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The paper presented introduces an innovative accident prevention system that employs deep learning models and sensors to identify and alert drivers who lose concentration while driving for a variety of reasons. The system targets drowsiness, cell phone use, and alcohol consumption, which are among the leading causes of automobile accidents. The proposed solution employs neural networks to identify specific patterns associated with driver inattention and generates an audible alert to refocus the driver's attention on the road. In addition, the system utilizes alcohol-detection sensors, as alcohol consumption is another significant cause of accidents. This system seeks to reduce the number of casualties by enhancing road safety considering the high number of daily collisions, which are primarily caused by speeding. The paper describes the design, architecture, and implementation of the system on a modest scale to provide a lifesaving, cost-effective solution. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most prevalent forms of cancer around the world. Initial diagnosis relies on visual assessment of the affected area, followed by detailed dermoscopic analysis. The development of an automated system for classifying skin lesions poses a considerable challenge due to inherent noise and subtle variability in lesion images. Deep convolutional neural networks have demonstrated exceptional proficiency in image classification tasks spanning diverse domains. This paper demonstrates an end-to-end classification architecture built on top of transfer learning with data augmentation. The proposed model capitalizes on pre-trained architectures such as DenseNet, VGG-16, and Inception for feature extraction, and employs fully connected dense layers for categorizing seven distinct types of lesions. Several data augmentation techniques are also used to handle the class imbalance problem. Extensive experimentation encompassing various hyperparameters, imbalanced data scenarios, and balanced datasets was conducted to refine the automated skin lesion system. The proposed approach achieved a notable accuracy of 98% on the HAM10000 dataset and 94% on the ISIC-2019 dataset. Importantly, the experimental findings surpassed the performance of current state-of-the-art models for lesion classification. Furthermore, this paper examines the impact of class imbalance and data augmentation on the model's accuracy. [ABSTRACT FROM AUTHOR]

Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and produce perceptually unrecognizable speech. These systems pose new protection and privacy risks to voice-driven interfaces. Fake audio has been used for malicious purposes and is difficult to classify what is real and fake during a digital forensic investigation. This paper reviews the issue of deep-fake audio classification and evaluates the current methods of deep-fake audio detection for forensic investigation. Audio file features were extracted and visually presented using MFCC, Mel-spectrum, Chromagram, and spectrogram representations to further study the differences. Harnessing the different deep learning techniques from existing literature were compared using five iterative tests to determine the mean accuracy and the effects thereof. The results showed a Custom Architecture gave better results for the Chromagram, Spectrogram, and Me-Spectrum images and the VGG-16 architecture gave the best results for the MFCC image feature. This paper contributes to further assisting forensic investigators in differentiating between synthetic and real voices. [ABSTRACT FROM AUTHOR]

The term "crowd counting" refers to the practise of counting the number of people present in a certain area. Urban planning, medical services, emergency preparedness, public security, strategic planning, and defence all seem to be domains where this method may be used. Occlusion, size and perspective distortion, and non-uniform distribution are all problems that crowd counting approaches face. As the population density grows, so does the complexity of the calculations. Great advances in deep convolution neural networks (CNNs) and datasets are largely responsible for the tremendous development in crowd count approaches seen in the past few years. In this paper we assess recent efforts and provide a complete evaluation of modern deep learning-based crowd counting systems. This paper discusses some classic and deep learning-based crowd counting approaches. We examine detection-based, regression-based, and classic density estimation approaches briefly. For the purpose of estimating the crowd density and count for the provided crowd scene image, we have evaluated the recent 10 publications on crowd counting using deep learning. We also go through the most widely used datasets. In conclusion, these investigations demonstrated a high degree of precision and offered good illustrations of the potential of AI in crowd counting. From paper to paper, there were significant differences in the approaches and algorithms used to address the crowd counting and density mapping challenge. We also examine the possible uses of crowd counting as well as the difficulties associated with it. As a result, fresh study is being conducted in this area. [ABSTRACT FROM AUTHOR]

Image Synthesis or generation is evolving at a high rate helping from diagnosis of diseases to traffic control. Deep Learning has touched a new height in which domains like Computer Vision have successfully developed Deep learning models for image generation. Recent improvements in deep learning enabled Generative Adversarial Networks or GANs to achieve a remarkable level of success in image synthesis. GANs help in generating significant and relevant outcomes from convoluted patterns as input. This piece of research aims at making a call towards conservation of tribal art culture through deploying neural models and computer vision techniques, in line with Warli tribal human synthetic stick figures of Asian tribes. This paper presents several methods for synthesizing human stick figures in Asian Tribal Art using modified GAN architecture. The modified GANs were quite different in terms of layers as well as the range and distribution of latent vector space. To provide input data for the training process, a customized data set was created based on spherical polar coordinates and the laws of coordinate geometry. The process of training the GAN architecture involves insertion of random vector points, and addition of neural layers, to generate outputs of higher and better resolution. The paper includes training of Generator and Discriminator following the concepts of Transfer learning and providing relevant affirmation to draw conclusions that the generated samples exhibit similarities and resemble the original input sample. Further work extends towards using of different color space data set images for generating outputs with better accuracy through the modified GAN models. The final generated output images along with other visualizations are also attached with this work to provide evidence for the fact that the output samples are analogous to the actual data. This piece of research tries to open a gate way of using artificial intelligence in terms of cultivating and protecting the traditional art cultures of various tribes across the globe. The proposed methodology performed quite well in terms of producing warli figures from given latent vector space, which is vindicated by the comparison of images at pixel level and image histograms. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) is a high-resolution radar extensively used for aerial and terrestrial images in numerous applications. The SAR images are all weather and day/night capable. It is extensively used in defense/military systems for surveillance, target detection and target tracking. In current times, the need for deep learning algorithms has increased due to its capability of generating SAR images, as it is very intensive and expensive task for the acquisition and formation of SAR images through sensors. Thus, alternative methods are required to produce SAR image data. In this paper, generative adversarial networks (GAN) are used to generate SAR images from a limited pre-existing dataset. This paper presents a novel way of translating optical images to SAR images, for the generation of paired, unpaired data set. Due to the unavailability of an open-source paired, unpaired image dataset, a synthetic paired image dataset is created for proving the concepts of CycleGAN and Pix-to-Pix algorithm by fine tuning the algorithms using generated datasets. The paper tried tackling this problem in two ways, both paired and unpaired image-to-image translation. [ABSTRACT FROM AUTHOR]

