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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]

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]

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]

Devanagari is an ancient script that is used to write Hindi, Nepali, Marathi, Maithili, Awadhi, Newari, and Bhojpuri, among other Indo-Aryan languages. Thousands of individuals in India use this script to write documents in Marathi and Hindi. Indian mythology is based on this script. Because of the script's prominence, handwritten Devanagari character identification has grown in popularity over time. Handwritten recognition of languages such as English has received a lot of attention, but Indian languages written in the Devanagari script are also a rich source of information. Most of the work on this problem statement has been done either using deep neural networks like CNN at its heart coupled with other machine learning techniques like SVM,Random Forest etc. In this paper we are utilising a recently introduced transformer model for computer vision known as Vision Transformer for the task of Devanagari Character Recognition. We have also compared our model with various pretrained CNN-based architectures like ResNet50,VGG16 and InceptionV3 and ViT has outperformed these models both on DHCD dataset and the modified slightly more complex version of it with accuracy scores of 99.68% on the original testing dataset of the DHCD dataset and accuracy score of 96.55% on the modified(blurred) slightly more complex version of the original testing dataset. The ViT model thus generalized better than standard CNN-based models on the problem of Devanagari Character recognition. [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]

It's possible that our Human Visual System (HVS) only sees a particular section of an image instead of the whole picture. This phenomenon is a trending and demanding topic in the fields of computer vision research field. For predicting co-salient features many deep learning algorithms have recently been used. This paper examines the visual saliency prediction ability of five cutting-edge deep CNN models namedResnet-50, InceptionResNet-v2, VGG-16, Xception and MobileNet-v2. By using the SALICON dataset, we used five deep learning pre trained models that are used to suggestco-saliency maps on four popular datasets: DUT-OMRON, TORONTO, CoSOD3k, MIT103. According to the data, the ResNet-50 model outperforms in obtaining desirable results and assisted in giving more accurate results that closely resembles the desired output. [ABSTRACT FROM AUTHOR]

Fuzzy neural networks have been widely applied in the medical field. In this paper, we apply cascade architectures of fuzzy neural networks to monitoring fetal health using Cardiotocography data. Cascade architectures of neural networks can select reduced size of input subspace by selecting useful inputs. For the optimization of the input subspace and the structure of the cascade architectures of fuzzy neural networks, genetic algorithms and gradient decent method are used. To verify the applicability of the proposed method, Cardiotocography data available on the Machine Learning Repository site at the University of California at Irvine is used. [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]

Machine learning has helped a lot in the field of cancer research. For the detection and diagnosis of cancer various algorithms like artificial neural networks, Support Vector Machine, Decision Tree, Bayesian Networks, Naive Bayes and K-Nearest Neighbor etc. have been used. Today Machine learning techniques are widely used in distinguishing and characterizing diseases by means of X-ray and CRT (Cathode Ray Tube) images. Using these techniques, one is attempting to anticipate the probability of building up of cancer growth before the disease occur. In this paper, a review has been done on different approaches used to detect different type of cancer using various machine learning techniques. Further the efficiency of these methods is compared on the basis of various parameters like Accuracy, Sensitivity, Specificity, F-measure and ROC (Receiver Operating Characteristics) Curve. [ABSTRACT FROM AUTHOR]

Improvement of correspondence advancements and web based business has made the Master card as them ost well -known method of installment for both on the web band standard buys. Along these lines, security during this framework is extremely expected to stop misrepresentation exchanges. Mis representation exchanges in Visa information exchange are expanding yearly. Toward this path, analysts additionally are attempting the novel strategies to distinguish hand stop such cheats. In any case, there is consistently a need of certain procedures that ought to unequivocally and productively identify these fakes. This article includes a plan for the purpose of con artists in the visa details, with the help of the neural network (NN), unsupervised learning in the process. The proposed method is superior to the common methods of Auto Encoder (AE), the Local Emission Factor (LOF), isolation forest (IF),and the K-means clustering. The proposed method for the localization of the expression is based on the NNR operates with an accuracy of 99.87%, compared to the existing, AE, AS, PRAISE, and 'K' Means as the strategies that will give an accuracy of about 97c/o,98c/o,98c/o, and 99.75c/o separately. Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions. [ABSTRACT FROM AUTHOR]

