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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 enormous benefits and applications of Image classification and recognition are umpteen. Machine learning algorithms and Deep Neural Networks are like windfall to fathom the objective proficiently in streamlined manner. The prevalent improvement in this technology is that these networks do not call for any prior blueprints in terms of algorithms as prerequisites. The presented paper is an attempt to create a Convolutional Neural Network from scratch to classify the images from the well-known dataset – Cats and Dogs into their relevant baskets. Manifold open source accessible approaches to amplify the efficiency of the network are no more onerous. Further, data augmentation technique boosts the efficiency tremendously by extending the dataset with reoriented features from the same images. To untangle the same problem, Transfer Learning is also a compelling technique in which all the layers, neurons in each layer, weights of each neuron and all other parameters are predefined and we can amend the output layer as per the classes in the respective problem statement. In the present paper, we have tried to obtain a comparable efficiency with a significant reduction in parameters. [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]

Handwriting Detection is a computer's approach or ability to access and interpret understandable handwritten source inputs such as paper documents, touch screens, images, etc. Recognition of handwriting is characterized as the task of turning a language into its symbolic representation represented in its spatial form of graphical marks. The progress is base on artificial neural networks, which is an artificial intelligence area of study. To build a Handwriting character recognition system, similar techniques and methods are used. When the data is implemented and translated into digital form by an OCR (Optical Character Recognition) device, it can be edited, manipulated, and processed in a computer-understandable form. For extracting the characteristics of the handwritten alphabets, a method of feature extraction is implemented and then we use the data to train the artificial neural network. It contributes enormously to the development of an automation process and in various applications will enhance the interface between man and machine. [ABSTRACT FROM AUTHOR]

Agriculture is essential for everyone to promote sustainable development, the farming, which combines image processing, artificial intelligence, Deep learning, and Internet of Things (IOT). World population incensing every day. Due to the rising demand in the Agriculture industry, the need to collectively improve a plants and growth its field is very useful. In this paper, it is important to maintain the crop during its initial time, and also at period of harvesting. The image processing and artificial networks are used as a different techniques to maintain the detecting the diseases on the leaves and correct time to harvesting. When we take images with help of drones, the images are divided and changed to disease described three things vectors namely the first one is color, one more is texture and morphology. The vectors morphology gives 95% accuracy and its give more compare to other two vector features. This research paper proposed effective and useful algorithms for detection of disease with help implementation of Artificial Neural network algorithms using MATLAB. Detection of leaf or plant diseases with some manual techniques are requires a lot of work by maintaining a huge farm of crops, and it's very early stages it detects different types of symptoms to different diseases on plants, when the displayed on crop leaves. In this research paper survey on various disease classification techniques that can be for plant leaves diseases detection. For this purpose Artificial Intelligence, Neural network algorithms and back propagation techniques for adjustment of training data sets. [ABSTRACT FROM AUTHOR]

This paper proposes an ANN based adaptive Auto Reclosing AR which integrated of an operation of distance measuring relay in HV Over Head Transmission Line OHTL. This work is brought to enhance stability synchronism, and reliability during the transient faults of OHTL. Thus, it leads to increase the continuity of the electric power system, especially of hydropower plants are connected to utility grid. This work are used Matlab\Simulink environment to design and simulate a single-phase auto-reclosing, which depends on the Artificial Neural Networks ANN integrated with a distance relay DR. In addition, the simulation model consists of Boolean algebra circuits, and timers are utilised to build Auto-reclosure of a circuit breaker in each phase. The ANN is here trained to recognize angle values of actual impedances (in the positive sequence only) in which it can classify which phase exactly is line to ground fault. The simulation results show the ability of this combination to distinguish among various fault types and giving multi pulses-shot of tripping and reclosing for the specific circuit breaker with a successful mechanism. In this paper, a realistic system with real parameters was represented by the important transmission line (Mosul Dam-East Mosul), which currently is only the main and alone line that connects the northern Iraq network with a national grid. [ABSTRACT FROM AUTHOR]

There has been a lot of research in recent years into employing optimization approaches to improve artificial intelligence (AI). In this research review paper, we have compared and contrast some of the most usual optimization algorithms, such as Backtracking searching method (BSA), the genetic algorithm (GA), particle swarm optimization (PSO), an artificial bee colony (ABC), and the genetic algorithm, which are all artificial neural networks (ANNs)-based algorithms. The number of recently developed optimization techniques, such as the lightning search algorithm (LSA) and the whale optimization algorithm (WOA) are also compared. All the techniques are categorized based on randomly generated populations. To produce the best possible results, the processing parameters are set within a certain range according to their knowledge. This review paper emphasis on applying optimization techniques to improve the accuracy of simply adjusting the parameters of the neural network. This review paper also presents some results for enhancing neural network performance using various optimization techniques like PSO, GA, and ABC optimization methods to get optimal processing parameters, such as the number of hidden layers, neurons and learning rate etc. The findings of this research review paper will aid in the improvement in the quality of plastic injection molded parts. [ABSTRACT FROM AUTHOR]

