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This scientific paper explores the use of correlation coefficients of vibration signals and machine learning algorithms for structural damage assessment in beams under moving loads. The paper discusses the challenges of maintaining structural integrity and the importance of automated, nondestructive monitoring techniques. Preprocessing techniques, such as the random decrement technique (RDT), are highlighted for improving data analysis. Machine learning algorithms are identified as valuable tools for structural damage assessment. The paper concludes by emphasizing the potential of machine learning in safeguarding critical infrastructures. The text also discusses trigger points and the vibration response of a slender beam under a moving load. An artificial neural network (ANN) is proposed as a computational model for identifying non-linear features. Experimental testing on a simulated bridge girder using accelerometers collected data to identify and locate damage in the beam. The ANN achieved high accuracy in detecting damage appearance and location, but further research is needed to improve accuracy in real-world situations. [Extracted from the article]

Today according to social media, the internet, Etc. Data is rapidly produced and occupies a large space in systems that have resulted in enormous data warehouses; the progress in information technology has significantly increased the speed and ease of data flow.text mining is one of the most important methods for extracting a useful model through extracting and adapting knowledge from data sets. However, many studies have been conducted based on the usage of deep learning for text processing and text mining issues.The idea and method of text mining are one of the fields that seek to extract useful information from unstructured textual data that is used very today. Deep learning and machine learning techniques in classification and text mining and their type are discussed in this paper as well. Neural networks of various kinds, namely, ANN, RNN, CNN, and LSTM, are the subject of study to select the best technique. In this study, we conducted a Systematic Literature Review to extract and associate the algorithms and features that have been used in this area. Based on our search criteria, we retrieved 130 relevant studies from electronic databases between 1997 and 2021; we have selected 43 studies for further analysis using inclusion and exclusion criteria in Section 3.2. According to this study, hybrid LSTM is the most widely used deep learning algorithm in these studies, and SVM in machine learning method high accuracy in result shown. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management has become a highly challenging issue for many countries at different levels of development since the growth of population and urbanisation has resulted in a large increase in MSW generation. It is difficult to forecast the solid waste generated and its characteristics due to the non-linear nature of the MSW system, hence the importance of introducing artificial intelligence (AI) techniques and machine learning (ML) methods. This paper discusses a systematic literature review (SLR) on the application of AI techniques in MSW management, including waste generation prediction, waste collection and transportation and waste treatment and final disposal. The study reviewed and analysed the research studies published between 2000 and 2021, and investigated the current challenges faced by researchers in implementing AI approaches in the MSW system. It was concluded that artificial neural networks are the most used approach in various MSW-related problems. However, the lack and reliability of data are limiting the advancement of AI techniques in this field. Additionally, most studies claimed that their results are accurate and can be implemented in real-life scenarios, with an absence of a clear baseline to assess the performance of the adopted approaches. The detailed gaps and future suggestions for AI techniques in MSW systems are also discussed for further research. [ABSTRACT FROM AUTHOR]

