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Fake news is false or misleading information presented as news. False stories tend to spread farther, faster, and more broadly than true stories. Some individuals and organizations intentionally applied misleading information for certain benefits. The internet goes quickly, mobile devices became popular, and social media users grow more significant in number, make no boundaries world and connect every user. As a result, information circulates in immense numbers makes to handle fake news manually is impossible. Fake news also affects human psychology. Hoax related to how to cure COVID-19 and the lack of toilet paper has led to casualties and riots. Fake news potentially differs in writing technique compare to real information. In this paper, we discussed a few studies related to fake news detection. Their algorithm can capture writing style in content viz lexicon, syntax, semantic, and discourse. Also, those studies implemented user-based, content-based, and social context-based. It used machine learning, deep learning, and reinforcement learning. Furthermore, education plays a vital role in making people understand the impact of fake news. Itis necessary to do a campaign for all internet users. The national government necessity collaborates with society implement the particular rule for better internet environment. [ABSTRACT FROM AUTHOR]

Deep learning has been increasingly popular in the area of information engineering over recent years (SE). However, there are so many unfinished concerns to be studied. How can researchers incorporate deep learning into SE issues? What SE phases are supported by in-depth learning? Are the professionals benefiting from deep learning? Responses allow clinicians and researchers to build functional deep learning models for SE activities. To address these questions, we perform a bibliography review of 98 SE academic articles using deep learning techniques. We notice that 41 SE activities have been enabled by deep learning optimised solutions in all SE phases. In which 84.7 percent of papers only use traditional deep learning techniques and their variants to overcome Software Engineering problems. Practicability is becoming a problem with the use of deep learning methods. More Software Engineering researchers may be attracted in the future to Enhance the effectiveness, efficiency, comprehensibility and testability with deep learning- based methods. [ABSTRACT FROM AUTHOR]

In the recent times, deep learning has gained significant importance in various domains. Computer Vision is one the domains in which deep learning has seen vast development and is still gaining a lot of attention. CNNs are deep architectures that are specifically applied for image classification and object detection in computer vision. Image classification has also been successfully applied in the field of agriculture. Using deep learning, various agricultural problems have been addressed and solved. Keeping in view the enormous uses of deep learning in agricultural domain, this paper gives a brief review of the various works proposed by the researchers working in the field of deep learning and agriculture. This paper will put some light on the various agricultural problems, the deep learning models that have been employed to solve the problem, any pre-processing techniques that have been applied on the images to prepare the dataset for the specific model and the results that have been achieved by the model in solving the problem they focus on. It will also provide any research gap found in the specific work. This work will be useful for future researchers to gain an overview of the work done so far and the deep learning techniques used in agriculture. [ABSTRACT FROM AUTHOR]

This paper reviews the complex task problems which are out of reach for a simple machine. So, there is a need for a solution for such a problem, so the solution is Reinforcement Learning with deep Q-Network. Reinforcement learning techniques are now being researched for their applicability in a wide range of situations. Perhaps because of the rising complexity and unpredictability in the generation and distribution sector of power systems, traditional approaches frequently encounter congestion while attempting to handle decision and control issues that are out of reach for a basic machine. Deep Reinforcement Learning (DRL) is one of these data-driven approaches that is considered true Artificial Intelligence (AI). DRL is a hybrid of Deep Learning (DL) and Reinforcement Learning (RL). Our study examines the fundamental concepts, models, methods, and approaches of DRL. It also presents power system applications such as smart grids, energy management, demand response, the electricity market, operational control, and many others. Furthermore, current advancements in DRL, the coupling of RL with other classical techniques, and the prospects and problems of its applications in the power system are explored. [ABSTRACT FROM AUTHOR]

