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Copyright of International Journal of Economic & Social Research is the property of Abant Izzet Baysal University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Basrah Journal of Science / Magallat Al-Barat Li-L-ulum is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using the Convolutional Neural Network (CNN) algorithm, this research aims to create a better lecturer attendance application that improves the attendance system and creates peace of mind when lecturers arrive at national universities. The author analyzes the results of applying deep learning algorithms to an experimental face recognition system that uses convolutional neural networks. The purpose of this study is to show that deep learning algorithms can improve the accuracy and efficiency of recording presence. In addition, the goal of this research is to create a timekeeping application using face recognition technology that is expected to have a high level of accuracy. In addition, this research includes a modification of the CNN model. This modification resulted in an epoch value of 75 for training of 100% and test of 95%. Analysis of results, drawing conclusions, and suggestions for additional development are the final stages of this research. Evaluation of the integrated system is done by collecting actual attendance data and comparing it with the attendance records created by the system. This validation will help explain the performance of the system and find problems or vulnerabilities that may need to be fixed. [ABSTRACT FROM AUTHOR]

Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluated based on their participation in two attendance trials. Successful presence has an achievement success value of 19, and 1 failed out of a total of 20 attempts. Several variables, such as illumination, and the use of marks on hats, that could have influenced attendance caused the experiment to fail. [ABSTRACT FROM AUTHOR]

Breastfeeding is widely recognized as the gold standard for infant nutrition, benefitting infants' gastrointestinal tracts. Stool analysis helps in understanding pediatric gastrointestinal health, but the effectiveness of automated fecal consistency evaluation by parents of breastfeeding infants has not been investigated. Photographs of one-month-old infants' feces on diapers were taken via a smartphone app and independently categorized by Artificial Intelligence (AI), parents, and researchers. The accuracy of the evaluations of the AI and the parents was assessed and compared. The factors contributing to assessment bias and app user characteristics were also explored. A total of 98 mother–infant pairs contributed 905 fecal images, 94.0% of which were identified as loose feces. AI and standard scores agreed in 95.8% of cases, demonstrating good agreement (intraclass correlation coefficient (ICC) = 0.782, Kendall's coefficient of concordance W (Kendall's W) = 0.840, Kendall's tau = 0.690), whereas only 66.9% of parental scores agreed with standard scores, demonstrating low agreement (ICC = 0.070, Kendall's W = 0.523, Kendall's tau = 0.058). The more often a mother had one or more of the following characteristics, unemployment, education level of junior college or below, cesarean section, and risk for postpartum depression (PPD), the more her appraisal tended to be inaccurate (p < 0.05). Each point increase in the Edinburgh Postnatal Depression Scale (EPDS) score increased the deviation by 0.023 points (p < 0.05), which was significant only in employed or cesarean section mothers (p < 0.05). An AI-based stool evaluation service has the potential to assist mothers in assessing infant stool consistency by providing an accurate, automated, and objective assessment, thereby helping to monitor and ensure the well-being of infants. [ABSTRACT FROM AUTHOR]

The identification of individuals through finger vein patterns has become a prominent biometric technique due to its non-invasiveness and uniqueness. Convolutional neural networks (CNNs) have been at the forefront of this technology, offering impressive recognition rates within large, labeled datasets. Despite their successes, the application of CNNs to finger vein recognition remains a challenging task, largely due to the high dimensionality of input data and the multitude of classification outputs required. This paper presents an optimized CNN model designed to address the intricacies of finger vein image classification. It is posited that increasing the number of feature extraction layers, coupled with a strategic selection of kernel sizes for each layer, significantly enhances model accuracy. Through a series of systematic experiments, the optimal layer configurations were identified, resulting in an architecture that surpasses previous models in classification precision. The proposed CNN architecture demonstrates a classification accuracy exceeding 99%, an improvement over existing method. It is noteworthy that the development of this model has been constrained by the limited scale of current finger vein databases, which poses risks of overfitting. Hence, the expansion of these databases is suggested as a future avenue to reinforce the robustness of the training process. The results depicted in this study underscore the potential of deep learning techniques in biometric security, with the advanced CNN model setting a new benchmark in finger vein recognition. [ABSTRACT FROM AUTHOR]

