Search

Various machine learning algorithms are developed with an aim to create precise and trustworthy models and extract knowledge from data sources. Deep expertise in the field of machine learning is required for the challenging task of choosing the right algorithms for a specific dataset. There is no single algorithm that outperforms all others across all applications and different datasets. The difficulty of choosing an appropriate algorithm for a specific task in specific domain is related to the properties of the dataset. Properties of the dataset are measured through meta-features. Meta-features describe task and can provide explanation how one machine learning approach outperforms other algorithms on a given dataset. Learning about the effectiveness of learning algorithms, or meta-learning was developed to deal with this issue. Focus is required because previous research papers have not successfully identified meta-features in particular domains. In this research, we have evaluated various meta-feature characterization methodologies and have concentrated on basic meta-features. Business domain data is in the focus of this paper. We computed basic (general) meta-features and illustrated several use cases for their applications. [ABSTRACT FROM AUTHOR]

The paper proposes a novel approach for recognizing and classifying gym exercises using human pose detection with Mediapipe and LSTM, followed by matching the accuracy of the exercises using the Hungarian algorithm and cosine similarity. The proposed approach involves the use of a combination of cutting-edge technologies, including computer vision and deep learning, to recognize and classify a range of gym exercises accurately and efficiently. This study presents several advantages of this approach, including the ability to leverage temporal information, robustness to variations in lighting, camera angle, and occlusions, and the ability to handle a wide variety of gym exercises. Experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods for gym exercise recognition and classification. The paper also compares the proposed approach with existing techniques, highlighting its strengths and cost-effectiveness. It concludes by outlining the methodology, including dataset collection and pre-processing, landmark extraction, LSTM model training, exercise classification, and similarity analysis. Finally, the paper discusses potential future research areas in this field. [ABSTRACT FROM AUTHOR]

Natural disasters cause devastation, chaos, destruction, death, displacement, and much more when they occur. With the advancement of recent technologies especially in artificial intelligence and deep learning, the severity and impact of natural disasters could be reduced by predicting their occurrence. In this research, the flood detection (using images) system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). In this paper, the main aim is to propose an automated System for precision disaster detection, in particular flood detection. The intelligent system will monitor vast problematic areas that are dubbed flood risk areas during the season. The intelligent automated system will systematically take images with real-time processing to detect floods. If a flood is detected, the system will automatically communicate to the ground station with the early warning to issue an early warning to all the inhabitants in the path of the flood. Since this paper will prove a concept, the other main contribution of this paper is a new dataset consisting of 9000 images collected from various sources on the Internet, labeled, and preprocessed. The Deep CNN architectures were trained and tested by constructing a new dataset of more than 9000 labeled images with flood and non-flood collected from different sources. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. Experimental results show that 92.50% accuracy was achieved using GoogleNet. The second highest performance was achieved using AlexNet with an accuracy of 92.20%. [ABSTRACT FROM AUTHOR]

Road traffic by vehicles is continuously increasing through the world and they lead to terrible traffic jamming and significant delay in emergency services at cross roads. The vehicles stuck in that traffic jam may reach their destination with more delay which was lead to loss of lives or a property in some cases for example, emergency vehicles when stuck in the traffic jam like ambulance, fire engine vehicles, police jeeps, etc. Therefore it becomes most significant to control the vehicle traffic on the roads so as to acquire the effective control over the vehicular traffic based on the priority that may leads to saving the life of people in emergency situations by providing delay less services. This paper presents a survey on various methods and systems developed for the traffic classification according to the priority of emergency vehicle and for scheduling the vehicles accordingly in traffic. In this paper, different deep learning methods explored by various works are presented for effective increasing of vehicles creating traffic on the route of emergency vehicle. Finally, the works presented in this paper are compared and result analysis can be illustrated. [ABSTRACT FROM AUTHOR]