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively. [ABSTRACT FROM AUTHOR]

In recent years, one of the deadliest malignancies is skin cancer. If it is not detected and treated in a timely manner, it is expected to spread to other body parts. An accurate automated system for skin lesion recognition is essential for early detection to save human lives. Although there are many other forms of skin cancer, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma are the three most prevalent. With early identification and appropriate treatment, these three kinds of skin cancer can be successfully treated by using deep learning techniques. One of the main benefits of using deep learning for skin cancer detection is its ability to accurately classify images with subtle differences. In this paper, Image pre-processing is employed at an initial diagnosis for removing the artifacts present in the raw dataset and further Convolutional Neural Network (CNN) is employed to improve classification and detection of skin cancer with improved accuracy. For analyzing enormous volumes of data, R-CNN algorithms are proved to be incredibly effective in terms of accuracy of 84.32%. Due to its precision, effectiveness, objectivity, and accessibility, R-CNN algorithm have proven to be very helpful in the identification and categorization of skin cancer. [ABSTRACT FROM AUTHOR]

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today's world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge, demanding early detection and diagnosis to enhance patient outcomes. However, existing brain tumor detection methods often suffer from inefficiency and inaccuracy. Presently, deep learning models have gained prominence in classifying brain MRI images due to their superior accuracy compared to traditional classifiers. Nonetheless, optimizing hyperparameters manually to achieve precise image classification is a daunting and time-consuming task. To address these challenges, we present a novel approach in this paper—the Manta Ray Foraging Optimized Convolutional Neural Network (MRFO-CNN) model to classify brain MRIs into tumorous and non-tumorous categories. We leverage the Manta Ray Foraging Optimization technique to automatically determine the optimal hyperparameters, specifically batch size and epoch settings. Our model is trained on a dataset comprising 3000 brain MR images and validated on an additional 351 brain MR images. The results unequivocally demonstrate the superiority of our proposed MRFO-CNN model over conventional CNN approaches, achieving an impressive training accuracy of 99.3% and a validation accuracy of 98.7%. This research not only showcases the potential of deep learning in brain tumours classification but also underscores the efficiency and effectiveness of automated hyperparameter optimization in medical image analysis. [ABSTRACT FROM AUTHOR]

Stress is a psychological or emotional strain that occurs due to adverse experiences in human life. This paper showcases the application of deep learning in detecting stress levels in continuous audio signals in the Distress Analysis Interview Corpus Wizard of Oz (DAIC-WOZ) database. The features that have been experimented with are Gammatone Frequency Cepstral Coefficients (GFCC), Log Filter Bank (Log-Filter Bank), Mel Frequency Cepstral Coefficients (MFCC), chroma, and Linear Predictive Coding (LPC). Five deep learning models were evaluated: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Bidirectional LSTM (Bi-LSTM), k-fold CNN with the k value as 5, and a fusion model of CNN, LSTM, and attention. Upon evaluating the performance metrics of all the models, it is concluded that the k-fold CNN model with k as 5 performs well with continuous audio signals. The model has achieved an accuracy of 80% when it is trained on MFCC, GFCC, and Log-F Bank features which are observed to be the optimal features in the stress analysis. [ABSTRACT FROM AUTHOR]

The concept of predictions has gained much attention over the last few years. Research on prediction based on experience is error-prone. Usually, a lot of data has been available with multiple variables, but not all features are relevant. This work uses different feature selection techniques and their combinations to select the best possible feature subset. This paper uses feature selection techniques for time series data prediction with deep learning models. In this work, we have used filter, wrapper, and embedded methods for feature selection. We combine these techniques and compare their performance with single feature selection techniques. We found that the embedded method alone performs well with fewer features, but its performance deteriorates when the number of features increases. We input these reduced feature sets into deep learning models such as recurrent neural networks (RNN), long short-term memory (LSTM), and 1d Convolutional with LSTM (1d CNN-LSTM). We assess the performance of different deep-learning models with reduced features. Our results analysis found that the right feature subset may help increase the performance of deep learning models for time series prediction. Performance improvement is specific to the dataset, feature selection techniques, and the deployed DL models; performance improvement varies on average, from a few percent to fifty percent. [ABSTRACT FROM AUTHOR]