This research aims to evaluate various traditional or deep machine learning algorithms for the prediction of groundwater level (GWL) using three key input variables specific to Izeh City in the Khuzestan province of Iran: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P) (with 3 km distance). Various traditional and deep machine learning (DML) algorithms, including convolutional neural network (CNN), recurrent neural network (RNN), support vector machine (SVM), decision tree (DT), random forest (RF), and generative adversarial network (GAN), were evaluated. The convolutional neural network (CNN) algorithm demonstrated superior performance among all the algorithms evaluated in this study. The CNN model exhibited robustness against noise and variability, scalability for handling large datasets with multiple input variables, and parallelization capabilities for fast processing. Moreover, it autonomously learned and identified data patterns, resulting in fewer outlier predictions. The CNN model achieved the highest accuracy in GWL prediction, with an RMSE of 0.0558 and an R2 of 0.9948. It also showed no outlier data predictions, indicating its reliability. Spearman and Pearson correlation analyses revealed that P and E were the dataset’s most influential variables on GWL. This research has significant implications for water resource management in Izeh City and the Khuzestan province of Iran, aiding in conservation efforts and increasing local crop productivity. The approach can also be applied to predicting GWL in various global regions facing water scarcity due to population growth. Future researchers are encouraged to consider these factors for more accurate GWL predictions. Additionally, the CNN algorithm’s performance can be further enhanced by incorporating additional input variables.

In this paper, GPS, WLAN, and INS are used as positioning information data sources, and multi-source fusion technology is used to build an intelligent positioning navigation device. The feature vectors of the original data are observed using the federal Kalman filter algorithm, and the information is continuously updated to assign weights to achieve the optimization of the positioning information. The three-dimensional information of the defined position is measured using the CNN algorithm, the faulty data in the filter is detected and removed, and the detected data enters the main filter instead of the faulty data for fusion. To prove the effectiveness of the device, experimental analysis is performed, and the results show that the device provides diverse, intelligent services with up to 95% localization accuracy and stronger signals.

In this paper, the image processing capability of the CNN algorithm under the chaotic recursive diagonal model is explored from two aspects of medical image fusion and compression. By analyzing the structure of the chaotic recursive diagonal model, it is possible to combine it with a neural network. A convolutional neural network is used to automatically extract the focusing features of an image and output the probability of a pixel focusing. Combining the convolutional layer to extract image features with the activation function to nonlinearly map the feature map to achieve the effect of image fusion. Focusing on the exploration of the CNN algorithm for image fusion in image compression application processes. The results show that in the image fusion experiments, the CNN algorithm for image fusion data MI mean value is 6.1051, variance is 0.4418. QY mean value is 0.9859. The variance value is 0.0014. Compared to other algorithms, CNN in the image fusion effect has the effect of better distinguishing the edge details and making the appropriate decision. The CNN algorithm of the compression time is shorter. The time used in the compression of the X-chest image is 2.75s, which is 0.42 less than other algorithms. This study provides a new research perspective for medical image processing and is beneficial to improving the efficiency of medical image processing.

Emotion recognition utilizing MindWave signals and neural networks presents a substantial challenge due to the inherent complexity of human emotions and the variability of individual brainwaves. The selection of the appropriate algorithm, dictated by the problem and available data, necessitates an understanding of each algorithm's unique strengths and weaknesses. Previous studies have predominantly focused on the classification of emotions through EEG signals employing various standalone neural network algorithms. However, our study fills a notable research gap by integrating Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). This innovative combination yields improved testing performance and accuracy, setting a benchmark in the realm of emotion recognition. The process encompasses the collection of MindWave data, the elimination of noise through preprocessing, the extraction of features indicative of emotional states, and the training of a neural network using labeled data. Finally, the network's accuracy is evaluated on novel data. By addressing the unique challenges and complexities associated with emotion classification using EEG signals, this study provides a promising and advanced approach towards the understanding and recognition of human emotions, paving the way for potential real-world applications. [ABSTRACT FROM AUTHOR]

