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The evaluation of musician performance has traditionally been subjective and challenging, relying on human judgment and personal preferences. However, recent advancements in artificial intelligence (AI) have opened up new possibilities for objective and automated assessment. This paper presents a comprehensive survey and analysis of AI-enabled techniques in evaluating musician performance. The research addresses the limitations of subjective evaluations and aims to enhance objectivity and efficiency in assessment. By leveraging AI and machine learning, various aspects of performance, including technical skills, musical interpretation, and emotional expression, can be objectively evaluated. The findings demonstrate the potential of AI-based assessment tools in providing objective feedback, enhancing music education, and enabling fair evaluations in competitions and auditions. The survey encompasses ten key papers that highlight different methodologies, advantages, and limitations in the field of automated music performance assessment. The reviewed literature showcases approaches such as deep neural networks, sensor localization algorithms, multimodal analysis, and machine learning techniques. Through a comparative analysis, this paper provides insights into the strengths and limitations of each approach. [ABSTRACT FROM AUTHOR]

Early identification and accurate diagnosis are crucial for successful therapy and improved patient outcomes of brain tumors. The study looks at how deep learning may help detect brain tumors, including how convolutional neural networks (CNNs) can change the face of diagnostic imaging. This underscores the need of precise and timely identification of brain cancer for efficient therapy and enhanced patient results. The study discusses the challenges associated with tumor segmentation and classification, such as variations in size, shape, and location. To overcome these challenges, deep learning techniques are utilized to analyze brain scans and identify tumor abnormalities. However, the paper underscores the importance of these models serving as tools to assist medical professionals rather than replacing their expertise. Additionally, the paper presents a comparative study that evaluates different techniques, artificial neural networks (ANN) achieve the highest accuracy at 99%, followed by Support Vector Machine (SVM) at 98.9%. vision transformer (ViT), Extreme Gradient Boosting (XG Boost), CNN-based dense Efficient Net, and Deep Neural Network (DNN) also show high accuracies above 98%. However, U-Net, Alex Net, hybrid ensemble method (Random Forest (RF), K-Nearest Neighbour (KNN), Decision Tree (DT), and Region-based convolutional neural networks (R-CNN)) have lower accuracies ranging from 91.66% to 97.305%. Overall, the findings highlight the potential of deep learning in enhancing the efficiency, precision of brain tumor diagnosis, leads to earlier treatment and enhanced patient prognosis. [ABSTRACT FROM AUTHOR]

The paper is focused on the determination of mechanical fracture parameters of alkali-activated concrete (AAC) specimens of nominal dimensions 80 × 80 × 240 mm with central notches of the following types: straight-through notch, chevron notch with 90° notch angle and chevron notch with 120° notch angle. In accordance with the results of the experimental research, a numerical simulation of the fracture test of the specimens tested in three-point bending was carried out. The 3D FEM computational model is based on the configuration of the laboratory test and was developed in ATENA-GiD software. Artificial neural network (ANN)-based inverse analysis was used to identify the mechanical fracture parameters of studied composite. A neural network ensemble was created, with one robust network for each type of notch, covering the entire domain of identified parameters. The main goal of this paper is to present the results of the identification of the mechanical fracture parameters of the AAC and their comparison with the values obtained by direct evaluation of the experiments. Another objective is to investigate the influence of the notch type on the mechanical fracture parameters of the AAC. [ABSTRACT FROM AUTHOR]

The rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time. [ABSTRACT FROM AUTHOR]

This paper describes an Artificial Neural Network (ANN) based speed estimation of BLDC drive using the three-phase current of the motor. The star point of three parallel resistors is used as the artificial star point of the motor. The comparators are connected to motor terminals and star point of resistors. This circuit determines the zero crossing of the back-EMF of the motor phases. Using zero crossing detection of the back-EMF with the estimated speed from the ANN, precise control of the BLDC motor is achieved, without the need for a sensor. The ANN is an offline trained network, trained using data obtained from simulations. The network is validated against different Inputs. The network is precise but at the same time simple enough to be implemented on a low-cost processor. The results are validated with simulations done in MATLAB using the trained neural network The findings and applications of neural networks to control the speed of PMSM are very well presented in the referenced papers, but the same for BLDC motors, especially those used in electric vehicles, have not been explored yet. The presented paper attempts to address the challenges and advantages of adopting this sensor-free approach for speed control in EVs. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of digital communication, ensuring the reliability and security of networks is paramount, with the escalating volume and complexity of network traffic necessitating robust anomaly detection mechanisms to safeguard against potential threats and disruptions. This paper introduces a novel hybrid deep learning system designed for effective network anomaly detection, employing a two-stage approach. The first stage utilizes a Convolutional Neural Network (CNN) as a binary classifier to identify broad anomalous patterns in network traffic. Subsequently, the second stage employs a Deep Neural Network (DNN) as a multi-class classifier to further categorize the detected anomalies into specific classes. Notably, a unique feedback mechanism is incorporated into the system, leveraging false results, including false negatives and false positives, to iteratively refine the model through retraining and reclassification, thereby mitigating false results. An innovative aspect of the proposed approach lies in the incorporation of a feedback loop that addresses false results. False negatives and false positives identified during the classification process are used to augment the training dataset, allowing the system to learn from its own mistakes. The model is then retrained, and the classification process is repeated, resulting in a more resilient and adaptive anomaly detection system. To evaluate the performance of the proposed hybrid system, extensive experiments are conducted using the CICIDS2017 dataset, a widely recognized benchmark in the field of cybersecurity. Comparative analyses with existing methods showcase the superior capabilities of the hybrid system in terms of accuracy, false positive reduction, and adaptability to evolving network threats. The results indicate that the hybrid deep learning system not only outperforms traditional approaches but also exhibits an enhanced ability to learn and adapt from misclassifications, thereby continually improving its performance over time. In conclusion, the presented two-stage hybrid deep learning system, coupled with the adaptive feedback mechanism, represents a significant advancement in network anomaly detection, promising heightened accuracy and robustness in the ever-evolving landscape of cybersecurity. [ABSTRACT FROM AUTHOR]

