Naive Algorithm Research Articles

Machine learning tools in the tasks of selecting the determinants of socio-economic situation and development potential of Russian regions

Subject. The article addresses the selection of determinants that are significant for assessing the level of socio-economic situation and development potential of Russian regions. Objectives. The aim is to study machine learning algorithms for the selection of determinants – predictors of the level of socio-economic situation and development potential of Russian regions, to build models of classification of regions, according to the level of socio-economic situation, using various machine learning algorithms. Methods. To build classification models, I used data from the Federal State Statistics Service, the Institute of Scientific Communications, the RIA Novosti news agency, and the TAdviser Internet portal. Procedures for data classification, model parameter estimation, selection of significant determinants and visualization of results are performed, using the basic functions of the PyCaret library. Cohen's Kappa statistics and Matthews correlation coefficient were employed as priority metrics for evaluating the model productivity. The algorithms for selecting determinants are implemented in the Google Colab analytical environment. Results. I constructed multiclass classification models based on simple and ensemble machine learning algorithms. Simple classification algorithms, including logistic and ridge regression models, naive Bayesian algorithm, decision tree, support vector machine, and k-nearest neighbor methods are characterized by accuracy values at 77%, however, Cohen's Kappa statistics and the Matthews correlation coefficient only show a satisfactory relationship between the actual and predicted value of the region class. Ensemble algorithms, including random forest, gradient boosting and extreme gradient boosting, are characterized by a close relationship between the actual and forecast estimates of the classifier at a level of more than 70%. Conclusions. The random forest algorithm is recognized as the most effective classification model. The gross regional product and investments in fixed assets are informative determinants for measuring the socio-economic status.

Construction and Implementation of Application-Oriented Undergraduate Teaching and Teaching Evaluation System Based on Multiple Information Fusion

At present, many schools implement teaching quality evaluation systems with student performance and practical activities as the core data of evaluation. Based on the introduction of the training objectives and discipline nature of the information management and information system specialty, this paper analyzes the construction principles of the multi-disciplinary curriculum system of the specialty and puts forward relevant countermeasures for the construction of the multi-disciplinary education system. This paper introduces the definition of practical teaching evaluation system and teaching evaluation algorithm. Among them, naive Bayesian algorithm can improve the sample accuracy by 95%, so it is necessary to build an application-oriented undergraduate practice teaching evaluation system.

Global Digital Compact: A Mechanism for the Governance of Online Discriminatory and Misleading Content Generation

With the continuous development of artificial intelligence (AI), algorithmic discrimination and discriminatory and misleading content (DMC) generated by AI have given rise to many negative effects in cyberspace, such as racial and gender discrimination, misinformation, and so on. The growing concern in society over AI governance urgently necessitates the establishment of an effective mechanism to supervise and govern AI-generated DMC. In this article, the discriminatory and misleading contents of AIGC (Artificial Intelligence Generated Content) were extracted according to Text Classification Model and then classified by Naive Bayesian algorithm. The results showed that under the Global Digital Compact (GDC), countries differed in their degrees of discrimination related to race, gender, religion, and age. The racial discrimination accounted for the highest proportion in the United States, with a score of 0.15; that in Britain and France took up a share of 0.06 and 0.07, respectively; and merely 0.03 in Germany. Discriminatory content of racial discrimination (M1) and gender discrimination (M2) in science and technology industry was relatively low, accounting for 0.05 and 0.08, respectively. Analyzing data within the Global Digital Compact (GDC) illuminates the disparities and trends in DMC generation across various countries, cities, industries, and individual users. This analysis provides valuable references for subsequent research and problem-solving initiatives under the compact. Furthermore, GDC plays a pivotal role in addressing issues related to AI-generated DMC, contributing significantly to the creation of a secure, reliable, and equitable cyberspace.

Sentiment Analysis of Domestic Violence Issues on Twitter Using Multinomial Naïve Bayes and Support Vector Machine

Cases of domestic violence (KDRT) always attract numerous public comments on Twitter's social media platform. This research aims to conduct a sentiment analysis classification regarding ongoing cases of KDRT on Twitter. The study employs the Multinomial Naive Bayes and SVM algorithms to test accuracy in classifying tweets. The research methodology includes the following steps: data collection from Twitter, data preprocessing, sentiment analysis, sentiment classification using SVM and Multinomial Naïve Bayes algorithms, and analysis of results from both algorithms. The research findings indicate that the SVM algorithm achieves the highest accuracy rate, reaching 73% at an 80:20 ratio. In comparison, the Multinomial Naïve Bayes algorithm attains an accuracy rate of 70% at the same ratio. Therefore, it can be concluded that the SVM algorithm exhibits better accuracy compared to the Multinomial Naïve Bayes algorithm.