In recent years, artificial neural networks have been widely used in the fault diagnosis of rolling bearings. To realize real-time diagnosis with high accuracy of the fault of a rolling bearing, in this paper, a bearing fault diagnosis model was designed based on the combination of VMD and ANN, which ensures a higher fault prediction accuracy with less computational time. This paper works from two aspects, including fault feature extraction and neural network structural parameter optimization to obtain an ANN bearing fault diagnosis model with high performance. The raw vibration signals of 10 fault types were divided into training, verification and testing datasets by the random step increment slip method. The variational mode decomposition method was used to decompose the raw vibration signal into several intrinsic mode functions. A new definition of the energy of each intrinsic mode function based on discrete Fourier transform and information entropy method were used as the input for the artificial neural network. Furthermore, the structural parameters of the artificial neural network were designed to obtain a high-performance neural network. The artificial neural network used in this paper had three hidden layers and 13 neurons in each hidden layer. Compared with several machine and deep learning algorithms, the artificial neural network can better fulfill the classification task of rolling bearing fault types with a mean prediction accuracy of 99.3% and computation time of 2.4 s based on a small training dataset. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the structure and function of the human brain. Their popularity has increased in recent years due to their ability to learn and improve through experience, making them suitable for a wide range of applications. ANNs are often used as part of deep learning, which enables them to learn, transfer knowledge, make predictions, and take action. This paper aims to provide a comprehensive understanding of ANNs and explore potential directions for future research. To achieve this, the paper analyzes 10,661 articles and 35,973 keywords from various journals using a text-mining approach. The results of the analysis show that there is a high level of interest in topics related to machine learning, deep learning, and ANNs and that research in this field is increasingly focusing on areas such as optimization techniques, feature extraction and selection, and clustering. The study presented in this paper is motivated by the need for a framework to guide the continued study and development of ANNs. By providing insights into the current state of research on ANNs, this paper aims to promote a deeper understanding of ANNs and to facilitate the development of new techniques and applications for ANNs in the future. [ABSTRACT FROM AUTHOR]

The rising development of power systems and smart grids calls for advanced fault diagnosis techniques to prevent undesired interruptions and expenses. One of the most important part of such systems is transmission lines. This paper presents a survey on recent machine learning-based techniques for fault detection, classification, and location estimation in transmission lines. In order to provide reliable and resilient electrical power energy, faster and more accurate fault identification tools are required. Costly consequences of probable faults motivate the need for immediate actions to detect them using intelligent methods, especially emerging machine learning approaches that are powerful in solving diagnosis problems. This paper presents a comprehensive review of various machine learning methodologies including naive Bayesian classifier, decision tree, random forest, k-nearest neighbor, and support vector machine as well as artificial neural networks such as feedforward neural network, convolutional neural network, and adaptive neuro-fuzzy inference system that have been used to detect, classify, and locate faults in transmission lines. [ABSTRACT FROM AUTHOR]

Energy is essential to sustain life and development and to fulfill daily requirements. A clean source of energy like the Solar Chimney Power Plant (SCPP) is essential or hybridized with conventional sources to meet the demand. Also, it will provide energy during the night and it will reduce the need and expense of physical batteries in comparison to the photovoltaic energy storage system. In this paper, a mathematical study has been done for the generation of 10 MW power from the SCPP. This paper optimized the core parameter of SCPP such as chimney height and collector radius under different operating conditions. It has been evaluated on the basis of data produced by the developed mathematical model. The current research work highlights the modern application of state‐of‐the‐art Deep Learning (DL) techniques that is, Functional Link Convolutional Neural Network (FLCNN) to map developed mathematical models. The proposed FLCNN is compared with various other Machine Learning algorithms including Support Vector Regression, K‐Nearest Neighbors, Random Forest, Naïve Bayes, and Multilayer Perceptron. Proposed FLCNN performed efficiently and achieved 91% and 93% of Adjusted R2 score for predicting values of collector radius and chimney height, respectively, which is comparable to other ML algorithms. [ABSTRACT FROM AUTHOR]

Geological studies rely heavily on facies classification since it offers vital information for reservoir characterization and hydrocarbon exploitation. Because facies are inherently complex and heterogeneous, traditional approaches frequently struggle to categorize them effectively. Artificial Neural Networks (ANNs) have shown great promise in recent years for improving the efficiency and accuracy of facies classification. This review assesses ANN applications for facies classification in geological investigations critically and it begins by delineating the essential principles of facies classification and the constraints of traditional methodologies. Then ANNs' theoretical underpinnings and applicability to tasks involving the classification of facies was explored. The different architectures and configurations of ANNs used in geological research were also examined, as well as the benefits and difficulties of their use. The several ANNs architectures and configurations used in geological research are examined, as well as the benefits and difficulties of putting them into practice. In order to enhance the efficacy of ANNs in facies classification, the paper also addresses the integration of auxiliary data sources, such as well logs, seismic, and core data. Furthermore, the application of new developments in Deep Learning methods, including Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), to facies classification were discussed. To guarantee solid and trustworthy classification results, factors including feature selection, data preparation, and model assessment metrics were also taken into account. Lastly, the review highlights possible avenues for future research and breakthroughs in leveraging ANNs for enhanced facies classification, precision and effectiveness in geological studies. [ABSTRACT FROM AUTHOR]