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively. [ABSTRACT FROM AUTHOR]

Breast cancer diagnosis from mammographic scans is a complex challenge, demanding accurate and efficient methodologies. Existing approaches often rely on time-consuming manual feature extraction or lack scalability when employed with diverse clinical image sets. To address these limitations, this paper presents an innovative solution: the MPC2EL (Multiple Phased Convolution model for Pre-emption of Breast Cancer via Extensive Learning Operations). The core problem tackled by this research is the need for improved breast cancer detection, especially concerning scalability, accuracy, and practicality. To achieve this, MPC2EL leverages extensive learning operations and multiple phased convolutions to process extensive clinical datasets containing both cancerous and benign images. Our novel approach begins with the conversion of these images into multimodal feature sets, encompassing frequency, cosine, wavelet, and Gabor domains. These feature sets enable enhanced classification of images into different cancer stages. The proposed methodology lies in a Dual 1D Convolutional Neural Network (D1D CNN) architecture, capable of categorizing images initially into malignant and benign types and subsequently refining classification for malignant images. Experimental results reveal a substantial 5.9% increase in classification accuracy and a 4.5% improvement in recall compared to conventional deep learning methods. Moreover, the model demonstrates lower processing delay and higher precision, making it a promising candidate for real-world clinical deployment. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, the road traffic system as one of the basis of national economy is also increasingly developed, which is followed by the traffic safety problems are increasingly prominent. Among them, driving violations such as not obeying traffic signs are the main cause of traffic accidents. In this research, Traffic Sign Recognition (TSR) has become a hot research topic. The research status and application field of traffic sign detection and recognition technology are introduced and analyzed in detail, and the improvement of traffic sign database is introduced. After that, the application of CNN in image processing and traffic sign recognition is introduced, and the existing problems in traffic sign recognition are analyzed. The CNN deep learning network used in this paper can conduct training while identifying targets, effectively improve the recognition accuracy of traffic signs under complex natural conditions and reduce the time consumption. [ABSTRACT FROM AUTHOR]

The increasing number of articles available in the digital library will require quite a long time and accuracy in sorting articles according to needs. The use of artificial neural networks in finding the right articles as needed through topic segmentation applications will helps this process in terms of speed and accuracy. Several neural network models used in segmentation topic applications include Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The Transformer model since its introduction in 2017 for Natural Language Processing (NLP) has a better level of accuracy compared to the RNN, CNN and LSTM models. Transformer model research for segmentation topic applications, especially articles in Indonesian language, is still very limited. This paper will discuss the use of the Transformer model in segmentation topic applications for Indonesian-language articles. The experimental results found that the accuracy produced by the Transformer model was higher than previous LSTM model with the WindowDiff value generated by the model proposed using Transformer is 0.249 and the LSTM baseline model is 0.363, while the while the Pk value generated by the proposed model is 0.279 and the LSTM baseline model is 0.394. [ABSTRACT FROM AUTHOR]

Medical image segmentation is one of the most challenging tasks in medical image analysis which aims to extract effective information and improve the level of clinical diagnosis. In last decades, automatic segmentation based on Deep Learning (DL) models such as U-Net and CNN architectures have been widely used to automatically extract organs or lesions contours in order to overcome manual segmentation limitations. In this paper, we performed a comparative study between CNN and U-Net performance for medical image segmentation applied to cardiac-MRI segmentation using U-Net from short-axis MRI images of ACDC database. The adopted architecture was trained and tested with and without data augmentation. The obtained results show a strong agreement between the labelled masks and the predicted ones with a mean DSC that reached 97,9% and a mean Hausdorrf Distance (HD) that reached 5.318 mm. A quantitative comparison was made on two levels. The first one is an intra-comparison made between the adopted model and methods based on the same architecture and having been trained and tested using the same database which proved that our method reached the highest performance and can be considered as a promising tool for medical image segmentation. The second one is an inter-comparison made between U-Net and CNN performance which proved that U-Net is more suitable for carrying out this task since it takes less time for training as it does not have a fully connected layer and offers a fairly significant similarity to ground truth compared to CNN. [ABSTRACT FROM AUTHOR]

In the last two decades, improvement in artificial intelligence and medical imaging technology have made healthcare sector to achieve some remarkable achievements in diseases analysis and prediction. Due to advancement in medical imaging technology the brain images are taken in different modalities, that gives 3D view of different sections of brain for tumor diagnosis. The ability to extract relevant characteristics from magnetic resonance imaging (MRI) scans is a crucial step for brain tumor classifiers. As a result, several studies have proposed various strategies to extract relevant features from different modalities of MRI to predict the growth of abnormal tumor. Most of techniques used conventional techniques of image processing for feature extraction and machine learning for classification. More recently, the use deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumor. Since tumors are located at different regions of brain, the localizing the tumor and classifying it to particular category is challenging task. In this paper, we have solved this problem by designing deep ensemble model. In the proposed approach, first shallow convolutional neural network (SCNN) and VGG16 network were designed with T1C modality MRI image and subsequently loss and accuracy were examined. To improve the performance of model in terms accuracy and loss information, the extracted features from both the deep learning model were fused to improve the classification accuracy of three types of tumors. The obtained results from ensemble deep convolutional neural network model (EDCNN), proved that the fusion of deep learning model improves the accuracy of multiclass classification problem and also tries to address the problem of overfitting of model for imbalance dataset. The proposed model tries to give classification accuracy up to 97.77%. Furthermore, the proposed framework, achieves competitive results when compared with other state of art studies. [ABSTRACT FROM AUTHOR]