Target strategy is an important part of low-carbon city construction, and reasonable formulation of target strategy can effectively improve resource utilization, reduce environmental pollution, and promote sustainable urban development. Therefore, this paper proposes an artificial neural network algorithm, which comprehensively analyzes the connotation of artificial intelligence, and discusses the low-carbon city research model under the background of artificial intelligence to determine the corresponding target strategy analysis. Firstly, artificial intelligence theory is used to analyze urban construction. The artificial intelligence the low-carbon city construction strategy, forms a strategy evaluation scheme, the target strategy results. the evaluation accuracy, energy efficiency and carbon emission level of low-carbon city construction strategy by artificial neural network algorithm are better than those of previous urban planning. [ABSTRACT FROM AUTHOR]

This paper delves into the training and testing of two pre-trained models of a Convolutional Neural Network (CNN) to classify images of clothing from the Fashion MNIST dataset and determine the classification accuracy and performance of both the models. The Fashion MNIST dataset is a collection of 60,000 28x28 grayscale images of 10 different types of clothing. The images are well-labeled and comparatively easy to classify, making them a good starting point for learning about CNNs. The article starts with a summary of the Fashion MNIST dataset. There are 60,000 training photos and ten thousand test ones in the dataset. T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag and Ankle boot are the pictures' ten constituent classes. The CNN architecture used in the tests is described in the next part of the study. CNN architecture is made up of two convolutional layers, two max-pooling layers, and two fully linked layers. The convolutional layers employ 32 3x3 filters, whereas the max pooling layers employ a pool size of 2x2. There are 128 neurons in the fully linked layer and 10 neurons in the unconnected layer. The Adam optimizer is chosen to train the CNN framework utilizing the learning rate of 1e-5. The machine learning algorithm underwent training for 30 epochs with a batch size of 64. The experiments' shortcomings are highlighted in the conclusion section. It is certainly possible that the CNN model would not perform as well on other datasets. Another limitation is that the experiments were conducted only for a set of hyperparameters. The outcomes of the experiments are presented in the results section. The training accuracy of the Convolutional Neural Network models VGG19 and Resnet50 are 94.58 and 99.41 respectively. The testing accuracies for VGG19 and Resnet50 models are 91.92 and 90.25 respectively. The inference latencies for both cases were found to be 5.439 and 3.522 respectively. [ABSTRACT FROM AUTHOR]

The crusher facility is recognized as being a principal preparation facility for processing and refining minerals. Installing the crusher requires a steep slope to convenience the feeding process. Currently, Mechanically Stabilized Earth (MSE) Wall has been decided to reinforce a steep slope on the crusher facility to increase stability. The crusher vibration and stockpile load on the crusher area widely account for the crusher facility's longs term stability. This paper discusses how to define the interaction of the crusher, MSE Wall, and stockpile that affects the crusher stability. GEO5 software has provided the Limit Equilibrium Method (LEM) in two-dimensional analysis using Mohr-Coulomb Failure Criteria and Morgenstern-Price Method. Back Propagation Artificial Neural Network (ANN) is used to validate the computational analysis from GEO5. The sum of variations models in this analysis was 25 slopes. Based on the variations, the data input for ANN included 20 models, while the five models are used to validate the ANN prediction result. The entire validation model meets the criteria for the value of R2 > 0.6. Still the same as the training model where only parameter Center of Rotation: y Axis Values has an SI of RMSE above 10%, which is 13.6%, which means that the neural network model is used in this study is still not able to estimate the shape of the Center of Rotation, however this Neural Network model can excellently estimate the value of the Safety Factor because based on the R2 value is 0.96 and the SI of RMSE is 0.59%. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess whether the utilization of the XGBoost technique, in contrast to the Artificial Neural Network (ANN) algorithm, enhances the precision of cervical cancer detection. Two distinct groups, denoted as Groups 1 and 2, were established, each comprising fifteen samples. The algorithms ANN and XGBoost were associated with Groups 1 and 2, respectively. The dataset was provided by High-risk human papillomavirus (HPV.CS), and the values for Alpha and G-power were determined as 0.94 and 80%, respectively. Employing an independent sample T-test with a significance level of p=0.007 (p<0.05), it was observed that the accuracy of the XGBoost method reached 87.333%, surpassing the accuracy of the artificial neural network approach, which was measured at 80.200%. The accuracy of the XGboost method is significantly greater at 87.333% when related to the ANN technique. It is concluded that the XGBoost algorithm outperforms the ANN by a wide margin in terms of accuracy for finding in the determination of cervical cancer. [ABSTRACT FROM AUTHOR]

The primary goal of this paper is to offer the wind speed forecast for Banda Aceh, which is located on the Indonesian coast. Wind speed forecasting is significant in Banda Aceh because of its many real-world uses in fields including aviation, shipping, and the creation of renewable energy. In these sectors, accurate wind speed predictions can streamline processes, cut costs, and improve safety. Banda Aceh is additionally susceptible to typhoons and tsunamis due to its coastal location. Through the provision of vital information regarding prospective risks and their effects, wind speed forecasting can play a crucial role in disaster preparation and response activities. The wind speed is examined in this study using neural networks (NN), and autoregressive integrated moving average (ARIMA). The study calculated statistical error metrics, including the root mean square error (RMSE), for contrasting the three forecasting techniques. In all three of the sites that were studied, the results of the analysis showed that hybrid algorithms predicted wind velocity more precisely than ARIMA and NN models. [ABSTRACT FROM AUTHOR]