As a common data-driven method, artificial neural networks have been widely used in electricity spot price forecasting. To improve the accuracy of short-term forecasts, this paper proposes an optimized artificial neural network model for monthly electricity spot prices forecasting. A genetic algorithm is applied to regulate the weights and biases parameters of the artificial neural network structure. This study uses various historical dataset at monthly periods selected from Nordic electricity spot prices. For efficiency comparison, one-step ahead forecast method based on Schwartz-Smith stochastic model and two other prediction models, artificial neural network trained by Levenberg–Marquardt and particle swarm optimization algorithms are also presented. The comparison results show that the prediction model based on the genetic optimization algorithm is more accurate than the other prediction models. The proposed forecasting model can be considered as an alternative technique for the electricity spot price forecasting in the Nordic regions. [ABSTRACT FROM AUTHOR]

In deep learning, the accuracy and generalization ability of the model largely depend on the optimization of the loss function. Up to now, dozens of optimization methods have been used in the deep learning models. Among them, stochastic gradient descent (SGD) is a popular and widely used method, and most of the other up-to-date optimization methods are variants or improvements of the original SGD. Among all the variations and improvements, the adaptive moment estimation (Adam) is one of the classics. However, Adam has also been pointed out to have non-convergence or error-convergence. Combining with the improvement points of the existing algorithms, this paper proposes an improved algorithm based on Adam, called NewAdam. NewAdam is modified from Adam in both search direction and learning rate. We perform a theoretical analysis on it and conduct numerical experiments on three data sets and two network architectures to illustrate the effectiveness of NewAdam. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence‐based diagnostic approaches, with a special focus on convolutional neural networks, the state‐of‐the‐art in artificial intelligence and deep learning. Methods: The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. Conclusion: The development of artificial intelligence‐based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction. [ABSTRACT FROM AUTHOR]

Summary: The rapid advancement and growth of technology have rendered cloud computing services indispensable to our activities. Threats and intrusions have since multiplied exponentially across a range of industries. In such a scenario, the intrusion detection system, or simply the IDS, is deployed on the network to monitor and detect any attacks. The paper proposes a feed‐forward deep neural network (FFDNN) method based on deep learning methodology using a filter‐based feature selection model. The feature selection strategy aims to determine and select the most highly relevant subset of attributes from the feature importance score for training the deep learning model. Three benchmark data sets were used to assess the experiment: CIC‐IDS 2017, UNSW‐NB15, and NSL‐KDD. In order to justify the proposed technique, a comparison was done using other learning algorithms ranging from classical machine learning to ensemble learning methods that can detect various attacks. The experiments showed that the FFDNN model with reduced feature subsets gave the highest accuracy of 99.53% and 94.45% in the NSL‐KDD and UNSW‐NB15 data sets, while the ensemble‐based XGBoost model performed better in the CIC‐IDS 2017 data set. In addition, the results show that the overall accuracy, recall, and F1 score of the deep learning algorithm are generally better for all the data sets. [ABSTRACT FROM AUTHOR]