A novel method to detect and classify Alzheimer using naive Bayes classification algorithm

A novel method to detect and classify Alzheimer using naive Bayes classification algorithm

Development of a patient health monitoring system based on the internet of things with a module for predicting vital signs

<span>Recent issues related to human health in the world have shown the importance of telemedicine considering necessities to perform the remote monitoring of patients. In this study, using a patient smart monitoring system (PSMS), we collected 5,000 samples of heart rate and blood saturation vital signs from 4 volunteers and tried to find better correlation algorithms to develop a module to predict what these vital signs will be in the next 60 seconds. The following regression algorithms recurrent neural network (long short-term memory) (RNN(LSTM)), autorregresive integrated moving average (ARIMA), value-added reseller vector autoregression (VAR) were used to forecast the patient's state of health in the next 60 seconds. Further, the support vector machine (SVM) and Naive Bayes classification algorithms use the data forecasted by the regression algorithms as input to predict the health status of the patients. When comparing algorithms, we focused on the F measure, a metric used to evaluate the performance of machine learning algorithms. As a result, RNN(LSTM) and SVM showed the performance score value of machine learning algorithms F 0.84, RNN(LSTM) and Naive Bayes 0.81, VAR and SVM 0.82, and VAR and Naive Bayes 0.75. Compared to them, the correlation of ARIMA regression algorithms and SVM classification showed a better F score of 0.86 for machine learning algorithms than the others.</span>

МОДУЛЬ ИНФОРМАЦИОННОЙ СИСТЕМЫ НА ОСНОВЕ НАИВНОГО БАЙЕСОВСКОГО КЛАССИФИКАТОРА ДЛЯ АВТОМАТИЗАЦИИ РАБОТЫ АПТЕКИ

The introduction of the automation and management principles for pharmacists’ activities using information systems is an effective way to increase profi ts and strengthen pharmacy profi tability. Companies use machine learning algorithms to adjust their strategy, study customer reviews about their organization through feedback analysis, and improve the company’s image. However, a signifi cant amount of pharmacists’ time and effort goes into the manual processing of incoming reviews. The article proposes automating the processing using the naive Bayes classifi er algorithm, which is implemented in Python. To train the classifi er, the authors created their own corpus of labeled review texts with two categories, with the total number of reviews being about 500. A parser written in Python was used to search for reviews. The following steps were taken during the preliminary processing of the reviews’ text: lemmatization, elimination of punctuation, the text’s letter conversion to lower case, tokenization, stop words removal, and text vectorization using the bag-of-words model. According to the numerical experiments carried out, the classifi er’s highest accuracy was achieved with an 80/20 ratio of training and test samples that did not include stop words. When using a classifi er, analyzing 100 reviews takes 8 times less time than manual reading. The classifi er itself can be presented as a separate application or as a module within an information system. Thus, the increasing number of positive reviews is an indicator of the company’s effi cient performance as well as the number of satisfi ed customers. The growth of its image will strengthen customers’ loyalty to the company, resulting in greater sales.

A Locally Instance Weighting Naive Bayes Text Classification Algorithm Based on Distance Correlation Coefficient

A Locally Instance Weighting Naive Bayes Text Classification Algorithm Based on Distance Correlation Coefficient

Hybrid Naïve Back Propagation Based iCOSϕ Algorithm for Enriching the Grid Performance with Photovoltaics/Battery/Ultracapacitor/Fuel Cell

This paper presents a novel method for the combination of photovoltaic (PV), fuel cell (FC), battery, and ultracapacitor systems with D-STATCOM (Distribution Static Compensator) for grid integration. In this study, a novel naive backpropagation algorithm (NBP) based iCOSϕ is proposed for obtaining fundamental components from load current for effective harmonics compensation and contributes power quality enhancement by delivering power to the grid and connected loads. Further, a modified incremental conductance (MIC) approach is employed to acquire maximum power in the event of different atmospheric conditions. The performance of the proposed method is investigated under four uncertain conditions such as (a) linear loads, (b) zero voltage regulation under dynamic loads, (c) non-linear loads, and (d) variations in solar irradiance. Moreover, the breakthrough of the developed method is compared with various existing techniques like synchronous reference frame (SRF) theory, iCosϕ, fuzzy logic controller (FLC), conductance fryze, adaptive neuro-fuzzy inference system, and gradient descent back propagation learning (GDBP) neural network (NN) based iCosϕ. The simulation outcomes disclosed that the proposed NBP based iCOSϕ technique rendered a prolific performance rather than other existing methods under all uncertain conditions.

