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Early identification and accurate diagnosis are crucial for successful therapy and improved patient outcomes of brain tumors. The study looks at how deep learning may help detect brain tumors, including how convolutional neural networks (CNNs) can change the face of diagnostic imaging. This underscores the need of precise and timely identification of brain cancer for efficient therapy and enhanced patient results. The study discusses the challenges associated with tumor segmentation and classification, such as variations in size, shape, and location. To overcome these challenges, deep learning techniques are utilized to analyze brain scans and identify tumor abnormalities. However, the paper underscores the importance of these models serving as tools to assist medical professionals rather than replacing their expertise. Additionally, the paper presents a comparative study that evaluates different techniques, artificial neural networks (ANN) achieve the highest accuracy at 99%, followed by Support Vector Machine (SVM) at 98.9%. vision transformer (ViT), Extreme Gradient Boosting (XG Boost), CNN-based dense Efficient Net, and Deep Neural Network (DNN) also show high accuracies above 98%. However, U-Net, Alex Net, hybrid ensemble method (Random Forest (RF), K-Nearest Neighbour (KNN), Decision Tree (DT), and Region-based convolutional neural networks (R-CNN)) have lower accuracies ranging from 91.66% to 97.305%. Overall, the findings highlight the potential of deep learning in enhancing the efficiency, precision of brain tumor diagnosis, leads to earlier treatment and enhanced patient prognosis. [ABSTRACT FROM AUTHOR]

The rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time. [ABSTRACT FROM AUTHOR]

Physical disabilities cause significant distress in people's daily routine, especially for leg amputees as their movement is restricted. The civilized society has the responsibility to provide them with better prosthetics so they can live a normal life. Previously, prosthetic legs were developed for lower leg amputees. Today's electronic prosthesis provides a wide range of mobility compared to a mechanical prosthesis. Powered prosthetics are quite expensive and hence not widely used. We have proposed a low-cost control system above-knee powered prosthetics leg that is reliable, and the complexity of the model is less. The prostheses works by taking input from a sensor placed in the model and six FSR402 sensor and an IMU sensor that record force muscle activity of the thigh during variation of leg movement. The value of motion of the joint knee angle is determined by sensors. In this paper, we are discussing and comparing potential deep learning control system namely artificial neural network (ANN) as baseline and recurrent neural network-long short term memory (RNN-LSTM) to predict human gait cycle. A multi-layer perceptron (MLP) method is implemented. In general, network architecture consists of input layer which is represents by six inputs of the FSR402 sensor and an IMU sensor, fully connected layers, and an output layer as joint knee angle. As the result model of ANN as baseline has metric value MAE and R squared are 6.21 and 68.7% respectively whereas model of RNN-LSTM has metric value MAE and R squared are 4.99 and 96.9% respectively. The result indicates the RNN-LSTM model is most likely to perform fewer errors based on the dataset. RNN-LSTM outperforms the task with a greater value compared to the ANN model as the baseline. This present study suggests that deep learning with the recurrent neural network-long short term memory (RNN-LSTM) is suitable for the prediction of human gait cycle movement from the FSR402 and an IMU signal in the overall walking task. [ABSTRACT FROM AUTHOR]

This paper investigates the most commonly used Deep Learning (DL) based techniques for estimating the wireless channel at the User Equipment (UE). The generalized Nakagami-m fading distribution is considered for deep learning network. The motive of this work is to provide an experimental investigation on the wireless channels using three different deep-feed forward neural networks, namely Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), and Convolution Neural Networks (CNN). The analysis includes the preprocessing approach for handling complex received signals as one of the inputs and actual channel coefficients as output. Bit Error Rate (BER) performance analysis is performed and is compared with the classical LS estimator. It is demonstrated that DL channel estimation outperforms the Least Square (LS) based estimation, and simulation results support that DL-based channel estimation is very efficient. [ABSTRACT FROM AUTHOR]

