Assessing Community Awareness of Water Infrastructure Encroachment Risks in Makause Informal Settlement, City of Ekurhuleni | Chapter 5 | Current Research on Geography, Earth Science and Environment Vol. 4

 

The management of the bulk water infrastructure is a critical aspect of urban resilience, particularly as cities expand and informal settlements increasingly encroach upon essential services. Encroachment presents significant threats to water supply systems, elevating the risks of infrastructural damage, contamination, and service disruptions. This study examines the risk of informal settlement encroachment on critical water infrastructure in the Makause informal settlement. It aims to identify the key factors influencing encroachment and to develop predictive models that support proactive, community-based infrastructure protection. A mixed quantitative–computational approach was employed, using survey data from 105 residents. Descriptive statistics and one-way ANOVA were applied to evaluate differences across categorical responses (“Yes”, “No”, “Unsure”). The ReliefF algorithm was used to rank variable importance in predicting encroachment risk. Key predictors were then used to train, validate, and test an artificial neural network (ANN) model to assess its suitability for risk forecasting. The ANN achieved high predictive accuracy, with correlation coefficients exceeding 0.95 and low mean squared error values across all modelling phases. ANOVA results confirmed statistically significant differences among selected variables. ReliefF identified community awareness, settlement proximity, and resource access as the most influential predictors. Model validation showed strong agreement between predicted and actual outcomes (p > 0.900), confirming robustness and reliability. This study proposes a novel, data-driven framework that integrates machine learning and statistical analysis for infrastructure risk assessment in informal settlements. It demonstrates how community-generated data can be combined with computational techniques to strengthen urban infrastructure management. The framework offers municipalities and water utilities a practical tool for engaging communities, prioritising interventions, and improving protection of critical infrastructure in rapidly urbanising environments. Results are based on a single case study in Makause and may reflect self-reporting bias. A broader application would require additional case studies and expanded datasets.

 

 

Author(s) Details

Mpondomise Nkosinathi Ndawo
Management College of Southern Africa, MANCOSA, Research Directorate, 26 Samora Machel Street, Durban 4001, South Africa.

 

Stephen Loh Tangwe
Central University of Technology, Resources and Operations Division, Bloemfontein 9301, Free State Province, South Africa.

 

Please see the book here :- https://doi.org/10.9734/bpi/crgese/v4/6697

 

Machine-Learning-Based Compact Modeling and Model Parameter Extraction for Sub-3-nm-Node Emerging Transistors | Chapter 7 | Current Approaches in Engineering Research and Technology Vol. 9

In this paper, we present an artificial neural network (ANN)-based compact model for evaluating the characteristics of nanosheet field-effect transistors (NSFETs), a promising next-generation nano-device. Using the Sentaurus TCAD simulator,  data was extracted that accurately reflects the physical characteristics of NSFETs. The ANN model, implemented in Verilog-A, predicts device currents and capacitances with high accuracy and efficiency using five key geometric parameters and two voltage biases. Extensive experiments demonstrate for the first time that the proposed model is several times faster and more accurate than existing compact models, significantly reducing compact model development, model parameter extraction, and circuit simulation time while maintaining high precision. This advancement has the potential to accelerate the design of sub-3-nm transistors, thereby lowering development costs and time. In particular, while the industry standard compact model typically requires 7-8 weeks to extract SPICE model parameters for a designed device, the proposed ANN-based compact modeling approach can provide a SPICE model within a day. Moreover, even in the absence of physics-based compact model development for next-generation semiconductors, this data-driven ANN-based compact modeling approach offers a less technology-dependent solution, providing real-time SPICE models for circuit simulations. Future work will explore the application of this model to other emerging transistor technologies, the further optimization of simulation speed and accuracy, and design technology co-optimization (DTCO) capabilities.

 

Author (s) Details

 

SangMin Woo
College of Information and Communication Engineering, SungKyunKwan University, Suwon -16419, South Korea.

 

HyunJoon Jeong
College of Information and Communication Engineering, SungKyunKwan University, Suwon -16419, South Korea.

 

JinYoung Choi
College of Information and Communication Engineering, SungKyunKwan University, Suwon -16419, South Korea.

 

HyungMin Cho
College of Information and Communication Engineering, SungKyunKwan University, Suwon -16419, South Korea.