The demand for improved Power Quality (PQ) in distributed power systems has raised due to the increase in sensitive irregular load integration and PV system-based power generation. In order to upgrade the entire system with developed PQ, a Unified Power Quality Conditioner (UPQC), a unique power device is proposed in this work. Using a shunt compensator, the PV system delivers active power to the load. Photovoltaic (PV) systems is used for uninterrupted energy flow to the load. The output voltage obtained from PV module is irregular, so that a DC-DC converter is required to step-up/step-down the output. In this paper, Luo converter is used in which the converter operation is regulated using Grey Wolf Optimisation (GWO) based Proportional Integral (PI) controller. PWM generator provides necessary pulses to the converter which in turn provides constant supply to the load. The battery converter is used for charging and discharging of the batteries in both direction for further use. To overcome harmonic distortion observed in the input AC source, an Artificial Neural Network (ANN) aids in the operation of shunt active filter of the UPQC. The proposed ANN controller reduces control complexity and effectively overcomes power quality issues. The AC source output is fed as input to ANN which generates reference current. PWM generator with hysteresis control converts the reference current as PWM pulses received from ANN and supplies to shunt converter of UPQC. Then the DC supply is converted to AC by reducing harmonic distortion using LC filter and it is fed to the load. On the basis of MATLAB simulations, the effectiveness of the proposed PV-UPQC configuration in eliminating load current harmonics and source voltage fluctuations thereby improving PQ is analyzed. [ABSTRACT FROM AUTHOR]

The quality of food is significantly influenced by temperature. In Cold Storage facilities, it is essential to maintain optimal chamber freezer conditions within a specific temperature range. To achieve this, regular automatic measurement of the freezer temperature becomes necessary, although it can be time-consuming, it ultimately enhances crew efficiency. Hence, the implementation of an automatic system for measuring freezer temperature is significant in alleviating the workload of the crew when it comes to monitoring freezer temperature. The boosting demand for viable and efficient cold storage has led to the advancement of new techniques and technologies. Vintage refrigeration systems are known for their high-energy consumption and substantial aid to the release of greenhouse gases. This proposed paper introduced a novel optimization tactic for cold storage, incorporating Internet of Things (IoT) technology and solar panels to minimize both energy expenses and the environmental influence of greenhouse gas emissions. The proposed system comprises essential components, including a DHT11 temperature and humidity sensor, a gas sensor, and a microcontroller, responsible for data collection and refrigeration system regulation along with a battery connected to a solar panel. The power supply section of proposed system is derived from a solar panel array, utilizing daytime sunlight and storing surplus energy in a battery for nighttime operation. Real-time data analysis enables prompt adjustments to the refrigeration system, ensuring optimal storage conditions for the products. A remarkable advantage of the proposed system is its self-governance from the electrical grid, providing independence in operation. This holds significant advantages in regions with unstable electricity provision or areas burdened by expensive power tariffs. These results underscore the considerable potential of combining IoT technology and solar panels to optimize cold storage systems, contributing to a more sustainable future. Moreover, a machine learning based approach has been developed which can classify ripe and unripe fruits/vegetables. Statistical feature extraction is followed by classification using a deep neural network. It has been shown that the proposed approach achieves an accuracy of 97% thereby rendering high classification accuracy. In summary, our results exemplify the immense potential of combining IoT and solar panels to optimize cold storage systems, fostering a more sustainable future. Furthermore, the automated classification mechanism would facilitate the continuous monitoring of fruits/vegetables thereby reducing the chances of spoilage. [ABSTRACT FROM AUTHOR]

Agriculture's production is heavily impacted by the state of the economy. Plant disease is increasingly common in agricultural settings and is now easier to detect as a result of the previously described issue. It is receiving more attention due to the monitoring of crops in a wide range of settings. Farmer transitions between different disease control strategies and encounters significant challenges. We are able to identify or detect leaf illnesses, which is how surveillance and monitoring experts usually find them. If the appropriate control measures aren't followed, the plants suffer a major consequence, which symbolizes how the quality of the plants' production will be impacted. The adoption of a mechanical approach or method for identifying diseases is effective and beneficial since it lessens the burdensome work of surveillance in the vast cultivation. The early stages of plant illnesses allow us to identify the signs even before they manifest on the plant's leaves. The early stage denotes the starting period which is one or two days after affecting the disease. The paper's primary objective is to use ResNet50 for early detection of plant disease. ResNet50 has been successfully applied to plant disease image classification tasks and has shown promising results. ResNet architecture, with its deep layers and residual connections, has the ability to capture intricate patterns and features, which is useful for identifying the differences in plant images. The results proved that the ResNet 50 has better accuracy, precision and recall than Support vector machines (SVM), artificial neural networks (ANN), Convolutional neural networks (CNN) for plant disease prediction. [ABSTRACT FROM AUTHOR]

Chronic Renal Disease (CRD) is a significant global health issue, with an increasing number of people affected by the disease. Accurate and timely prediction of CRD is crucial for effective treatment. In recent years, machine learning approaches have gained popularity as a tool for CRD prediction due to their high accuracy and efficiency in predicting disease progression. In this paper, we have proposed a Chronic Renal disease prediction using Artificial neural network with Feature selection Technique (CRAFT) that is a combination of Artificial Neural Network with Principal Component Analysis for the prognosis of CRD. The performance of the proposed model was evaluated using various criteria and is compared with five commonly used machine learning classification approaches, namely K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Naïve Bayes. Our results show that CRAFT outperforms all the other methods, with a higher accuracy and AUC-ROC score. This study provides valuable insights for researchers interested in leveraging machine learning classification approaches for the prognosis of CRD, with ANN being the most effective method. [ABSTRACT FROM AUTHOR]

Image based Steganography has been in existence for a very long time and Kurak and McHugh were one of the earliest who proposed a method in which the secret data or image is embedded into the fourth least significant bit. But this method was not of much use since the intruder can easily access the hidden image or data since they know exactly the position of the hidden data. This is an example to show that this is an area in which security and efficiency can be improved by using different methodologies. This paper is proposed to build an architecture to hide single or multiple images also called hidden images which are the inputs to be hidden inside an outer image and then the combined image is converted into an Stego image, and the image looks like the outer image except that it is filled with the bits of information from the hidden image. From this Stego-image can be retrieved the original outer image using disclosure component which is the reveal network. A deep learning model has been developed and trained which consists of two layers namely preprocessing layers and embedding layers which will hide the data or image inside the outer image and convert it into a Stego image. Then from this Stego image the hidden data is extracted using reveal network. Only this reveal network can extract the data and no intruder can extract it since the embedding is done by the model and it does not use same or fixed pattern. This method helps to transmit sensitive information in a more secure and efficient manner. The extracted image from the outer image has an accuracy of 95 percent only for the first image and it decreases for each added hidden image and the MSE value is 0.01 and it increases for increasing number of hidden images. The model has a F1 score of 0.96. [ABSTRACT FROM AUTHOR]