The development of computer vision and artificial intelligence has advanced in recent years. Object detection system based on deep learning have been extensively investigeted. In this paper, we proposed deep learning based method that will be used to detect and classify moving vehicles in complex traffic flows. The YOLO (You Only Look Once)-v3 network will be implemented to handle this case. This paper consist of saveral stages. In the first stage we collect vehicles dataset on one-way street in the form of a video. The second stage is converting video into frames and labelling frames. The third stage is to feed the labeled frames as input in training this model. The last stage is training and testing the model to detect and classify moving vehicles. The experimental was completed and the proposed method can classify moving vehicles using recorded data video appropriately. The results showed that 0.9648 average accuracies of moderate traffic condition and 0.9256 average accuracies of crowded traffic condition. [ABSTRACT FROM AUTHOR]

This paper presents the systematic literature review of the application machine learning method in detecting a distributed of service (DDoS) attack. Several relevant research papers were selected and they were reviewed based on the method using to provide the best performances and evidence in machine learning technique applications. The researchers are dedicating their efforts to analyzing, summarizing, and evaluating various machine learning methods for detecting DDoS attacks. Therefore, the purpose of this study is to evaluate several machine learning approaches for detecting DDoS attacks in computer networks. These mechanisms are characterized into five categories, the Multiple Linear Regression method, Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) method, Recurrent Neural Network (RNN) with Autoencoder, Deep learning-based method, and LSTM with Singular Value Decomposition (SVD). The paper also debates several open research questions and the research technique, parameters, and metrics. Also reviewed and contrasted were summaries of analyses and gaps in deploying a predictable machine learning model. Thus, the paper is expected to benefit academicians and researchers in developing an efficient solution for the machine learning mentioned above in detecting DDoS attacks. [ABSTRACT FROM AUTHOR]

In recent years, AI deep learning technology has been recognized as a potential solution in many fields and applications. It has the ability to solve practical problems, especially the ability to identify and classify images. In addition to ordinary daily image recognition, it also includes applications in medical images. On the other hand, music therapy has been commonly used for children with developmental delays to stimulate children's visual, auditory and tactile sensory abilities and enhance children's developmental skills in different aspects. However, there was a lack of clear quantitative evidence of response time in previous music therapy treatments, mainly as there were no extra human resources to measure the response time accurately during the treatment process. In view of this, this paper aims to identify the time point when children turn their heads after being stimulated by the sound and calculate the reaction time after the sound stimulation for children with developmental delays in the process of music therapy, by using AI deep learning technology. In the experimental analysis, the reaction time is measured for children. The experimental results show that the average error of the reaction time measurement method proposed in this paper is less than 1 %, achieving the desired goal of accurately measuring the reaction time and analyzing the time error calculation. The method reduces the average error by about 6.5 times. [ABSTRACT FROM AUTHOR]

Diseases can have a detrimental effect on plants depreciating their market value. Disease diagnosis is important for improving plant yield. Over the years, farmers are relying on Soft Computing (SC) techniques for the purpose of early diagnosis of various plant diseases. The paper builds an application using a Deep Learning (DL) model for timely disease diagnosis. The research employs pre-trained Convolution Neural Networks (CNN) for identifying crop diseases and insect pests. CNN efficiently tackles the challenges related to image stability and the number of parameters. PlantVillage dataset is used for training and testing the model. The dataset comes with fifteen different classes of infected and healthy leaves. The work applies image pre-processing techniques and data augmentation for enhancing the image recognition rate. The paper implements a crop disease recognition model comprising of Inception-ResNet-v2, Visual Geometry Group 16, DenseNet201 and, Inception_V3 for training. The model and subsequently the application are validated and tested using standard methods. The convolution layer of the networks uses a non-linear activation function-ReLU which has gained popularity in the deep learning domain for its simple activation strategy. Later the name of the crop reporting the highest matching degree is displayed. Lastly, the accuracy of all the networks is compared against the traditional convolution network. It is inferred that the system thus aims to efficiently identify crop diseases and produces better recognition accuracy than the traditional convolution networks. [ABSTRACT FROM AUTHOR]

Currency has become absolutely necessary part in human life. At present recognition of counterfeit and real currency notes is very important. If the application learns to classify the denominations of the currency and detect the counterfeit currency then it will improve the accuracy of the recognition and confidence of the people. Implementation of deep learning techniques on various applications is increasing rapidly, and now many researchers are using deep learning techniques for the applications related to currency notes. In this paper various techniques which have been used for extracting the features from currency note, recognizing the currency note and detecting the counterfeit note have been presented. [ABSTRACT FROM AUTHOR]