Variable selection is one of the most important contents of machine learning research and a large number of variable selection methods have been proposed at present. From deep learning perspective, we propose a new variable selection method based on multi-layer artificial neural networks in this paper. The method propagates errors gained form a trained network model through the network from output layer to input layer and the values gained by input layer give information about the relative contribution. The variables with big contribution value are selected, which we call neural inverse propagation (NIP). Specifically, after training a neural network, the gained errors between the actual network outputs and the desired outputs go through the network from top to down and arrive at the input layer. The absolute values gained by input units provide information about the relative importance (contribution) of input units, which is that the larger the value is, the more important the corresponding input variable is. We remove k variables corresponding to the first k minimum absolute value gained by input units to the predictive variables, to perform variable selection. The efficiency of the proposed method is illustrated through its application to classification and regression tasks on a number of synthetic datasets and a real-world image classification dataset. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) refers to the development of computer systems capable of performing tasks that normally require human intelligence. AI and its subsets, machine learning and deep learning, have been incorporated into several aspects of dentistry including pediatric dentistry. However, there is a lack of documentation and analysis of the current applications of AI in pediatric dentistry. Aim: The aim of this systematic review was to evaluate the effectiveness of AI as a diagnostic tool in pediatric dentistry. Materials and Methods: The literature for this paper was identified by performing a thorough search in electronic databases such as PubMed, Google Scholar, and Cochrane Library from the years 2011 to 2021. The following keywords and Boolean operators were used: AI AND pediatric dentistry, artificial neural networks AND pediatric dentistry, convolutional neural networks AND pediatric dentistry, and machine learning AND pediatric dentistry. After applying appropriate inclusion and exclusion criteria, 13 articles were selected, fully read, and systematically analyzed as per a specific research question. Results: Among the 13 selected articles, it was found that AI is a useful tool for dental diagnosis/classification, cephalometric landmark identification, identification of early childhood caries patterns, chronological age assessment in children, assessment of facial attractiveness in cleft patients, dental plaque detection, and oral health education. Conclusion: The selected articles indicate that AI is an effective diagnostic tool and has the potential for assisting several aspects of pediatric dentistry. However, further studies are required to assess the clinical effectiveness of these AI models. [ABSTRACT FROM AUTHOR]

Nonconvex penalties are utilized for regularization in high-dimensional statistical learning algorithms primarily because they yield unbiased or nearly unbiased estimators for the parameters in the model. Nonconvex penalties existing in the literature such as SCAD, MCP, Laplace and arctan have a singularity at origin which makes them useful also for variable selection. However, in several high-dimensional frameworks such as deep learning, variable selection is less of a concern. In this paper, we present a nonconvex penalty which is smooth at origin. The paper includes asymptotic results for ordinary least squares estimators regularized with the new penalty function, showing asymptotic bias that vanishes exponentially fast. We also conducted simulations to better understand the finite sample properties and conducted an empirical study employing deep neural network architecture on three datasets and convolutional neural network on four datasets. The empirical study based on artificial neural networks showed better performance for the new regularization approach in five out of the seven datasets. [ABSTRACT FROM AUTHOR]

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen's Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a tool to identify and measure the emotion in a piece of text. Negation handling is an important aspect of natural language processing (NLP) for Twitter data. It is a process of correctly interpreting the sentences containing negation words, such as, "never", "no", "neither" and so on. Negation words are used in machine learning to express negative sentiment or indicate the absence of something. In this article, a negation handling technique using deep learning models. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) for classification is proposed. The system is evaluated on SemEval-2017 dataset. The classification performance is improved by using ANN and CNN on the negative tweets. The study aims to improve the classification accuracy by considering negation words in the text. The paper compares the performance of ANNs and CNNs in handling negation words and evaluates them on the tweets data. This study provides insights into the effectiveness of using deep learning techniques for negation handling in sentiment analysis and highlights the importance of considering negation words in the text for improved sentiment analysis performance. The proposed negation strategy attains a superior performance accuracy over machine learning models by preventing misclassified tweets. [ABSTRACT FROM AUTHOR]