India ranks second in production of cement. In order to produce one ton of cement about one ton of Co2 is released into atmosphere. As per Global Cement and Concrete Association, Cement is the third highest contributor of Green house gases in the atmosphere. In order to decrease the negative impacts of cement in the atmosphere a new type of concrete is evolved by conducting countless researches that is Geopolymer Concrete. The Amount of Cement Content in Geopolymer Concrete is null and void. Artificial Neural Networks is bough of Artificial Intelligence. It is one of the sophisticated Computed technique to prognosticate the properties of Geoploymer Concrete with minimum Percentage of error. In this paper more than 170 hunks of data gathered from the literature to train and test the data. In this study evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). After performing ANN the statistical parameters found be 0.92, 0.934 and 0.905. which resembles that ANN outcomes are satisfactory and can be used in evaluating the properties of the Geopolymer concrete. [ABSTRACT FROM AUTHOR]

The deep learning technology is growing on day by day with increasing the internet access. The techniques of deep learning used to process information as human brain activities like learning some knowledge, solving the problems, and making the decision. Generally, deep learning provides self.training. In deep learning, CNN architecture is commonly used in many areas such as image processing, video processing, speech recognition, natural language processing etc. In the paper, the overview of deep learning is represented in which artificial intelligence, machine learning deep neural network are introduced. After that, layers of CNN architecture with its layers and techniques of deep learning are discussed. The CNN architecture is also used in image processing for security purposes. [ABSTRACT FROM AUTHOR]

Fossil fuels are depleting at a significantly higher rate, global warming reaching its new high and entire ecosystem suffering from air pollution. Biodiesel has emerged as potential substitute of diesel fuel that can be operated in a conventional diesel engine without any sort of major modification and provides comparable performance and lower emissions. One of the cumbersome tasks is to find the optimum blend of diesel-biodiesel and engine operating parameters. In this regard, many optimization and simulation methods have been used to optimize the biodiesel percentage in blend and engine operating parameters, which gives maximum power output and minimum emissions. There are several optimization techniques such as Genetic algorithm, Ant colony optimization, Grey relational analysis, Response surface methodology, Taguchi, artificial neural network, VIKOR and TOPSIS, which have been employed to optimize the working of a diesel engine run on biodiesel blends. In this research paper, the various optimization methods that have been used in a diesel engine for predicting & optimizing the several output parameters of the input model and ranking them accordingly are discussed. [ABSTRACT FROM AUTHOR]

Road traffic crashes result in a significant number of fatalities and economic losses worldwide. Rear-end collisions, caused by insufficient distance between vehicles, are a major contributor to these accidents. To address this issue, the use of Dedicated Short-Range Communication (DSRC) technology has been developed prominence in last decade. However, uncertainties associated with DSRC-based systems, such as GPS positioning errors and transmission delays, pose challenges to the development of robust collision warning models. In this paper, a design of three DSRC-based rear-end collision warning models to mitigate the impact of uncertainties. The models include the vehicle kinetic model (VK), the maximum compensation vehicle kinetic model (MC VK), and the artificial neural network model (NN). The VK model shows both a high possibility of false warning and missing. The MC VK model shows a 0% possibility in missing warning, which can be determined as the early warning boundary. The NN model shows low probability in both false and missing warning, and 80% correct warning makes it a good candidate for emergency warning boundary. [ABSTRACT FROM AUTHOR]

Utilizing bio-oils in liquid pumping unites are considered as a one of the most important parts in the water filtration and pumping stations especially in the area of automatic control. In this paper using bio-oils are being investigated which have high degradability and it is environmentally friendly. The thermal performance features of the liquid pumping unit lubricated with bio sunflower seed vegetable oil was investigated and compared with the performance features with mineral oil (MO10W-30). The study includes, oil temperature, density, power and tourq under different rational speed (1200-1500 rpm) for the pump. From experimental investigations carried out in this study, it can be concluded that biodegradable vegetable oils are adequate alternative to the mineral oil as a renewable bio cooling and lubricate. An automatic pumping unit was implemented (three tanks one as a source and two auxiliary) based on two types of controllers. These two are: PID controller and artificial neural networks. The results in neural back propagation that more accurate than PID controller. The over shoot is improving in artificial network is zero where compare with PID result 0.8 m3/s flow rate [ABSTRACT FROM AUTHOR]

An adaptive artificial neural network model of a fuzzy neural inference system is evaluated in this paper to predict the performance of a nanofluidic evacuated tube solar collector. Variable intake temperatures and weight percentages of the aluminum oxide nanofluid (1 percent, 3 percent, 5 percent), as well as different flow rates (3, 9, 15) l/min were investigated. In this study, three ANFIS inputs were used: inlet temperature, solar radiation rate, and ambient temperature. Where one output is obtained from the artificial neural network, which is the values of the angles of the reflective electronic curtain made of acrylic mirrors, which enhance the performance of the solar collector as well as control the temperature of the solar collector when the curtain is closed. We can note that the practical results are consistent with the prediction results by predicting the performance of the solar collector produced by ANFIS. [ABSTRACT FROM AUTHOR]

In this paper, the author analyzes the use of a trained artificial neural network to precisely evaluate hybrid rocket engine performance in chemical equilibrium condition, avoiding the use of an high fidelity chemical model. A three-stage small satellite launcher is considered as test case, and the optimization of the engine design and the corresponding ascent trajectory is performed by means of an in-house coupled optimization procedure. The net evaluates specific heat ratio, characteristic velocity and thrust coefficient for given mixture ratio, combustion chamber pressure and nozzle expansion area ratio. The results show that the use of the artificial neural network can improve model accuracy at an affordable computational cost. [ABSTRACT FROM AUTHOR]