With the development of science and technology, electromechanical integration and the Convolutional Neural Network (CNN) have developed rapidly. At present, one of the more widely used fields is the electric energy metering management system. Data analysis is one of the focuses of research in this field. Therefore, this paper introduces CNN algorithm and explains the advantages and disadvantages of the CNN algorithm in previous studies and the direction of optimization. Secondly, the target detection algorithm and data analysis are described, and the application of the target detection algorithm to image information processing and information analysis in the current research is introduced. Additionally, two methods are proposed for optimizing the CNN algorithm, and the optimization model is re-optimized by introducing the migration model. Finally, comparative experiments are conducted to verify the effectiveness and rationality of this model. The experimental results show that the detection rate of the two optimization methods is higher than that of the traditional model. The detection rate of CNN based on Region Proposal Network (RPN) is higher than that based on Region of Interest (ROI) pooling. Simulation experiments are carried out in different power metering management systems in the second experiment. The RPN-CNN model was introduced into the migration model. In system 1, the maximum difference between the detection rate and the traditional model is 0.2. In system 2, the maximum difference in detection rate is 0.12, which verifies the effectiveness of this model. Additionally, the stability of the RPN-CNN is better than that of the traditional model in the slope comparison of the curve, which proves the feasibility of the model. Therefore, this paper has certain reference significance for the data analysis of the power metering management system. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study was to develop a deep convolutional neural networks (CNNs) algorithm for automated assessment of stool consistency from diaper photographs and test its performance under real‐world conditions. Methods: Diaper photographs were enrolled via a mobile phone application. The stool consistency was assessed independently according to the Brussels Infant and Toddler Stool Scale (BITSS) by paediatricians. These images were randomised into a training data set and a test data set. After training and testing, the new algorithm was used under real‐world conditions by parents. Results: There was an overall agreement of 92.9% between paediatricians and the CNN‐generated algorithm. Post hoc classification into the validated 4 categories of the BITSS yielded an agreement of 95.4%. Spearman correlation analysis across the ranking of 7 BITSS photographs and validated 4 categories showed a significant correlation of rho = 0.93 (95% CI, 0.92, 0.94; p < 0.001) and rho = 0.92 (95% CI, 0.90, 0.93; p < 0.001), respectively. The real‐world application yielded further insights into changes in stool consistency between age categories and mode of feeding. Conclusion: The new CNN‐based algorithm is able to reliably identify stool consistency from diaper photographs and may support the communication between parents and paediatricians. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people’s livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommenda)tion algorithm based on the neural network framework. Through the text convolutional network, the input layer in CNN has transformed into two channels: static ones and non-static ones. Meanwhile, the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher. The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding. It obtains data name features through a convolution kernel. Finally, the top pooling layer obtains the length vector. The attention system layer obtains the characteristics of the data type. Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage. The proposed algorithm shows excellent accuracy and robustness. [ABSTRACT FROM AUTHOR]

There have been several security solutions based on artificial intelligence (AI), such as intrusion detection systems (IDS),cyberattacks are increasing because big data is increasing by using the internet on all sides of life, therefore, unbalanced data poses a serious problem in intrusion detection systems. The proposed detection system that is based on deep learning Convolutional Neural Network(CNN )partitions data into training and testing., Creating the classifier model for the Principal Component Analysis (PCA )technique of reducing features, is required for the development of intelligent analytic tools that need data pre-treatments and deep learning algorithm-performance enhancement. The UNSW-NB15 data set is used According to experimental findings, We employed a number of evaluation tools to assess the proposed NIDCNN strategy relying on the UNSW-NB15 data set that takes 30% of it for testing and after processing this part of the data became used to evaluate the proposed system. Measures such as a classifier's F-Score, precision, and sensitivity (Recall) are evaluated. Classifier performs better than the other approaches at determining if the data stream is normal or malicious. which is used to assess deep learning's effectiveness, the suggested model results from a high level of accuracy. The experimental findings demonstrate the suggested system's ability to accelerate the intrusion detection process while reducing memory and CPU usage. Experimental results prove the theoretical considerations.Because the UNSW-NB15 data set contains a wide range of patterns that accurately represent contemporary real network traffic, New NIDS algorithms can therefore be assessed using it. [ABSTRACT FROM AUTHOR]