In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immense number of resources and is time consuming. Each reservoir prediction experiment needs tens or hundreds of simulation runs, taking several hours or days to finish. In this paper, we attempt to overcome these issues by creating machine learning and deep learning models to expedite the process of forecasting oil and gas production. The dataset was provided by the leading oil producer, Saudi Aramco. Our approach reduced the time costs to a worst-case of a few minutes. Our study covered eight different ML and DL experiments and achieved its most outstanding R2 scores of 0.96 for XGBoost, 0.97 for ANN, and 0.98 for RNN over the other experiments. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) can track the brain waves which contain the neural activity of the brain. EEG signals help to understand the physiological and functional details and activities of the brain. In the era of Artificial Intelligence (AI), machine learning algorithms were useful in brain disorder detection and classification. Recently, a rapid increase in using Deep Learning (DL) methods in various applications in EEG signals not only helps in the detection of brain disorders but also facilitates the recognition of human emotions and various psycho-neuro disorders. In order to offer a beneficial and broad perspective, a detailed survey on the application of deep learning architecture in EEG signals has been carried out in this paper. Different deep learning methods, using varied architecture in EEG signal analysis, offer an understanding to develop the next level of AI-based systems. This review will provide information about how deep learning methods are used in EEG signals and the challenges and limitations of each method in classification; moreover making it helpful for those who are exploring EEG signals using DL algorithms. [ABSTRACT FROM AUTHOR]

Optical time-of-flight sensors have potential in the revolution of distance measurement. These sensors can continuously monitor the distance and track the movement of objects. However, the existing sensing methods for such distance optical sensors mainly calculate the flight time, e.g. pulse transmission and receiving time, without considering the environmental effects. Therefore, the measurement accuracy is severely reduced. There are other technologies with higher accuracy in distance measurement. Nonetheless, they are too expensive due to the high accurate power supply. In this paper, we innovatively improve the accuracy of continuous distance measurement using the artificial neural network (ANN) technique. The proposed method can be applied for very cheap optical distance sensors with analog output in a real-time system. Moreover, the proposed method can self-calibrate and be miniaturized for cheap analog sensor applications. The prototype is built with the infrared sensor GP2Y0A02YK0F and an Arduino control board (ESP32_DevC), and the ANN is implemented using the deep learning algorithm. The test results show that the distance measurement accuracy is significantly improved and the measuring range is increased from 15 to 150 cm. In addition, we calculate mean squared error, mean absolute error, mean bias error, and R 2 for further performance evaluation. The experimental results have proven the superiority of the proposed ANN method in optical distance measurement. The proposed method can be applied to many types of sensors. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry. [ABSTRACT FROM AUTHOR]

Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site. [ABSTRACT FROM AUTHOR]

Aim/Purpose This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendations for Researchers Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the dataset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India. [ABSTRACT FROM AUTHOR]

Rock types, pore structures and permeability are essential petrophysical outputs, and they contribute considerably to the highest degree of uncertainty in reservoir characterisation. These factors have a strong influence on exploration and field development decisions. Core analysis is the best approach for estimating permeability, assigning rock types and characterising pore networks. Wireline logs are the most often employed method for estimating the parameters at each data point of reservoirs since there are more un-cored wells than cored wells. Artificial intelligence, on the other hand, is gaining popularity in the geosciences due to the ever-increasing complexity and volume of available subsurface data. This is also obvious in the demand for faster and more accurate interpretations in order to identify reservoir characteristics in increasingly difficult and complicated petroliferous basins. Artificial Neural Networks and Self-Organizing Maps are examples of machine learning approaches that can be used in both supervised and unsupervised modes for modelling and prediction. Eocene carbonates of Mukta oilfield are the major pay rocks of strong geological heterogeneity in terms of their porosity and permeability relationship with pore structures. This paper outlines a novel method of rock fabric classification, pore structure characterization, flow unit classification and robust reservoir permeability modelling based on an integrated approach that incorporates core measurements, log data and machine learning techniques. The pore structure has been characterised by the combination of conventional core, capillary pressure and nuclear magnetic resonance data. Artificial neural network has added an adequate benefit in accurate permeability modelling by utilizing the concepts of rock classifications and hydraulic flow units. [ABSTRACT FROM AUTHOR]

