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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Ibrahim A Alharbi Ali Jaber Almalki Cliff C. Zou dept. Computer Science University of Central Florida Orlando USA

ISBN: (纸本)9781665458429

Educational Data Mining is the process of extracting information from datasets with educational relevance. This process of information extraction can be extremely insightful and useful for policy decision making but also small scale interventions. In our research, we optimize model configurations and hyperparameters of student performance prediction pipelines. Our models target to predict student performance based on data gathered in Portuguese schools pertaining to the subjects of Mathematics and Portuguese. The target variable is either cast in two or five bins and we train separate models for each of the tasks. We search model configurations amongst three different feature selection algorithms and 7 different classifiers implemented in scikit-learn. Our search spans 2000 iterations overall different experiment setup permutations. We successfully develop novel model configurations that perform exceptionally well. Furthermore, we set an important precedent showing the utility of hyperparameter selection and model search for Educational Data Mining.

关键词： Scientific computing Pipelines Predictive models prediction algorithms Mathematical models Data models Data mining

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Accurate Load prediction algorithms Assisted with Machine Learning for Network Traffic

Accurate Load Prediction Algorithms Assisted with Machine Le...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Yin Gao Man Zhang Jiajun Chen Jiren Han Dapeng Li Ruitao Qiu Wireless Product R&D Institute ZTE Corporation Shanghai China ZTE Corporation Tianjin China

With the increasingly higher demand on radio access networks, problems such as serious energy consumption and network load imbalance have aroused, catching more attention from the network operators. To solve these problems, load prediction has been considered as one possible solution. This paper investigates two load prediction models to predict the traffic load in cells, in order to improve the energy saving strategies. One of the proposed algorithm is a linear ensemble model which is composed of three sub-models, to predict the traffic load in terms of time, space and historical pattern respectively. This model is named as the ensemble model. Different methods such as time series analysis, linear regression and regression tree are applied to the sub-models, after which adjusted weights are calculated and allocated to each of the three sub-models to create the ensemble model. The second model investigated in this paper is convolutional neural network (CNN) based, which utilizes the residual learning network (ResNet) structure to train the collected data and at the same time improves the calculation efficiency. This model is thus referred as ResNet model in this paper. The collected data of traffic load in Milan area is put into training and verification by the proposed models. The performance of proposed methods is evaluated. The result shows that ensemble model provides a higher prediction accuracy than the other base line models and the ResNet model improves the efficiency of calculation.

关键词： Training Wireless communication Machine learning algorithms Telecommunication traffic Predictive models Tools prediction algorithms

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AI-Driven Dynamic Water Resource prediction and Optimization algorithms for Precision Irrigation

AI-Driven Dynamic Water Resource Prediction and Optimization...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Xiaoben Zhong Yunnan College of Business Management Anning China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

Addressing the escalating global scarcity of water resources and the pressing need for efficient irrigation in agricultural production, this research introduces a novel AI-powered framework for dynamic water resource prediction and optimization tailored for precision irrigation [1]. This framework innovatively integrates advanced machine learning techniques to achieve accurate, real-time forecasting of farmland water demand and subsequently optimizes irrigation strategies based on these predictions. Distinct from conventional approaches relying on static or simplified models, this study innovatively fuses multi-source heterogeneous information, including meteorological data, soil moisture levels, and crop growth stages, to construct a deep learning-based time-series forecasting model[2]. This model effectively captures the non-linear dynamic characteristics of water resource requirements. Furthermore, we designed a reinforcement learning-based irrigation decision optimization algorithm capable of dynamically adjusting irrigation parameters based on the predictive outcomes and real-time environmental feedback. This approach aims to minimize water consumption while concurrently maximizing crop yield. Experimental results demonstrate that the proposed algorithm significantly surpasses traditional methods in water resource prediction accuracy. Moreover, in practical irrigation scenarios, it effectively enhances water utilization efficiency, boosts crop productivity, and reduces energy expenditure, thereby providing robust technological support for sustainable agricultural development. This research exhibits significant innovation in both algorithm design and system integration, effectively overcoming the limitations of existing precision irrigation technologies regarding dynamic environmental adaptability and intelligent decision-making capabilities.