Artificial intelligence is the simulation of human intelligence and seeks to mimic human knowledge acquisition by using planning, reasoning, and research. Sometimes AI faces challenges due to a large set of rules, and growing demand for learning and searches optimization. Computational intelligence is a form of computing modeled on the methods by which humans learn. Artificial neural networks system learns by being given different image examples, and contain many layers of learning, some of them more complex than others. This paper presents a comprehensive survey on the various artificial neural network open source software for implementing computational intelligence as they seem to be effective in simulation, prediction, building, and studying artificial neural networks. This paper helps the researchers to find which software they should prefer by knowing their requirements. [ABSTRACT FROM AUTHOR]

The constant change in the components and variables of the economic environment is one of the most important phenomena that characterize this environment at present, and these changes have made the goal of survival in the market a priority due to the prevalence of financial distress in companies. The main purpose of this paper is to test the impact and effectiveness of the model of artificial neural networks and Naive Bayes technology and to see how accurate they are and their ability to classify companies. In this paper, the information of the companies listed on the Iraq stock exchange for 2017, which represents 36 companies with high financial distress, 20 companies with medium financial distress, in addition to 43 non-distressed companies for a group of 99 companies. According to the experimental results, the model of artificial neural networks gives the highest rating accuracy of 96.97% and an error rate compared to Naive Bayes, which gave a rating accuracy of 74.74%. [ABSTRACT FROM AUTHOR]

Associative Hopfield Memory is a very useful form of Artificial Neural Networks. This paper outlinesan optimization relaxation approach for signature verification based on the Hopfield neural network (HNN). The standard sample signature of the customer is cross matched with the one supplied on the check. The difference percentage is obtained by calculating the different pixels in both the images. The network topology is built so that each pixel in the difference image is a neuron in the network. Each neuron is categorized by its status, which in turn signifies that if the particular pixel is changed. The network converges to unwavering condition based on theenergy function which is derived in experiments. The Hopfield‟s model allows each node to take on two binary state values (changed/unchanged) for each pixel. The performance of the proposed technique is evaluated by applying it in various binary and gray scale images. This paper contributes to finding an automated scheme for verification of authentic signature on bank check. The derived energy function allows a trade-off between the influence of its neighborhood and its own criterion. This device is able to recall as well as complete partially specified inputs. The network is trained via a storage prescription that forces stable states to correspond to (local)minima of a network "energy" function. [ABSTRACT FROM AUTHOR]

The wireless networks of the future generation are evolved as complex systems due to broadening in service prerequisites, application heterogeneity and networking of gadgets. In recent times, the major step forward of machine learning techniques is deep learning. However, in wireless/heterogeneous networks, the deep learning application for network traffic control is relatively new. The key disputes in the wireless backbone networks since the advancement of wireless networks are resource allocation and efficient network traffic control such as routing. In larger scale of networks and tangled radio environments, the method of adding intelligence to wireless networks is achieved by Deep Learning (DL). The tangled wireless networks supported with several nodes and their quality of variable link can be investigated by Deep Learning. This paper is presented with a perception of harnessing the next generation communication networks with the artificial neural networks. This work helps the readers to explore the unsolved issues to pursue their research and deeply understand the wireless network design with DL based state of the art facilities. In this work, we integrate the deep learning and wireless networking research with a widespread survey. Finally, this paper summarizes the disputes and benefits of acquiring ML and AI for next generation wireless systems. [ABSTRACT FROM AUTHOR]

Driving while drowsy is one of the significant causes of road accidents, and detection of drowsiness of drivers is necessary to minimize such accidents. There are many methods proposed for drowsiness detection. In this paper, we present the details of a drowsiness detection system using deep learning techniques with ECG as its input. Continuous wavelet transforms (CWT) are applied on the input signal, segmented to 1-minute duration, and then it is converted into scalogram images to be fed as input to deep neural networks (DNN). We use pre-trained transfer learning models, AlexNet, ResNet-50, and VGG-19 for this work. The best results were obtained for Reset-50 with an accuracy of 88.31% without dropping the final layers, which is an improvement of 4.5% over the spectrogram-based implementation. This paper evaluates the effectiveness of wavelet-based features, and we note that the time-frequency domain features outperform. To check the robustness of the net-work, a second model is implemented by dropping the final layers, and the validation accuracy of AlexNet is 51.5%, ResNet-50 is 67.2%, and VGG-19 is 81.8%. [ABSTRACT FROM AUTHOR]

