Ranking Algorithm Research Articles

Measuring sustainability performance with SWARA-MEREC-COBRA multi-criteria model: A case study of Anadolu insurance company

The aim of this study is to assess and rank Anadolu Sigorta Company's sustainability performance between 2018 and 2022 using the hybrid SWARA-MEREC-COBRA model. The sustainability performance evaluation criteria's importance weights were determined using both subjective and objective methods. The SWARA algorithm was used to determine the weights subjectively based on expert opinion, while the MEREC algorithm was used to determine them objectively. The final importance weights were obtained by combining the results of both methods. When evaluating the sustainability performance of Anadolu Sigorta Company, the most important criterion was total paper consumption, while the criterion with the least impact was the number of female employees. The COBRA ranking algorithm was used to rank the alternatives, and it was determined that the company's best sustainability performance was in 2022, while the worst was in 2018. Different sensitivity analyses were used to test the consistency of the proposed model.

Personalized product ranking system for enhanced user experience

Businesses looking to engage and satisfy their online audience must prioritize the user experience in the quickly changing digital landscape. In order to address this challenge, this project proposes and implements a sophisticated algorithmic solution designed to produce extremely accurate and user-centric product rankings. This represents a groundbreaking approach. The system attempts to predict and present the most relevant product suggestions by meticulously considering a wide range of factors such as user preferences, historical interactions, product popularity trends, and user similarity metrics. Our methodology is distinguished by the use of a dynamic simulation environment in which user profiles, product categories, and interaction pat- terns are manipulated to replicate authentic real-world scenarios. The dynamic framework facilitates the comprehensive testing and optimization of customized ranking algorithms, guaranteeing their flexibility in response to changing user preferences and behaviors. The project’s effectiveness is evaluated using precise evaluation metrics that offer quantitative information about how well the system understands and responds to each unique user’s preference, ultimately resulting in a more fulfilling and rich online shopping or content consumption experience.

Construction and validation of a new media marketing influence assessment model

Abstract In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.

Alleviating Cold Start in the EOSC Recommendations: Extended Page Rank Algorithm

Alleviating Cold Start in the EOSC Recommendations: Extended Page Rank Algorithm

Optimizing the Efficiency of Web Mining through Comparative Web Ranking Algorithms

Optimizing the Efficiency of Web Mining through Comparative Web Ranking Algorithms

Как учесть мнение населения при промышленном освоении Арктики

The article deals with the issues of taking into account the opinion of the local population when justifying and implementing projects for industrial development of the Arctic. The mechanism and procedures for assessing and taking into account the opinion of the population at all stages of the life cycle of a project for exploration and extraction of minerals from its initiation to completion are proposed by informing the population about the project of planned activities, holding public hearings, ethnological expertise, as well as ethnological audit and ethnosocial monitoring. The model of interaction of stakeholders (mining companies, local authorities and indigenous peoples of the North) is based on the concept of sharing joint benefits from the industrial development of the Arctic, which is based on the cooperation of the parties and corporate social responsibility of business in the area of planned activities. It is proposed to protect the interests of indigenous peoples by signing agreements on the socio-economic development of the territory, creating compensation funds and implementing compensation projects of socio-economic, environmental orientation and projects to preserve the culture and ethnos of indigenous peoples, which are formed on the basis of population surveys. 
 The algorithm of multi-criteria evaluation, ranking and selection of compensation projects, taking into account priorities for various groups of the population, as well as economic regulators for supporting compensation projects, including direct action tools (compensation fund, etc.), and indirect action tools (tax incentives, subsidies to support traditional crafts, etc.) is proposed.

Analysis of regularized least squares ranking with centered reproducing kernel

The existing results of the regularized least squares ranking (RLSR) algorithm are mainly related to the reproducing kernels. In this paper, we go beyond this limitation by investigating the convergence rates of ranking with centered reproducing kernel (CRK). We develop a new error analysis framework by means of CRK and integral operator. The result shows that the convergence rate of RLSR is still optimal as those in the traditional analysis framework.

Classification of Multiple H&E Images via an Ensemble Computational Scheme.