In 2050, the worldwide populace is assessed to be about 9.7 billion, because of which there will be incredible food inevitability. So as to address this issue, it is important to build the current arrangement of agriculture system. The conventional method of agribusiness is fine, yet at the same time it won't meet the world's whole food necessities. Here utilization of past information mining methods in assessment of yields and environmental change is used to take better decision of crop for farmer, choices made for cultivating and increase the necessary financial return. A huge issue that can be beaten reliant on past experience is the issue of yield assessment. Thus, from crop cultivation to crop market systematic approach is proposed using CNN framework with Smart Farm IoT. Utilizing Temperature, Humidity, Rainfall (THR) concept to get to trim creation design in light of climatic conditions, for example, precipitation, temperature, humidity and so on. Harvest expectation is a precondition, and forecast of illness is a post- condition for the assortment of information from a field or zone from a climate boundary test. Furthermore, utilizing this proposed framework farmer gets correct fertilizer prediction for diseases as well as the nearest fertilizer shops are recommended. [ABSTRACT FROM AUTHOR]

The agricultural sector plays a crucial role in the majority of developing countries like India. But in recent times agriculture production is following a downward trend due to various plant diseases with an increase in investment costs. This work conducts a survey on deep learning techniques training issues related to a Chili leaf diseases dataset. Especially the work focused on the viability of the Squeeze-Net training architecture on the Chili leaves to train the two classes of diseased leaves namely Geminivirus and Mosaic. The dataset comprised of 160 Chili diseased photographs deployed from the Kaggle public domain is subjected to the Squeeze-Net convolutional neural network (CNN) to test the training accuracy. The obtained training accuracy ranges from 50% to 100% by considering various training properties like CNN optimizers SGDM, ADAM, and RMSPROP w.r.t Max_Epoches and assigning Dropout probability, Strides, Dilation factor, and padding values as constants. From the simulation is observed that the Squeeze-Net CNN architecture is achieving 100% accuracy in ADAM, and RMSPROP, where Max_Epoches are 40 and 35 respectively. But it suggested that the applicability of RMSPROP is good for training the Chili Dataset, where Max_Epoches are very less compared to the ADAM and SGDM. [ABSTRACT FROM AUTHOR]

A great deal of research indicates that the duality of agricultural production can be reduced because of various factors. Plant diseases are among the most critical aspects of this category. As a result, reducing plant diseases allows for significant improvements in quality. The article uses Grape Leaf Disease Detection Technique (GLDDT) with Faster Region based Convolutional Neural Network (FRCNN) - GLDDT-FRCNN techniques to automatically diagnose plant diseases. Once trained, the software can diagnose plant leaf disease without requiring any experimentation. The primary focus of this research is grape leaf disease. The basic technique manipulates H & colour histograms 25 channels 24. Excluding the final 26 phases, where the person decides which channel (H or a) offers the best separation, the algorithm method is fully automated. A GLDDT is also proposed in this article, which uses two-pronged processes for the evaluation, recognition and categorization of traits. The analysis process, testing on a benchmark set of data reveals that the disease diagnosis system might be a better fit than existing methods because it recognizes and identifies infected/diseased areas. The researchers achieved a precision rate of 99.93% for the detection of Isariopsis, black rot and Esca using the proposed disease detection method.

Since the early days of 20 20, COVID-19 has tragic effects on the lives of human beings all over the world. To combat this disease, it is important to survey the infected patients in an inexpensive and fast way. One of the most common ways of achieving this is by performing radiological testing using chest X-Rays and patient coughing sounds. In this work, we propose a Convolutional Neural Network-based solution which is able to identify the positive COVID-19 patients using chest X-Ray images. Multiple CNN models have been adopted in our work. Each of these models provides a decision whether the patient is affected with COVID-19 or not. Then, a weighted average selection technique is used to provide the final decision. To test the efficiency of our model we have used publicly available chest X-ray images of COVID positive and negative cases. Our approach provided a classification performance of 88.5%. [ABSTRACT FROM AUTHOR]