The paper presented the various techniques that can be applied for vehicle number plate identification. The extensive literature survey has been carried out on already presented technologies. Out of these technologies, three technologies have been selected and explained in this paper, the technologies are: OCR Method, Deep Learning Method and OpenCV Method. The comparison of three different technologies also available for vehicle number plates identification has been carried out on the basis of accuracy, complexity and cost. [ABSTRACT FROM AUTHOR]

The rapid developments of deep learning techniques are the process that can easily create and use a fake media known as Deepfakes. Deep learning techniques can generate facial motion transferring, gender-changing, and face swapping with others. Currently, it is possible to create hyper-realistic digital media like images and videos through deep learning techniques that are easily accessible. However, that becomes a threat for everyone, if used for harmful purposes. This work presents how deep learning is used for Deepfake generation and detection. There are two methods that are most popular for Deepfake generation: Generative Adversarial Networks (GANs) and autoencoder techniques that is presented in this work. It also, introduces a survey of some researchers' works and methods that used for Deepfake detection during the recent years. This paper proposed a method to discriminate if that video is real or fake by using a Support Vector Machine (SVM) classifier, the method presented in this work for deepfake detection is based on Gray Level Co-occurrence Matrix (GLCM) as a feature extraction method applying on 4 bands to obtain 13 feature form each one to achieve good accuracy of the classifier with a score 70.47% that batter than previous work that didn't exceed 60% of accuracy. [ABSTRACT FROM AUTHOR]

Coronavirus Disease (COVID-19) pandemic has emerged as one of the biggest challenge faced by researchers, especially in public health system. Since the virus spread and mutates quickly, strategies are needed so that the public health system doesn't collapse due to overcapacity. Modeling and forecasting accurately the COVID-19 daily new cases is very important to understand and help carry out risk management for the outbreak control. To obtain the estimation of the Indonesian daily cases of COVID-19 based on daily confirmed cases, we present a comparative study between three deep learning methods in this paper: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Long Short-Term Memory (Conv-LSTM). The data used is daily confirmed cases from September 1st to September 7th, 2021. The result is Conv-LSTM model produces better performance than other methods. Here we also provide 7 day out-of-sample forecast for daily cases from September 1st to September 7th, 2021 using each model. [ABSTRACT FROM AUTHOR]

Timely identification of diseases in crops and taking immediate actions to prevent them from being completely destroyed is a key part of agricultural production. Environmental challenges like unfavorable weather conditions and climate changes directly affect the plant growth and it causes viral infections that can lead to the eradication of the crops. Microorganisms such as bacteria and fungi can also cause various diseases in plants. In order to provide the accurate and better solution for a plant problem, the farmers first need to identify the exact kind of illness that the plant is having. The existing method of manual diagnosis of plant diseases by a field expert is time consuming as well as costly. Farmers cannot afford the cost of frequent visits to remote laboratories for diagnosis. The use of advanced and effective deep learning models can solve this problem and predict plant diseases at an early stage. IoT has become very popular in the agricultural industry as it provides state of the art solutions to plant problems. This paper aims to study the recent related works on this area and to analyze the advanced methods and models used in the implementation and to allow future research to learn larger capabilities of IoT and Deep Learning in proactively detecting the plant diseases with improved system performance and accuracy. [ABSTRACT FROM AUTHOR]

Techniques for detecting oral cancer are a developing area in medicine that includes cancer diagnosis and early detection. For the classification of oral cancer, we have tried to develop machine learning models like logistic regression (LR), naive Bayes (NB), K-Nearest Neighbors (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), as well as hybrid models like KNN-SVM and SVM-KNN and deep learning models like Xception, VGG, CNN, Inception, and MobileNet. A few researchers have already used the aforementioned models to categorize oral cancer using genetic data. Nevertheless, the database used for this study includes pictures of the lips and tongue that have been divided into malignant and non-cancerous categories. With the assistance of ENT doctors, the photos were categorized after being captured in various ENT hospitals in Ahmedabad. To determine which model is the most effective, the ML, hybrid, and DL models are evaluated based on numerous evaluation criteria. Experimental result shows that the accuracy, precision, recall, and Fl score of classification of the ML, Hybrid, and deep learning are discussed in this research paper. Out of which hybrid model SVM-KNN with PCA has 74%, Gradient boosting with PCA with 73%, and deep learning model VGG has I 00% accuracy. Hence, it may be concluded that Gradient boosting with PCA and advanced deep learning models like VGG, and Xception is the most favorable model for predicting the survival rate of oral cancer patients by classifying them into cancerous or non-cancerous. This research study motivates towards the early prediction comparatively reduces the severity, and economic cost as the treatment for early diagnosis is less. [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]