In the present era of Deep learning algorithms whose impact is at a greater extent in identifying and classifying the electronic medical images based on Content Based Image Retrieval (CBIR) systems where a user queries an histopathological images based on the visual content which leads to non accurate results. In this paper after reviewing all the references we propose an image annotation approach which automatically assigns keywords to images based on the visual content by which querying task will be easier one as we classify the images based on supervised learning aspects to perform mapping between features and concepts or class labels called as Bag of Words (BoW). In this paper we compared the remarkable researches from 2006 to till date publications and compared them in terms of methodology adapted. [ABSTRACT FROM AUTHOR]

In this paper, we develop a risk based predictive analytics model utilizing the available dataset to train the model. To predict the likelihood of a new strain of Covid-19 syndrome in patients, the researchers used a deep learning classifier called Recurrent Neural Network (RNN). The study considers diabetes patients as the respondents and the data is collected from the diabetes patients. The risk of covid-19 effects on diabetes patients are deeply analyzed using RNN. The collected datasets are initially pre-processed and then the features are extracted with final classification using RNN. The experimental analysis is conducted to validate the efficacy of the predictive analytics using RNN. The findings indicate that the suggested RNN outperforms other approaches for forecasting covid-19 risk in diabetic patients. [ABSTRACT FROM AUTHOR]

Trash classification is an important activity which helps in identification of waste. In this paper, a Classical- Quantum Transfer learning model, namely TrashQNet is proposed to classify trash into two classes, Organic and Recyclable trash. Classical-Quantum Transfer learning is a combination of machine learning and quantum computing. The proposed TrashQNet model uses a pre-trained DenseNet169 network for the feature extraction process and a variational quantum circuit as a classifier. The performance of TrashQNet is compared with classical machine learning models K- Nearest Neighbour & Support Vector Machine, deep learning model - Convolutional Neural Network and transfer learning model. TrashQNet outperforms all these models, it achieves an accuracy of 94% on test dataset. [ABSTRACT FROM AUTHOR]

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]

The performance of university students during their academic session are vital to their overall grade throughout their term in the university. There are multiple factors that could lead to the loss of performance but the foremost factor is their level of emotions. Previous research has shown that to determine the performance of the students, the best way to do so is by analysing their attention levels. With the development of portable Electroencephalogram (EEG) devices and machine learning algorithms, it is easy to obtain the students attention and emotion level during their academic sessions. This paper aims to present a method of obtaining the EEG signals using a portable EEG device and classifying it into the type of emotions that are present in the human brain. The EEG device will obtain the attention level and EEG signals during two scenarios which are lectures/tutorials and exams/quizzes. The signals are then compiled and analysed to determine the emotion labels based on a normalization process that categories the signals into positive or negative emotions. The dataset and labels are then used to train and evaluate multiple machine learning models and a deep learning model in order to determine which model has the best accuracy and performance. The chosen model is then used to predict the emotions of several students during both scenarios and the average emotions are then compared with their average attention to determine the effect of emotions on the students' performance. Hence, this paper will first provide a method on obtaining the emotion labels, followed by the models' development and finally correlating the predicted emotions with the students' performance during their academic sessions. [ABSTRACT FROM AUTHOR]

The enormous benefits and applications of Image classification and recognition are umpteen. Machine learning algorithms and Deep Neural Networks are like windfall to fathom the objective proficiently in streamlined manner. The prevalent improvement in this technology is that these networks do not call for any prior blueprints in terms of algorithms as prerequisites. The presented paper is an attempt to create a Convolutional Neural Network from scratch to classify the images from the well-known dataset – Cats and Dogs into their relevant baskets. Manifold open source accessible approaches to amplify the efficiency of the network are no more onerous. Further, data augmentation technique boosts the efficiency tremendously by extending the dataset with reoriented features from the same images. To untangle the same problem, Transfer Learning is also a compelling technique in which all the layers, neurons in each layer, weights of each neuron and all other parameters are predefined and we can amend the output layer as per the classes in the respective problem statement. In the present paper, we have tried to obtain a comparable efficiency with a significant reduction in parameters. [ABSTRACT FROM AUTHOR]