Now a days, Video Summarization is highly explored research area by the research community, which has off late gained a lot of interest from researchers globally. It comprises extraction of important frames from the big size video input in order to extract the important events from the video into a much smaller yet comprehensively summarized form. Towards effective video summarization, a novel methodology is proposed in this paper, that works on AoA (attention over attention) strategy. The proposed deep model is based on multiple attention modules including spatial, channel, and multi-headed attention. The AoA enables us to capture inter spatial and inter channel relationship between the features effectively. The proposed attention module is applied over the set of existing features from video summarization datasets. Progressively applying attention ensures to highlight the most important contents from the input frames, thereby producing more effective key frames. Several ablation studies have also been performed to analyze the position spatial and channel attention to determine the best possible architecture based on experiments alongside the theoretical proofs of AoA architecture. For the experimental work, two benchmark dataset have been used and compared the performance with existing methods. [ABSTRACT FROM AUTHOR]

Agricultural sector is one of the main sectors in India. Productive growth and high yield production of fruits is important and required for the agricultural industry. Application of computer vision and image processing has aid agriculture to boost yield rating, disease spotting, irrigation, fruit classification and maturity grading. Computer vision and image processing approach can be used to lower the time utilization and has made it economical. A convolutional neural network (CNN) is a type of artificial neural network used in image identification and processing that is mainly planned to process pixel data. This paper utilized a convolutional neural network (CNN) to classify bananas into raw or ripe without requiring labour. Bananas were classified by their colour appearance in the CNN classification and not only this it can be also used to classify many other types of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Automation in the agricultural field is a requirement of all the countries. Usually, plant diseases are observed in the form of visual symptoms and many deep learning-based models have achieved outstanding results in the classification of plant leaf diseases in recent years. Beans plant diseases like bean rust disease and angular leaf spot disease reduce the bean crop yield. To treat the problem at an early stage, an appropriate diagnosis for this crop disease is required. In this paper, three deep learning-based pre-trained models namely MobileNetV2, EfficientNetB6, and NasNet were used to perform transfer learning on the Beans Leaf image dataset containing 1295 images with three different classes. Furthermore, different optimization techniques were also used to highlight the variation in performance of different Convolutional Neural Network (CNN) models. The analysis of experimental results shows that EfficientNetB6 performs better with 91.74% accuracy than other models. This study would be helpful to understand the role of different optimizers on the CNN models. Furthermore, agricultural scientists could employ a real-time-based application of the best-suited model for the farmers to adopt prevention measures in disease-vulnerable areas. As a result, prompt action would aid in minimizing plant productivity loss. It will further revenue the economic growth and agricultural productivity. [ABSTRACT FROM AUTHOR]

The notion of smart farming is gaining traction in the agricultural industry these days, and it makes use of sensors and a variety of machine learning based technologies. According to recent surveys, 56 percent of the agricultural industry is facing significant losses because of diseases developing on plant leaves. It's critical to keep track of the disease's spread and enhance agricultural yields. To prevent the disease from spreading, we must first recognize it on time and prevent it. As a result, we may solve this problem by putting in place some algorithms for detecting sickness on leaves. This paper presents a comparative analysis between support vector machines (SVM) model, K-Nearest Neighbor (KNN) model and convolution neural network (CNN) model. The three different models are presented and examined in this research, and they can detect eight different leaf diseases. The CNN model has achieved an accuracy of 96 percent when trained with the images of soyabean leaf disease dataset, outperforms the KNN and SVM models, which have accuracy of 64 percent and 76 percent, respectively [ABSTRACT FROM AUTHOR]

Human-Computer Interaction (HCI) in an intelligent way, which aims at creating scalable and flexible solutions. Big tech firms and businesses believe in the success of HCI as it allows them to profit from on-demand technology and infrastructure for information-centric applications without having to use public clouds. Because of its capacity to imitate human coding abilities, facial expression recognition and software-based facial expression identification systems are crucial. This paper proposes a system of recognizing the emotional condition of humans, given a facial expression, and conveys two methods of predicting the age and gender factors from human faces. This research also aims in understanding the influences posed by gender and age of humans on their facial expressions. The model can currently detect 7 emotions based on the facial data of a person - (Anger, Disgust, Happy, Fear, Sad, Surprise, and Neutral state). The proposed system is divided into three segments: a.) Gender Detection b.) Age Detection c.) Emotion Recognition. The initial model is created using 2 algorithms - KNN, and SVM. We have also utilized the architectures of some of the deep learning models such as CNN and VGG - 16 pre-trained models (Transfer Learning). The evaluation metrics show the model performance regarding the accuracy of the Recognition system. Future enhancements of this work can include the deployment of the DL and ML model onto an android or a wearable device such as a smartphone or a watch for a real-time use case. [ABSTRACT FROM AUTHOR]