Soil is an essential part of the ornamental plant industry because the soil stores a lot of nutrients and microflora, which are very useful for plant growth. However, ordinary people or beginners in the ornamental plant industry sometimes do not understand the type of soil used, so they get less than optimal results in plant maintenance. Thus, this sometimes results in losses and decreased production income. So, to help the layman understand the type of soil that is good for use in the plant industry, this research will help classify the soil. In conducting soil classification, this research uses a method, namely CNN. CNN method is the most effective method in classifying. This paper aims to provide steps for the classification of 5 soil types. [ABSTRACT FROM AUTHOR]

In recent years, improvements in natural language processing and computer vision have come together to provide automatic image caption generation. Image captioning is the process of creating a description for an image. Captioning an image needs the recognition of significant items, their properties, and their connections within the image. Additionally, it must create phrases that are syntactically and semantically accurate. Deep learning approaches can address the complexities and difficulties associated with image captions. This paper describes a joint model which is capable of automatically captioning images using EfficientNet-B0 and a transformer with multi-head attention. The model is an aggregation of an EfficientNet & Transformer single encoder and decoder. The encoder utilizes EfficientNet-B0, a convolutional neural network-based algorithm that generates a detailed input image, represented by embedding them into a fixed-length vector. The decoder employs a transformer, and a multi-head attention mechanism to selectively concentrate attention on certain regions of images to predict the sentence. The proposed model was trained using a large dataset Flickr8k to optimize BLEU N-Gram (N=1,2,3,4), METEOR score, and CIDEr, to assess the probability of the target description phrase given in the training images. Our studies show that the proposed model can produce captions for images automatically. [ABSTRACT FROM AUTHOR]

The agricultural industry is the most prominent sector for the economic growth of the country. Cotton is an important crop that brings out major returns to the economy. In the current era, smart agriculture technology is incorporated which includes various modern techniques such as sensors, artificial intelligence, and computer vision to digitalize the whole process. This digitalization through modern technology enhances the fast and intelligent productivity of crops. In this paper convolutional neuralnetwork-based methods have been used to predict the maturity of the cotton balls using image features of mature and immature images. Real-time maturity detection is a challenging task for cotton fiber because of the many constraints in natural light conditionsand environmental effects. The proposed scheme uses annotation to classify the cotton balls. The classification is labeled as matureand immature images then the annotated images are used for training the deep learning models Resnet 50 and VGG 16. The averageaccuracy achieved from these methods is 92.47% for Resnet 50 and 96.58% for VGG16. The loss of the model is 21.05% for Resnet50 and 14.91% for VGG16. [ABSTRACT FROM AUTHOR]

The stock market serves as a vital investment platform, attracting numerous financial investors due to its growing capitalization. However, predicting stock prices accurately has always been a challenging task, requiring advanced algorithmic techniques. This research paper presents an innovative approach to improve stock market predictions by leveraging the strengths of Long Short-Term Memory (LSTM) based on a comparative analysis with Multi-Level Perceptron (MLP) models. The study compares the predictive capabilities of Random Forest, MLP, and LSTM models, with MLP demonstrating superior accuracy compared to Random Forest in the initial analysis. Building upon this finding, LSTM is employed by incorporating MLP predictions to forecast future market trends. The proposed technique demonstrates reliable results by combining machine learning and deep learning approaches. The MLP analysis further enhances the suggested model, resulting in improved accuracy and reduced mean square error. The result highlights the potential of integrating LSTM and MLP models for enhanced stock market predictions, offering valuable insights for financial forecasting and opening avenues for further research and refinement in this field. [ABSTRACT FROM AUTHOR]