This paper presents a proposed design and investigation of an impressed current and Single-Ended Primary Inductive Converter (SEPIC) converter based on an artificial neural network to assess the potential difference required to control cathodic protection system. The cathodic protection methods proved their effectiveness in the metallic equipment protection in the industrial applications that contain liquids and that are buried in electrolyte mediums against the electrochemical corrosion process, where D.C power supply plays an important role in the cathodic protection system. The proposed design consists of an intelligent d.c power supply and a group of anodes. This model has been adjusted to be able to equalize the normal voltage difference between the surrounding medium and the metal part being protected. The proposed design has been tested and investigated the cooling water carrying pipeline of AL-Quds electrical power station as a part of the unified Iraqi power network. The protection voltages and currents were calculated with a permissible tolerance based on; the pipeline characteristics, its coating, and the buried soil along the pipeline length to ensure better performance of protection. The obtained experimental and simulation results showed high effectiveness and accuracy by maintains the negative potential of the pipeline within the permissible and standard limits of the Iron metal of the pipeline. [ABSTRACT FROM AUTHOR]

Nowadays, regular intake of nutritious foods is essential for maintaining a healthy eating routine and avoiding obesity in the human body.. In this paper, a novel framework dependent on AI that naturally performs exact grouping of food pictures and gauges food credits. In the preparation section of the model system, it proposes a profound learning model that includes a convolutional neural organization that orders food into explicit classifications. The principle reason for the proposed technique is to improve the exactness of the pre-preparing model. The paper plans a model framework dependent on the customer worker model. The customer sends a picture location solicitation and cycles it on the worker side. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Network (CNN) are the three classifiers that have been modified to analyze the framework's enhanced precision. Exploring various avenues about a variety of food groups, each with a large number of images, using Artificial Intelligence (AI) to achieve higher grouping precision. [ABSTRACT FROM AUTHOR]

This article describes a method for evolvement predicting of an emergency zone on the example of a forest fire using neural networks and applying the Hurst exponent to increase the accuracy and reduce the computational complexity of the algorithm. The inputs in this method are a set of environment state maps and the fire characteristics. The lack of a sufficient number of maps and a large amount of available data has a negative effect on the forecast accuracy and the overall system performance. It proposed to divide the fire area into sectors and perform the forecast by sectors with an accuracy that depends on the sector behavior characteristics. The sector behavior characteristic in this paper presented by the persistence analysis of the sector vectors, and be replaced by any other indicator. Persistence indicates trend stability in the behavior of the time series and, accordingly, the forecast reliability. In the case of low forecast reliability, the sector is iteratively divided into segments, which are analyzed and characterized. In this case, forecasting carried out for each sector and segment, which generally increases overall forecast accuracy. The paper presents the key mathematical calculations and the results of experimental studies. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption is important, especially in building these days. The energy consumption of Mechanical – Ventilating – Air conditioning (MVAC) systems always the largest proportion in total energy consumption. One of the most effective approach is the estimation and prediction as close as possible to the real operation mode of the MVAC in reality. However, the tasks of energy consumption estimation request large efforts, time consuming but low precise results. Most designers roughly calculate the energy consumption at first and the using automation control to manipulate the system operation later. With the help of the newest, cutting edge technology in soft computing like ANN, ANFIS and intelligent optimization methods, the designers now can apply these technologies to achieve the reliable, rapid, precise results for the further convincing designs. In this paper, the authors simply apply the Multi Layers, Back Propaganda Neural Network (MLB ANN) to estimate the building Heating Load (HL) and Cooling Load (CL). The Mean Square Errors (MSE) and Regression R Values (R) are used as the indicators for ANN with the dataset provided open access by UCI. The results prove that ANN can be reliable approach to predict energy consumption for complex, multi design option building. [ABSTRACT FROM AUTHOR]

Despite advances in technology Handwritten text is still considered as a method for correspondence and recording data in everyday life. Offline handwritten text recognition is an infamously challenging problem in Artificial Intelligence. Recent studies show great potential of Neural Networks for handwritten text recognition segmented at word level. In this paper, a prototype is presented for offline handwritten text recognition using deep neural networks. Preprocessing techniques like data augmentation and line-level segmentation enrich the input images and hence benefits the classifier. The proposed approach performs thorough feature engineering on preprocessed input images and propagates them through a three layered architectures comprising of CNN (Convolutional Neural Network), LSTM(Long Short Term Memory) and CTC(Connectionist Temporal Classification). [ABSTRACT FROM AUTHOR]

