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Using Twitter Sentiment Analysis for Sustainable Improvement of Business Intelligence in Nigerian Small and Medium-Scale Enterprises 4

Using Twitter Sentiment Analysis for Sustainable Improvement...

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4th IEEE-Nigeria-Section International Conference on Disruptive Technologies for Sustainable Development (IEEE NIGERCON)

作者： Orji, Ugochukwu E. Ezema, Modesta E. Ujah, Jideofor Bande, Ponsak S. Agbo, Jonathan C. Univ Nigeria Dept Comp Sci Nsukka Enugu Nigeria

ISBN: (纸本)9781665479783

For most Small and Medium-scale Enterprises (SMEs) and startups in Nigeria, conducting market research and evaluation is solely based on customers' verbal feedback and demands, which is often flawed and highly limited. Social media (Twitter), as a business intelligence tool, can help SMEs gain insight into customers' perspectives of their products and services through Sentiment Analysis. This research presents a Twitter Sentiment Analysis for business intelligence using three machine learning algorithms;Bernoulli Naive Bayes (BNB), Linear Support Vector Classification (SVC), and Logistic Regression (LR) algorithm, to get the text polarity of tweets (Negative/positive). The dataset used for the research was gotten from Kaggle;the tweets were processed and analyzed using Python programming libraries on Kaggle's Jupyter Notebook cloud environment. Our result showed that Logistic Regression achieved better performance with an accuracy of 83% based on Precision-Recall evaluation metrics.

关键词： SMEs Business Intelligence Twitter Sentiment Analysis machine learning algorithm

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Research and Design of Credit Risk Assessment System Based on Big Data and machine learning 6

Research and Design of Credit Risk Assessment System Based o...

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IEEE 6th International Conference on Big Data Analytics (ICBDA)

作者： Wen, Song Zeng, Bi Liao, Wenxiong Wei, Pengfei Pan, Zhihao Guangdong Univ Technol Sch Comp Guangzhou Peoples R China

ISBN: (纸本)9781665415132

Since the outbreak of the COVID-19, small and medium-sized enterprises have been greatly affected. In order to cope with the difficulty of capital turnover for small and medium-sized enterprises, the government has successively introduced a series of financial policies to increase credit support and reduce financing costs. The rapid development of technology has also prompted further innovations in the operating models of banks and other credit platforms. However, banks and credit platforms must consider practical issues such as their own capital costs and risk assessment while they help small and medium-sized enterprises reduce financing costs. This paper aims to study and design a credit risk assessment system based on big data technology and machine learning algorithms. It is hoped that the system will enhance the bank's ability to identify the credit risks of small and medium-sized enterprises, so as to solve the problem of difficult and expensive financing for small and medium-sized enterprises. At the same time, it will reduce the bank's own bad loan ratio and increase profit margins. Achieving a win-win situation for small and medium-sized enterprises and banks, it's crucial to promote jointly the development of economy.

关键词： credit risk assessment big data technology machine learning algorithm

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Cricketer?s tournament-wise performance prediction and squad selection using machine learning and multi-objective optimization ?

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APPLIED SOFT COMPUTING 2022年 129卷

作者： Tirtho, Devopriya Rahman, Shafin Mahbub, Shahriar Ahsanullah Univ Sci & Technol Dhaka Bangladesh

The selection of a proper squad for a competition is vital in any sport, and cricket is no different. The diversity of squad formation is vital because those in charge of squad formation must consider all characteristics of the players. The squad must be broad and diverse enough for any event that lasts a long time since the squad must be competent in every situation, regardless of whether the side is playing at home or against. We aim to make the procedure as computationally efficient as possible. machine learning algorithms appear to have gone a long way in anticipating a system based on the information gained from the provided data. The article attempts to forecast player performance for a future tournament while considering most important characteristics of a cricket player. The prediction appears more accurate than an approximation. Several machine learning algorithms are utilized and analyzed to select the best one to use in our proposed work. The second phase employs Multi-objective optimization (MOO) techniques to perceive optimum squads from a list of players based on anticipated performance and some additional attributes. However, the problem appears to have certain conflicting aspects that may be handled by Multi -objective evolutionary algorithms (MOEA). Finally, a comparison is made between well-known MOEAs, and the results suggest that NSGA-II performs better for the problem.(c) 2022 Elsevier B.V. All rights reserved.