This research paper explores the application of text classification and natural language processing techniques for enhancing hate speech detection. The study employs machine learning (ML) and deep learning models, including transformer models such as BERT, RoBERTa, and DistilBERT, to improve the accuracy of hate speech classifiers. Through a comprehensive empirical analysis on three diverse datasets (Data-ICWSM, Data-ALW2, Data-OLID), the study demonstrates the effectiveness of these models in accurately identifying hate speech. Compared to traditional baselines, the BERT models exhibit a significant performance boost in macro and weighted F1-scores. Additionally, the study addresses the challenge of imbalanced class distributions in the datasets by employing sampling techniques during training. Overall, the research highlights the potential of transformer models for hate speech detection and provides insights for future exploration in this domain. [ABSTRACT FROM AUTHOR]

Sign language is a crucial mode of communication for individuals who are deaf or hard-of-hearing, and a reliable sign language recognition system can greatly aid in bridging the communication gap. According to statistics provided by the Government of India, there are only around 250 accredited sign language interpreters in India, servicing a deaf population of 1.8 million to 7 million individuals. This paper presents a real-time sign language recognition system for Indian Sign Language (ISL) using YOLOv8 for object detection. The system uses a single convolutional neural network (CNN) architecture to process the entire image in one forward pass and predict the bounding boxes and class probabilities for each sign in the image. The results of the study demonstrate the feasibility and potential of the system in revolutionizing communication for the deaf and hard of hearing community and have applications in gesture-controlled robotics, home automation, game control, human-computer interaction, and sign language interpretation. [ABSTRACT FROM AUTHOR]

Run 3 of the Large Hadron Collider (LHC) of the data-taking period poses unprecedented challenges to the computing models used in the high-energy physics experiments of the LHC accelerator. Only in the LHCb experiment, the luminosity has increased by a factor of five. Recent results show that deep learning solutions techniques can significantly improve the performance of the cluster reconstruction in calorimeters when high occupancy is expected. In this paper, we will review selected results of the LHC experiments and, in particular, focus on the investigated convolutional (CNN) and graph neural network (GNN) solutions for planar, LHCb-inspired calorimeters with hybrid granularities. We will also show how the most recent developments in the new experiment-independent simulation framework – Gaussino – can be used to produce training datasets for these models. [ABSTRACT FROM AUTHOR]

last years the risk of dying from depression has increased, Depression creates suicidal thoughts for people that cause serious disabilities in daily life. Sentiment analysis (SA) is a topic has been on research for decades, which aim to find the nature of the text and classifies into positive, negative and neutral, for today progression of technology a lot of data is available for SA from text and image. This paper aim to apply pre-processing, feature extraction and classification methods on Twitter tweets for perform SA focusing on depression. Deep Belief Network (DBN) technique used for the classification of the text data obtained from depression dataset, where the tweets are classified as positive or negative. The results are presented using standard evaluation metrics precision, recall, f-score and accuracy, which results are 99.97%, 98.47%, 99.21% and 98.77 respectively. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious disease which is spreading fast and some variants of the coronavirus, such as Delta and Omicron, are spreading more easily between people and this spreading can be alleviated to some extend by vaccination. In this context, the government is demanding for producing the vaccination certificate for the person to be in certain public places. But there can be chances where people do not know their Aadhar number and they do not carry their vaccination certificates especially in rural areas. And also it takes a lot of time in waiting in a long queue for verification purpose. So to tackle all these problems we propose an automated system for identifying vaccinated person without much human interventions. The system uniquely identifies the person through iris recognition using various deep learning models. In this paper we made a comparative analysis of different deep learning and CNN architectures (MobileNet, EfficientNetB3,InceptionV3, DenseNet169) for building a model for person recognition and select the best model with maximum accuracy. Here, we used kaggle dataset (1000 iris images) for testing and training. Also using an iris scanner, the live iris images can be captured and used for model building. Using some deep learning algorithms, the features of iris image of the person is obtained and it is compared with those in Aadhaar database to get the Aadhar number. Using this Aadhar number, the details of the vaccinated person can be obtained from vaccination database and system will identify whether a person is vaccinated or not. By installing our proposed system, vaccinated person can be easily identified without much human interventions, avoiding touch and reducing the long queue for verification, thereby maintaining social distancing. [ABSTRACT FROM AUTHOR]