In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.

Sustainable Medical Waste Management Using an Intuitionistic Fuzzy-Based Decision Support System

The growing urban population and increased use of healthcare services have brought significant attention to the safe and sustainable management of medical waste. Selecting the proper technology in medical waste management (MWM) represents one of the most critical challenges for decision-makers to ensure public health. In order to evaluate and choose the best MWM methodology, the current research provides a novel multi-criteria decision-making (MCDM) strategy for a variety of social stakeholders, to compute criteria weights, decision-making weights, and alternative ranking algorithms. The suggested structure addresses uncertain assessments of alternatives by extending weighting and ranking methods to acquire the decision-making weight and rank the MWM alternatives based on uncertain conditions. It also uses ‘intuitionistic fuzzy’ linguistic variables to indicate criteria weights. To assess all the factors pertaining to the sustainability of MWM actions, this study suggests the creation of a decision support system (DSS). Our DSS is built upon a novel strategy that utilizes a collection of MCDM models that are grounded on contemporary intuitionistic fuzzy logic methodologies. Alternative scenarios have been assessed for the instance of Greece, after specialists in the healthcare management field imposed 17 criteria and sub-criteria. The IF-MCDM methodologies used were the Intuitionistic Fuzzy DEMATEL, TOPSIS, and CORPAS. The alternative scenarios ranged from the prioritizing of safety laws and regulations to public acceptance and awareness, with the handling of hazardous risks and transportation playing a crucial part in the process. All ensemble methods produced the same ranking of the alternatives, demonstrating that safety and risk avoidance is the most significant scenario for sustainable urban development and public health.

Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods

COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.

Identification of key gene expression associated with quality of life after recovery from COVID-19.

Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, "Better," "The Same," and "Worse." Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life.

Colorectal cancer detection based on convolutional neural networks (CNN) and ranking algorithm

With the development of targeted therapies, many treatments are based on molecular studies, which require sampling tumor tissue from paraffin blocks for sequencing. An automated solution could potentially reduce the workload of pathologists by acting as a screening device and may reduce the subjectivity in diagnosis. In tissue-based diagnostics, most of the work still needs to be done manually by a pathologist using a microscope to examine stained slides. The foundation of such tasks is to accurately distinguish cancer/malignant cells from normal/benign cells. However, the determination of tumor content is poorly reproducible with significant variation. As the size of tumor regions can be very small, pathologists are often required to use high magnification for detecting tumor cells. This requirement significantly increases the workload for pathologists. As digital pathology datasets have become publicly available and have opened up the possibility of evaluating the feasibility of applying deep learning techniques to improving the efficiency and quality of histologic diagnosis. The model proposed in this work is an application to detect colorectal cancer based on the Convolutional Neural Network and Ranking algorithm. Based on the performance evaluation, it is found that the proposed model is yielding better results in diagnosis of Colorectal Cancer than the existing methods in terms of Recall, Precision and Accuracy.

Heuristic Search for Rank Aggregation with Application to Label Ranking

Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications but posing a computationally challenging problem. In this paper, we provide an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and an enhanced late acceptance local search method reinforced by a relaxed acceptance and replacement strategy and a fast incremental evaluation mechanism. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on both synthetic and real-world benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, a well-established machine learning task. We additionally analyze several key algorithmic components to gain insight into their operation. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72371157] and Shanghai Pujiang Programme [Grant 23PJC069]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0019 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0019 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Network‐based Genetic Subtyping for Target Prioritization and Drug Repositioning: The Artificial Intelligence for Alzheimer’s Disease (AI4AD) Consortium