The manual detection of exoplanets is subject to human error and very labor-intensive. This paper introduces, classical machine learning techniques for the detection of exoplanets. For the testing, three different machine learning algorithms, i.e., Logistic Regression, SVM, and Decision Tree, with and without filter are used. The model is designed to detect transiting planets, with the help of light curves by searching a regular pattern in the flux of the host star. The finding demonstrates that these algorithms can also perform well as compared to the deep learning models and provide a reliable system for detection. The final model after implementation of filter called SMOTE achieves an accuracy of 94.21%, recall of 98.54%, and f1-score of 94.02%. [ABSTRACT FROM AUTHOR]

Sign language is a method for the dumb and deaf society to connect with the exterior world. The dumb and deaf people find it very difficult to express their feelings to the normal people, since the normal people have the insufficiency knowledge of the sign language used by the mute and deaf people. Thus the development of a system to fill the communication gap in between mute/deaf and the normal people who are well able to speak and hear would be of great utility to the society. A sign language recognition model involves of accurate and effective tool to translate sign language into text/speech. Gesture recognition identifies a significant expressions of a man-made gesture by hands. Gesture identification from video sequences has significant deviations in the field of behavioral understanding and computer vision. This will provides a platform for conversion of predictable gestures in sentences that are cumulative of words used in an approved sequence. With the advance of deep learning, artificial intelligence has completed significant achievement in modern growths in gesture recognition technology for videos based on deep learning. As compare to others gesture recognition methods, dynamic hand gesture recognition using deep learning to progress the recognition accuracy. This paper presents a research gap survey on various hand gesture recognition methods. Hand gestures are categorized in two types-1) Static hand gestures and 2) Dynamic hand gestures. The first type as static hand gesture recognition is the fingerspelling level detection of alphabets, words and numbers. The second type as dynamic hand gesture recognition are used for identification of sign language sentences. Sign language recognition systems using gesture play a significant part in Human-computer interaction (HCI) to establish communication between dumb/deaf societies and normal people. In this paper, a literature review on hand gesture recognition methods are presented. [ABSTRACT FROM AUTHOR]

Detection of disease in plants is a paramount issue in the agricultural sector of any country. Crop failure adversely affects the farmer and has an enormous impact on the economy. Modern technology has developed various tools using deep learning techniques to perform errorless predictions and to originate a rapid treatment. The purpose of this paper is to propose a deep learning based method to detect disease in plants using leaf images. Our main goal is to develop an efficient methodology to provide reliable predictions. We have considered CNN (Convolution Neural Network) algorithm as our principal detector. The proposed system can approach complex scenarios from the plant's body. The result achieved is very promising of 98.33 accuracy and 0.0047 loss on validation data. [ABSTRACT FROM AUTHOR]

Segmentation of problematic area is important for many computer visions aided detection tasks. The medical field has benefited a lot from current advancements in computer vision techniques. Pneumothorax is such a condition that if its diagnosis and treatment are not done on time then it can be deadly. Chest X-rays images help in the diagnosis of this deadly disease. But to diagnose an expert and experienced radiologist is required and many people don't have instant access to such a facility. Some of the cases in which a person can be suffering from this condition are accidents and, in those cases, a quick diagnosis is required. The paper explores image segmentation techniques to assist doctors in taking instant decisions if a particular individual is suffering from pneumothorax or not. Segmentation models predict a mask indicating a problematic area. With the help of this mask, doctors can make decisions on whether to operate or not. Two models Mask RCNN and U-net with Xception net backbone are compared in this paper. 12047 chest X-ray images were collected from Kaggle and out of those 9638 images were utilised for training and 2409 were utilised for testing and validation purpose. The performance measurement was done using Dice Coefficient and IoU matrices. The work in this paper focuses on deep learning and methods of segmentation and does not claim any medical validation. [ABSTRACT FROM AUTHOR]

