A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

Gollapalli, Mohammed; Rahman, Atta; Musleh, Dhiaa; Ibrahim, N. S.; Khan, Muhammad Adnan; Abbas, Sagheer; Atta, Ayesha; Khan, Muhammad Murtaza; Farooqui, Mehwash; Iqbal, Tahir; Ahmed, Mohammed Salih; Ahmed, Mohammed Imran Basheer; Almoqbil, Dakheel; Nabeel, Majd; Omer, Abdullah Khalid

doi:10.32604/cmc.2022.027925

Cited by 19 publications

(14 citation statements)

References 25 publications

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“…Several other machine learning, deep learning and AI based techniques have been applied for disease predictions in the literature and it is among the hottest areas of research in healthcare and several other disciplines [17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ahmed¹,

Zaghdoud²,

Al-Abdulqader³

et al. 2023

MMEP

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Epilepsy is a chronic non-communicable illness that affects brain individuals and impacts more than 50 million people globally. To predict epileptic seizures, we proposed machine learning-based ensemble learning technique in this study. In the preprocessed stage, we applied some important techniques such as Power line noise reduction and dividing the record into windows of 5 seconds. The project is created by the help of ensemble machine learning technique, which employs several machine learning algorithms, we used the following algorithms: decision tree, support vector machine, artificial neural networks, and convolutional neural networks. We used a dataset from PhysioNet website that contains adult EEG signals. Several convolutional layers were used to extract features from the EEG signals, after that, the feature set is utilized to train a classifier model, which combines the results. Our approach successfully reached 91% accuracy while 91% sensitivity and 91% specificity, respectively.

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Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ahmed¹,

Zaghdoud²,

Al-Abdulqader³

et al. 2023

MMEP

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“…One may add or remove a few steps based on requirements. Heat maps and Pair plots are a great method to correlate the input of the data with the output [21,22].…”

Section: Data Synthesis and Preparationmentioning

confidence: 99%

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Kumar¹,

Pal²

2023

Computers, Materials &Amp; Continua

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Increasing energy demands due to factors such as population, globalization, and industrialization has led to increased challenges for existing energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable, cheap, and easily available sources of energy. Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions. But the integration of distributed energy sources and increase in electric demand enhance instability in the grid. Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid. Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control, reinforcement of the grid demand, and generation balancing with cost reduction. But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data. Machine learning and artificial intelligence have recognized more accurate and reliable load forecasting methods based on historical load data. The purpose of this study is to model the electrical load of Jajpur, Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression (GPR). The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past, current, and future dependent variables, factors, and the relationship among data. Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead. The study is very helpful in grid stability and peak load control management.

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“…Recent comparative study [72] shows that GROBID, CERMINE and ParsCit pose best results among the various open-source extractors. In future, the schemes involving deep learning models and other hybrid intelligent approaches such as federated learning and transfer learning [91][92][93][94][95][96][97][98][99][100] can be anticipated more successful and worthy to explore for the IE from published scholarly articles of diverse nature.…”

Section: Information Extraction Toolsmentioning

confidence: 99%

Assessment of Information Extraction Techniques, Models and Systems

Rahman¹,

Musleh²,

Nabil³

et al. 2022

MMEP

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The present article aims to review and evaluate the practiced and classical techniques, tools, models, and systems concerning automatic information extraction (IE) from published scientific documents like research articles, patents, theses, technical reports, and case studies etc. IE is performed for various reasons such as better indexing, archiving, searching, and retrieving. That is mainly used by the search engines and the indexing services as well the digital libraries and semantic web. In this regard, several studies have been conducted targeting various nature of documents. The study pays special consideration to the successful IE models, algorithms and approaches applied to structural IE from published documents. To grasp this, the paper is classified into several segments and each segment covers a significant aspect of IE. Furthermore, to validate their benefits and drawbacks, a comparative study of all the approaches have been conducted in terms of various performance factors like precision, accuracy, recall and F-score. Potential areas of improvement have been emphasized as research gap for the scholars in the closely related areas. Ultimately, a comprehensive summary of the evaluation is presented in tabular form and review is concluded. It was observed that the hybrid methods outperform the other methods due to their versatile nature to address various document formats.

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A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

Cited by 19 publications

References 25 publications

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ensemble Machine Learning Based Identification of Adult Epilepsy

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Assessment of Information Extraction Techniques, Models and Systems

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