglitazone- and ciglitazone-pretreated groups, respectively, were significantly decreased compared with the TGF-β1-stimulated group. TGF-β1 (5 ng/mL) enhanced p-Smad2/3 protein expression in a time-dependent manner. Compared with the TGF-β1-stimulated group, p-Smad2/3 protein induced by TGF-β1 in PPAR-γ agonists-pretreated groups significantly decreased with no statistical difference amongst the three pretreated groups. Conclusion. PPAR-γ agonists could inhibit TGF-β1-induced renal fibroblast activation, CTGF expression and ECM synthesis through abrogating the TGF-β1/Smads signaling pathway

A Novel Web Page Filtering System by Combining Texts and Images

With the rapid development of the Internet, people benefit much from the sharing of information. Meanwhile, the WWW era is a double-edged sword which spreads harmful and erotic content widely. In this paper, a new statistical approach has been exploited by combining the results of two or more different classification methods using our filtering system. We first briefly introduce the classification of discrete texts, continuous texts and images separately, and then describe the specific way we have been exploring to merge the text and image classification result. Also there is a section illustrating our system framework. Finally we assess our method by demon-strating the experimental results and comparing it to some common-used filtering methods

Male Clients of Male Sex Workers in China: An Ignored High-Risk Population.

BackgroundThere is a high prevalence of HIV/syphilis among male sex workers, but no formal study has ever been conducted focusing on male clients of male sex workers (MCM). A detailed investigation was thus called for, to determine the burden and sociobehavioral determinants of HIV and syphilis among these MCM in China.MethodsAs part of a multicenter cross-sectional study, using respondent-driven and snowball sampling, 2958 consenting adult men who have sex with men (MSM) were recruited, interviewed, and tested for HIV and syphilis between 2008 and 2009. The distributions of sociodemographic characteristics, risk behaviors, and HIV/syphilis prevalence were determined and compared between MCM and other MSM.ResultsAmong recruited MSM, 5.0% (n = 148) were MCM. HIV prevalences for MCM and other MSM were 7.4% and 7.7%, whereas 18.9% and 14.0% were positive for syphilis, respectively. Condomless anal intercourse (CAI) was reported by 59.5% of MCM and 48.2% of MSM. Multiple logistic regression revealed that compared with other MSM, MCM were more likely to have less education [for ≤ elementary level, adjusted odds ratio (aOR) = 3.13, 95% confidence interval (95% CI): 1.42 to 6.90], higher income (for >500 US Dollars per month, aOR = 2.97, 95% CI: 1.53 to 5.77), more often found partners at parks/restrooms (aOR = 4.01, 95% CI: 2.34 to 6.85), reported CAI (aOR = 1.49, 95% CI: 1.05 to 2.10), reported a larger sexual network (for ≥ 10, aOR = 2.70, 95% CI: 1.44 to 5.07), and higher odds of syphilis (aOR = 1.54, 95% CI: 1.00 to 2.38).ConclusionsThe greater frequency of risk behaviors and high prevalence of HIV and syphilis indicated that HIV/syphilis prevention programs in China need to pay special attention to MCM as a distinct subgroup, which was completely ignored until date