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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]

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]

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]

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]

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]

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]

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]