Forecasting of HOSR for Different Mobile Carriers in Kano Using Conventional and Intelligent Techniques

Abdullahi, Sunusi Bala

doi:10.17781/p002599

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANFIS is an AI approach, composing Artificial Neural Network (ANN) and fuzzy inference networks (FIS) as a single ANFIS model, leveraging individual limitations of ANN and FIS methods [25], [26]. One major advantage of ANFIS is its ability to make good representation of complex and non-linear connections among features [25]. In addition, its soft computing and rapid convergence raised the interest of using ANFIS in prediction of complicated relationships [27]- [30].…”

Section: A Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

et al. 2022

View full text Add to dashboard Cite

Addressing crime detection, cyber security and multi-modal gaze estimation in biometric information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms such as Support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) have been proposed to recognize distinct and discriminant features of biometric information (intrinsic hand features and demographic cues) with good classification accuracy. Unfortunately, due to nonlinearity in distinct and discriminant features of biometric information, accuracy of SVM and ANFIS is reduced. As a result, optimized AI algorithms ((ANFIS) with subtractive clustering (ANFIS-SC) and SVM with error correction output code (SVM-ECOC)) have shown to be effective for biometric information recognition. In this paper, we compare the performance of the ANFIS-SC and SVM-ECOC algorithms in their effectiveness at learning essential characteristics of intrinsic hand features and demographic cues based on Pearson correlation coefficient (PCC) feature selection. Furthermore, the accuracy of these algorithms are presented, and their recognition performances are evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), scatter index (SI), mean absolute deviation (MAD), coefficient of determination (R 2 ), Akaike's Information Criterion (AICc) and Nash-Sutcliffe model efficiency index (NSE). Evaluation results show that both SVM-ECOC and ANFIS-SC algorithms are suitable for accurately recognizing soft biometric information on basis of intrinsic hand measurements and demographic cues. Moreover, comparison results demonstrated that ANFIS-SC algorithms can provide better recognition accuracy, with RMSE, AICc, MAPE, R 2 and NSE values of ≤ 3.85, 2.39E+02, 0.18%, ≥ 0.99 and ≥ 99, respectively.

show abstract

Section: A Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Our method adopted artificial intelligence (AI) approach, which is the hybridization of Artificial Neural Network (ANN) and fuzzy logic [17] networks as one Adaptive Neuro fuzzy inference System (ANFIS) model, to make up the FIGURE 1: Procedures of the proposed method shortfalls of the ANN and fuzzy inference methods. One benefit of this architecture is a reliable representation of sophisticated non-linear relationships among unions.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…Emergence of Adaptive neuro fuzzy inference system (ANFIS) tries to overcome these hindrances using trial and error learning. ANFIS is a machine learning approach, with hybridization of Artificial Neural Network (ANN) and fuzzy [17] logic networks to establish Adaptive neuro fuzzy inference System model, to make up the shortfalls of ANN and fuzzy inference methods. One benefit of ANFIS architecture is reliable representation of sophisticated non-linear relationships among unions of real life cases [16], [18], [19], and conquers disadvantages of classical approaches.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

Abdullahi¹,

Muangchoo

Abubakar

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some points through trial and error procedures. In this work, we tune ANFIS using Grid partition algorithm to handle unseen data effectively with fast convergence. This model is initialized using a careful selection of effective parameters that discriminate climate conditions; minimum temperature, maximum temperature, average temperature, wind speed and relative humidity. These parameters are used as inputs for ANFIS, whereas confirmed cases of COVID-19 is chosen as dependent values for two consecutive months and first ten days of December for new COVID-19 confirmed cases according to the Department of disease control (DDC) Thailand. The proposed ANFIS model provides outstanding achievement to predict confirmed cases of COVID-19 with R 2 of 0.99. Furthermore, data set trend analysis is done to compare fluctuations of daily climatic parameters, to satisfy our proposition, and illustrates the serious effect of these parameters on COVID-19 epidemic virus spread.

show abstract

Intelligent Fuzzy Network for Dynamic Sign Words Recognition from Spatial Features

Abdullahi

Chamnongthai

2022

2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

View full text Add to dashboard Cite

Forecasting of HOSR for Different Mobile Carriers in Kano Using Conventional and Intelligent Techniques

Cited by 3 publications

References 0 publications

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

Intelligent Fuzzy Network for Dynamic Sign Words Recognition from Spatial Features

Contact Info

Product

Resources

About