关键词： machine learning algorithm Prediction of performance Squad formation Multi-objective evolutionary algorithm

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Using machine learning to quantify sources of light-absorbing water-soluble humic-like substances (HULISws) in Northeast China

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ATMOSPHERIC ENVIRONMENT 2022年 291卷

作者： Hong, Yihang Cao, Fang Fan, Mei-Yi Lin, Yu -Chi Bao, Mengying Xue, Yongwen Wu, Jiyan Yu, Mingyuan Wu, Xia Zhang, Yan-Lin Nanjing Univ Informat Sci & Technol Atmospher Environm Ctr Joint Lab Int Cooperat Climate & Environm Change Minist Educ ILCEC Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Appl Meteorol Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Reading Acad Nanjing 210044 Peoples R China Univ Reading Sch Polit Econ & Int Relat Halifax RG66UR England

Water-soluble humic-like substances (HULISws ) are a group of heterogeneous organic compounds in the atmosphere, greatly impacting climate change and human health. As the most significant biomass burning (BB) and fossil fuel burning polluted area in China, Northeast China Plain could generate more light absorbing HULISws to the atmosphere. In this work, daily fine particle (PM2.5) samples were collected in Changchun, located in Northeast China Plain, from October 17th to November 29th, 2016, to investigate the potential sources of optical properties of HULISws . Here, the whole sampling period was divided into three sub-periods using the density of fire spots and the timeline of the central heating: the non-heating (October 17th to October 24th), early heating (October 25th to November 13th), and normal heating (November 14th to November 29th) periods. The mean mass concentrations of HULISws were 4.2 +/- 1.2, 8.6 +/- 3.6, and 2.4 +/- 1.3 mu g m(-3) during the non-heating, early heating, and normal heating periods, respectively. The positive matrix factorization (PMF) model results suggested that the contribution of primary BB, secondary BB, and fossil fuel burning emissions were 46%, 24%, and 19% during the non-heating period, 22%, 29%, and 17% during the early heating period, and 13%, 16%, and 50% during the normal heating period, respectively. Combining the PMF results with the random forest (RF) algorithm, the contribution of each source to the optical properties was quantified. Here, BB still dominated the optical properties of HULISws in Northeast China. During the study period, the primary BB and secondary BB contributed 25% and 34% in the light absorption coefficient (Abs), 22% and 33% in the mass absorption exponent (MAE), and 17% and 33% in the absorption Angstrom exponent (AAE), respectively. Other sources like cooking and fossil combustion contributed 25% and 35% in Abs, 35% and 26% in MAE, and 16% and 33% in AAE, respectively.

关键词： Humic-like substances Heating season Optical properties Source apportionment FLEXPART model machine learning algorithm

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Investigation on the use of Artificial Neural Network Equalizer in Indoor Visible Light Communication Systems 13

Investigation on the use of Artificial Neural Network Equali...

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13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

作者： Ertunc, Ezgi Younus, Othman Isam Ciaramella, Ernesto Ghassemlooy, Zabih Scuola Super Sant Anna Inst Commun Informat & Percept I-56124 Pisa Italy Northumbria Univ Fac Engn & Environm Opt Commun Res Grp Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (数字)9781665410441

ISBN: (纸本)9781665410441

In this paper, we investigate a non-line-of-sight visible light communication system with the artificial neural network (ANN)-based equalizer that uses the machine learning algorithm Levenberg-Marquardt (LM). We investigate the system performance in terms of the bit error rate for 2-, 4-, 8-, 16-, 32-of pulse amplitude modulation (PAM) scheme using an ANN-based equalizer with 4, 5, 10, 17, and 20 hidden neurons that are optimized. The signal to noise ratio (SNR) penalties are below 10 dB at a bit error rate of 10(-4), which is below the 7% forward error correction limit of 3.8x10(-3). We also compare the LM algorithm over Broyden-Fletcher-Goldfarb-Shanno) quasi-newton, resilient backpropagation, and gradient descent backpropagation. LM offers the best result with a 7 dB SNR penalty at a BER of 2x10(-4). Lastly, a 1 Mbit/s 4-PAM lin with an ANN-based equalizer with 5 hidden neurons is demonstrated over transmission distances of 1, 3, and 6 m is performed, with the lowest SNR penalty of 0.5 dB for the 1 m link.

关键词： artificial neural network equalizer equalization machine learning algorithm visible light communication

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Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第21期28卷 26571-26583页

作者： Jumin, Ellysia Basaruddin, Faridah Bte Yusoff, Yuzainee Bte. Md Latif, Sarmad Dashti Ahmed, Ali Najah Univ Tenaga Nas Dept Civil Engn Coll Engn Kajang 43000 Selangor Darul Malaysia Univ Tenaga Nas Dept Mech Engn Coll Engn Kajang 43000 Selangor Darul Malaysia Univ Tenaga Nas Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.

关键词： Solar radiation prediction Weather parameters machine learning algorithm Boosted decision tree regression Neural network and linear regression Correlation coefficient

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Spatio-temporal K-NN prediction of traffic state based on statistical features in neighbouring roads

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9059-9072页

作者： Priambodo, Bagus Ahmad, Azlina Kadir, Rabiah Abdul Univ Kebangsaan Malaysia IIR4 0 Bangi 43600 Selangor Malaysia Univ Mercu Buana Fac Comp Sci Jakarta Indonesia

Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having 'high relationship' with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only.