关键词： Productivity Irrigation Adaptation models Heuristic algorithms Predictive models prediction algorithms Dynamic scheduling Real-time systems Water resources Optimization

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Identification of Candidate Lung Disease Biomarkers through prediction Analysis algorithms

Identification of Candidate Lung Disease Biomarkers through ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： R. Premkumar Amit Kumar Shrivastav Shiv Shankar Shankar Department of Electronics and Communication Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India ARKA JAIN University Jamshedpur Jharkhand India Department of Mathematics Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This examine aimed to discover biomarkers associated to precise lung diseases from gene expression statistics thru the use of prediction analysis algorithms. To this end, the authors advanced a framework based totally on a Euclidean distance help vector machine (DSVM) and a recursive feature removal (RFE) manner. The dataset beneath evaluation changed into made from gene expression information from 380 bronchial brushings people- 188 of wholesome people and 192 of diseased individuals with muscular dystrophy, chronic obstructive pulmonary sicknesses, bronchial asthma and post-radio-chemotherapy. The DSVM version became able to achieve a mean popularity rate of ninety five. 6%, without compromising the specificity price at 88. n i n e %. The diagnosed biomarkers have been further enriched towards many databases and the authors located an association of a number of them with bronchial sicknesses. This result recommend the identification of potentially new biomarkers, supplying novel findings that is probably beneficial in future studies about lung diseases.

关键词： Support vector machines Radio frequency Accuracy Lung Biomarkers prediction algorithms Vectors Classification algorithms Gene expression Standards

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Development and Evaluation of a Machine Learning Model for Cobot Action prediction: Optimizing algorithms Through Cognitive Workload Metrics

Development and Evaluation of a Machine Learning Model for C...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Lala Rukh Yasir Ahmad Uzair Khaleeq Uz Zaman Mohsin Islam Tiwana Afshan Naseem Department of Engineering Management NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechanical Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechatronics Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Collaborative robots (Cobots) have replaced traditional industrial robots on the factory floors, enhancing automation and efficiency. From the perspective of Industry 5.0, centering around H.F. (Human Factors), concerns about cognitive ergonomics persist as Cobots' unpredictable actions can increase cognitive workload and reduce productivity. Identifying and prioritising the operators' mental and physical well-being is necessary for safer and more efficient Human Robot Collaboration (HRC). This paper proposes a novel machine-learning model for Cobot action prediction and evaluates which algorithm is best for predicting Cobot actions based on operators' cognitive states. 75 participants with different levels of technophobia, technical expertise, and age collaborated with a Cobot to solve a complex maze puzzle. Firstly, each participant's Cognitive Workload (CWL) was measured using a combination of subjective and objective metrics (NASA-Task Load index, Heart Rate Variability, and Error Rate), followed by the development of a machine learning model that takes human factors as input parameters and predicts Cobot's actions accordingly. This study evaluates various machine learning models for Cobot action prediction and identifies that the Random Forest Classifier outperforms other models with an accuracy of 90%. The model can be implemented in various industrial settings to improve HRC safety and operators' cognitive ergonomics.

关键词： Productivity Service robots Ergonomics Collaborative robots Human factors Predictive models prediction algorithms Safety Random forests Load modeling

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Review of Research on prediction algorithms of Bus Headway

Review of Research on Prediction Algorithms of Bus Headway

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Lian Lian Jiashuo Shang Xuejun Zong College of Information Engineering Shenyang University of Chemical Technology Shenyang China Graduate school Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

Public transportation plays an important role in the city's intelligent transportation system. Accurate bus headway forecast is crucial for passengers to arrange their travel time and for bus operators to provide high quality service. This article first studies the three main factors affecting bus headway prediction. Then we focus on the analysis of several bus headway prediction models. Finally, we summarize the current difference and development trends of the research on bus headway.

关键词： Computational modeling Urban areas Predictive models prediction algorithms Market research Public transportation Cybernetics

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Benchmarking Explicit Rating prediction algorithms for Cosmetic Products

Benchmarking Explicit Rating Prediction Algorithms for Cosme...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Raditya Nurfadillah Fariz Darari Radityo Eko Prasojo Yasmin Amalia Faculty of Computer Science Universitas Indonesia Depok Indonesia Tokopedia-UI AI Center of Excellence Depok Indonesia Kata.ai Jakarta Indonesia

ISBN: (数字)9781728184067

ISBN: (纸本)9781728184074

Recommendation systems have become a staple feature for any e-commerce sites. The ability to predict whether a customer likes an unseen product forms the very foundation of a recommendation system. In this paper, we concern the issue of explicit rating prediction over cosmetic products. Given a dataset of cosmetic product ratings, we analyze the characteristics of the dataset and implement a wide range of algorithms, such as KNN and matrix factorization, to predict such ratings. We evaluate the performance of these algorithms using MAE and RMSE measures, and discuss factors that may contribute to their performance results. Our experiments have shown that the SVD++ technique performs the best among all with an MAE of 0.7699 and an RMSE of 0.9696. We hope that our paper can shed new light on the selection of explicit rating prediction algorithms not only in the domain of beauty products, but also in wider scenarios.