As airfoil design plays a crucial role in achieving superior aerodynamic performances, optimization has become an essential part in various engineering applications, including aeronautics and wind energy production. Airfoil optimization using high-fidelity CFD, although highly effective, has proven itself to be time-consuming and computationally expensive. This paper proposes an alternative approach to airfoil performance assessment, through the integration of a deep learning algorithm and a stochastic optimization method. NACA 4-digit parametrization was used for airfoil geometry generation, to ensure feasibility and to reduce the number of input variables. An extensive dataset of airfoil performance parameters has been obtained using an automated CFD solver, laying the foundation for the training of an accurate and robust Artificial Neural Network, capable of accurately predicting aerodynamic coefficients and significantly reducing computational time. Due to the ANN’s predictive capabilities of efficiently navigating vast search spaces, it has been employed as the fitness evaluation method of a multi-objective Genetic Algorithm. Following the optimization process, the resulting airfoils demonstrate significant enhancements in aerodynamic performance and notable improvements in stall behavior. To validate their increased capabilities, a high-fidelity Computational Fluid Dynamics (CFD) validation was conducted. Simulation results demonstrate the approach’s efficacy in finding the optimum airfoil shape for the given conditions and respecting the imposed constraints. [ABSTRACT FROM AUTHOR]

The prediction of wear in cutting tools is pivotal for boosting productivity and reducing manufacturing costs. Although current data-driven models in machine learning and deep learning have advanced predictive capabilities, they often lack generality and demand substantial data training. This paper presents a novel approach using Bayesian Regularized Artificial Neural Networks (BRANNs) to precisely forecast wear in milling tools. Unlike conventional machine learning models, BRANNs merge the strengths of artificial neural networks (ANNs) and Bayesian regularization, yielding a more robust and generalized predictive model. We utilized three open-access datasets from the literature alongside an in-house dataset generated by our milling setup. Initially, we assessed the model's predictive ability by training and testing it against individual open-access datasets. We investigated the impact of input features, training data size, hidden units, training algorithms, and transfer functions on the model's predictive capability. Subsequently, we trained the model using three open-access datasets and tested it against our in-house data. Our findings demonstrate that the developed model surpasses existing state-of-the-art models in accuracy and transferability [Display omitted] • Milling tool wear prediction using Bayesian Regularized Artificial Neural Networks. • Proposed BRANN model outperforms existing models in predicting tool wear. • Process parameters and monitoring signals are used as input to predict tool wear. • Training based on open-access data to predict wear for in-house experimental data. [ABSTRACT FROM AUTHOR]

The computer vision community has proposed numerous formulations for object description based on human perceptivity and vast knowledge of the problem domain. In order to reduce human intervention, deep learning techniques are widely used to learn features automatically. However, they lack the property of explainability; that is, a human being understands the meaning of all the parts that make up the calculation of a feature. In this paper, we empirically show how Artificial Intelligence can automatically discover explainable formulations of a topological feature for binary images called the Euler characteristic. The training images are represented by bit-quad patterns, and a single-layer artificial neural network automatically learns the optimal combination of bit-quads to provide valid formulations to correctly calculate the Euler characteristic. We report the results on binary images of different complexities and sizes and compare them with state-of-the-art machine learning algorithms. Finally, we present 14 new equations to calculate the Euler number never reported in literature. [ABSTRACT FROM AUTHOR]

For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to "learn" the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm. [ABSTRACT FROM AUTHOR]

Presently, the use of a credit card has become an integral part of contemporary banking and financial system. Predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. For now, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default data, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE). The SSAE was optimized to achieve improved performance. The proposed SSAE learned excellent feature representations that were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods. [ABSTRACT FROM AUTHOR]

As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL. [ABSTRACT FROM AUTHOR]

Digital image processing involves the manipulation of images by digital means, and its use has been increasing exponentially in recent decades. It is applied in a diverse range of fields including medicine, remote sensing, robotic vision, and audiovisual processing. Image processing technologies are widely used as a proficient tool in the agriculture sector. The combination of digital image processing and machine learning not only provides new insights into crop health and environmental circumstances, but also helps farmers reach timely and precise decisions. Using machine learning methods, this study examines digital image processing applications in agriculture, focusing on plant disease identification, fruit quality evaluation, weed detection, yield mapping, and robotic harvesting, to other issues. In essence, this study integrates existing knowledge at the interface of digital image processing, machine learning, and agriculture, providing insights into a promising and growing topic with significant implications for sustainable and resilient food production. [ABSTRACT FROM AUTHOR]

Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations. [ABSTRACT FROM AUTHOR]

Mobile providers must be preparing for a rapid increase with mobile traffic in future, given the significant growth in demand for wireless data capacity for communications link every year. This motivates researchers and scientist for the higher spectrum bands. The major challenges for these bands are their more sensitive nature toward environmental factors like rain, dust, clouds etc. for the prediction of rain attenuation, various mathematical and empirical models are existed. Those models are not so accurate and complex. As technologies like Machine Learning and Artificial Intelligence are emerging to make any system smarter. Telecom engineers are looking for these technologies for making telecom system smarter. This research develops a model based on Artificial Neural Networks (ANN). The AMSER2 satellite supplied real-time data for this model's training. When compared to previous research, the suggested model will be more accurate. The development of next-generation communication systems will benefit from this model technique (6G). [ABSTRACT FROM AUTHOR]

• Developed a novel neural network for CVD mortality prediction with clinical data. • Proposed a more time-efficient feature-based artificial neural network (ANN). • Investigated feature representation to improve ANN for practical guideline. • Boosted calibration of neural networks and enabled model to make less mistakes. • Made model updates in clinical context more accessible, flexible and reliable. Cardiovascular (CVD) is the leading cause of death worldwide and a significant public health concern. Therefore, its mortality prediction is crucial to many existing treatment guidelines. Medical claims data can be used to accurately foresee the health outcomes of patients contracting to a variety of diseases. Many machine learning algorithms, especially deep learning artificial neural networks, can predict mortality rate among patients with CVD using clinical data. Calibration of probabilistic prediction is essential for precise medical interventions as it indicates how well a model's output matches the probability of the event. However, deep learning neural networks are poorly calibrated. Through experiments, we observe that feature representation is an important factor influencing calibration. This paper proposes a novel feature-based deep learning neural network framework to predict the mortality rate among patients with CVD. Our focus is to present a comprehensive study to achieve advanced performance calibration of mortality prediction on CVD in leveraging deep learning architecture and feature representations. Our study demonstrates that the proposed feature-based neural network framework integrated with Principal Component Analysis or Autoencoders significantly reduces training time and boosts calibration, making model updates in clinical context more flexible and decision-making in medical prevention more reliable. [ABSTRACT FROM AUTHOR]

This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented. [Display omitted] • AI, ML and DL are transforming the building and construction industry 4.0. • Robotics, IoT, Digital Twins, 4D Printing, Cloud AR/VR, Blockchain are future trends. • AI techniques can be used throughout the whole building lifecycle. • Sensor and vision-based data acquisition can facilitate ML/DL implementation. [ABSTRACT FROM AUTHOR]

Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of networks has been rapidly increasing and network intrusions have become more frequent and less detectable. The increase in complexity pushed researchers to boost NIDS effectiveness by introducing machine learning (ML) and deep learning (DL) techniques. However, even with the addition of ML and DL, some issues still need to be addressed: high false negative rates and low attack predictability for minority classes. Aim of the study was to address these problems that have not been adequately addressed in the literature. Firstly, we have built a deep learning model for network intrusion detection that would be able to perform both binary and multiclass classification of network traffic. The goal of this base model was to achieve at least the same, if not better, performance than the models observed in the state-of-the-art research. Then, we proposed an effective refinement strategy and generated several models for lowering the FNR and increasing the predictability for the minority classes. The obtained results proved that using the proper parameters is possible to achieve a satisfying trade-off between FNR, accuracy, and detection of the minority classes. [ABSTRACT FROM AUTHOR]

The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others. [ABSTRACT FROM AUTHOR]