AbstractBackgroundAlzheimer disease (AD) is a progressive neurodegenerative disease with complicated underlying disease mechanisms. The clinical and neuropathological heterogeneity of AD may be explained by specific disease mechanisms at a brain cell‐level. We postulate that the path to effective treatment of AD depends on identifying distinctive subtypes linking to specific disease mechanisms at brain cellular level.MethodWe developed a novel precision medicine framework using polygenic risk scores defined by co‐regulated cellular networks to stratify individuals into low and high‐risk groups (genetic subtypes) for brain cell‐type specific networks. We prioritized genes from the cell‐based networks using a graph‐based ranking (PageRank) algorithm. We investigated molecular signatures of the prioritized genes for the subtypes using autopsied brains from the Framingham Heart Study (FHS). We validated the signatures using isogenic APOE lines of human pluripotent stem cell (hiPSC)‐derived astrocytes. Existing drugs targeting for the genetic subtypes were identified using drug perturbation database and clustering algorithms using compound structures from the PubChem databaseResultWe identified six preserved and AD associated co‐expressed gene networks for astrocytes, oligodendrocytes, and oligodendrocyte progenitor cells. The top‐ranked astrocyte network was enriched with Parkinson’s disease, Alzheimer’s disease, mineral absorption, and ribosome pathways. The most important gene in the astrocyte network was the APOE gene, which its expression was increased in AD compared in control brains with elevated complement C4A level. We confirmed elevated expression of both APOE and C4A levels in APOE4 compared in APOE3 hiPSC‐derived astrocytes. We identified three drugs targeting the astrocyte subtype, all of them previously modulated expression of the prioritized genes in the drug perturbation database. Clustering algorithm identified multiple compounds targeting the prioritized genes in the astrocyte network with at least 80% similarity in structure.ConclusionThe newly established framework provided the potential implication in genetic subtype driven drug repositioning in AD. In future, we will develop novel graph neural network approaches to identify comprehensive genetic subtypes and drugs targeting the subtypes in collaboration with internal and external members from the AI4AD consortium.

A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer

<span lang="EN-US">Automatic text summarization generates a summary that contains sentences reflecting the essential and relevant information of the original documents. Extractive summarization requires semantic understanding, while abstractive summarization requires a better intermediate text representation. This paper proposes a hybrid approach for generating text summaries that combine extractive and abstractive methods. To improve the semantic understanding of the model, we propose two novel extractive methods: semantic latent Dirichlet allocation (semantic LDA) and sentence concept mapping. We then generate an intermediate summary by applying our proposed sentence ranking algorithm over the sentence concept mapping. This intermediate summary is input to a transformer-based abstractive model fine-tuned with a multi-head attention mechanism. Our experimental results demonstrate that the proposed hybrid model generates coherent summaries using the intermediate extractive summary covering semantics. As we increase the concepts and number of words in the summary the rouge scores are improved for precision and F1 scores in our proposed model.</span>

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells

Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to analyze CTC-expressed genes to identify potential cancer biomarkers. In this regard, several studies have used gene expression changes in blood to predict the presence of CTC and, consequently, cancer. However, the CTC mRNA data has not been used to develop a generic approach that indicates the presence of multiple cancer types. In this study, we developed such a generic approach. Briefly, we designed two computational workflows, one using the raw mRNA data and deep learning (DL) and the other exploiting five hub gene ranking algorithms (Degree, Maximum Neighborhood Component, Betweenness Centrality, Closeness Centrality, and Stress Centrality) with machine learning (ML). Both workflows aim to determine the top genes that best distinguish cancer types based on the CTC mRNA data. We demonstrate that our automated, robust DL framework (DNNraw) more accurately indicates the presence of multiple cancer types using the CTC gene expression data than multiple ML approaches. The DL approach achieved average precision of 0.9652, recall of 0.9640, f1-score of 0.9638 and overall accuracy of 0.9640. Furthermore, since we designed multiple approaches, we also provide a bioinformatics analysis of the gene commonly identified as top-ranked by the different methods. To our knowledge, this is the first study wherein a generic approach has been developed to predict the presence of multiple cancer types using raw CTC mRNA data, as opposed to other models that require a feature selection step.