Prediction of electric power performance of the exhaust waste heat recovery system of an automobile with thermoelectrical generator under real driving conditions by means of machine learning algorithms

In internal combustion engines, approximately 40% of the thermal power obtained from fuel burning is thrown out to the environment from the exhaust system. Implementations of waste heat recovery systems with thermoelectrical generator over exhaust system have become widespread as it is the waste heat resource with the highest temperature in a vehicle. During literature research, experiments related to waste heat recovery under real road conditions are very few and no study on estimation of system performance by machine learning algorithms has been found; therefore, Toyota Corolla has designed an air-cooled waste heat recovery system using 3 thermoelectrical generators for the exhaust system of the automobile. During the road tests, temperature and electric power generation values obtained as per gear, vehicle speed and engine speed have been recorded. In the study, a dataset consisting of 10 attributes was created with each record as a result of the path test. Using this dataset, Random Forest, Support Vector Machine (SVM) and Naive Bayes machine learning algorithms estimated the electrical power to be generated from the thermoelectric generator recovery system. In the study, 7 electric power classification estimates were made. In the estimation process, 76% of the dataset was used for training and 24% was used for testing. In terms of estimation success; an estimation success of 96.6% has been achieved by Random Forest method; 94.6% by Support Vector Machine (SVM) method; and 76.7% by Naive Bayes method. The results show that prospective electricity generation estimation can be achieved with high level of accuracy.

Efficient Exact Enumeration of Single-Source Geodesics on a Non-Convex Polyhedron

In this paper, we consider enumeration of geodesics on a polyhedron, where a geodesic means locally-shortest path between two points. Particularly, we consider the following preprocessing problem: given a point [Formula: see text] on a polyhedral surface and a positive real number [Formula: see text], to build a data structure that enables, for any point [Formula: see text] on the surface, to enumerate all geodesics from [Formula: see text] to [Formula: see text] whose length is less than [Formula: see text]. First, we present a naive algorithm by removing the trimming process from the MMP algorithm (1987). Next, we present an improved algorithm which is practically more efficient on a non-convex polyhedron, in terms of preprocessing time and memory consumption. Moreover, we introduce a single-pair geodesic graph to succinctly encode a result of geodesic query. Lastly, we compare these naive and improved algorithms by some computer experiments.

Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm

The influencing factors of building seismic capacity are analyzed, the basic cause events of the assessment target based on fault tree analysis (FTA) are determined, the basic cause events in the FTA model are classified and summarized, and a judgment system of building seismic capacity is built. The weight of each index factor in the Gini index calculation system is used, and the importance of the index is analyzed. On the basis of the Spearman correlation coefficient calculation of the index, the improved naive Bayesian algorithm is combined with the importance of the index to build a judgment model for the seismic capacity of housing buildings. The sample set is constructed based on the judgment system with the basic data of some housing buildings in Huoshan County. In order to improve the generalization ability and avoid overfitting, the K-SMOTE algorithm for mixed sampling was modified to improve sample balance, and random k -fold cross validation method was used for sample division and model optimization, achieving the determination of seismic capacity level of building. The research results indicate the following: (1) the accuracy of model evaluation is 93%, with model accuracy and recall rates of 0.913 and 0.93, respectively, indicating strong generalization ability of the model. (2) Selecting some actual examples of a building, the model judgment results are consistent with the actual results, verifying the correctness of the proposed method for building the model, which can be effectively used for determining the seismic capacity of building structures. (3) Applying the proposed method to the seismic capacity assessment of buildings in the Ta-pieh Mountains of Lu’an, it is concluded that the seismic capacity of urban buildings is common, while that of rural buildings is poor.

FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs

As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, graph data are stored separately in multiple distributed parties in some practical scenarios, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo issues while preserving each party’s privacy (or client). Nevertheless, different graph data distributions of various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this article, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, in which each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.

Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN

Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm.

Densest Periodic Subgraph Mining on Large Temporal Graphs

Densest subgraphs are often interpreted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">communities</i> , based on a basic assumption that the connections inside a community are much denser than those between communities. In a graph with temporal information, a densest periodic subgraph is the most densely connected periodic behavior which needs to be captured. Unfortunately, the existing work do not model the densest periodic subgraph in temporal graphs, and the current algorithms for mining the densest subgraph cannot be applied to detect the densest periodic subgraph in the temporal networks. To tackle this problem, we propose a novel model, called the densest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> -periodic subgraph, which presents the densest periodic subgraph whose period size is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> . We prove that finding the densest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> -periodic subgraph can be solved in polynomial time, but it is still challenging because the naive algorithm needs to repeatedly invoke a maximum flow algorithm for many periodic subgraphs. To compute the densest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> -periodic subgraph efficiently, we first develop an effective pruning technique based on the degeneracy of the graph to significantly prune the number of the periodic subgraphs. Then, we present a more efficient algorithm that can reduce the computations for the degeneracy and maximum flow. Next, we develop a greedy algorithm that can compute the approximate densest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> -periodic subgraph and achieve an approximation ratio of 1/2. Finally, the results of extensive experiments on several real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms.

Exploring the application of teaching evaluation models incorporating association rules and weighted naive Bayesian algorithms

Teaching evaluation is a judgment of the value of teachers’ teaching and students’ learning, and has become an important part of teaching management and teaching processes in universities. However, the workflow of implementing teaching evaluation is relatively cumbersome, often requiring the completion of a large amount of data calculation tasks. Therefore, how to apply modern science and technology to establish a comprehensive, objective and feasible teaching evaluation system and optimize the evaluation process is an important issue that urgently needs to be solved. The study first uses the Apriori algorithm to explore the correlation between evaluation indicators and results, and then optimizes the teaching evaluation indicators. On this basis, incremental learning is used to improve the classification training ability of the weighted naive Bayesian algorithm, and it is combined with the Apriori algorithm for teaching evaluation. The results show that the fused algorithm takes only 50 seconds to process 500 transactions, and the running speed improves rapidly. As the minimum support threshold decreases, the increase in the time required by the algorithm gradually decreases, resulting in a higher running speed. In the self-built university teaching evaluation database, compared with the BP (Error Back Propagation) algorithm, the combined algorithm has a relatively small fluctuation of accuracy in classifying teaching data, stable at 80 % to 95 %. Meanwhile, in the efficiency comparison, the algorithm requires a slow increase in time as the amount of data increases, resulting in higher efficiency. The application of the algorithm after the fusion of the two in teaching evaluation has not only high accuracy, but also higher efficiency, providing reference technical support for the optimization of university teaching evaluation.

Classification and Prediction Evaluation of a Fuzzy Rule-Based Model for Diabetes Diagnosis, Level of Care and Pima Indians Diabetes Datasets

— Classification and prediction of diseases is crucial in decision-making for the healthcare sectors, especially diabetes, a chronic disease. The availability and accessibility of diabetes datasets assists medical practitioners in the diagnosis process as well as researchers in various fields and these datasets are valuable sources. However, diabetes datasets are exposed to vagueness and uncertainty issues. This research work improved an interrelated decision-making model for diabetes by proposing a fuzzy rule-based model to handle the vagueness and uncertainty issues. The research methodology starts with pre-processing of the simulated diabetes diagnosis and level of care datasets that were validated by medical experts, as well as the Pima Indians Diabetes Dataset (PIDD). This is followed by the design of the fuzzy model and the construction of the fuzzy rules. Next, the testing of the fuzzy model using six supervised machine learning algorithms namely J48, Logistic, Naive Bayes Updateable, Random Tree, Bayes Net and AdaBoostM1. Lastly, the evaluation of the fuzzy model in terms of the accuracy, precision, recall, F1-Score and confusion matrix. Experimental results show 100% accuracy for the diabetes diagnosis fuzzy model, for all the five machine learning algorithms mentioned except AdaBoostM1 with 79.8165% accuracy. In addition, for the level of care fuzzy model, the highest accuracy produced is 97.1098% for J48 algorithm and the lowest accuracy is 93.1049% for Naive Bayes Updateable and Bayes Net algorithms. Furthermore, for the PIDD fuzzy model, the highest accuracy obtained is 74.8698% for J48 and AdaBoostM1 algorithms and the lowest accuracy is 70.1823% for Random Tree algorithm. Overall, the proposed fuzzy model produced a good accuracy and working as expected associated to the previous interrelated decision-making model.