Nowadays, there has been much attention on computer vision regarding human-computer interaction, especially facial expression recognition (FER). Many researchers have explored and suggested systems for this field. In this paper, we propose the Deep Learning architecture to improve the performance of models from the previous work. Additionally, we propose the BiLSTM-CNN model, which combines our proposed CNN and BiLSTM model. Besides that, we also compare the model to our CNN and LSTM-CNN models. We conduct the experiments on the CK+ dataset and evaluate the accuracy rate of the built models. Data augmentation is used in the dataset to improve the model's performance and prevent overfitting. The results demonstrate that the BiLSTM-CNN method achieves a state-of-the-art accuracy rate compared to other methods from previous work. The highest accuracy of 99.43% is reached by the BiLSTM-CNN model with data augmentation. [ABSTRACT FROM AUTHOR]

The best scheduling of water distribution systems may be supported by accurate and trustworthy water demand forecasts, which is a positive assurance for the development of smart cities and smart water services. This paper studies a new multivariate time-series prediction model that is based on the Convolution Neural Network (CNN) and Gate Recurrent Unit(GRU), taking into account the limitations of the prediction of multivariate time series using a single model. According to the regularity of water use during the time period, the characteristics of water users are clustered and classified to form two types of users: tidal type and irregular type. The factors that affect the daily water consumption such as user type, week, water consumption of the day of the previous three weeks, major events, etc. are used as input vectors, and the 2018-2021 resident daily water consumption time series data of a domestic water company is used as the training sample, respectively establish CNN-GRU models.CNN-GRU approach is checked using the root mean square error(RMSE),mean absolute error(MAE),and mean percentage absolute error(MAPE).The results are compared with Long-Short Term Memory (LSTM),CNN and GRU.Results show that CNN-GRU improves water demand prediction. The CNN-GRU model's RMSE dropped by around 0.648, 0.82, 0.82 when compared to the LSTM, CNN, and GRU models.the MAE decreased by about 0.418,0.47,0.462;and the MAPE decreased by about 0.722,0.649,0.712. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) may provide more detailed information than general sentiment analysis. It aims to extract aspects from reviews and predict their polarities. In this paper, we focus on aspect extraction sub-task. We propose three weakly-supervised systems based on contextual language models and topic modeling. We evaluate and compare our systems on SemEval-2016 restaurant french benchmark. The experimental results reveal that our systems is quite competitive in aspect extraction from user reviews. We obtain 60.65% as F1 score with our best system. The latter outperforms the existing supervised ones. We deduct that weakly-supervised systems are efficient in terms of performance, time and human effort. [ABSTRACT FROM AUTHOR]

Musical instrument recognition is an important task within the broad field of Music Information Retrieval. It helps to build recommendation systems, compute similarity between musical compositions and enable automatic search in music collections with regard to the instrument. The task has two variants differed by the difficulty level. The simpler one is classification based on the sound of a single instrument, while the more difficult challenge is to recognize the predominant instrument in polyphonic recordings. In this paper, we used a convolutional neural network to solve both of these problems. As the analysis of monotimbral recordings is relatively easy, we used the knowledge acquired during solving it to train a neural network to tackle the more complex predominant instrument recognition problem. Within this staged training scenario, we also examined the impact of introducing some intermediate stages during the training sessions. The results showed that such a training approach has a potential to improve the accuracy of classification. [ABSTRACT FROM AUTHOR]

The paper examines the accuracy of classification performed by selected deep learning methods on a large number of ECG data streams. Specifically, it investigates the performance of two types of neural networks: the convolutional neural network (CNN) and residual neural network (ResNet). The results were compared with those obtained by the classic k-nearest neighbors (k-NN) approach. A number of significant signal and method-specific parameters were taken into account, like the duration of the signal examined, the distance between consecutive R waves, the analysis window, the number of learning epochs, etc. The investigation was performed on two data sets: PTB and MIT (Arrhythmia). [ABSTRACT FROM AUTHOR]

This paper studies the effect of word representations on gender classification using deep learning. There are two main objectives: how well do popular deep learning architectures, namely LSTM and CNNs, perform on gender classification task and investigate how the choice of word representation effects the performance. Three networks, LSTM, CNN and LeNet-5, were trained on a dataset containing about 18000 names from India, Western countries, Sri Lanka and Japan. These names, encoded using the popular One-Hot representation and Word Embeddings in addition to Integer representation and an Enhanced Integer representation (proposed in this paper), were given as Input and the performance is evaluated on accuracy, training times and size of input layer. Experimental results show that LSTM in combination with word embedding derived from the proposed Enhanced Integer representation gives the best performance of about 85%. One-Hot representation is superior to Integer and Enhanced Integer representation but appears to perform lower than word embeddings. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, commonly known as diabetes, is a disease that affects a vast majority of people globally. Diabetes cannot be cured; it can only be kept under control. In this paper, diabetes is diagnosed by the analysis of Heart Rate Variability (HRV) signals obtained from ECG signals. We employed deep learning networks of Convolutional neural network (CNN) and CNN-LSTM (LSTM = Long short term memory) combination to automatically detect the abnormality. Unlike the conventional analysis methods so far followed, deep learning techniques do not require any feature extraction. We initially performed classification splitting the database into separate training and testing data. The maximum accuracy obtained for test data is 90.9% using CNN-LSTM. Using 5 fold cross-validation, CNN gave an accuracy of 93.6% while CNN-LSTM combination gave the maximum accuracy of 95.1%. As per our best knowledge, this is the first paper in which deep learning techniques are employed in distinguishing diabetes and normal HRV. The accuracy obtained using cross-validation is the maximum value achieved so far for the the automated detection of diabetes using HRV. [ABSTRACT FROM AUTHOR]

