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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]

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]

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]

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]

Deep learning has played a pivotal role in solving a wide array of problems. These powerful models can also be used for detecting mental illness. Knowing this in advance shall help individuals to utilize appropriate prophylactic measures and might even help cure them. Reddit is used to identify one's language patterns through social media posts. Machine Learning and Deep Learning are used to develop promising models that predict if a person is likely to show signs of mental illness. Machine Learning(ML) and Deep Learning(DL) based models like Logistic Regression, Artificial Neural Networks(ANN),Convolution Neural Networks(CNN) and state-of-the-art models like Bi-Directional Encoder Representations from Transformers are leveraged. Results showcase that Deep Learning shows promising results and can be utilized as an 'initial measure' of finding if a person has a mental illness and with accuracy scores of above 80% they can prove to be a 'game changer'. [ABSTRACT FROM AUTHOR]

Bone disease means any of the diseases or injuries that affect human bones. Diseases and injuries of bones are major causes of abnormalities of the human skeletal system. Although physical injury, causing fracture, dominates over disease, but fracture is one of several common causes of bone diseases. Bone fracture disease can indirectly lead to death, as fractures and their associated complications can in some cases trigger a downward spiral in health. Approximately 20 percent of the hip fracture patients died within a year of the fracture. The aim of the study was to conduct a systematic literature review of bone abnormalities that used machine learning or Deep Learning as a mean of detection and classification of bone abnormalities, by systematic review technique which included study reference, methods, dataset, programming language used, and the best accuracy obtained. 17 studies published between 2002 and 2020 were selected for analysis. Seven of which uses X-ray images, five uses CT images and five used MRI Images. The results showed that among the classification techniques examined, artificial neural networks, Convolutional Neural Network and Residual neural network widely used with average accuracy values between 73.42% and 94.70%. Most of the selected studies used X-ray images. The result is that it is possible in the future develop a deep learning model to detect abnormalities bone with more accurate results, by modifying the techniques used and adding more effective algorithms. [ABSTRACT FROM AUTHOR]

Textual expressions are frequently not only direct using emotional terms, but also derive from a grasp of the meaning of concepts and how they interact in the text document, making identifying an individual's emotional state appear difficult but necessary several times. In order for organizations and individuals to be able to provide clients with optimal services, it is important for them to recognize the various emotions felt by individuals. There are variety of methods available to detect emotions in texts, from traditional machine learning models to deep neural networks. The previously described models were unable to capture the emotional connections between words. In this work we have proposed a noble model which captures semantic and emotion information using transfer learning technique. The proposed model used BERT technique for contextual information encoding and second is emo2vec which is used for emotion information encoding. The results reveal that the proposed model improves the system's emotion recognition quality by a resulting in F1 score of 1.0. [ABSTRACT FROM AUTHOR]