MI[formula omitted]C: Mining intra- and inter-image context for person search

Person search aims to localize the queried person from a gallery of uncropped, realistic images. Unlike re-identification (Re-ID), person search deals with the entire scene image containing rich and diverse visual context information. However, existing works mainly focus on the person’s appearance while ignoring other essential intra- and inter-image context information. To comprehensively leverage the intra- and inter-image context, we propose a unified framework termed MI3C including the Intra-image Multi-View Context network (IMVC) and the Inter-image Group Context Ranking algorithm (IGCR). Concretely, the IMVC integrates the features from the scene, surrounding, instance, and part views collaboratively to generate the final ID feature for person search. Furthermore, the IGCR algorithm employs group matching results between query and gallery image pairs to measure the holistic image matching similarity, which is adopted as part of the sorting metric to yield a more robust ranking among the whole gallery. Extensive experiments on two popular person search benchmarks demonstrate that by mining intra- and inter-image context, our method outperforms previous state-of-the-art methods by conspicuous margins. Specifically, we achieve 96.7% mAP and 97.1% top-1 accuracy on the CUHK-SYSU dataset, 55.6% mAP, and 90.8% top-1 accuracy on the PRW dataset.

An Exome Capture-Based RNA-Sequencing Assay for Genome-Wide Identification and Prioritization of Clinically Important Fusions in Pediatric Tumors

This study reports the development of an exome capture-based RNA-sequencing assay to detect recurring and novel fusions in hematologic, solid, and central nervous system tumors. The assay used Twist Comprehensive Exome capture with either fresh or formalin-fixed samples and a bioinformatic platform that provides fusion detection, prioritization, and downstream curation. A minimum of 50 million uniquely mapped reads, a consensus read alignment/fusion calling approach using four callers (Arriba, FusionCatcher, STAR-Fusion, and Dragen), and custom software were used to integrate, annotate, and rank the candidate fusion calls. In an evaluation of 50 samples, the number of calls varied substantially by caller, from a mean of 24.8 with STAR-Fusion to 259.6 with FusionCatcher; only 1.1% of calls were made by all four callers. Therefore a filtering and ranking algorithm was developed based on multiple criteria, including number of supporting reads, calling consensus, genes involved, and cross-reference against databases of known cancer-associated or likely false-positive fusions. This approach was highly effective in pinpointing known clinically relevant fusions, ranking them first in 47 of 50 samples (94%). Detection of pathogenic gene fusions in three diagnostically challenging cases highlights the importance of a genome-wide and nontargeted method for fusion detection in pediatric cancer.

Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo

Cloud computing has emerged as a transformative technology in the healthcare industry, but selecting the most suitable CV (“cloud vendor”) remains a complex task. This research presents a decision framework for CV selection in the healthcare industry, addressing the challenges of uncertainty, expert hesitation, and conflicting criteria. The proposed framework incorporates FFS (“Fermatean fuzzy set”) to handle uncertainty and data representation effectively. The importance of experts is attained via the variance approach, which considers hesitation and variability. Furthermore, the framework addresses the issue of extreme value hesitancy in criteria through the LOPCOW (“logarithmic percentage change-driven objective weighting”) method, which ensures a balanced and accurate assessment of criterion importance. Personalized grading of CVs is done via the ranking algorithm that considers the formulation of CoCoSo (“combined compromise solution”) with rank fusion, providing a compromise solution that balances conflicting criteria. By integrating these techniques, the proposed framework aims to enhance the rationale and reduce human intervention in CV selection for the healthcare industry. Also, valuable insights are gained from the framework for making informed decisions when selecting CVs for efficient data management and process implementation. A case example from Tamil Nadu is presented to testify to the applicability, while sensitivity and comparison analyses reveal the pros and cons of the framework.

Multiple change-points estimation in panel data models via SaRa

Multiple change-points estimation in panel data models is one of the popular topics in statistics. In this article, we investigate the multiple change-points estimation in the mean of panel data model based on a screening and ranking algorithm. Firstly, the possible change-points are initially screened based on local statistics. Secondly, the threshold is used to further screen the change-points. Finally, the final change-points are screened out using the information criterion. Furthermore, the consistency of the change-point estimators is proved. The Monte Carlo simulation results show that the proposed method can estimate the number and locations of change-points accurately even if the error terms are serially correlated or cross-sectionally correlated, and finally the method is used to analyze the annual GDP growth rate data of 47 countries in the word from 1971 to 2019 to illustrate the effectiveness of the method.