 

Jeong-Taek Kong
College of Information and Communication Engineering, SungKyunKwan University, Suwon -16419, South Korea.

 

SoYoung Kim
College of Information and Communication Engineering, SungKyunKwan University, Suwon -16419, South Korea.

 

Please see the book here:- https://doi.org/10.9734/bpi/caert/v9/2557

Study of Dynamic Adsorption from Wastewater into Mineral Activated Carbon Using Artificial Neural Network | Chapter 9 |Current Approaches in Engineering Research and Technology Vol. 10

Water bodies are the most common natural resources that have been contaminated as a result of different human activities. Actually, in industries various methods of treatment of wastewater and many adsorbent materials used for purification of effluents have existed. An artificial neural network (ANN) was used in this study to predict the removal of sodium decanesulfonate using actived carbon obtained by the calcination of mineral biomass under different conditions. The structure of [3-3-1] was obtained and given a good correlation coefficient (R² = 0.9965) with root mean squared error (RMSE = 0.0276). For the stage of interpolation and extrapolation, the results present a high correlation coefficient close to 1 which provides the robustness and the high capacity of ANN developed model.

 

Author (s) Details

 

Sediri Meriem
Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Médéa, 26000, Médéa, Algeria.

 

Hanini Salah
Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Médéa, 26000, Médéa, Algeria.

 

Please see the book here:- https://doi.org/10.9734/bpi/caert/v10/2974

 

Benchmarking ANN Setup Options for Trade-Off Functions of Heat Transferring Baffle Inserted Tubes | Chapter 3 | Engineering Research: Perspectives on Recent Advances Vol. 1

Artificial Neural Networks (ANNs) have been widely used for thermal analysis of heat exchangers during the last two decades. This present research uses artificial neural networks (ANNs) to determine Nusselt numbers and friction factors for nine different baffle plate-inserted tubes. MATLAB toolbox was used to search for better network configuration prediction by using commonly used multilayer feed-forward neural networks (MLFNN) with backpropagation (BP) learning algorithm with five different training functions with adaptation learning function of mean square error and TANSIG transfer function. In this research, eighteen data samples were used in a series of runs for each of nine samples of baffle-inserted tube. The uncertainties of experimental quantities were computed by using the method presented. The uncertainty calculation method used involves calculating derivatives of the desired variable with respect to individual experimental quantities and applying known uncertainties. Up to 70% of the whole experimental data was used to train the models, 15% was used to test the outputs and the remaining data points which were not used for training were used to evaluate the validity of the ANNs. The results show that the TRAINBR training function was the best model for predicting the target experimental outputs.  The study concluded that artificial neural network methodology has been successfully applied to transient forced convective heat transfer to determine the time-averaged values of Nusselt number, friction factor, entropy generation number and irreversibility distribution ratio. It is obvious that all of the training functions are in good agreement with the experimental data set but the TRAINLM training function is the best training function for the prediction of output layer parameters.

 

Author (s) Details

Ahmet Tandiroglu
Department of Mechanical Engineering Technology, Vocational High School of Erzincan, Erzincan University, Turkey.

 

Please see the book here:- https://doi.org/10.9734/bpi/erpra/v1/2702

The Prospect of Artificial Intelligence in Dentistry | Chapter 13 | Medical Research and Its Applications Vol. 6

 Artificial intelligence (AI) is one of the fields of technology that is expanding tremendously in a short span of time. AI in the healthcare sector is evolving and has a promising future. Key applications in dentistry include patient care, diagnostics and treatment guiding, and administrative tasks. As a result, this AI system enables dentists to become acquainted with this cutting-edge technology, as dentistry will soon combine artificial intelligence and dentistry in an incredible way. The need for accurate patient records, and dependable robotic surgery treatment has increased the use of these software technologies in helping dentists diagnose and treat patients in a way that is both satisfying and practical. This chapter aims to highlight the role of artificial intelligence in dentistry, yet technical advancements like this are still in their early stages.

 

Author(s)details:-

 

Madhulika Banerjee
Department of Periodontics, Vishnu Dental College & Hospital, Bhimavaram, Andhra Pradesh- 534202, India.

 

Meetika
Baba Farid University of Health Sciences, Faridkot, India.

