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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]

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]

Face recognition is a biometric based application software capable of verifying or identifying exclusively a person by analyzing and comparing person's facial contours using patterns. The main applications of face recognition system is found in security systems, law enforcements and other initiatives. The variation in illumination, effects immensely on recognizing faces or images. Even for the familiar faces, illumination variations lead to alteration in the shadow shapes, shift in locat ions, fluctuations in features and contract gradient variations. Under these conditions, larger impact is observed to recognize these face images. A face recognition system is developed under these varying illumination conditions in this paper. Various methods of illumination normalization like Self-Quotient Image (SQI), Histogram equalization (HE), Gross & Barovick's anisotropic smoothing (GB), Gross & Barovick's anisotropic smoothing (GB) and Multiscale Retinex (MSR) are comparatively analyzed. Illumination invariant face recognition is developed by first acquiring 40 illumination variants face s. Face detection is performed by deformable template with scaled color map and its negative. Further face alignment is estimated by supervised learning by estimating the locations of a set of facial landmarks. Later image normalization is performed using Tan Trigg's Normalization Technique (TTVT) which includes Gamma correction, MaskingDifference of Gaussian (DOG) and contrast stretching methods [1]. The resultant normalized face image is classified using Convolutional Neural Networks. The evaluation and performance analysis are discussed to determine the applicability and suitability for various applications. [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]

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]

Nowadays, regular intake of nutritious foods is essential for maintaining a healthy eating routine and avoiding obesity in the human body.. In this paper, a novel framework dependent on AI that naturally performs exact grouping of food pictures and gauges food credits. In the preparation section of the model system, it proposes a profound learning model that includes a convolutional neural organization that orders food into explicit classifications. The principle reason for the proposed technique is to improve the exactness of the pre-preparing model. The paper plans a model framework dependent on the customer worker model. The customer sends a picture location solicitation and cycles it on the worker side. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Network (CNN) are the three classifiers that have been modified to analyze the framework's enhanced precision. Exploring various avenues about a variety of food groups, each with a large number of images, using Artificial Intelligence (AI) to achieve higher grouping precision. [ABSTRACT FROM AUTHOR]

Despite advances in technology Handwritten text is still considered as a method for correspondence and recording data in everyday life. Offline handwritten text recognition is an infamously challenging problem in Artificial Intelligence. Recent studies show great potential of Neural Networks for handwritten text recognition segmented at word level. In this paper, a prototype is presented for offline handwritten text recognition using deep neural networks. Preprocessing techniques like data augmentation and line-level segmentation enrich the input images and hence benefits the classifier. The proposed approach performs thorough feature engineering on preprocessed input images and propagates them through a three layered architectures comprising of CNN (Convolutional Neural Network), LSTM(Long Short Term Memory) and CTC(Connectionist Temporal Classification). [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]

Timely analysis and recognition of leaf diseases in plants ensures good quality and productivity of crops in the field of agriculture. Recognition of rice leaf diseases is one of the biggest challenges for the farmers due to incomplete awareness regarding latest sophisticated techniques in the field of leaf disease identification. Much of the growth of rice is disturbed due to the prevalence of rice leaf diseases. Earlier detection of rice leaf diseases done manually by farmers which is very laborious and time consuming. However, requisite of automatic identification of rice leaf diseases helps farmers to save their agricultural crops more efficiently. Innovations in the field of computer vision and deep learning meets the expectations and proves to be the best solution for classification of rice leaf diseases using convolutional neural networks using samples of rice leaf diseases. In this review paper, the major focus is on performance analysis of detection of rice leaf diseases based on the architectures employed. [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]

The article is devoted to a comparative study of the deep convolutional neural network (CNN) architectures recognizing basic emotions in human conversational speech using spectrograms of the speech audio signals. The paper considers a number of the most common CNN architectures: AlexNet, VGG-13, ResNet-18, MobileNet-V2, EfficientNet-B0. The research was carried out on the IEMOCAP dataset, which was labeled into four classes of basic emotions: Anger, Happiness, Neutral, Sadness. Accuracy, precision, recall, f1-score were chosen as quality metrics. A comparative analysis of recognition accuracy showed that the best results were achieved in accuracy for the simplest and least deep architectures AlexNet and VGG13 (accuracy was 0.649 and 0.662, respectively). The study also showed that with an increase in the depth and complexity of the architecture, the recognition results decrease. [ABSTRACT FROM AUTHOR]

Healthcare providers confront obstacles to deliver cost-effective and high-quality patient care. In specific, enhancing the efficacy of illness diagnosis and preventive is important, particularly in the context of heart disease that is perceived a leading cause for human mortality. Electrocardiogram tests the pulse rate of the heart. It is used as a diagonostic tool in analysing the heart diseases. A deep learning approach for the detection of HRV using supervised and unsupervised training models is promoted in this paper. As a supervised training model, Convolution Neural Networks are employed and Stacked Autoencoders and Restricted Boltzmann Machines are employed as an unsupervised training model and perhaps a Neuro-Fuzzy Inference System (ANFIS) classifier, a form of Neuro-Fuzzy classifier, is inevitably developed. The primary focus of the analysis is to recognize typical ECG pulse rate and correlate ANFIS outcomes with artificial neural networks. The clinical decision support system automatically detected the parameters of the ECG and identified the type of heart disease that the patient is facing, for now arrhythmia ECG signals has been analysed. The ECG records were taken from MIT-BIH Arrhythmia Database. [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]