A large segment of revenue in many developing countries comes from agriculture. But the perennial incline of diseases in crops is much severe issues and seeks attention of researchers during the past few years. Agriculturists are unable to make timely prediction of diseases due to lack of knowledge and large cultivation areas. Presently, Machine learning (ML) and its sub branch Deep learning (DL) brings a sea level change in conventional disease prediction methods and incline accuracy also. Using visualization techniques many DL architectures are implemented for identification and classification of plant diseases. This paper provides a systematic review of various existing DL and ML models for content-based image retrieval for data sets related to agriculture and investigate the future scope which helps to learn the potential of DL along with multiple visualization methods for recognizing plant diseases which is fruitful for farmers. Prediction and analysis of diseases instead of leaves at other parts of plants is not done yet, DL can help to work from this perspective also. The main motive of this study is to prediction of diseases at various parts of plant to overcome the issues in agriculture field to incline the profit of Farmers. [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]

From numerous sources, a vast volume of text data has been developing daily. This enormous volume of data contains crucial knowledge and information that must be skillfully condensed to be usable. In this era of digitalization, vast information is available to us, which are being uploaded online from all over the world in the form of the web pages, and social media etc. To expand quick knowledge from this massive content, the summary of text can help understand the concept fast. Whenever we talk about the text summarization in research article or paper, we must be aware that this colossal amount of data can be generic or scientific and with this we will have an astronomical number of publications. There would be great support to the researchers if the approach of automatically summarization were applied on the scientific articles to increase the speed of understanding. Due of its applicability in numerous fields, automatic summarization of natural language is a prominent academic and industrial focus in computer science. While doing text summarization the essence or the context of the text remains same this could be the primary requirement. It is due to the high usage of Internet as a medium text summarization is developed as an outstanding tool for interpreting text information. Extraction of important information from the source text material is known as text summarization. In this procedure, the extracted data is produced as a concise report and sent to the user as a succinct summary. Broadly text summarization is classified into two methods extractive and abstractive techniques for summarization. Defining a rank from the text file based on the word and features of the sentence, collate them to develop a summary where feature of sentences will be notified based on the statistical and linguistic trait, which we name as extractive summarization method. In other case first we have to understand the details and main points of the documents, after which we need to define these concept in terms of natural language is defined as Abstractive Summarization method. This paper will give you the comparative study of various Abstractive Summarization methods. [ABSTRACT FROM AUTHOR]

In our contemporary days, relying on machine learning has become a matter that we see permeates all life sciences and engineering, and there is no doubt that the exploitation of machine learning in various medical matters, the most important of which is the early detection of types of cancerous diseases, which has become an imperative due to its relevance to human life, in this research paper we will address Incorporating both machine learning in early detection of cervical cancer and then applying the recommendation system to the final results and comparing them with traditional results, and here it should be noted that after 80 years of the invention of the Pap test, this test is still the most used method. Cell pathologists search for microscopic abnormalities inside and beside the cells, and here in this paper, we introduce deep learning techniques for cell fragmentation, as these techniques are able to detect abnormal cell clusters with the high overlap rate of digital photos for conventional Pap smears. Our method of research relies on ignoring low-load images by applying a system of advice to the results received. In the database, there are images from real cervical swabs from 113 fields of view that contain abnormal cells, and there are 96 swabs containing normal cells out of millions of cells. The results of our search on cells show an accuracy ratio (MAP=0.924) and it works faster than the currently tiring methods. [ABSTRACT FROM AUTHOR]

In many fields, Convolutional neural networks have been efficiently used and by giving more attempts in the area of Seismic imaging. Seismic image analysis is important in a variety of industrial applications like finding underground salt bodies plays a vital role in detecting oil and gas reservoirs. Seismic image analysis still requires experts to examine the Salt body, which is a time taking process. In this paper, we present a method for detecting and optimizing deep-supervised edges that accurately segments the salt body. We create an edge-prediction branch to detect the salt body's border, which aids feature learning by supervising boundary loss. We also used a Sigmoid function (Logistic) which gives output effectively and accurately. We have compared our model with two existing approaches which ended up with good results. [ABSTRACT FROM AUTHOR]