 

Rajat Sharma
Department of Orthodontics, Maharaja Ganga Singh Dental College & Research Centre, Sriganganagar, Rajasthan, India.

 

Deepshikha Gahlot
Maharaja Ganga Singh Dental College & Research Centre, Sriganganagar, Rajasthan, India.

 

Ramanjeet Kaur Grover
Department of Prosthodontics, Maharaja Ganga Singh Dental College & Research Centre, Sriganganagar, Rajasthan, India.

 

Komal Sihani
Department of Periodontics, Triveni Institute of Dental Sciences Hospital & Research Centre, Bilaspur, Bodhri, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/mria/v6/762

Classification Offered by the Artificial Neural Networks (ANNs) for the Patient Information: An Overview | Chapter 6 | New Visions in Medicine and Medical Science Vol. 6

 

Purpose: Study of the classification offered by the artificial neural networks (ANNs) for the “Patient Information” variable in the “Not” category in all data groups: Training, Testing and Holdout.

Background: Informed consent is a principle of medical ethics, medical law and media studies because a patient must have sufficient information and understanding before making decisions on their medical care. Pertinent information may include risks and benefits of treatments, alternative treatments, the patient’s role in treatment, and their right to refuse treatment.

Methods: This study collects data from hospitals in the Burgos University Hospital, Spain, for two years, configuring a data file with 647 cases and 9 variables, 7 of them referring to the attitude to Informed consent, Sex and Age. We perform a descriptive analysis to have information about the variables that make up the classification/prediction model (Artificial Neural Network), and how the data are distributed by category (“Yes” and “Not”) of the “Patient Information” variable.

Results: The structure of the most efficient artificial neural network found in the classification of the categories of the “Patient Information” variable (“Yes” and “Not” categories) is the binomial Hidden layer-Output layer: Hyperbolic tangent-Softmax Dependent variable: (“Patient Information”; Partition: Training 60%, Testing 20% and Holdout 20%).

Conclusions: The classification/prediction of the “Patient Information” variable using the artificial neural network, perceptron, offers us the low classification/prediction of the “Not” category, which is the object of this study.  An empirical investigation demonstrates that adding a new covariant variable, like "Consultation time," to the network enhances the classification of the "Not" category.

Author(s) Details:

Elena Martín Pérez,
Institute of Forensic and Legal Medicine of Zamora, Spain.

Jacobo Salvat Dávila
Orthopedic Surgery and Traumatology Service, University Hospital of Burgos, Spain.

 

Quintín Martín Martín
Department of Statistics, University of Salamanca, Spain.

Please see the link here: https://stm.bookpi.org/NVMMS-V6/article/view/14282

Reliability of Artificial Neural Network Approach for Predicting the Performance of Transient Forced Convective Heat Transfer Applications | Chapter 8 | Theory and Applications of Engineering Research Vol. 8

 Artificial Neural Networks (ANNs) have been successfully used in many engineering applications to simulate nonlinear complex systems without requiring any input and output knowledge such as dynamic control, system identification and performance prediction of thermal systems in heat transfer applications. This present research uses artificial neural networks (ANNs) to analyze and estimate the influence of transfer functions and training algorithms on experimentally determined Nusselt numbers, friction factors, entropy generation numbers and irreversibility distribution ratios for nine different baffle plate inserted tubes. Nine baffle-inserted tubes have several baffles with various geometric parameters used in the experiments with a baffle area blockage ratio of two, with different pitch-to-diameter ratios, different baffle orientation angles and different baffle spacings. The experimental set-up of this study consists of three parts flow entrance section, flow development section, test section and flow exit section. The actual experimental data sets were used from previous author’s studies and applied as an input data set of ANNs. MATLAB toolbox was used to search for better network configuration prediction by using commonly used multilayer feed-forward neural networks (MLFNN) with backpropagation (BP) learning algorithm with thirteen different training functions with adaptation learning function of mean square error and TANSIG transfer function. In this research, eighteen data samples were used in a series of runs for each of nine samples of baffle-inserted tubes. Reynold number, tube length to baffle spacing ratio, baffle orientation angle and pitch-to-diameter ratio were considered as input variables of ANNs and the time-averaged values of Nusselt number, friction factor, entropy generation number and irreversibility distribution ratio were determined as the target data. Up to 70% of the whole experimental data was used to train the models, 15% was used to test the outputs and the remaining data points which were not used for training were used to evaluate the validity of the ANNs. The results show that the TRAINBR training function was the best model for predicting the target experimental outputs. Almost perfect accuracy between the neural network predictions and experimental data was achieved with a mean relative error (MRE) of 0,000105816% and correlation coefficient (R) that was 0,999160176 for all datasets, which suggests the reliability of the ANNs as a strong tool for predicting the performance of transient forced convective heat transfer applications.