In this paper, we aim to provide a method based on deep learning to help clinicians diagnose lumbar spine MRI for stenosis more efficiently and with instruments that produce noisy images to make maximal usage of medical resources. To achieve the same, this paper evaluates the effect of various MRI noises, such as Gaussian, salt and pepper, speckled, etc. in boundary delineation of stenosis in MRIs of the lumbar spine using recently introduced semantic segmentation algorithm based on deep neural networks. A U-Net model was trained for segmentation and its performancewas tested on various MRI images with added noise. The proposed methodology adopted in this paper is successful in producing a considerably good performance with higher scores, which helps in identifying Lumbar Spinal Stenosis in a more efficient manner. [ABSTRACT FROM AUTHOR]

Healthcare systems deal with a bulk of digital data everyday. Most of the healthcare organizations are focusing on using deep learning for supporting better patient care. Deep learning is nothing but the subset of Artificial Intelligence. The concept of deep learning arises from artificial neural networks. Deep learning performs very well on unstructured data. This paper aims at reviewing deep learning along with its various techniques and also its applications in medical healthcare system. [ABSTRACT FROM AUTHOR]

Ayurvedic medicines plays significant role in maintaining the biological state of human beings. The major components of ayurvedic medicines are plant leaves, roots, stem, bark, leaf stalk, flowers etc. Ayurvedic plants cure several disorders such as lung diseases, gastro-intestinal disease, cardiac disorder, skin disorder, excretory diseases etc. So knowledge about these plants sustained by generations must be conserved. Also appropriate classification and identification of ayurvedic plants is essential for researchers, botanist, cultivator and doctors. Manual identification will be time consuming, imprecise and inaccurate. This paper proposes an automatic recognition system for the identification of different types of ayurvedic plant images using deep Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) based on transfer learning method. For the performance evaluation the dataset is generated by capturing images using mobile device from the environment and from Google network. The dataset consist of 424 plant images of 21 types of ayurvedic plants. A Graphical User Interface (GUI) is also developed to display the informations of the detected ayurvedic plant. [ABSTRACT FROM AUTHOR]

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

To create, the deep reinforcement learning technique combines deliberative reasoning with visual perception. It does this by using an end-to-end learning approach to merge the reinforcement learning's decision-control abilities with convolution neural networks' perceptual abilities. It's utilized frequently. It has been applied to tackle issues with complex decision control and high-dimensional visual input ever since it was created. MPC works best when I understand what is required of me in terms of authority and responsibility as well as the state constraints that always exist in real-world scenarios, which validates its application in this case. DRL is then used to create the algorithm, which includes observation states. By utilizing the reward function, network structure, and parameter optimization for a 2D environment, the laborious 3D environment effort is avoided. We use the tried-and-true method to obtain the converged network's parameters, such as the deep neural networks (DNN) weights and biases, in a simple 3D environment. This research gives a general overview of how the agent should be represented in technologies employing applied Markov decision processes. This paper presents a proof-of-concept application scenario to illustrate the effect of multichannel Kalman filtering on the reduction of uncorrelated noise in magnetotelluric data. We evaluate our strategy using a variety of maps that depict the kinds, locations, and numbers of obstacles. The experimental findings show strong generalization, the superiority of G2RL over prior technologies, and its capacity to perform almost as well as entirely despite relying on distributed architectures and modern benchmarks at their core. [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]

A key component of tumour diagnosis, treatment planning, and creating an outcome for assessing brain tumours is highly accurate and dependable segmentation. Magnetic resonance imaging (MRI) is a tool used in medicine to help find very small abnormal growths in any human body part. Deep neural networks (NNs) and machine learning methods are efficient at segmenting 2D images, but it is challenging for NNs to distinguish significant organs from 3D medical MR images. The segmentation for tumour detection includes a number of processing techniques thatfall into the categories of pre-processing, segmentation, optimization, and feature extraction. The study focuses mainlyon 3D-based Convolution neural networks (CNN), ANN (Artificial Neural Networks), SVM, and Multi-class Support vector machines for deeper segmentation. In order to lessen the computational burden of processing 3D medical scans,this survey paper will examine recent developments in image segmentation and image classification based on effective and efficient processing of tumor-infected human brain MRI adjacent image patches that can pass through the network. The focus will be on gliomas. More discriminative 3D NNs and computational machine learning are used to process the input images at different scales simultaneously. This article also suggests the state of the art in tumor-based imageprocessing using deep learning models for tumour segmentation and detection. [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]