With the support of Internet of Vehicles technology, UBI (Usage Based Insurance) car insurance rate determination has certain guiding significance for achieving accurate pricing of car insurance rates and satisfying the personalized needs of users. Based on the CNN (Convolutional Neural Networks) algorithm and SVM (Support Vector Machine) algorithm, this paper establishes a rating model for UBI car insurance rates. The model first performs a series of operations such as convolutions, pooling and nonlinear activation function mapping using the CNN algorithm so that it can extract the features from the driving behavior data of UBI customers. Then, it introduces the Hull Vector to optimize the operating efficiency of the SVM algorithm. The HVSVM (Hull Vector Support Vector Machine) algorithm classifies customers according to their driving behavior, and thus obtains UBI customer car insurance rate grades. Therefore, this paper proposes a UBI car insurance rate grade determination model based on the CNN-HVSVM algorithm. The empirical results of the model show that the CNN-HVSVM algorithm has higher discrimination accuracy in the risk rating process of UBI customer driving behavior than the CNN algorithm, BP neural network algorithm and SVM algorithm; and when dealing with large training sets, it has a speed advantage over the CNN-SVM algorithm. Furthermore, it is easy to realize in the process of establishing the UBI car insurance rate determination model and it has good robustness, which can adapt to diverse data sets, thus achieving better results in the car insurance rate determination process. Therefore, the CNN-HVSVM model can predict the grade of UBI car insurance users more accurately and efficiently, and the prediction results are more consistent with the actual situation, which has strong applicability and flexibility. The UBI car insurance premium rate determination model based on the CNN-HVSVM algorithm can determine driver behavior more fairly and reasonably, and has certain practical significance for promoting car insurance rate market reform, which can better promote future UBI research work.

For the purpose of analyzing the application of back propagation (BP) neural network model in retinal vascular segmentation of color fundus images, in this study, the traditional BP neural network was first improved by adopting the additional momentum method. Second, adaptive histogram, morphological background subtraction, Gauss preprocessing matching filter and Heisen matrix were used to enhance the image and extract the features. Third, the retinal vascular segmentation algorithm for color fundus images was constructed based on optimized BP neural network. Finally, the DRIVE and MESSIDOR data sets of color fundus images were introduced to compare the proposed algorithm with the convolutional neural network (CNN) and pulse-coupled neural network (PCNN) algorithms in terms of performance. In addition, the three algorithms were also compared in terms of the sensitivity (Se), specificity (Sp), accuracy (Ac), average operation time for each image and F1 value. The results show that the BP neural network algorithm proposed in this study shows obvious advantage over the other two algorithms in the segmentation of color fundus images. In the DRIVE data set, the Se (81.37%), Sp (90.55%) and Ac (95.82%) of BP algorithm are the highest among the three; in the MESSIDOR data set, the Se (85.22%), Sp (91.08%) and Ac (96.16%) of BP algorithm are also highest; in the DRIVE and MESSIDOR data sets, the operation time of BP algorithm is (28.46 ± 3.19 ms; 24.73 ± 4.81 ms, respectively), which are significantly less than the other two algorithms. Besides, the F1 value of the proposed algorithm is obviously higher than that the other two algorithms. As a result, it is concluded that compared with the CNN algorithm and the PCNN algorithm, the proposed algorithm is more effective in the retinal vascular segmentation of color fundus images; the capillaries have better connectivity, and the proposed algorithm can improve the segmentation Ac while reducing the operation time. [ABSTRACT FROM AUTHOR]

The COVID-19 is an unmatched emergency inciting an immense number of incidents and security issues. To reduce the spread of Covid, individuals regularly wear shroud to promise themselves. This makes the face attestation an especially infuriating undertaking since unequivocal pieces of the face are hidden. A basic mark of the intermingling of specialists during the progressing Covid pandemic is viewed as plans to deal with this issue through fast and suitable strategies. Face Detection has made a remarkable issue in Image preparation and Computer Vision. Different new figurings are being envisioned utilizing convolutional developments to make the most of them as exact as could be viewed as ordinary. These convolutional models have made it conceivable to eliminate even the pixel subtleties. We desire to plan an equivalent face classifier that can perceive any face present in the bundling paying little psyche to its course of action. Beginning from the RGB picture of any size, the method utilizes Predefined Training Weights of Architecture with arranging is performed through Fully Convolutional Networks. This is to correspondingly set up to see an unmistakable facial cover in a solitary edge. [ABSTRACT FROM AUTHOR]