Author(s) Details:

Ahmet Tandiroglu,
Department of Mechanical Engineering, Engineering Faculty, Erzincan Binali Yildirim University, 24109, Erzincan, Turkey.

Please see the link here: https://stm.bookpi.org/TAER-V8/article/view/13821

Using Artificial Neural Network to Discriminate Parkinson’s Disease from other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images: A Retrospective Study | Chapter 2 | Research Developments in Medicine and Medical Science Vol. 5

 It is still troublesome to distinguish Parkinson's disease from nervous system disorder brought on by other disorders at an beginning. To process images from dopamine bearer single-photon emission computerized axial tomography scanner, we used an pretended neural network (ANN) (DAT-SPECT). Training and test sets of abnormal DAT-SPECT figures of individuals accompanying Parkinson's disease and parkinsonism brought on by additional disorders were created. To identify Parkinson's disease from parkinsonism brought on by added disorders, the striatal regions of the representations were segmented using an alive contour model and secondhand as the data for transfer learning on a pre-prepared ANN. For comparison, a support heading machine was prepared using semi-determinable measurement limits such as the specific binding percentage and asymmetry index. The ANN classifier's predicting accuracy (86%) was higher than the support heading machine classifier's (68%). The ANN classifier had a feeling and specificity of 81.8% and 88.6%, respectively, in concluding Parkinson's disease. The ANN classifier performed better than established biomarkers at distinguishing between Parkinson's disease and nervous system disorder brought on by added disorders. At an early stage of the parkinsonian disorders, ANN can be a hopeful biomarker, particularly when it concentrates on the putamen.

Author(s) Details:

Chung-Yao Chien,
Department of Biomedical Engineering, National Cheng Kung University, Tainan 704, Taiwan and Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.

Szu-Wei Hsu,
Department of Nuclear Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.

Tsung-Lin Lee,
Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.

Pi-Shan Sung,
Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.

Chou-Ching Lin,
Department of Biomedical Engineering, National Cheng Kung University, Tainan 704, Taiwan and Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.

Please see the link here: https://stm.bookpi.org/RDMMS-V5/article/view/9885

Computational Rainfall Forecasting Models | Chapter 03 | Recent Recent Advances in Mathematical Research and Computer Science Vol. 8

 Rainfall forecasting is critical for planning and understanding rainfall variability, which aids agricultural management in making decisions. Rainfall forecasting is a key scientific concern in meteorology. Many academics sought to develop linear models to predict rainfall, but the revelation of nonlinearity in the nature of weather data has changed the attention to nonlinear rainfall prediction. Through a simple procedure utilising historical data, neural networks can automatically construct a forecasting model. This type of training allows the neural system to capture complex and non-linear correlations that are difficult to examine using traditional approaches. Neural networks are a novel type of computational technology that can be used to investigate the dynamics of a wide range of hydrological applications. An Artificial Neural Network (ANN) is a massively parallel, distributed information processing system with a highly flexible architecture that captures nonlinearity well. In hydrologic modelling, Artificial Neural Networks with one hidden layer are often utilised. The standard data pre-processing approach of Moving Average (MA) was combined with Artificial Neural Network as MA – ANN model in this research study to improve rainfall forecast in Tamilnadu. The MA – ANN hybrid model is a better tool than the Moving Average and ANN models when used separately, according to the experimental results.