In recent times, several fields that exist in engineering have been heavily dominated through the integration of artificial intelligence, however machine vision systems are simultaneously being studied and observed, they encapsulate certain tools that help in deriving technical information such as the working, errors and relative faults. Such systems play a vital role in manufacturing production lines, where important factors such as efficiency and productivity are sought to be at optimum levels. This paper captures the essence of using convolutional neural networks and also the addition of VGG-19 that are used in the detection and inspection of the various type of bottle caps. The respective bottle cap classes that have been pre-determined include normal cap, no cap a misplaced cap. The implementation of this system should enable us to determine whether a bottle with a specific cap class should be rejected or accepted. High metrics of precision and accuracy of the system should be observed and beneficial to the existing and upcoming processes. [ABSTRACT FROM AUTHOR]

Many countries use the AFIS (Automated Fingerprint Identification System) to identify individuals via their fingerprints. In this system, fingerprint inputs are compared with all the images stored in the given database. Many comparisons are required when the database is enormous; thus, fingerprint classification is used to eliminate many comparisons (reduced database penetration rate). Deep learning techniques have extremely advanced several domains, especially computer vision processing. This paper surveys research on fingerprint classification utilizing deep convolutional learning. It provides an overview of convolutional deep neural networks and some types of CNN models. Moreover, it presents a comparison and complete analyses of 10 research projects based on: CNN models, dataset, and the accuracy achieved hyperparameters. The most common CNNs used in fingerprint classification are AlexNet with an accuracy of 99.79%, VGG-16 with an accuracy of 98.30%, and ResNet with 95.7%. [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]

Time series forecasting is a decisive step in data modeling and a significant area in machine learning. This paper presents Long short-term memory (LSTM) network, a deep learning neural network for predicting Covid-19 cases in India. The neural network models are trained and tested with Covid-19 case data sets obtained from PRS Legislative Research database. Further, the parameter optimization is carried out for choosing the optimal network. The parameters considered for evaluating the performance of LSTM network are RMSE, number of epochs, accuracy and loss. The results are compared with various recurrent neural network models and autoregressive model. The results revealed an improved accuracy of 92.8% for LSTM network in predicting the transmission of Covid-19 in India. [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]

Simulating the properties of complex alloys is an extremely challenging scientific task. The model should take into account a large number of uncorrelated factors, for many of which information may be absent or vague. The individual contribution of one or another chemical element out of a dozen possible ligants cannot be determined by traditional methods, and there are no general analytical models describing the effect of elements on the characteristics of alloys. Artificial neural networks are one of the few statistical simulation tools that may account many implicit correlations and establish correspondences that cannot be identified by other, more familiar mathematical methods. However, networks require complex tuning to achieve high performance. Data engineering and data preprocessing also makes a great contribution. This paper focuses on combining deep network configuration selection based on physics and input engineering to simulate the solvus temperature of nickel superalloys. [ABSTRACT FROM AUTHOR]

The prediction models of association football can be categorized into three natural clusters, which are the statistical models, the machine learning and probabilistic graphical models and rating systems. The prediction may focus on matches outcomes prediction (win, draw and lose) or the number of goals scored obtained by home and away team. Match result prediction is very important as a benchmark for the management team to assess the chances of winning, drawing and losing for a particular team before the match starts. In the 2017 soccer challenge, the conventional machine learning and ensemble methods, as well as probabilistic graphical models such as k-Nearest Neighbor (k-NN), Gradient Boosting Tree (GBT) and Bayesian Networks (BN) have been a choice of researchers that participate the challenge and successfully dominate the challenge. Even so, there are some well-known techniques such as Neural Networks (NN) and Deep Neural Networks (DNN) that are left in this challenge although this technique has advantages in producing good results in many fields including solving real world problems which the results are comparable and sometimes superior to expert knowledge in some cases. In this paper, we propose a football match outcome prediction based on pi-rating system using TabNet, a DNN architecture for tabular data. The experiment cover two parts namely: (1) generates pi-rating system from 216,743 instances of raw football dataset and (2) predicts 206 football match outcomes using TabNet. As a result, the proposed prediction model based on the pi-rating system using TabNet successfully overcomes the existing model for football match outcomes for both scoring rules, in terms of accuracy and Rank Probability Score (RPS). The findings from this study are important because they can be used for future researchers in developing new football match outcome prediction models that incorporate several new features along with other features. This is important to improve the predictive performance of football prediction models using other advanced techniques. [ABSTRACT FROM AUTHOR]