Tampered Medical images and scans are a very serious issue in the medical field. The results of tampered scans can range from mild to serious results. For example, a tampered scan or medical image can lead to misdiagnosing a patient with a serious condition or a patient who suffers from a serious condition can be misdiagnosed as healthy leading to a delay in receiving proper treatment. In this paper, we propose a deep learning-based methodology to detect tampered/fake cancers in 3D CT scans of human lungs. We use a publicly available dataset of deepfakes that include both tampered and genuine cancers and propose the use of a modified Convolutional Neural Network (CNN), in particular the AlexNet with Transfer Learning. The model was pre-trained on a large dataset and fine-tuned on the medical images dataset to detect tampering. Experimental results achieve a high level of accuracy in detecting tampering. Using the AlexNet with Transfer learning, we achieved an accuracy of 89.47%, Recall of 89.47%, Precision of 89.47%, and F1 measure of 89.47%. This is higher accuracy than similar methods using the same dataset reported in the extant literature. Achieving this high accuracy for a multiclass complex problem is considered an excellent achievement. Expert radiologists are mostly unable to distinguish between real and tampered cancer images. The results demonstrate the potential for deep learning-based models in improving accuracy and efficiency in tampered medical scan detection. [ABSTRACT FROM AUTHOR]

Due to its extraordinarily contagious nature, the Corona virus disease (COVID-19), which initially started in December 2019, has seriously imperiled the world's healthcare systems. One of the most destructive global pandemics in history is due to its great infectiousness and high mortality rate. As a result, it is crucial to implement a variety of techniques to quickly identify the Corona virus. With the enhancement of early diagnostic and disease severity detection technologies the load on the healthcare structure can be lessened and the effectualness of treatment and rehabilitation for patients can be increased. In light of the severity classification of COVID 19 procedures, this survey analyses numerous methodologies. 25 research papers that focused on different techniques will be used in this study, and an overview of the techniques will be given. The analysis will then be highlighted in the work based on the year of publication, performance metrics, software utilized, datasets and success of the study approaches toward various severity levels of COVID 19and the most utilized techniques related to COVID-19 severity classification is deep learning techniques. At last, drawbacks and issues involved in COVID 19 severity classification techniques will be considered concluding declaration and future scope. [ABSTRACT FROM AUTHOR]

Nowadays the use of Artificial Intelligence (AI) around the globe is constantly growing. These processes were accelerated even more due to the COVID-19 pandemic. In the cybersecurity realm, the real implementations of AI solutions are also constantly growing. For some enterprises, AI is considered more as a risk for overall cybersecurity while others see AI as the way forward to achieve cyber resilience. That kind of uncertainty points out that cybersecurity leaders are still not united around AI and do not fully understand the effects of utilizing it. That lack of understanding is emphasizing again the need for more research into AI for cybersecurity and in addition to that the urgency of attracting young talent to the cybersecurity career path. On the other hand, cyber threat actors are getting more and more sophisticated and may already be on the way to integrating AI into their future attacks. In this paper, we are analyzing the artificial intelligence methods for cybersecurity in Industry 4.0. In addition, we detail the challenges, motivations, and pre-conditions to be taken into consideration when using these AI methods for improving overall cybersecurity in Industry 4.0. [ABSTRACT FROM AUTHOR]