Human faces are a fascinating class of objects and it can be utilized in wide ways. Even though there has been a considerable progress made on the issue of generic deblurring,when it comes face blurred image the current solutions are less efficient. The performance measure of cutting-edge image deblurring algorithms is taken primarily from indirectly or specifically restoring the outstanding kernel edges. The efficiency of the available methods is less, because kernel estimation for blurred images can restore only very number of edges. The problem that facial structures exploit is discussed in this paper. Deep learning has proved itself to be one of the leading machine learning methods for a number of inferior tasks in recent years. It provides transformative possibilities in optical microscopy for the reconstruction and improvement of images in addition to its significant applications such as classification. This paper proposes numerous methods proposed by several authors based on the blind deblurring process, which are important for us to predict a blur kernel. A clear image has a distinctive rim of colour distribution when compared with a blurry image. In this paper we propose a filter to describe the edges which will be a reference image to recover a blurred image and the blur kernel will be calculated for this reference image. The blurred image is then unparalleled to add a latent image into the approximate blurred kernel. [ABSTRACT FROM AUTHOR]

Agriculture is essential for everyone to promote sustainable development, the farming, which combines image processing, artificial intelligence, Deep learning, and Internet of Things (IOT). World population incensing every day. Due to the rising demand in the Agriculture industry, the need to collectively improve a plants and growth its field is very useful. In this paper, it is important to maintain the crop during its initial time, and also at period of harvesting. The image processing and artificial networks are used as a different techniques to maintain the detecting the diseases on the leaves and correct time to harvesting. When we take images with help of drones, the images are divided and changed to disease described three things vectors namely the first one is color, one more is texture and morphology. The vectors morphology gives 95% accuracy and its give more compare to other two vector features. This research paper proposed effective and useful algorithms for detection of disease with help implementation of Artificial Neural network algorithms using MATLAB. Detection of leaf or plant diseases with some manual techniques are requires a lot of work by maintaining a huge farm of crops, and it's very early stages it detects different types of symptoms to different diseases on plants, when the displayed on crop leaves. In this research paper survey on various disease classification techniques that can be for plant leaves diseases detection. For this purpose Artificial Intelligence, Neural network algorithms and back propagation techniques for adjustment of training data sets. [ABSTRACT FROM AUTHOR]

The wireless networks of the future generation are evolved as complex systems due to broadening in service prerequisites, application heterogeneity and networking of gadgets. In recent times, the major step forward of machine learning techniques is deep learning. However, in wireless/heterogeneous networks, the deep learning application for network traffic control is relatively new. The key disputes in the wireless backbone networks since the advancement of wireless networks are resource allocation and efficient network traffic control such as routing. In larger scale of networks and tangled radio environments, the method of adding intelligence to wireless networks is achieved by Deep Learning (DL). The tangled wireless networks supported with several nodes and their quality of variable link can be investigated by Deep Learning. This paper is presented with a perception of harnessing the next generation communication networks with the artificial neural networks. This work helps the readers to explore the unsolved issues to pursue their research and deeply understand the wireless network design with DL based state of the art facilities. In this work, we integrate the deep learning and wireless networking research with a widespread survey. Finally, this paper summarizes the disputes and benefits of acquiring ML and AI for next generation wireless systems. [ABSTRACT FROM AUTHOR]

The objective of this paper is to build an Object Recognition model using 'TINY YOLO' and FPGA. Object Recognition is a CV (i. e., Computer Vision) technique for identifying and detecting objects in images or videos. Object recognition is a key output of DL (i. e., Deep Learning) and ML (i. e., Machine Learning). When we humans look at an image or video, we can readily spot people, object, scene and visual details. The aim of this is to teach a computer to do what comes innately to a human being. Object recognition is done with Image Classification, Object Localization and Object Detection that combines these two tasks and localizes and classifies an object in an image. Object Recognition has become a pivotal technology for driverless cars, security purpose, traffic surveillance etc., It is also used in a various application such as disease detection in bio imaging, industrial examination, and robotic vision. Hence, in a nutshell, 'Object Recognition is the staple of automation industry'. [ABSTRACT FROM AUTHOR]