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

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

In recent years face recognition system is emerging technology in research areas. Face recognition is used in many applications such as attendance management system, People tracking system and access control system etc. Major challenges faced in face recognition are detection and recognition because it is not easy to detect multiple faces in one frame and difficult to recognize the faces with poor resolution. Therefore, the main objective of this paper is to obtain better accuracy by using the combination of FaceNet and Deep Neural Network (DNN). In this proposed system, FaceNet is used for feature extraction by embedding 128 dimensions per face and DNN is used to classify the given training data with extracted feature of FaceNet. The outcome of the system is practical, reliable and consumes less time. [ABSTRACT FROM AUTHOR]

Noise removal is one of the chronic problems while dealing with the images. In this paper, we propose Futuristic Flask with Convolution Neural Network (FFCNN), a residual learning model in deep convolutional neural network which is trained with large dataset, showing up excellent results for removing Gaussian noise from digital Images. FFCNN is not only intended for training neural model with performance metrics but also intended to have presentation metrics for a user, using a micro web framework "Flask" to enable interactions. The architecture has feed- forward denoising neural network structure that conducts discriminative learning for image denoising especially for Additive White Gaussian Noise (AWGN) with specific noise level and also for blind Gaussian noise. The proposed algorithm is optimized and speeded by layers of batch normalization with GPU computing. The resulting PSNR and SSIM obtained are excellent proving efficiency and effectiveness of the model for several general image denoising task. [ABSTRACT FROM AUTHOR]

Face Mask recognition has developed as an extremely admired issues in several domains such as image processing, computer vision and artificial intelligence. Several novel approaches are being developed through deep learning based Convolutional Neural Network model to detect the face mask identification. In this paper, we propose "FMNet" model which indicates Face Mask detection Neural Network model for recognition of face mask person on publicly available face masks images as resources. Furthermore, we applied image preprocessing, feature extraction have performed using CNN, applied FMNet model for face mask recognition from images, and finally perform classification technique to categorize the images as person "wearing masks" and "not wearing masks". Our experimental outcome generates better performance in finding face mask by achieving accuracy as 99.7%. Moreover, comparing our proposed algorithm results with pre-trained models namely VGG 16 attains 99.6%. This system has capability to perform in real time application creates it applicable to identify people in airports, buses, schools etc. [ABSTRACT FROM AUTHOR]

The entire human race is currently facing a huge disruption of everyday life due to the rapid spread of the novel Corona Virus disease 2019 (COVID-19). It is essential to develop a tool or model for fast diagnosis of the disease which is pandemic and also the model should be able to justify the result for trustworthy in the field of medicine. Machine learning (ML) and Deep Learning (DL) models play a vital role in identifying COVID-19 patients by visually analyzing their Computed Tomography (CT) scan images. In this paper, few publicly available convolutional neural network models (CNN) were analyzed to classify the CT scan images of lungs into two classes, COVID-19 positive and negative cases. In addition to that, Local Interpretable Model-agnostic Explanation (LIME) framework is used as an explanation technique for interpretability. The pixel of relevancy responsible for the outcome of classification is visually explained through LIME technique which gives trustworthiness in the field of healthcare. [ABSTRACT FROM AUTHOR]

If the driver does not have proper rest, he/she tends to fall asleep causing an accident. The main objective of the project is to design a system that can detect the driver drowsiness and alert them to reduce road accidents. The system takes the input images through a camera which focus on the driver. Initially the face is detected using Naive Bayes Region of Interest (NB_ROI) algorithm, and then the eye and mouth regions are separated. In this paper, a single layer Artificial Neural Network is used along with the auto encoder module of the Deep Learning Toolbox to categorize them to one of the classes as 'drowsy' or 'alert' based on the eye closure detection. The proposed method offers better accuracy in driver drowsiness detection. [ABSTRACT FROM AUTHOR]