The paper introduces an original permutation approach to assessment of the predictive ability of models based on artificial neural networks. Three models based on artificial neural networks (a multilayer perceptron, a network with the radial basis function, and a neural network with the generalized regression) were implemented to illustrate the permutation approach. Data on the spatial distribution of copper in the upper soil layer of Novy Urengoy (Yamalo-Nenets Autonomous Okrug, Russia) were used for modeling. To evaluate the performance of the models, three different methods were used: error indices estimation, a graphical approach (the Taylor diagram), and a randomization estimation of the probability of obtaining the divergence between the observed and predicted series under assumption that both these datasets are taken from the same population. In the permutation approach, two statistics were used: the difference in means and the correlation coefficient. The permutation approach proved to be productive, as it allowed assessing the significance of the divergence between the observed and predicted datasets. [ABSTRACT FROM AUTHOR]

Waste rubber tires have been used in building materials to support the environment and green construction. There is a growing demand for rubberized materials as they are cost-effective and useful from a sustainable standpoint. There are several properties of the rubber tire that could be applied usefully such as low density, and waterproofing properties. Rubberized concrete has composed of waste rubber as natural aggregate and is an alternative solution to the use of tire rubber particles in the production of concrete. It has been proven that the addition of waste rubber tires to concrete starts to low strength and this restriction its application in structural elements but benefits to enhance the ductility, impact resistance, thermal conductivity, and acoustic properties. This paper presents a review of the recent studies on the application and development of artificial neural networks (ANNs) to predict the compressive strength of rubberized concrete. From this review, predicting the compressive strength of rubberized concrete by ANNs is generally more accurate, and their development is inexpensive and time-saving. In this review, the advantages, and limitations of ANNs to predict the compressive strength of rubberized concrete, as well as some new research trends have been addressed. [ABSTRACT FROM AUTHOR]

An original infection has spread over the global village in form of pandemic and the regular lifestyle of human beings got stuck with misfortunes. As it has been declared by the global organization of health sector namely WHO, this Covid got initiated from southern city of China during the last weeks of the year 2019. Based on the multiple observations, it was declared as most infectious as well contagious spread through wind or by coming in close contact with a tainted individual. Various methods have been implemented to cease off the fast spreading infectious disease. For example, keeping a social separation, that is to say, keeping a legitimate actual separation among individuals and reducing close contact with one another, and wearing a facial covering to stay away from the beads that are passed by means of wind air. Consequently, this paper put forth the observation made to explore the initiatives for maintaining physical distance in the public domain and to identify whether a Face Mask is worn or not. The architectural framework provides the exact location and position of an object along with an acknowledgment about facial coverage as per the WHO norms under the moving images of the persons who are into public domain. Our suggested model implements the existing procedures such as dual shot face detector, You look only once and finally ResNet algorithms. Irrespective of the origin location of an individual and their interaction among themselves taking into consideration the space between them and the facial expressions in a continuous form were recognized. A general outcomes dashboard has displayed the result composing the measures with regard to the abuses of an individual person and breaking the protocol of maintaining separate distance measures. Subsequent to carrying out and conveying the procedures of process, the above said exploration model accomplished an accuracy of maximum performance. Subsequently, this exploration project had come up with demonstrated realities that maintaining physical distance along with the usage of facial mask decreases the escalation of the disease, which lead to consequently constructs a procedures to assist with identifying the mentioned actions. [ABSTRACT FROM AUTHOR]

Accident prediction and Black Spot identification have a significant role in improving traffic safety and urban traffic management. This paper deals with developing the model to identify black spot locations using the Classical approach that relies on historical accident occurrence and the Computational Intelligence approach. A hybrid approach with a combination of the best fit Artificial Neural Network (ANN) model and classical Accident frequency method results in 97.72% accurate prediction of the black spots. Combining the best fit ANN model and the Accident severity method results in 93.18% accuracy in predicting black spots. It concluded that combining Computational Intelligence and a Classical approach ensures better accuracy in identifying black spots. The uniqueness of the applied model in this research also includes combining Classical methods and Fuzzy C-means clustering to identify the road segments recognized as black spots. It also identifies the road segments categorized in the medium possibility range expected to be promoted to highly vulnerable groups. [ABSTRACT FROM AUTHOR]

Due to the high labour costs in industrialised countries and the increasing demands on the quality of plastic moulded parts produced by the injection moulding process, companies are challenged to increase their production efficiency. Data-based approaches provide an opportunity for systematic and objective process set-up and control. Especially the process set-up can be a tedious, iterative and complex task which can consume a significant amount of time in the preparation of mass production. Artificial neural networks (ANN) can be used by evolutionary algorithms to search for suitable machine setting parameters for the desired part and process quality features. This often results in locally optimal solutions, which do not guarantee the best possible machine settings. Invertible neural networks (INN) could be a solution as with the help of these models, globally optimal solutions may be identified for definitive optimisation as the entire solution space for given quality parameters can be mapped in short time. In our approach, data for the implementation of the INN is collected in practical tests. The resulting weight and several geometric dimensions of the parts are determined as quality parameters. Based on these data, ANN are trained and utilized for the generation of synthetic data for the training of the INN. The creation of synthetic data is necessary because INN have millions of trainable parameters and therefore degrees of freedom which need to be determined using a bigger amount of training data in comparison to traditional feedforward ANN. In this paper, two different databases with a size of 10,000 samples each are being investigated. The two databases differ in the distributions of holding pressure and cooling times in order to investigate the influenceability of the prediction quality of the INN by training data. The sample settings for the injection velocity, packing pressure, mould temperature and melt temperature are uniformly distributed. Two different types of conventional and conditional invertible neural networks are implemented based on literature recommendations. Measured on validation data, the model quality (R² value) for the prediction of the part weight is 0.995 and 0.975 for the part dimensions. The machine setting parameters proposed by the INN can be analysed in regard to their impact. The holding pressure time is identified as the machine setting with the greatest influence on both part weight and part dimension. The predictions of the INN are validated in practical experiments with an R² value of 0.997 for the part weight. The weight of the parts produced with the proposed machine settings deviates on average by 0.24 % from the target. [ABSTRACT FROM AUTHOR]