As next-generation wireless communication technologies advance and spectrum resources become scarcer, Cognitive Radio Networks (CRN) must incorporate learning and reasoning capabilities. Spectrum Sensing (SS) in particular has emerged as the primary target for investigation to solve the problems of CRN. Deep learning techniques have been used to add new aspects to SS. This paper is based on a statistical analysis of the classic energy detection scheme, which is solely dependent on the number of samples and secondary users' signal-to-noise ratio. The detection of channel occupancy is based on well-established analytical techniques such as Maximum Ratio Combining (MRC) and AND/OR rules. Furthermore, the deep learning Long Short Term Memory (LSTM) algorithm is used for SS and compared to analytic techniques. the results demonstrate that the LSTM technique presents a better trade-off between the probability of false alarms and the probability of missed especially for fading channels with accurate detection of 87.4%. [ABSTRACT FROM AUTHOR]

The potential for service continuity across uncovered and under-covered regions, service ubiquity, and service scalability are all offered by Satellite Communication (SC) for the recent decade. However, to take advantage of these benefits, it is necessary to address several obstacles first. This is because the management of resource, control and security of network, the efficient utilization of spectrum, and efficient utilization of satellite-based communication networks are more difficult to accomplish than those of Terrestrial Networks (TNs). Thereby, Artificial Intelligence (AI), which encompasses Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has been continuously expanding as an area of study and has shown good outcomes in a variety of applications, including wireless communication. As a consequence of this, several publications put forth a variety of algorithmic solutions to the problem involving the management of various applications for SCs. Such an algorithm will employ various DL methods to improve the effectiveness of SCs. As a consequence of this, the aim of this paper is to provide an exhaustive overview of the utilization of various AI methods for a broad aspect of SCs. Following this, a general overview of the most recent details related to various AI methods and their related classifications will be presented. In addition, the study provides a summary of the many bodies of research that apply AI-based approaches to the resolution of satellite-related problems. In addition to this, AI may also be used to spot trends in satellite traffic and utilize bandwidth more effectively, making better use of the resources. [ABSTRACT FROM AUTHOR]

Due to the fast spread of the new Coronavirus called SARS-CoV-2 (otherwise known as COVID-19 virus) worldwide and its continuous mutations, such large viral outbreaks require early elucidation to determine the genetic sequence of the virus to design an effective system for identifying variants Different known and unknown virus. Identifying specific variables facilitates the comprehension and modelling of viral propagation patterns to treat and manage outbreaks, create efficient reduction techniques, and avoid future outbreaks. In addition, it serves a vital role in assessing the efficiency of known vaccines against each variation and modelling the probability of superinfection. It is known that the genomic sequence carries the vast majority of genetic information pertaining to variations and variants of the Coronavirus. This paper proposes a system that depends on deep learning to predict the mutations of complete COVID-19 virus genomes based on the Convolutional Neural Network (CNN) algorithm as an alignment-free method. The k-mer technology is applied to fragment the DNA of coronavirus mutants to create a unique vocabulary. The proposed approach can correctly predict and characterize other coronavirus strains, such as MERS-CoV, Alpha-CoV, SARS-CoV-2, SARS-CoV, Gamma, Beta, and other strains, regardless of missing information and sequencing errors with an accuracy rate of 99% on the data set. [ABSTRACT FROM AUTHOR]

After a deep questioner among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies. [ABSTRACT FROM AUTHOR]

Online education's popularity has been continuously increasing over the past few years. Many universities were forced to switch to online education as a result of COVID-19. In many cases, even after more than two years of online instruction, colleges were unable to resume their traditional classroom programs. A growing number of institutions are considering blended learning with some parts in-person and the rest of the learning taking place online. Nevertheless, many online education systems are inefficient, and this results in a poor rate of student retention. In this paper, we are offering a primary dataset, the initial implementation of a virtual teaching assistant named VTA-bot, and its system architecture. Our primary implementation of the suggested system consists of a chatbot that can be queried about the content and topics of the fundamental python programming language course. Students in their first year of university will be benefited from this strategy, which aims to increase student participation and involvement in online education. [ABSTRACT FROM AUTHOR]