关键词： prediction algorithms Measurement Recommender systems Singular value decomposition Seminars Privacy Matrix decomposition

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Research on Movie Rating prediction algorithms

Research on Movie Rating Prediction Algorithms

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Xiaoyue Li Haonan Zhao Zhuo Wang Zhezhou Yu College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141121

In the era of data explosion of movie and video websites, “informahon overload” and “information labyrinth” have brought serious troubles, but movie recommender systems can efficiently solve such problems. In order to solve these problems, we propose the RF that uses the users' activity and rating to select suitable experimental data and proved this method can efficiently reduce RMSE and MAE of various recommendation algorithms. On this basis, we propose the MCBF-SVD, which is a movie rating prediction algorithm based on explicit data and implicit data from movie datasets to predict the future ratings of movies from users with a certain degree of activity. Firstly, the MCBF-SVD uses weighting factors to discuss the impact of movie categories on predicting future rating behavior of users, and also improves the filtering method based on movie categories. Finally, the MCBF is combined with SVD algorithm which has good performance in CF. Compared with several existing algorithms, our MCBF-SVD greatly enhanced the accuracy of rating prediction, and improved the scalability and efficiency of personalized recommender systems.

关键词： Motion pictures prediction algorithms Radio frequency Recommender systems Matrix decomposition Collaboration

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Indoor Localization and Movement prediction algorithms with Light-Fidelity

Indoor Localization and Movement Prediction Algorithms with ...

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International Conference on Transparent Optical Networks

作者： Hamis Hesham Tawfik Ismail M. Saeed Darweesh WINC: School of Engineering and Applied Sciences Nile University Egypt National Institute of Laser Enhanced Sciences Cairo University Egypt

ISBN: (数字)9781728184234

ISBN: (纸本)9781728184241

Indoor localization has recently attended an increase in interest due to the potential for a wide range of services. In this paper, indoor high-precision positioning and motion prediction algorithms are proposed by using light fidelity (LI-FI) system with angular diversity receiver (ADR). The positioning algorithm uses to estimate the location of an object in the room. Furthermore, the prediction algorithm applies to predict the motion of that object. The simulation results show that the average root mean squares error of the positioning algorithm is about 0.6 cm, and the standard deviation from 1.188 ×10 -8 to 0 cm. Meanwhile, the accuracy of the motion prediction reaches 89.5%.

关键词： prediction algorithms Light fidelity Optical transmitters Optical receivers Wireless communication Integrated optics

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Protein Topology prediction algorithms Systematically Investigated in the Yeast Saccharomyces cerevisiae

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BIOESSAYS 2019年第8期41卷 1800252-1800252页

作者： Weill, Uri Cohen, Nir Fadel, Amir Ben-Dor, Shifra Schuldiner, Maya Weizmann Inst Sci Dept Mol Genet IL-7610001 Rehovot Israel Weizmann Inst Sci Dept Life Sci Core Facil IL-7610001 Rehovot Israel

Membrane proteins perform a variety of functions, all crucially dependent on their orientation in the membrane. However, neither the exact number of transmembrane domains (TMDs) nor the topology of most proteins have been experimentally determined. Due to this, most scientists rely primarily on prediction algorithms to determine topology and TMD assignments. Since these can give contradictory results, single-algorithm-based predictions are unreliable. To map the extent of potential misanalysis, the predictions of nine algorithms on the yeast proteome are compared and it is found that they have little agreement when predicting TMD number and termini orientation. To view all predictions in parallel, a webpage called TopologYeast: was created. Each algorithm is compared with experimental data and a poor agreement is found. The analysis suggests that more systematic data on protein topology are required to increase the training sets for prediction algorithms and to have accurate knowledge of membrane protein topology.

关键词： membrane proteins prediction algorithms termini orientation transmembrane domains yeast

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