The reasons for pollution and the limitations of fossil fuels have driven the use of electric vehicles. Electric vehicles are synonymous with electric batteries that require periodic charging. There has been an increase in research on battery charging for electric vehicles. There is also the use of renewable energy resources such as photovoltaics for battery charging stations. This paper focuses on prototyping battery charging stations from solar photovoltaic energy using ANN-based MPPT controllers within the framework of optimizing solar energy harvesting and comparing them with conventional MPPT methods. Test results showed that the prototype of the proposed charging system took 33.3 minutes to charge a 7Ah battery. Compared to conventional MPPT methods, the charging time of MPTT methods with Neural Network is 6.01% faster. [ABSTRACT FROM AUTHOR]

Vertical farming (VF) allows for more plants to be cultivated in a particular land area, maximizing land-use efficiency. VF employs multileveled shelves enabling crops to be planted at each level. This paper aims to develop an optimization model that could generate a required setup of a vertical shelf that would meet the sunlight requirements of strawberries. Firstly, the simulation model was developed using Rhino (Rhinoceros) software with Grasshopper plugin, which can predict the sunlight availability at each VF shelf level. The model is validated by the experiment in Chiang Mai province, Thailand, at an elevation higher than 310 m above mean sea level. The experiment set up of the VF shelf with three levels was constructed, and the solar radiation values were recorded and validated against the radiation predicted by the model. The validated model is used to simulate the sunlight radiation on each level of the shelf for different shelf designs with varying vertical spacings between each level. These sets of predicted radiation values and vertical spacings are used to train the ANN (Artificial Neural Network) model. The ANN simulation model was developed using MATLAB software with Simulink and Neural Net Fitting App. The trained ANN model has an MSE (Mean Squared Error) of 0.00019621 and an R (the correlation coefficient) value of 0.9992. As a result, the optimization model could successfully give out the vertical spacing between each level of the VF shelf with an input of desired sunlight availability on each level. For the PPFD (Photosynthetic Photon Flux Density) of 600 µmol/m2/s on both levels, the spacing should be 51.34 cm from the top to the middle (y1) and 48.69 cm from the middle to the bottom (y2). To receive 600 and 500 µmol/m2/s on the middle and bottom level respectively, the levels should be placed 66.77 cm (y1) and 30.89 cm (y2) apart. [ABSTRACT FROM AUTHOR]

Multiresolution analysis has wide-ranging applications in signal processing such as fault detection and isolation in sensors. Wavelet transforms can be used for feature extraction and compressing the signals without losing their characteristics. Coefficients of wavelet transform of a signal can be represented in the form of scalogram images. Neural networks helped researchers in replacing human interventions which can also be trained for specific tasks. Researchers have also demonstrated the high level of accuracy and usefulness of Deep Neural Networks(DNNs) in image classification. DNNs pre-trained with millions of images can be used for feature extraction or can be modified and fine-tuned for specific tasks. Scalograms contain the fault signatures of the transformed signal, which can then be classified with the help of a Deep Neural Network(DNN). Though DNNs are efficient in image classification it requires a lot of computing resources for training. To overcome this disadvantage we used transfer learning i.e. modifying and fine-tuning a pre-trained DNN. In this paper, we proposed a model for fault detection and isolation in sensors using transfer learning of DNNs and scalograms. A publicly available temperature sensor dataset was used for fault injection and classification purposes. The proposed model successfully classified sensor stuck faults with 95% accuracy by using scalograms as input to the fine-tuned DNN. [ABSTRACT FROM AUTHOR]

This research employs a novel approach to headcounting by comparing the performance of the ANN algorithm with that of the SVM algorithm. To determine how many individuals are moving under an overhead monitoring system, researchers have developed a revolutionary paper counting approach based on head recognition and tracking. Substances and Techniques: Twenty samples in all were taken from an electronic resource. We maintained a significance level of 0.05, a confidence interval of 95% (95% CI), an enrollment ratio of 1, and a significance level of 80% (G power). The precision value is calculated using the SPSS software. Result: The results of two separate t-tests show that the ANN algorithm outperformed the SVM method, although the difference is not statistically significant (p=0.721; p>0.05). In summary, it seems that the ANN algorithm outperforms the SVM method on the analysed datasets. [ABSTRACT FROM AUTHOR]

Using Regression Neural Networks and Probabilistic Neural Networks, implement a comparative overall performance evaluation of estimating food volume and monitoring meal intake for hospitalised affected persons using neural networks. This evaluation will be done using neural networks. Both the Materials and the Methods: Regression neural network and probabilistic neural network, with the same sample size (N=10) for each of the two groups, for the purpose of determining the accuracy of intake food estimation. Results show that the accuracy rate of the Regression neural network is 91.5 percent based on the obtained result, which is higher than the accuracy rate of the probabilistic neural network, which is 89.3 percent. According to the results of an independent sample T test, the statistical significance difference that was observed between two groups was P=0.00 (P0.05), 2-tailed. It has been determined through the research presented in this paper that the Probabilistic Neural Network is less accurate than the Regression Neural Network. [ABSTRACT FROM AUTHOR]