Aim: The main aim of this paper is to recognize mango leaf disease using convolutional neural networks in google co-laboratory. Materials and Methods: A dataset of 120 photographs of diseased and normal mango leaves was used to distinguish mango leaves using the CNN technique which is based on a deep learning algorithm. Results: The proposed 5D CNN model has a 98.67 % accuracy and 2D CNN has a 89.05% accuracy in leaf disease classification, so the novel 5D Convolution Neural Network algorithm appears to be better than 2D CNN standard deviation is 1. Conclusion: In mango leaf disease detection, the proposed novel 5D Convolution Neural Network based leaf disease recognition model shows less training time and high training accuracy. This demonstrates the adaptability of CNN models in real-time implementations. [ABSTRACT FROM AUTHOR]

The massive amount of marine debris generated from land to the marine ecosystem could bring various negative impacts on the flora and fauna that resides there. To properly remove marine debris from the marine ecosystem, the marine debris needs to be identified and measured first so relevant parties can execute the job easily with the right tools and methods. Various studies had been conducted to detect and quantify marine debris using deep learning models and remote sensing devices to capture the debris footage. But most of the studies don't include a platform such as a web or mobile application for executing the developed model. In this paper, a concept mobile application called MarinaWatch is proposed that brings marine debris monitoring through drone detection and quantification using a deep learning model on a single mobile platform with the combination of cloud computing. [ABSTRACT FROM AUTHOR]

Manual traffic surveys take a lot of time and effort as they require many human resources to conduct transportation surveys. Incorporating some level of automation (smart surveys) can help reduce the workload of human operators performing manual surveys and reduce the time and cost of obtaining that data. Intelligent surveys have become more important than ever due to the significant development in technology and the trend towards smart cities that are managed by various intelligent systems, including the transportation system. This paper develops a new method for intelligent surveys of real-time transport data using deep learning. In this research, a system was programmed to determine the types, counts, and speeds of vehicles passing through a specific road section by using the comprehensive database of video surveillance data obtained using high-resolution digital cameras. In addition, an application has been programmed to display the traffic situation in real-time to road users. In general, the results showed that the proposed method works as required and under different conditions without being significantly affected by weather conditions. The software also achieved high results in the accuracy of counting and classifying vehicles, reaching 98%, and regarding the accuracy of calculating the speed of vehicles, it reached 97%. [ABSTRACT FROM AUTHOR]

Several techniques have been developed to predict the stability of shallow foundations over the years. However, methods for predicting the necessary degree of detail and consistency have not yet been developed. This study applied the method to a deep neural network (DNN). The expected shallow settlement parameters are carefully selected. It was concluded that a deep neural network (DNN) appears to be a viable solution as it has been used successfully in many diagnostic applications in geotechnical engineering. In this paper, the precipitation values of gypsum soils were predicted under the influence of applied load using the deep learning technique. The study found that this model is very good in predicting soil precipitation and found convergence between real and expected values. The deep neural network model showed a more significant performance with mean absolute and mean squared errors, which were 0.0290 and 0.0387. The DNN model recorded the efficiency and variance computation coefficient at a good rate. It is concluded that a deep neural network model can be used to predict the stability of a shallow foundation on gypseous soil. [ABSTRACT FROM AUTHOR]

Legal Analytics is a blooming field of research that draws attraction from various fields such as computational linguistics, natural language processing, machine learning, and data science. Improvement in text analytics has paved the way to a great level in eCommerce, banking, medical and legal sectors after the incoming of deep learning models. The large quantity of digital information in the legal domain and the challenges faced has made this an important area of legal research. Historically, judges and other legal personnel have frequently been required to manually review legal documents to acquire an inclusive judgment justification. This practice is highly challenging also laborious and time-consuming. Most of the existing works predict the legal outcomes based on case facts, but predicting judgment solely on Casefact gives low efficiency. Automation in judgment prediction systems is at the budding stage. Choosing the appropriate word embedding technique in language modeling, Classifier model and loss functions pave way for better results. Selecting the appropriate learning algorithm for text classification on real-time problems is a great challenge as legal documents are lengthy and unstructured. This paper emphasizes the limitations and important aspects of different components of NLP such as tokenization, word embeddings, and classifiers which makes the computer interpret and extract maximum contextual information from documents to provide the best performance for assisting litigants for verdict prediction of countries that use common law system. It also reviews different ideas applicable to legal case facts in a broad manner. [ABSTRACT FROM AUTHOR]