Forecasting PM 2.5 concentration based on integrating of CEEMDAN decomposition method with SVM and LSTM

With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.

Prediction model of gleason score upgrading after radical prostatectomy based on a bayesian network

ObjectiveTo explore the clinical value of the Gleason score upgrading (GSU) prediction model after radical prostatectomy (RP) based on a Bayesian network.MethodsThe data of 356 patients who underwent prostate biopsy and RP in our hospital from January 2018 to May 2021 were retrospectively analysed. Fourteen risk factors, including age, body mass index (BMI), total prostate-specific antigen (tPSA), prostate volume, total prostate-specific antigen density (PSAD), the number and proportion of positive biopsy cores, PI-RADS score, clinical stage and postoperative pathological characteristics, were included in the analysis. Data were used to establish a prediction model for Gleason score elevation based on the tree augmented naive (TAN) Bayesian algorithm. Moreover, the Bayesia Lab validation function was used to calculate the importance of polymorphic Birnbaum according to the results of the posterior analysis and to obtain the importance of each risk factor.ResultsIn the overall cohort, 110 patients (30.89%) had GSU. Based on all of the risk factors that were included in this study, the AUC of the model was 81.06%, and the accuracy was 76.64%. The importance ranking results showed that lymphatic metastasis, the number of positive biopsy cores, ISUP stage and PI-RADS score were the top four influencing factors for GSU after RP.ConclusionsThe prediction model of GSU after RP based on a Bayesian network has high accuracy and can more accurately evaluate the Gleason score of prostate biopsy specimens and guide treatment decisions.

Computing Generalized Convolutions Faster Than Brute Force

In this paper, we consider a general notion of convolution. Let D be a finite domain and let D^n be the set of n-length vectors (tuples) of D. Let f :Dtimes Drightarrow D be a function and let oplus _f be a coordinate-wise application of f. The f-Convolution of two functions g,h :D^n rightarrow {-M,ldots ,M} is (g⊛fh)(v):=∑vg,vh∈Dns.t.v=vg⊕fvhg(vg)·h(vh)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} (g \\mathbin {\\circledast _{f}}h)(\ extbf{v}) {:}{=}\\sum _{\\begin{array}{c} \ extbf{v}_g,\ extbf{v}_h \\in D^n\\\\ \ ext {s.t. } \ extbf{v}= \ extbf{v}_g \\oplus _f \ extbf{v}_h \\end{array}} g(\ extbf{v}_g) \\cdot h(\ extbf{v}_h) \\end{aligned}$$\\end{document}for every textbf{v}in D^n. This problem generalizes many fundamental convolutions such as Subset Convolution, XOR Product, Covering Product or Packing Product, etc. For arbitrary function f and domain D we can compute f-Convolution via brute-force enumeration in widetilde{{mathcal {O}}}(|D|^{2n} cdot textrm{polylog}(M)) time. Our main result is an improvement over this naive algorithm. We show that f-Convolution can be computed exactly in widetilde{{mathcal {O}}}( (c cdot |D|^2)^{n} cdot textrm{polylog}(M)) for constant c {:}{=}3/4 when D has even cardinality. Our main observation is that a cyclic partition of a function f :Dtimes Drightarrow D can be used to speed up the computation of f-Convolution, and we show that an appropriate cyclic partition exists for every f. Furthermore, we demonstrate that a single entry of the f-Convolution can be computed more efficiently. In this variant, we are given two functions g,h :D^n rightarrow {-M,ldots ,M} alongside with a vector textbf{v}in D^n and the task of the f-Query problem is to compute integer (g mathbin {circledast _{f}}h)(textbf{v}). This is a generalization of the well-known Orthogonal Vectors problem. We show that f-Query can be computed in widetilde{{mathcal {O}}}(|D|^{frac{omega }{2} n} cdot textrm{polylog}(M)) time, where omega in [2,2.372) is the exponent of currently fastest matrix multiplication algorithm.