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

To keep up with rapid human population growth, food demand should increase. Quickly spreading illnesses can fundamentally affect plant yields, in any event, slaughtering whole harvests. As a consequence, detecting and preventing disease at an early stage is critical. Traditional approaches depend on lab studies and human experience, both of which are typically costly and in short in supply in most developing countries. As smartphones have become more popular, even in remote regions, researchers have turned to automated image processing to identify plant disease. This paper compares deep learning to conventional machine learning algorithms and explores the most recent developments in this field. AI, crop infections, and deep learning are a portion of the terms that ring a bell when considering this exploration. [ABSTRACT FROM AUTHOR]

The problem of face recognition gets more challenging when only a single face image per person is available. Further variations encountered in unconstrained scenarios impose more intricacies to the recognition mechanism such as illumination, pose, occlusion, expression and, so on. In this paper, we propose an augmentation technique that combines a set of photometric augmentation techniques to increase the training samples. A face embedding from the augmented face images is extracted using the classical FaceNet deep learning model. Furthermore, an SVM-based classifier has been deployed to recognize faces. Our results show that the proposed technique yields result on a par with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Corals are critical as indicators of the health and diversity of the marine ecosystem. Corals, on the other hand, are complex organisms to study due to their sensitivity to changes in temperature, acidity, and pollution. Marine ecosystems frequently have complex scenes, making them difficult to monitor manually. Automated technology is required to monitor the health of our oceans and to aid in the efficient detection and identification of coral reefs. Manually covering the entire research region takes a long time, especially when it comes to the coral classification procedure. It was a typical sequential activity that took longer to complete, was more expensive, required human participation, and produced results with a low degree of accuracy. The researchers are looking for a new method for automatically classifying significant image data of coral reefs with a faster computation time and a higher degree of accuracy, with little or no human intervention. This review paper emphasized the use of deep learning as a method for classifying coral reef components. The deep-learning system may gradually acquire the ability to extract high-level features, obviating the need for expert knowledge. As a consequence, deep learning outperformed conventional methodology in studies of coral reef classification. The study also emphasized the manual methodology, particularly for the classification of coral reefs. Implementing a deep learning methodology has been shown to reduce costs, increase accuracy, and monitor changes in marine ecosystems. This paper will present the results of a previous study on coral reef classifications using various types of deep learning approaches. The purpose of this review paper is to share and combine successful findings from previous research in order to demonstrate the deep learning approach's superior performance in coral reef classification research. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the commonest diseases globally and accounts for more deaths from lung cancer than other ones. We did an exact review of the distributed things on the study of disease transmission, conclusion, and treatment of lung cancer and as the dispersion of lung cancer is rapidly going on, it reaches the epidemic range or peak level. Besides, it has beaten the earlier commonest outline of cancer and is presently the commonest harm in men and women. Different modalities for early recognition through screening are being researched and it is observed that the majority of the patients have died due to late detection of this disease so recognition at the premature stage, as well as accurate detection of a lung nodule, is very much necessary so the most challenging thing is to develop a robust method for nodule detection that is why the main objective of this paper is early detection, minimizing the false rate, as well as improving accuracy. In the paper, a new model is proposed based on deep-learning for the pinpointing of region precisely, as we know there is a huge advancement in Deep convolutional neural networks in pre-processing, we adopted Three-dimensional Convolutional Neural Network) for the prognosis of lung cancer from the CT images of the long-sufferer. As one knows about the convolution neural network, it makes things easier to extract significant data by processing the images of the dataset. Gradient-weighted class activation mapping strategies can convey the information by visualization for precise judgment in lung knot by point-up the adversely affected area. [ABSTRACT FROM AUTHOR]

COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19: Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. [ABSTRACT FROM AUTHOR]