Stand-alone and the combined model of Convolutional Neural networks (CNNs) and Long Short-Term Memory (LSTM) and Deep neural Networks (DNNs) have shown great improvements in a variety of Speech Recognition tasks. In this paper we also combined these networks but in this paper we used them for multilingual speech recognition, for the prediction and correction (PAC) architecture, in order to calculate the state probability. Our proposed model is known as PAC-MCLDNN. In this paper, we present experiment results for multilingual training on AP16-OLR task. Furthermore, cross-lingual model transfer and multitask learning for under resourced languages such as Uyghur and Vietnam are also performed which further improved the recognition results. [ABSTRACT FROM AUTHOR]

In the paper, the automated evolutionary approach FEDOT-NAS for the design of convolutional neural networks is proposed. It allows building object recognition models for remote sensing tasks. The comparison of the proposed approach with state-of-the-art tools for neural architecture search is conducted for several examples of satellite-related datasets. The results of the experiments confirm the correctness and effectiveness of the proposed approach. The implementation of FEDOT-NAS is available as an open-source tool. [ABSTRACT FROM AUTHOR]

The pandemic of Covid-19 has caused a shift of paradigm of education, from face-to-face to e-learning. E-learning leads to an escalation in digitalization of handwritten documents because it requires submission of homework and assignments through online. To help teachers in checking digitalized handwritten homework, this paper proposes an automatic checking system based on a convolutional neural network (CNN) for handwritten numeral recognition. The CNN is used to recognize four arithmetic operations in mathematical questions consisting of addition, deduction, multiplication and division. The performance CNN in handwritten numeral recognition have been optimized in terms of activation function and gradient descent algorithm. The proposed CNN is also trained and tested with the MNIST handwritten data set. The experimental results show that the recognition accuracy the improved CNN improves to a certain extent as compared to before optimization. [ABSTRACT FROM AUTHOR]

Epilepsy is a chronic disease caused by sudden abnormal discharges of neurons which lead to transient brain disfunction. For those patients who need surgery, the recognition of the epileptic foci is one of the most important steps. In this paper, a combination of EEG signals processed by Short-time Fourier Transform and processed by Continuous Wavelet Transform is introduced. A deep learning model with an accuracy of 91.3% trained by Convolution Neural Network has been obtained. The experiment uses a methodology that has not been widely tested so far, and it also demonstrated the practical values by its relatively small time consumption and computing power requirements meanwhile ensuring high accuracy. Furthermore, it proves the feasibility of improving accuracy by combining different feature extraction methods. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning sequence-to-sequence approach to improve the task of automatic Romanian lemmatization. The study compares 24 systems using different combinations of recurrent, convolutional and attention layers, while the text input consists of word-lemma pairs, both at word and trigram level. As Romanian is a low resourced language in the field of text processing, the aim of this study is to use as little input information as possible. Thus, to increase the lemmatization accuracy, additional lexical features (such as context and POS tags) have been provided only gradually. For the trigrams case, two scenarios were proposed: to predict the lemma for every word from the sequence or to predict the lemma only for the word in the middle. The lemmatizers have been analyzed on two Romanian datasets: the Romanian Explicative Dictionary (DEX) and the belletristic subset of the CoRoLa corpus. For the DEX dataset, the best results were obtained with the LSTM-based systems at both word (99.32%) and character level (99.43%). For the CoRoLa subset, the CNN-based architecture outperforms at trigram (95.86%) and word level (99.09%) while the LSTM-stacked system obtained the highest accuracy at character level (98.78%). [ABSTRACT FROM AUTHOR]

Along with the growing popularity of deep learning to handle classification problems, various deep learning models have emerged having complex architectures. The disadvantage of a model with a complex architecture is the computational problem in which takes longer training time than a simple model. The desire to take advantage of sentiment classification in real-time applications is the reason for using a simpler model architecture but still paying attention to the model performance. Furthermore, this study also investigates how fastText embedding affects the performance of a sentiment classification model. In this paper, we propose two sentiment classification models with simple architecture. The first model is the single-layered Bidirectional Gated Recurrent Unit (BiGRU) model with fastText embedding, and the second one is the single-layered Convolutional Neural Network (CNN) model with fastText embedding. Both models can provide competitive results compared to the baseline model that has previously been compared with other models with complex architectures. The best accuracy is produced by the fastText + CNN model, with 80% of accuracy for the MR dataset and 84% of accuracy for the SST2 dataset. This study shows that the use of CNN for sentiment classification can provide competitive results compared to the BiLSTM and BiGRU models. This study also indicates that the use of fastText embedding can improve the performance of the single-layered BiLSTM model. [ABSTRACT FROM AUTHOR]