Author(S) Details

M. Nirmala
Sathyabama Institute of Science and Technology, Chennai, India.

View Book:- https://stm.bookpi.org/RAMRCS-V8/article/view/5743

Forecasting of Losses Due to Pod Borer, Pod Fly and Yield of Pigeonpea (Cajanus cajan) for Central Zone (CZ) of India by Using Artificial Neural Network | Chapter 08 | Current Topics in Agricultural Sciences Vol. 5

 Pigeonpea (Cajanus cajan L.) is a valuable food legume that may be cultivated with minimal inputs under rainfed conditions. Starch, protein, calcium, manganese, crude fibre, fat, trace elements, and minerals abound in pigeonpea. The need of having a timely forecast of productivity and pod damage caused by key insect-pests in pigeonpea has become a serious issue due to high domestic consumption and large losses due to major insect-pests. The built Artificial Neural Network (ANN) model for forecasting productivity (Kg/ha.) and percent pod damage by two important insect-pests, Helicoverpa armigera and Melanagromyza obtusa, of medium ripening pigeonpea in the Central Zone (CZ) of India, is reported in this paper. The model's performance was evaluated using mean squared error values, and it was determined to be suitable for the task at hand.

Author(S) Details 

Prity Kumari
Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.

G. C. Mishra
Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.

C. P. Srivastava
Department of Entomology and Agricultural Zoology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.


View Book:- https://stm.bookpi.org/CTAS-V5/article/view/5261

Compound Stratum Practice | Book Publisher International

 A person's face can reveal a lot of information about them, including their age, gender, and identity. Faces play a crucial part in estimating and predicting a person's age and gender simply by looking at them. Computer vision and psychophysics researchers encounter issues such as perceiving human faces and modelling the specific aspects of human faces that contribute most to face recognition. Many strategies for age and gender classification based on face features have been proposed in the literature.

In this book, feed forward propagation Neural Networks at the coarser level are used to categorise human age and gender. In the Compound level, the final classification is done utilising 3-sigma control limits. The proposed method effectively distinguishes three age groups: children, middle-aged adults, and senior citizens. Similarly, the proposed Compound Stratum technique defined two gender groupings as Male and Female.

Compound Stratum Practice with 3 Sigma Control Limits applied to the output of the Artificial Neural Network classifier improves the system's performance even further. On the output given by the Neural Network classifier, the Mean and Standard Deviation were considered, and 3 sigma control limits were used to set the range of values for the specific category of age and gender. The system's efficiency is proved by the experimental results obtained utilising benchmark database images.

Author(S) Details

M. R. Dileep
Department of Master of Computer Applications, Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India.

View Book:- https://stm.bookpi.org/CSP/article/view/4435

A Comparison of the Performance of Artificial Neural Network Algorithms in Facial Expression Recognition | Chapter 1 | Current Approaches in Science and Technology Research Vol. 12

In this study, the methodologies for distinguishing facial expressions are described. The purpose of this research is to show how to train the Single-Layer Neural Network (SLN), Back Propagation Algorithm (BPA), and Cerebellar Model Articulation Controller (CMAC) for identifying facial expressions using a texture-oriented strategy combined with dimensional reduction. Because the proposed approaches can account for changes in face expressions and so perform better for untrained facial expressions, they are referred to as intelligent methods. Face expressions must follow to particular rules, which limits traditional methods. To achieve expression identification accuracy, the Gabor wavelet is used from various perspectives to extract probable textures of the face expression. To improve the accuracy of the suggested method, the higher dimensions of the collected texture characteristics are further decreased using Fisher's linear discriminant function. Fisher's linear discriminant function is used to convert a higher-dimensional feature vector into a two-dimensional vector for training the proposed algorithms. Some of the facial emotions employed are anger, disgust, happiness, sadness, surprise, and fear. The performance of the proposed algorithms is compared.

Author (S) Details

Amira Elsir Tayfour
King Khalid University, Saudi Arabia.

Altahir Mohammed
Sudan University of Science & Technologies, Sudan.

Moawia Elfaki Eldow
University of Khartoum, Sudan.