this study was to analyze the effect of optimization algorithm based on convolutional neural network (CNN) on the image quality of magnetic resonance enterography (MRE) for small intestine, and explore the diagnosis and prognosis of epidemic inflammation bowel disease (IBD) with the MRE under the optimization algorithm and nonlinear equation model. The residual network structure (RNS) was incorporated into the CNN algorithm to establish an optimization algorithm, which was compared with other algorithms in terms of the peak signal to noise ratio (PSNR), structural similarity (SSIM), and high frequency error norm (HFEN). 392 patients suspected as IBD in the Gastroenterology Department were selected as the research objects. They were divided into a Crohn’s disease (CD) group (243 cases), an ulcerative colitis (UC) group (78 cases), an indeterminate enteritis (IDE) group (49 cases), and a non-IBD group (22 cases). The intestinal manifestations, clinical manifestations, MRE imaging manifestations, and gastrointestinal lesion distribution of patients in different groups were compared to analyze the diagnostic value of MRE for epidemic IBD. It was found that the optimized CNN algorithm showed better PSNR, SSIM, and HFEN than other algorithms. The percentages of patients with diarrhea in the CD group and UC group were 67.90% and 100%, respectively. The pediatric ulcerative colitis activity index (PUCAI) score of patients in the UC group showed that the number of patients with mild activity was at most 40 (51.28%). The Crohn’s disease activity index (CDAI) score showed that there were 197 of patients in the CD group (81.07%) with severe activity. In the CD group, the numbers of patients suffered with lesions in proximal colon, terminal ileum, and upper gastrointestinal tract were 125 (51.44%), 127 (52.26%), and 132 (54.32%), respectively. In the UC group, there were 32 (41.03%), 51 (65.38%), and 25 (32.05%) patients with lesions in the rectum, distal colon, and proximal colon, respectively. The diagnostic sensitivity and specificity of MRE for epidemic IBD was 95.18% and 46.7%, respectively. It indicated that the image quality of the optimization algorithm based on the CNN algorithm was improved greatly. Under the nonlinear equation model, the image characteristics of MRE in patients with different diseases were various, and the diseased parts were greatly different. MRE showed high sensitivity and specificity in the diagnosis of epidemic IBD. This study could provide a reference basis for the clinical diagnosis and prognosis of patients with epidemic IBD.

This paper focuses on the implementation, architecture and on-going design of a vehicle when it encounters with an object. The vehicle is driven, guided and controlled by utilizing an array of sensors and software. Many collision warning and avoidance systems were made known at the beginning of 21st century but automobiles won't necessarily be able to make judgment whether it is a child or an empty cardboard box which can be avoided. Collision can be avoided depending upon the interaction between the human and the car. Firstlyalgorithms were used to reach the destination. When a car encounters a collision, it naturally comes to a standstill or identifies the vehicle's next position or lets the vehicle go past it. There are two applications which are highlighted in this paper, one in which there is one route and the human driver takes control of the vehicle and the other one where the human does not interact with the vehicle. [ABSTRACT FROM AUTHOR]

A challenging task in measuring droplet size is the ability to perform in-situ droplet size distribution analysis on multiphase fluids in their native states in the undisturbed environment. In this study, an inline two-dimensional low cost–high accuracy technique is presented for continuous measurement of spherical or non-spherical droplets in emulsions using image processing. The characteristic of the droplets is evaluated and the describe drop size distributions in different ranges is determined. This droplet size determination algorithm is based on both cellular neural networks and linear matrix inequality. Our main work focuses on the performance of the proposed methodology for exploring the dynamical evolution of such droplet size distributions by in-situ measurement. Moreover, the results were compared with those obtained using laser diffraction analyzer technique. It was proved that this method can efficiently characterize the quality of dispersed phase by determining droplet size distribution. [ABSTRACT FROM PUBLISHER]

To date, the authors are not aware of an in‐depth investigation about embedded applications of the convolutional neural network (CNN) algorithm on small, lightweight, and low‐cost hardware (e.g. microcontroller, FPGA, DSP, and Raspberry Pi) applied to detect faults in structural health monitoring (SHM) systems. In this Letter, the authors implement and evaluate both feasibility and performance of an embedded application of the CNN algorithm on the Raspberry Pi 3. The CNN‐embedded algorithm quantifies and classifies dissimilarities between the frames representing healthy and damaged structural conditions. In a case study, the CNN‐embedded application was experimentally evaluated using three piezoelectric patches glued onto an aluminium plate. The results reveal an impressively effective 100% hit rate. This performance may significantly impact the design and analysis of CNN‐based SHM systems where embedded applications are required for identifying structural damage such as those encountered by aerospace structures, rotating machineries, and wind turbines. [ABSTRACT FROM AUTHOR]