关键词： Classification algorithm clustering algorithm machine learning algorithm nearest neighbour search intelligent transportation system

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Hybrid FCMG-OP-FIS model approach to convert regression into classification data for machine learning-based AQI prediction

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Heliyon 2024年第21期10卷 e39759页

作者： Ordenshiya, K.M. Revathi, G.K. Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Chennai Tamilnadu Chennai 600127 India

Air pollution from vehicle emissions, industrial activities, and medical facilities poses significant health risks in urban areas, underscoring the necessity for robust air quality index (AQI) monitoring. This paper presents a novel method for AQI prediction by integrating a fuzzy centre merge graph with an optimal value-based fuzzy inference system (FCMG-OP-FIS) and machine learning (ML). Traditional ML techniques encounter difficulties when converting regression datasets into classification formats, particularly when unable to label the dataset using the traditional method. The proposed FCMG-OP-FIS model efficiently converts regression data into a classification framework. Unlike traditional AQI prediction methods that rely solely on pollutant data, this approach incorporates both pollutant and meteorological data to improve prediction accuracy. The innovative fuzzy centre merge graph (FCMG) balances the dataset for optimal solutions and facilitates input grouping for Simulink, simplifying rule management. The FCMG-OP-FIS model generates a regression output for AQI, which is subsequently classified into levels (healthy, moderate, or unhealthy) using IF-THEN rules. To enhance accuracy further, a random forest classifier (RFC) is trained on the FCMG-OP-FIS classified output data. The regression output of the FCMG-OP-FIS model is validated using metrics such as RMSE (0.48), MSE (0.23), MAE (0.23), and MAPE (1.77%). Additionally, the classification output from the RFC model employs advanced validation techniques including stratified shuffle validation, grid search cross-validation, and confusion matrix analysis, achieving an accuracy rate of 99%, with the F1 score, precision, and recall over all at 99%. These results demonstrate the effectiveness of the proposed model in accurately labelling data for classification and predicting AQI through ML, highlighting its potential for practical application in environmental monitoring and management. © 2024 The Author(s)

关键词： Air quality index Fuzzy centre merge graph Fuzzy graph Fuzzy inference system machine learning algorithm Optimal solution and simulink

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Predicting Available Expert Developer for newly Reported Bugs using machine learning algorithms 5

Predicting Available Expert Developer for newly Reported Bug...

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IEEE 5th International Conference for Convergence in Technology (I2CT)

作者： Sawarkar, Rucha Nagwani, Naresh Kumar Kumar, Sanjay NIT Raipur Dept Informat Technol Chhattisgarh India NIT Raipur Dept Comp Sci Chhattisgarh India

ISBN: (纸本)9781538680759

Bug triaging is a concept of assigning newly reported bug to the appropriate developer. This task of assigning bug to developer is done by manager of the project. As number of reported bugs increases this task becomes tedious because manager should have knowledge of efficiency of all developers. In order to overcome from this problem, an automated bug triaging system is introduced using machine learning algorithm. In existing papers, it has been applied on dataset without considering developers availability to solve newly assigned bugs. In this paper a new algorithm is proposed using existing machine learning algorithm to analysis the text data of a bug repository independent of developer. An extensive analysis is done on natural language processing for analysis of text data to estimate accuracy, precision, recalls and F-score using different machine learning algorithm.

关键词： Software bug repositories machine learning algorithm Natural language processing

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Post-Transplant Liver Monitoring Utilizing Integrated Surface-Enhanced Raman and AI in Hepatic Ischemia-Reperfusion Injury Animal Model

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INTERNATIONAL JOURNAL OF NANOMEDICINE 2025年 20卷 6743-6755页

作者： Lee, Sanghwa Kwon, Hyunhee Oh, Jeongmin Kim, Kyeong Ryeol Hwang, Joonseup Kang, Suyeon Lee, Kwanhee Namgoong, Jung-Man Kim, Jun Ki Asan Med Ctr Biomed Engn Res Ctr Seoul 05505 South Korea Univ Ulsan Coll Med Asan Med Ctr Dept Surg Seoul 05505 South Korea Univ Ulsan Coll Med Dept Convergence Med Seoul 05505 South Korea

Background: While liver transplantation saves lives from irreversible liver damage, it poses challenges such as graft dysfunction due to factors like ischemia-reperfusion (IR) injury, which can lead to significant cellular damage and systemic complications. Current diagnostic tools for detecting IR injury have limitations, necessitating advanced methods for timely intervention. This study explores the integration of surface-enhanced Raman spectroscopy (SERS) with artificial intelligence (AI) to improve diagnostic accuracy for liver IR injury. Materials and Methods: IR injury was induced using a mouse model, and histopathological and hepatic functional evaluations were conducted alongside SERS measurements. Raman signals obtained via SERS chips, which selectively filter nano-biomarkers and enhance signals, were analyzed using machine learning algorithms. Results: The PC-LDA derived from spectra achieved an accuracy of 93.13%, while a machine learning algorithm based on PC-derived PLS-DA improved accuracy to 98.75%. Discussion: Our findings emphasize the potential of combining SERS with AI to detect and specifically identify dysfunction due to liver damage early, potentially advancing patient management in liver transplantation.

关键词： liver ischemia-reperfusion injury liver function surface-enhanced Raman spectroscopy discriminant analysis machine learning algorithm

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