In this paper a hybrid approach for spectral analysis and voice profiles recognition by techniques on the base of Machine Learning and Artificial Intelligence (AI) have been proposed. For voice-processing procedures was applied the algorithm of Fast Fourier Transformation (FFT) with several different window types, respectively Hamming, 4 Term BHarris, Flat Top and Hanning. The spectral feature extraction is used for pre-processing training sets about k-Nearest Neighbours (k-NN) classification models and Feed-Forward Neural Networks (FFNN) for individual's personal identity about target group of people. Euclidean, Cityblock, Minkowski and Chebychev metric distances were applied in k-NN model creation. The design models are evaluated through resubstitution and cross-validation techniques. Levenberg- Marquardt learning algorithm was used to FFNN architectures with Linear and Tangent Sigmoid activation functionsin network outputs. High quality k-NN and FFNN models in regard to personal voice identification with level of accuracy achieved 97.68 % and 100.0 % were synthesized. [ABSTRACT FROM AUTHOR]

The aim of statistical modeling is to predict with good accuracy the variable of interest and sometimes to determine the contribution of the independent variables towards the dependent variable. Over the years, researches have always tried to produce better models in order to increase the accuracy of their predictions. This paper will discuss some statistical techniques that were used frequently in environmental research as well as recent techniques and advances in statistical modelling. The first technique that will be discussed is the multiple linear regression (MLR) models. Some other regression models will also be given. Next probability distributions will be discussed. This is to obtain statistical distributions that best fit the variable of interest. This will enable the researcher to predict the return period of an event. The application of Bayesian statistics has increased over the years. The used of least squares method in MLR and hypothesis testing is known as classical statistical techniques. The Bayesian statistics is based on the probability distributions of the prior and likelihood of the Bayes theorem. The results of using classical and Bayesian statistics will be discussed. Machine learning method will be deliberated next. Machine learning is the application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being programmed explicitly. Some machine learning methods that will be discussed are boosted regression trees, artificial neural networks and support vector machines. Finally, hybrid models that are the combination of two statistical models will be deliberated next. It is hoped that by combining two statistical models, a more accurate and improved model will be obtained. [ABSTRACT FROM AUTHOR]

Animals including human being are often with others in groups such as people crowds, fish schools, and flocks of birds. In such groups, they are interacting with each other to tell their intentions. By analyzing such collective behavior, we can obtain plenty of information not only for understanding the ecology of animals but also for practical applications including aquaculture and social safety. Hence, collective behavior analysis has been an active research area of both ecology and engineering. In this paper, we will introduce our recent research projects for visual surveillance of collective behavior analysis. First, we will present small object detection based on a deep neural network. The method is specially designed for detecting small swimming fish. Although training deep neural network usually requires a large number of annotated data, our proposed method reduces the cost of manual annotation. Next, we will present social group detection based on multiple instance learning. The multiple instance learning enables us to extract meaningful information from given data. We examined both of the proposed method using actual data to show their performance. [ABSTRACT FROM AUTHOR]

New learning methods are presented in the paper to train neurons in a neural network. The methods are based on the principle "successive group optimization / identification", which states that parameters of a feedforward neural network may be estimated through layer by layer successive optimizations and iteration. Estimations on the parameters may be achieved in different ways; two possibilities are shown in the paper. Solutions of basic control problems with the proposed learning method and previously developed methods are also dealt with. [ABSTRACT FROM AUTHOR]

The defect detection of X-ray weld images is an effective method to improve product quality and safety. Due to the low contrast of the image, the way of using traditional feature extraction and machine learning has low accuracy. In this paper, combined theory with practice, the technique based on improved convolutional neural network is proposed to classify X-ray weld images. In comparison with conventional methods, it avoids de-noising, extracting and enhancing features. Experimental results on the images obtained from the actual production show that the introduced method has superior accuracy of the classification. [ABSTRACT FROM AUTHOR]

This paper builds an overloaded truck detect system called ITMD to help traffic department automatically identify the engineering transport vehicles (commonly known as ‘dirt truck’) in CCTV and determine whether the truck is overloaded or not. We build the ITMD system based on the Single Shot MultiBox Detector (SSD) model. By constructing the image dataset of the truck and adjusting hyper-parameters of the original SSD neural network, we successfully trained a basic network model which the ITMD system depends on. The basic ITMD system achieves 83.01% mAP on classifying overload/non-overload truck, which is a not bad result. Still, some shortcomings of basic ITMD system have been targeted to enhance: it is easy for the ITMD system to misclassify other similar vehicle as truck. In response to this problem, we optimized the basic ITMD system, which effectively reduced basic model’s false recognition rate. The optimized ITMD system achieved 86.18% mAP on the test set, which is better than the 83.01% mAP of the basic ITMD system. [ABSTRACT FROM AUTHOR]