View Book :- https://stm.bookpi.org/CASTR-V12/article/view/2593

Recent Development of Intelligent Shunt Fault Classifier for Nigeria 33-kV Power Lines | Chapter 9 | Newest Updates in Physical Science Research Vol. 9

 This research describes a novel way to employing artificial neural networks (ANNs) to improve transmission line protection. The suggested technique feeds four different neural network structures instantaneous voltages and currents on a transmission line during normal and fault conditions. The structures are then expertly merged to provide a system that can more effectively detect and diagnose shunt problems. The report goes into great detail about the design process as well as the many simulations that were run. The accuracy and mean square error (MSE) of the created system are examined, and the findings reveal that this approach is capable of identifying and classifying all probable shunt faults on the 33-kV Nigeria power lines in less than 1ms with a high level of precision. When evaluated under various shunt fault types with varying resistances and distances, the system's performance demonstrates that it can be used to improve distance line protection in 33-kV Nigeria power lines.

Author (s) Details

A. A. Awelewa
Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria.

P. O. Mbamaluikem
Department of Electrical/Electronics Engineering, Federal Polytechnic Ilaro, Ilaro, Ogun State, Nigeria.

View Book :- https://stm.bookpi.org/NUPSR-V9/article/view/1940

Probabilistic Fatigue Assessment for Complex Engineering Structures with Time-dependent Surrogate Modelling | Chapter 4 | Recent Developments in Engineering Research Vol. 12

 Thousands of simulations with nonlinear characteristics and hyperparameters are required for the dynamic probabilistic analysis of complex engineering structures, indicating that unacceptable computational loads exist. In this case, the efficiency and accuracy of complex structural dynamic probabilistic analysis are directly determined by the model's performance. We present a time-dependent particle swarm optimization (PSO)-based general regression neural network (GRNN) surrogate model (TD/PSO-GRNN) that integrates the proposed space-filling Latin hypercube to improve the computational efficiency and accuracy of probabilistic fatigue life prediction for complex structures. PSO-GRNN regression function and sampling technique The related theory and method of the TD/PSOGRNN model are first thoroughly investigated. The probabilistic fatigue life prediction framework is then demonstrated using the TD/PSO-GRNN surrogate model. Furthermore, the TD/PSO-GRNN model is validated by performing a probabilistic fatigue life prediction of an aircraft turbine blisk under multi-physics interaction. We obtain the fatigue failure cycle's distributional characteristics, reliability degree, and sensitivity degree, which are useful for turbine blisk design. By contrasting the direct and The TD/PSO-GRNN surrogate model is promising for performing the probabilistic fatigue life prediction of the turbine blisk with high computational efficiency and acceptable computational accuracy, as demonstrated by simulation (FE/FV model), RSM, GRNN, PSO-GRNN, and TD/PSO-GRNN. This study's efforts provide useful insight for the probabilistic design optimization of complex structures, as well as enrich mechanical reliability theory and methods.

Author(s) Details

Chengwei Fei
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, P.R. China.

Lei Han
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, P.R. China.

Cheng Lu
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, P.R. China.

Yan Hu
Commercial Aircraft Engine Co., Ltd, AECC, Shanghai 200241, P.R. China.

Bo Huang
Commercial Aircraft Engine Co., Ltd, AECC, Shanghai 200241, P.R. China.

Liu Yuan
Commercial Aircraft Engine Co., Ltd, AECC, Shanghai 200241, P.R. China.

View Book :-  https://stm.bookpi.org/RDER-V12/article/view/1481

An Approach of Short Term Road Traffic Flow Forecasting Using Artificial Neural Network | Chapter 13 | Recent Studies in Mathematics and Computer Science Vol. 2

In recent days, road traffic management and congestion control has become major problems in any busy junction in Hyderabad city. Hence short term traffic flow forecasting has gained greater importance in Intelligent Transport System (ITS). Artificial Neural Network (ANN) models have been fruitfully applied for classification and prediction of time series. In this chapter, an attempt has been made to model and forecast short-term traffic flow at 6.no. junction in Amberpet, Hyderabad, Telangana state, India applying Neural Network models. The traffic data has been considered for peak hours in the morning for 8A.M to 12 Noon, for 5 days. Multilayer Perceptron (MLP) network model is used in this study. These results can be considered to monitor traffic signals and explore methods to avoid congestion at that junction.

Author(s) Details

V. Sumalatha 
Department of Statistics, OSMANIA University, Hyderabad, India.

Manohar Dingari
Department of Mathematics School of Technology, GITAM University, Hyderabad 502329, India.

Prof. C. Jayalakshmi
Department of Statistics, OSMANIA University, Hyderabad, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/182