Physical disabilities cause significant distress in people's daily routine, especially for leg amputees as their movement is restricted. The civilized society has the responsibility to provide them with better prosthetics so they can live a normal life. Previously, prosthetic legs were developed for lower leg amputees. Today's electronic prosthesis provides a wide range of mobility compared to a mechanical prosthesis. Powered prosthetics are quite expensive and hence not widely used. We have proposed a low-cost control system above-knee powered prosthetics leg that is reliable, and the complexity of the model is less. The prostheses works by taking input from a sensor placed in the model and six FSR402 sensor and an IMU sensor that record force muscle activity of the thigh during variation of leg movement. The value of motion of the joint knee angle is determined by sensors. In this paper, we are discussing and comparing potential deep learning control system namely artificial neural network (ANN) as baseline and recurrent neural network-long short term memory (RNN-LSTM) to predict human gait cycle. A multi-layer perceptron (MLP) method is implemented. In general, network architecture consists of input layer which is represents by six inputs of the FSR402 sensor and an IMU sensor, fully connected layers, and an output layer as joint knee angle. As the result model of ANN as baseline has metric value MAE and R squared are 6.21 and 68.7% respectively whereas model of RNN-LSTM has metric value MAE and R squared are 4.99 and 96.9% respectively. The result indicates the RNN-LSTM model is most likely to perform fewer errors based on the dataset. RNN-LSTM outperforms the task with a greater value compared to the ANN model as the baseline. This present study suggests that deep learning with the recurrent neural network-long short term memory (RNN-LSTM) is suitable for the prediction of human gait cycle movement from the FSR402 and an IMU signal in the overall walking task. [ABSTRACT FROM AUTHOR]

In this study, we compare the effect of different hyperparameters of the deep neural network while solving partial differential equations. We consider feed-forward neural networks, and the hyperparameters such as the number of hidden layers, number of neurons, activation function, optimization methods, and learning rate are studied in this paper. Numerical results for the effect of all the hyperparameters while solving fourth-order partial differential equations are mentioned in this study. [ABSTRACT FROM AUTHOR]

Stepped, sinusoidal, and multi-level inverters are among the numerous types of inverters depending on the output voltage waveform. Inverters are most commonly used in commercial and industrial applications. Low output voltage harmonics are a distinguishing feature of the multilevel inverter. Inverters with several levels come in a range of designs and sizes. Because they require fewer components, cascaded multilevel inverters, for example, have attracted a lot of interest in the power industry. As the number of levels develops, the consonant substance of the yield voltage waveform diminishes. This paper illustrates a Cascaded H-Bridge fifteen-level inverter possible with the fewest switches. As the level increments, the synthesized yield waveform has more steps, coming full circle in a staircase wave that approaches the specified yield voltage waveform. By employing a limited number of switches, switching and power losses can be avoided. A fifteen-level inverter generally requires 28 switches, however the suggested inverter just requires 12. So that the inverter's cost can be kept as low as possible. The artificial neural network is trained to perceive the open circuit switches in the multilevel inverter and evaluated using data sets in order to eliminate problems that occur in inverters. Thus, using the suggested fault diagnosis method, a better understanding of fault behaviour, diagnostics, and detections of a multilevel inverter can be acquired with the help of ANN based fault detection using MATLAB Simulink and the results are analyzed. [ABSTRACT FROM AUTHOR]

The efficiency of the systems for the automated formation of artificial neural networks is beyond any disputes. Such systems make it possible to automatically generate ready-to-work models without participation of a domain expert while creating a model. The present paper considers a modification of the evolutionary algorithm for the automated formation of artificial neural networks of direct propagation, which helps build recurrent neural networks. The peculiarity of the algorithm lies in the network structure encoding in the form of a tree allowing the network structure to be represented more compactly, that well correlates with text analysis tasks, the dimensions of which vary from hundreds to thousands of features. To test the approach, the problems of classifying textual information were solved. The averaged test results as well as some of the obtained models are presented to demonstrate the approach. It is showed that the algorithm successfully builds models with a recurrent structure. This approach can be potentially improved by adding various recurrent links. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) shows the plot of the bio-potential produced by the movement of the heart and is utilized by doctors to foresee and treat different cardiovascular illnesses. The arrangement of electrocardiogram (ECG) signals assumes a significant function in the conclusions of heart illnesses. An exact ECG grouping is a difficult issue. Early and precise discovery of arrhythmia, Congestive Cardiovascular breakdown types is significant in distinguishing heart infections and picking suitab le treatment for a patient. Various classifiers are accessible for ECG orders. Among all classifiers, Convolution Neural Organizations (CNNs) like ALEXNET have become exceptionally famous and most broadly utilized for ECG grouping. This paper examined the issues engaged with ECG order and presents a definite studyof pre-handling strategies, ECG information bases, highlight extraction methods, CNN-based classifiers, and execution measures to address the referenced issues using the IoT-based mechanisms. [ABSTRACT FROM AUTHOR]

This paper investigates the most commonly used Deep Learning (DL) based techniques for estimating the wireless channel at the User Equipment (UE). The generalized Nakagami-m fading distribution is considered for deep learning network. The motive of this work is to provide an experimental investigation on the wireless channels using three different deep-feed forward neural networks, namely Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), and Convolution Neural Networks (CNN). The analysis includes the preprocessing approach for handling complex received signals as one of the inputs and actual channel coefficients as output. Bit Error Rate (BER) performance analysis is performed and is compared with the classical LS estimator. It is demonstrated that DL channel estimation outperforms the Least Square (LS) based estimation, and simulation results support that DL-based channel estimation is very efficient. [ABSTRACT FROM AUTHOR]

Floods are a serious problem for communities located on the banks of rivers. They can destroy buildings, endanger lives, and cause economic losses. Hydropower plants can also affect the behavior of rivers and increase flood risk. In recent years, neural networks have proven to be a powerful tool for modeling various processes. In this paper, we will consider the development of a model of flood water passage, taking into account the impact of hydroelectric power plants, using neural networks. As a model object, we have chosen a settlement located in the northern part of Krasnoyarsk region at the confluence of two full-flowing rivers Angara and Yenisei. The model was developed using the python programming language. The recurrent network with long short-term memory LSTM using 64 neurons was chosen as the network architecture. The data array for modeling was obtained from Yenisei Basin Water Department and FGBU Sredne-Sibirskoye UGMS. The input parameters were selected empirically. The analytical forecasting method and the neural network model were compared. [ABSTRACT FROM AUTHOR]