Cancer is the leading cause of non-accidental deaths worldwide. Specifically, nearly 10 million people died globally from cancer in the year 2020. Breast Cancer (BC) is a common and fatal disease among women worldwide, and ranks fourth among the fatal diseases among various cancers, such as cervical, colorectal, and cervical tumors and brain tumors. In addition, the number of new breast cancer patients is expected to increase by 70% in the next 20 years. Therefore, early and accurate diagnosis plays a pivotal role in improving prognosis and increasing the survival rate of cancer patients from 30 to 50%. With technical advances in healthcare, machine learning and deep learning play an important role in processing and analyzing a large number of medical images. The aim of this study is to identify studies that have been done on the application of classification techniques in diagnosing BC and analyze them from four perspectives: classification techniques used, Dataset used, Programming language used and best accuracy. We conducted a systematic literature review of 32 selected studies published between 2002 and 2020. The results showed that among the classification techniques examined, artificial neural networks, support vector machines and k-nearest neighbor were the most widely used. Moreover, artificial neural networks, support vector machines, and group classifiers have been implemented better than other techniques, with average accuracy values between 83.45% and 99.30%. Most of the selected studies used Wisconsin CSV dataset and a few of the studies used different types of images such as mammography, ultrasound, and micro images. [ABSTRACT FROM AUTHOR]

Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms. [ABSTRACT FROM AUTHOR]

Decision making is the thinking activity that people choose or judge, and scientific decision-making has always been a hot issue in the field of research. Analytic Hierarchy Process (AHP) is a simple and practical multi-criteria and multi-objective decision-making method that combines quantitative and qualitative and can show and calculate the subjective judgment in digital form. In the process of decision analysis using AHP method, the rationality of the twodimensional judgment matrix has a great influence on the decision result. However, in dealing with the real problem, the judgment matrix produced by the two-dimensional comparison is often inconsistent, that is, it does not meet the consistency requirements. BP neural network algorithm is an adaptive nonlinear dynamic system. It has powerful collective computing ability and learning ability. It can perfect the data by constantly modifying the weights and thresholds of the network to achieve the goal of minimizing the mean square error. In this paper, the BP algorithm is used to deal with the consistency of the two-dimensional judgment matrix of the AHP. [ABSTRACT FROM AUTHOR]

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance. [ABSTRACT FROM AUTHOR]

Weld bead geometry is an important characteristic of the weld joints in evaluating its quality. However, weld geometry is characterized by three parameters namely weld width, weld height and weld depth. The measurement system of three parameters is often time consuming, particularly for intelligent robots used in the welding processes. Hence, in this paper, we introduced a penetration coefficient (PC) that effectively minimizes the complexities involved in existing measurement system of weld geometry. Further, various machine learning approaches are used to predict the penetration coefficient. Cold metal transfer welded AA6061 sheets are chosen to obtain the data of penetration coefficient. Linear regression (LR), support vector machine (SVM) regression and Gaussian process regression (GPR) models and artificial neural network (ANN) model are used for predictive modelling. The statistical performance factors of the models reveal the superior performance of ANN model. The lowest mean absolute error of 0.15 is observed for ANN followed by the SVM (0.31), GPR (0.39) and LR (0.41). [ABSTRACT FROM AUTHOR]

This paper gives an approach based on a fitting technique to detect faulty bearings by employing artificial neural networks (ANNs). At present, many companies follow very expensive, scheduled maintenance procedures for checking the bearing condition of the electric drives. Therefore, there is considerable demand to reduce the maintenance costs and to prevent unscheduled downtime of electrical drives in company. To overcome the above crisis, online monitoring of vibrations in electric motors at running condition can be implemented. Most of the vibration measuring equipment measure the total vibration of the electric motor. Deducing the bearing fault from the total vibration value of the motor is not accurate. Hence it is quintessential to split the total vibration value of the motor into its sub-components namely bearing vibration and the vibration due to loading. This splitting could visualize the exact occurrence of fault in the machine. For the known loading conditions of the motor, the total vibration, bearing vibration and the vibration due to loading are tabulated. Some tabulated data are used to train the ANNs for the known machine conditions. Once trained, the ANNs are examined using the remaining data. The final evaluated ANNs can readily split the total vibration value into its sub-components enabling the computation of component value. These sub-component vibration values are analyzed individually for abnormalities to find the bearing fault accurately. [ABSTRACT FROM AUTHOR]