Point-of-Interest (POI) recommendation systems can give the user a list of locations that the user may be interested in, but the recent check-ins may contain some daily check-ins that users are not really interested in. To address the issues of mining users' actual preferences from users' recent check-ins, this paper proposes a Convolutional Neural Networks with Attention (CNNA), which consists of a latent representation method and a deep convolutional neural network. We have carried out several sets of experiments to test the validity of CNNA, and the experimental results show the effectiveness of CNNA. [ABSTRACT FROM AUTHOR]

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. CubeSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation and research and have the potential to enhance satellite constellation resilience. However, the inherent size, weight and power limitations of CubeSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution. Low resolution limits the utility of CubeSat images for military planning purposes and national intelligence applications. This paper implements a super-resolution deep learning architecture and proposes potential applications to CubeSat imagery. [ABSTRACT FROM AUTHOR]

A challenge for implementing deep learning research in the real-world is the availability of techniques that explain predictions of a model, particularly in light of potential legal requirements to give an account of algorithmic outcomes for certain use-cases. Convolutional neural networks are regularly proposed as an effective method of Android malware detection at scale. However there is a lack of studies into actually explaining the predictions of these classifiers, and how well they correlate with other state-of-the-art explainability methods. In this paper we present two research contributions that begin to address this issue. Firstly, a novel method to identify locations deemed important by our CNN in an Android app's opcode sequence which appear to contribute to malware detection. Secondly, a comparison of such locations highlighted by the CNN with those locations considered important from the state-of-the-art explainability method LIME. Using the publicly available Drebin benchmark dataset, our results show that for eight different malware families, when averaging location importance across all samples in each family, locations in an opcode sequence considered most malicious by our CNN match closely to those considered most malicious by LIME. This gives confidence the CNN is able to focus its attention on patterns of malicious opcodes in an Android app. As well as helping to validate use of CNNs for Android malware detection, our method may benefit the wider field of Android malware analysis to highlight opcode sequence locations worthy of investigation. [ABSTRACT FROM AUTHOR]

This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership. The relative performance of a subset of machine learning techniques is examined, focusing on Convo-lutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. Furthermore Sequential CNN-LSTM, Parallel CNN-LSTM, Augmented Sequential CNN-LSTM are explored. All models are benchmarked against a Fixed Effects Ordinary Least Squares regression. Historical ridership data has been provided in the framework of a project focusing on the impact that the opening of a new metro line had on ridership. Results show that the forecasts produced by the Sequential CNN-LSTM model performed best and suggest that the proposed framework could be utilised in applications requiring accurate modelling of demand for public transport. The described augmentation process of Sequential CNN-LSTM could be used to introduce exogenous variables into the model, potentially making the model more explainable and robust in real-life settings. [ABSTRACT FROM AUTHOR]

Sign Language Recognition (SLR) is a relatively popular research area yet contrary to its popularity, the implementation of SLR in daily basis is rare; this is due to the complexity and various resources required. In this literature review, the authors have analyzed various techniques that can be used to implement an automated sign-language translator through the analysis of the methodologies and models used to make a working model of any sign-language translator from various sources. The purpose of this study is to explore various possible ways to implement Artificial Intelligence technology to improve the automated American Sign Language translator that is applicable. The authors have identified 22 different research papers within the period of the years 2015 - 2020. The analysis showed that every research studies picked have achieved respectable results, however, they are not perfect, since each research demonstrates its own unique strengths and weaknesses. There are some methods that might be suitable for our need to create an applicable Sign Language Translator, that is by using standard video camera for obtaining data, and either Convolutional Neural Network or Support Vector Machine can be used for the classification. [ABSTRACT FROM AUTHOR]

The Coronavirus epidemic first appeared in Wuhan-China on December, 31st, 2019. This has put the world's hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04th, 2020, the World Health Organization reported more than 167515 confirmed cases in more than 100 countries worldwide. The diagnosis of the epidemic will increase the burden on overburdened testing laboratories. Several screening methods have been proposed in parallel in order to facilitate and, above all, to make rapid diagnosis easier. At this level, X-Ray images seem to be a good accompanying solution for emerging countries to help rapid screening. The solution proposed in this paper consists on a collaborative and smart platform based on the Convolutional Neural Network for Classification and Detection namely VGG16. The platform ensures the fast download of the X-Ray image, with the entry of the patient's personal information followed by the launch of a 5 seconds test. The platform generates, as a result, a PDF file containing all patient information. [ABSTRACT FROM AUTHOR]

As a method to judge the benign or malignant polyp from endoscope images, some methods have been proposed using an ultra-high magnification endoscope. The ultra-high magnification endoscope enables the diagnosis at the cell level. However, it tends to spend many times for diagnosis and requires specific expensive devices. There are three types of images that are taken for diagnosis by the regular endoscope: white light, dye, and narrowband image (NBI) in general. This paper proposes a benign/malignant polyp classification method using these images taken by the regular endoscope. Each image features derived from endoscope images are extracted by adapting a pre-trained CNN to each domain. Finally, polyps are classified using extracted features. Experiments confirmed that the proposed method enabled the classification of benign or malignant colorectal polyps with over 90% accuracy. [ABSTRACT FROM AUTHOR]