Facial expression is an important basis for judging human emotions. The driver's mood can be accurately determined by recognizing their facial expression. Because the facial expression data set based on ordinary life scenes cannot accurately show the relationship between the driver's facial expression and his psychological state in the real driving environment. This paper uses 1000 facial expression images of drivers based in the real driving environment. These images include 6 different facial expressions to express 6 different emotions. This paper uses Edge Impulse to train several neural network models capable of recognizing these 6 different facial expressions and to realize the purpose of judging the driver's psychological state. From the experiment results, the MobileNetV2 model used has the highest accuracy rate of 99.1% on training data and 97.4% on testing data. This shows that the lightweight neural network model MobileNetV2 can accurately recognize the driver's facial expressions in the real driving environment. [ABSTRACT FROM AUTHOR]

Automatic speech recognition is a research field for enabling computers to receive voice feedback from humans and to translate it with the greatest probability of correctness. Speech is the easiest way to communicate the message in basic context. Speech recognition is referring the allowing of machines to perceive and accept speech information from humans for the greatest probability of accuracy. Speech recognition will play an ever more important role in the future of human with machine interfaces. Arabic speech recognition (ASR) is a most complex job and has received little attention than other languages. In this paper we reviewing the most important deep learning methods used in speech recognition in Arabic language. Through the search the main finding in this review that the Arabic is one of the most spoken languages and least highlighted in terms of speech recognition. One of the emerging techniques is using neural networks with deep learning for speech recognition. Deep Neural Networks (DNNs) have reached to suitable performance, Recurrent Neural Networks (RNN) is one of the best models applied for sequential data and convolution neural network (CNN) has obtained a best accuracy rate in speech recognition. [ABSTRACT FROM AUTHOR]

Reliability, efficiency, and cost-effectiveness of smart grids are enhanced with power demand softening by means of efficient load management in electric vehicles. In such initiatives, the involvement of EV users may reduce due to the lack of adaptable user-centric approaches. During the connection sessions, the EV charging time is determined using a deep learning algorithm-based smart charging strategy proposed in this paper. Here, the total energy cost of the vehicle is minimized by making charging decisions considering demand time series, pricing, environment, driving, and other auxiliary data. The memorization technique is used for the estimation of the optimal solution of the existing connection sessions in the initial stage. The deep learning models are trained with this existing data and optimal decisions to make suitable decisions in real-time scenarios where car usage or future energy price values are undetermined. A significant reduction in the charging cost is observed by training the neural network with the proposed model. The results obtained are compared to the optimal charging costs computed and are found to be closely similar. [ABSTRACT FROM AUTHOR]

Timely analysis and recognition of leaf diseases in plants ensures good quality and productivity of crops in the field of agriculture. Recognition of rice leaf diseases is one of the biggest challenges for the farmers due to incomplete awareness regarding latest sophisticated techniques in the field of leaf disease identification. Much of the growth of rice is disturbed due to the prevalence of rice leaf diseases. Earlier detection of rice leaf diseases done manually by farmers which is very laborious and time consuming. However, requisite of automatic identification of rice leaf diseases helps farmers to save their agricultural crops more efficiently. Innovations in the field of computer vision and deep learning meets the expectations and proves to be the best solution for classification of rice leaf diseases using convolutional neural networks using samples of rice leaf diseases. In this review paper, the major focus is on performance analysis of detection of rice leaf diseases based on the architectures employed. [ABSTRACT FROM AUTHOR]

In today's world, a means to communicate with virtually no loss is highly essential, with innovations in SDR and improvements in cognitive computing, we could achieve a better way of communication than existing ones, and newer algorithms make it less hard to develop and test these systems. The cognitive radio, which allows dynamic allocation of the spectrum, could serve as a viable solution to congestion in the network and paves the way for dynamic spectrum allocation. The Cognitive Radio may be implemented, may vary with location and architecture, but the most common Software-Defined Radio could be converted into Cognitive Radio CR by a simple predictive and decisive algorithm. Here, we aim to analyze one of the techniques that could improve spectrum sensing of SDR implementation of CR. SDRs can tune to any wireless standard with modulation type, bandwidth, and variable carrier frequency; This paper proposes a Goodness of Fit technique to re-enforce and predict better channels for transmission or reception. [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]