The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners (physicians) to accelerate diagnosis and treatment processes. In this paper, the sparse matrix approach was incorporated in neural network learning algorithm for scalability, minimize higher memory usage/storage capacity, enhancing implementation time and speed up the analysis of the data. The proposed intelligent classification system maximizes the intelligently classification of data and minimizes the number of trends inaccurately identified. For robustness, the proposed method was tested with three different datasets, namely, Hepatitis, SPECT Heart and Cleveland Heart. Therefore, an attempt was made to determine the performance indicators efficacy. Compared to some similar existing methods, the approach presented achieves improved performance. The program used for implementation of the proposed model is MATLAB R2016a (version 9.0) and executed in the 4.0 GB RAM processor of PC Intel Pentium Quad Core N3700. [ABSTRACT FROM AUTHOR]

Spices and beverages plays an important role in every cuisine. The quality of these products should be good enough for their best use. The processing steps involved during their manufacturing and the implementation methodology determines the quality of these products. Among spices, Pepper is known as the king of spices. For assuring the quality of pepper, better methods during processing must be adopted. Withering is the most crucial process in the manufacturing of black pepper. It determines the quality and durability of the final product. In this paper, a system is implemented to attain the desired Moisture Loss (ML) during the withering process of black pepper. This is achieved by predicting in advance the ML using Machine Learning algorithm. The main factors influencing the prediction process are inlet and outlet temperature and relative humidity. A prototype trough is developed with smart sensor nodes placed at the inlet and outlet for measuring moisture and temperature. The data measured is saved in a database and this is utilised to predict the ML using ANN(Artificial Neural Network). The error between predictedML and actual ML due to weight loss is analysed. [ABSTRACT FROM AUTHOR]

In this paper, artificial neural networks (ANNs) are applied to predict protein concentrations of a biological system. The input data are generated from a nonlinear mathematical model of the protein concentration. The protein concentrations from CDC6 data with actual kinetic parameter are taken as the target output. The data are then trained using multilayer perceptron (MLP) neural network with a 6-6-6 configuration. The allocation of the data will be distributed into 3 categories that are 80% as training data, 10% as validation data, and 10% as test data. The learning rules used in this work to determine the best model are gradient descent, conjugate gradient, scaled conjugate gradient. It is found that the MLP with scaled conjugate gradient learning rule gives the best prediction rate. [ABSTRACT FROM AUTHOR]

Financial markets are currently a key source of growth for the local and international economies, as they are the means by which economic units are fed. Iraq is one of the countries attempting to improve and modernize its financial industry in order to stay up with technology advancements and the digital revolution. Forecasting and early detection of financial distress is one of the important methods that contribute to increasing confidence between investors and the market and help to make sound decisions in a timely manner in order to avoid reaching bankruptcy. This paper aims to employ smart models in the detection of financial distress, and to select the best model capable of classifying the financial situation of companies into three categories (non-distress, medium distress and high distress) by selecting (14) financial ratio that directly affects the situation of companies. The researcher used artificial neural networks algorithms such as the reverse error propagation algorithm, Using data mining methods and deep learning algorithms, the C4.5 algorithm, Naive Bayes simple classifier algorithm, Convolutional neural networks algorithm, and a multi-layered algorithm support vector machine were used to classify a company's financial state. The C4.5 method and SVM had the highest rating accuracy by a tiny margin in all levels (98, 96.9, and 91.9) respectively, according to the results of the analysis. The most essential recommendations included the fundamental requirement of using smart technology in recognizing financial challenges of companies in order to support and consolidate the economic stability of enterprises in particular and the market in general in the adoption of the Iraqi stock market. [ABSTRACT FROM AUTHOR]

It is known that combination of geostatistical interpolation techniques (e.g. kriging) and machine learning (e.g. neural networks) leads to better prediction accuracy and productivity. The paper deals with application of the artificial neural network residual kriging (ANNRK) to the spatial prediction of soil pollution by Chromium (Cr). In the work, we examined and compared two neural networks: Multilayer Perceptron (MLP) and Multilayer Perceptron Residual Kriging (MLPRK). The case study is based on the survey on surface contamination by Cr at the subarctic Noyabrsk, Russia. The proposed models have been built, implemented and validated using ArcGIS and MATLAB software. The models frameworks have been developed using a computer simulation based on a minimization of the root mean squared error (RMSE). Both models showed almost identical results. [ABSTRACT FROM AUTHOR]

With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected. [ABSTRACT FROM AUTHOR]