Applications of deep learning to solve complex problems related to computer vision and human level control system has been successfully implemented in real life. On the other hand, deep leaning based systems can also cause harm to the privacy and security of the personnel or a system. One of the threats that is powered by deep learning and AI is deep fake technique which is used to create fake images, video and audios that are not identified by human visual or audio system. This makes it difficult for human to distinguish between the original data and the deep fake data. This paper aims at discussing the techniques or algorithm require to create deepfake and to detect the deepfake in audiodata. [ABSTRACT FROM AUTHOR]

Medical imaging has seen substantial improvements not only in image procurement but also in terms of interpretational methodologies. The practice of using technologies such as image processing and artificial intelligence has shown remarkable results in the detection, classification, and localization of human bone fractures. Breakage or cracks in the bone occur when an external force is applied to a bone that is significantly larger than the bone's tolerance, which may lead to dislocation requiring extensive treatment. However, manual examination by experts is a complex and arduous process with a high chance of false detection. Many approaches under Artificial Intelligence and its subsets are proposed to facilitate fracture diagnosis and prevent misinterpretation, including Deep Learning and Convolutional Neural Networks (CNN). This paper is a comprehensive study of the various techniques and models designed using image processing approaches and the advances in machine learning and Deep Learning that have demonstrated improved performance in the precise detection of fractures in human bones. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) / Deep Learning (DL) has been applied in the development of the healthcare domain in recent years due to its increased potential and performance. It provides better support to clinicians and patients in various aspects, such as automating medical billing, clinical decision support, disease diagnosis, etc. It also plays a vital role in pattern recognition, online recommendation, finance, robotics, information security, etc. It can learn and realize the pattern from the healthcare records and gives better predictions about the disease. The goal of this review paper is to provide an overview of: (i) Machine Learning concepts; (ii) Machine Learning applications, techniques and its purpose in the healthcare domain; (iii) challenges and issues in maintaining health records; and (iv) finally, it helps to minimize the research gap with best decision support system using Machine Learning. [ABSTRACT FROM AUTHOR]

Now a days, air pollution is a critical environmental issue in developing countries due to urbanization and industrialization. The pollution in air causes severe health issues for human beings, affects plants and animals due to acid rain, creates loss in economy, agricultural production and commercial production. In order to prevent or control it, the air quality forecasting necessitates. Accurate air quality prediction is a prominent task to be done to control the pollution level and to take countermeasures for securing the human life. The complex, uncertain and non-linear nature of the air quality pollutants impose complications in achieving accurate forecasting. The advent of artificial intelligence techniques helps to improve the air pollution forecasting results compared to traditional statistical methods at the great extent. This paper focuses the complications of air pollution, significance of air quality prediction, air quality index, and reviews the applications of machine learning and deep learning methods in air quality prediction. Literature shows that the machine learning and deep learning methods along with feature engineering and optimization techniques guarantees a promising air quality prediction. [ABSTRACT FROM AUTHOR]

Environmental conditions continue to debilitate each year on account of advancement of development and expanding messy discharges from enterprises and cars. Notwithstanding the way that air is a fundamental resource always; various people are indifferent with the reality of air tainting or have actually seen the issue. Among different kinds of contaminations, air contamination and water contamination are the most hazardous and extreme, causing environmental change and perilous sicknesses. Air contamination is one among the principle players, prompting worldwide environmental change which has caused abnormalities inside the temperature design, crops creation and water contamination has prompted coming of fresher sicknesses causing broad demolition. In this paper, IOT Device, Data Science and Deep learning Technologies are used to validate the air and water pollution from the factory by the factory management and pollution control board. If any pollution violation occurs in factory, then the IOT device intimate the factory management as well as pollution control board regarding this violation. The pollution control board is provided with each and every detail of pollution emission from factory as a graph. [ABSTRACT FROM AUTHOR]