In the time on structured surfaces (walls, roofs, bridges, streets, etc.) cracks appear and influence from the aesthetic point of view, but also from the point of view of their resistance and quality. Traditionally, crack detection is performed by human visual inspection, which is dangerous (when they need to climb buildings), subjective (depending on their experience in detecting the severity of a crack), and time-consuming (if we consider hundreds of buildings). Increasingly, artificial intelligence continues to evolve and we can use it to improve human performance and automate the process of crack detection. In order to improve this problem, we present an application that detects cracks in buildings that are difficult to access or would endanger human life. The architecture of our application is based on Convolutional Neural Network. In this paper, three different approaches are described and compared. [ABSTRACT FROM AUTHOR]

Bearings are vital part of rotary machines. A failure of bearing has a negative impact on schedules, production operation and even human casualties. Therefore, in prior achieving fault detection and diagnosis (FDD) of bearing is ensuring the safety and reliable operation of rotating machinery systems. However, there are some challenges of the industrial FDD problems. First, there are unbalanced samples because industrial faults rarely occur. Conse-quently, the labeled data which can refer to failure information are limited in the industry and data augmentation methods are critical pre-processing be-fore training data driven models. Second, due to many learnable parameters in model and data of long sequence, both lead to time delay for FDD. There-fore, this paper proposes various data preprocessing methods and Light-Convolutional Neural Network (LCNN). [ABSTRACT FROM AUTHOR]

Authentication plays a very important role to manage security. In the modern era, it is one, in all the priorities. With the appearance of technology, the interaction with machines is turning automatic. Therefore, the need of authentication increases rapidly for various security purposes. Because of this, the biometric-based authentication has gained a drastic momentum. It is a kind of boon over other techniques. However, this event is not a replacement of drawback but varied ways are adopted to verify folks. Signature is one of the first broadly practiced biometric features for the verification of an individual. This paper proposes a method for the pre-processing of signatures to make verification simple. It also proposed a novel method for signature recognition and signature forgery detection with verification using Convolution Neural Network (CNN), Crest-Trough method and SURF algorithm & Harris corner detection algorithm. The proposed system attains an accuracy of 85-89% for forgery detection and 90-94% for signature recognition. [ABSTRACT FROM AUTHOR]

An automated method for classification of multispectral satellite images using image fusion is presented in this paper. Principal Component Analysis(PCA),and convolutional neural network (CNN).This proposed method first take multispectral satellite image and apply Brovey transform method for fusion of three RGB bands with panchromatic band shaving spatial resolution of 30m and 15m respectively. Then Principal Component Analysis is applied on fused images for reducing dimension. After that Convolutional Neural Network has been applied for classification purpose. Classification outcomes of the proposed method are matched with FCM, GIFP-FCM,and FKLICM. [ABSTRACT FROM AUTHOR]

Continues growth of social networking web users, people daily shared their ideas and opinions in the form of texts, images, videos, and speech. Text categorization is still a crucial issue because these huge texts received from the heterogeneous sources and different mindset peoples. The shared opinion is to be incomplete, inconsistent, noisy and also in different languages form. Currently, NLP and deep neural network methods are widely used to solve such issues. In this way, Word2Vec word embedding and Convolutional Neural Network (CNN) method have to be implemented for effective text classification. In this paper, the proposed model perfectly cleaned the data and generates word vectors from pre-trained Word2Vec model and use CNN layer to extract better features for short sentences categorization. [ABSTRACT FROM AUTHOR]

Automatic electrocardiogram beat classification is a prevalent field of research among the biomedical and computer science researchers. In the last decade, the research has seen a transition from feature centric classification approach to a classifier centric approach. The performance of conventional approach based classifiers such as probabilistic neural networks, support vector machines, is highly dependent on the performance of pre-processing and feature extraction modules. Whereas, modern approach based classifiers such as deep neural networks, convolutional neural networks, do not need handcrafted features. They can generate characteristic features automatically from raw ECG signal. This paper aims to provide a comparative analysis of conventional and modern approaches. Further, deep learning based classifier models have been investigated to design a road-map for researcher new to this field. [ABSTRACT FROM AUTHOR]

Online reviews, social media data contains wealth of information about customer's likes and dislikes about product or service. Extreme opinions in terms of praises and complaints are more informative as compared to plain positive and plain negative sentences and are difficult to find from customer reviews. Existing approaches to sentiment analysis classify the sentences as positive negative or neutral and do not consider informativeness of sentences. This paper compares Machine Learning, Ensemble and Deep Learning based approaches for praise or complaint classification using hybrid features based on linguistic properties of extreme opinions. The performance of the Machine Learning classifiers with proposed hybrid features and Deep Learning based classifiers using Dense Neural Network, CNN, Multichannel CNN were evaluated by training and testing the deep neural network with a set of important words such as nouns, adjectives, intensifiers, verbs, etc present in the sentence. The proposed Ensemble classifier uses SVC, Random Forest, Decision Tree, MLP classifiers. Hotel domain reviews were evaluated using the parameters Accuracy, Precision, Recall, and F1-score. The evaluation results show that the proposed Ensemble classifier and Deep Learning Models provides better classification accuracy as compared to existing methods. [ABSTRACT FROM AUTHOR]