Stress and emotion assessment play a pivotal role in healthcare, aiding in the early detection and prevention of various diseases. In this research paper, we propose a novel healthcare model that harnesses the power of artificial neural networks (ANN) and discrete wavelet transform (DWT) to assess stress and emotion through speech signal recognition. The proposed model extracts essential features from speech signals using DWT and employs ANN for the classification of stress and emotional states. Both ANN and DWT are established signal processing techniques that find applications in diverse domains. We present a comprehensive analysis of our healthcare model, which effectively analyzes speech signals to identify the stress and emotional states of individuals. The model's performance is rigorously evaluated using a publicly available dataset comprising speech signals recorded from subjects experiencing varying stress and emotional states. Experimental results demonstrate that the proposed model achieves high accuracy and surpasses existing state-of-the-art techniques. By employing recurrent neural networks (RNN), we visualize speech's phonetic characteristics, such as Mel-frequency cepstral coefficients (MFCCs), transforming them into sequences of phonemes or words. RNNs, designed to handle sequential data, form the basis of our speech recognition using neural networks, contributing to the model's effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms. [ABSTRACT FROM AUTHOR]

Heart disease forecasting is a key issue in the clinical data analysis field. Artificial Neural Network (ANN) is a machine learning tool that can help doctors diagnose heart diseases more accurately and quickly. However, the design of ANN is complicated because it needs to identify optimal weight values and suitable network structures. This paper aims to optimize the weights of ANN for forecasting the existence of heart disease among humans by using Artificial Bee Colony (ABC) algorithm to train ANN and select optimal network weights. The experimentations are carried out on the UC Irvine Machine Learning Repository (UCI) heart disease datasets and tested by the MATLAB software machine learning tools. The accuracy, specificity, sensitivity (recall), precision, and F-score of the ANN trained by the ABC model (ABC-ANN) and ANN trained by ABC with backpropagation (ABC-BpNN) model are investigated. Between ABC-ANN and ABC-BpNN, it was shown that the ABC-BpNN model has a better predicting ability and could reach considerably higher accuracy with 90% and 90.9%, 88.9%, 90.8% and 90.9% for precision, specificity, recall, and F-score, respectively. The findings showed significant enhancements compared with previous studies that have utilized the same dataset. [ABSTRACT FROM AUTHOR]

Face recognition is a biometric based application software capable of verifying or identifying exclusively a person by analyzing and comparing person's facial contours using patterns. The main applications of face recognition system is found in security systems, law enforcements and other initiatives. The variation in illumination, effects immensely on recognizing faces or images. Even for the familiar faces, illumination variations lead to alteration in the shadow shapes, shift in locat ions, fluctuations in features and contract gradient variations. Under these conditions, larger impact is observed to recognize these face images. A face recognition system is developed under these varying illumination conditions in this paper. Various methods of illumination normalization like Self-Quotient Image (SQI), Histogram equalization (HE), Gross & Barovick's anisotropic smoothing (GB), Gross & Barovick's anisotropic smoothing (GB) and Multiscale Retinex (MSR) are comparatively analyzed. Illumination invariant face recognition is developed by first acquiring 40 illumination variants face s. Face detection is performed by deformable template with scaled color map and its negative. Further face alignment is estimated by supervised learning by estimating the locations of a set of facial landmarks. Later image normalization is performed using Tan Trigg's Normalization Technique (TTVT) which includes Gamma correction, MaskingDifference of Gaussian (DOG) and contrast stretching methods [1]. The resultant normalized face image is classified using Convolutional Neural Networks. The evaluation and performance analysis are discussed to determine the applicability and suitability for various applications. [ABSTRACT FROM AUTHOR]

Currently, many companies are competing to produce robots that can help humans. Robots can work with high accuracy, efficiency, and low power in the production of goods. Automated Guided Vehicle is one form of robot that is useful in industry. Automated Cars are another form of functional industrial robot that transports with programmable paths and directions automatically. In this paper, we will discuss the navigation system in Automatic Cars based on previously mapped paths. The robot wheel serves to adjust the direction and position of the robot. The automatic movement can be carried out using computer vision. Warehouses that are not neatly arranged cause the navigation system to be less than optimal. The types of controllers used include classical controllers, fuzzy logic, artificial neural networks, and H∞. The purpose of this research is to produce a precise car front view monitoring system using image processing with high enough quality for automatic car navigation. The benefit for science is the development of automatic cars, which so far have not been widely carried out in Indonesia. The results of this study can be useful for the development of methods to build an automatic car system. The benefit for the electronics industry is the availability of control equipment designs to be able to control cars automatically. The effect on society is in the form of future vehicles that can move automatically without a driver. Based on the computer vision system test for Raspberry Pi-based car navigation, it can be ascertained that the car works well and has a small physique, so it looks neat and concise. The movement of the car from the first coordinate point to the next coordinate point is very precise and precise. By installing the L298N motor driver module, it is easier to set the program in python, so that the results sent to the DC motor can be used directly. Using a DC motor that has an excellent performance in terms of torque and speed. [ABSTRACT FROM AUTHOR]

The soil under any foundation should be capable enough to support the transmitted loads so that the structure– foundation–soil system is stable and safe against any settlement. Any change in the unconfined compressive strength is one of the principal causes of settlement. In this paper, an approach to forecast the unconfined compressive strength of gypseous soils based on basic soil properties was developed using the Artificial Neural Networks technique. An equation was developed to estimate the unconfined compressive strength using a backpropagation algorithm to train multi-layer perceptron, in which good agreements were achieved. The model inputs were; the depth, gypsum content, liquid limit, plastic limit, plasticity index, passing sieve #200, dry unit weight, water content and initial voids ratio. The output was the forecasted unconfined compressive strength. A parametric study was conducted to investigate the generalization and robustness of the model. The findings indicated that the model was reliable within the range of utilized data. The sensitivity analysis of the model revealed that the water content had the highest relative effect, followed by passing sieve #200, dry unit weight, liquid limit, plastic limit and gypsum content, while depth, initial voids ratio and plasticity index have lower relative effects. [ABSTRACT FROM AUTHOR]