A weapon is an item that threatens a person's life. Therefore, the use of weapons needs very close supervision. These supervisory activities could be carried out automatically using deep learning. Deep learning enables a computer to have a vision like humans. One of the deep learning methods is Single Shot Multibox Detector (SSD). SSD is one object detection method that has impressive accuracy and speed [1]. This paper used SSD MobileNet V2 and SSD ResNet 50 methods. The difference between SSD MobileNet V2 and SSD ResNet 50 lies in the backbone used. SSD MobileNet V2 used MobileNet V2 as a backbone, while SSD ResNet 50 used Residual Network 50 (ResNet 50) as a backbone. Weapons that could be detected in this paper are handguns and knives. The most excellent accuracy that is achieved by SSD MobileNet V2 is 79% and SSD ResNet 50 is 62%. [ABSTRACT FROM AUTHOR]

Un-proctored online test/examination increases the occurrence of academic dishonesty which is a major factor in maintaining academic integrity. A major challenge for the course instructor is the lack of resources to conduct proctored test. To tackle the challenge posed by the COVID-19 pandemic, an alternative strategy to traditional invigilated test or examination is the conduction of online open book assessment. The focus of the online open book assessment is to gauge individual student's development of knowledge, learning and experience against established academic/professional standards. In the current work, quality of the examination is assessed based on the performance of the students. This is done to ensure that the students meet the adequate program outcomes to complete the requirements for higher studies or a work setting. The question paper setting pattern and analysis of the learner's performance are very critical to ensure learning according to Bloom's Taxonomy level. A case study of Engineering Chemistry open book quiz and test analysis is performed to improve the quality of the question paper. Question papers are prepared using Achievable Matrix focusing on Bloom's Taxonomy level. Preparing questions focusing on Bloom's Taxonomy level is an effective tool for potent learning that supports long term retention. The quantitative analysis is done using Normal Distribution plot and item analysis such as facilitation value and discrimination index. The mathematical analysis has given insight to improve the quality of question paper while preparing for deeper learning in higher education. In our current study we have observed the effect of quality of question paper on the distribution of marks obtained by the students. [ABSTRACT FROM AUTHOR]

One of the most challenging tasks of a researcher in the area of stock market is accurate prediction because of the high volatile and non-linear behavior of the trade market. The main intention is to improve the efficiency of forecasting, since the investors in the stock market are concerned about the future stock market business for smart investments. And hence, advanced methods for prediction are needed that are reliable targeting to increase the profits. In this paper, a comprehensive review of the various prediction techniques for stock marketing is presented in a crisp way. This paper emphasizes on stock market prediction using reinforcement learning, machine learning, deep learning, deep reinforcement learning and sentiment analysis techniques. [ABSTRACT FROM AUTHOR]

The world research community have led data science to the level that it can mimic human capabilities very well. Prominent research is going on in deep learning areas such as speech recognition, image identification and prediction making, Natural Language Processing, Generative Pre-Training, language models, Generative adversarial networks (GANs), causal reasoning, etc. These all-Machine Learning and Deep Learning based research foundations are based on data and how the model trains data to solve real-life problems. Invalid and missing data may lead to problems such as underfitting, overfitting or even to the black-box model. So, it is vital to feed true and sensible data to an algorithm since the efficiency and accuracy of deep learning models depend majorly on them. This paper reviews some state-of-art practices referred to in the literature for handling missing data for Machine Learning and Deep Learning algorithms. This paper discusses types of missing value imputation techniques used by researchers and unveils the power of imputation along with a literature summary exhibiting all positive and negative impacts of imputations used in the literature. Additionally, it showcases the future research possibilities for the community to take the work forward since 60% of an algorithm's efficiency depends on data fed into it. This review winds up with some observations to contemplate while imputing missing values. [ABSTRACT FROM AUTHOR]

In their highest grade, brain tumors are the most widespread and dangerous diseases with a very short life span. Therefore, early automatic brain tumor detection is required to lower the fatality rate. Due to this, MRI is a commonly used imaging technology for diagnosis; however, it is practically impossible to do manual segmentation of the volume of data generated by MRI promptly. This paper is intended to analyze the suitable tools, techniques and language for automatic detection of Brain Tumor. From the nature of the problem, it is quite evident that it requires high precision of accuracy in detecting such a deadly disease in a very short period and if possible, in real-time, for a large number of datasets will be required not only to train the algorithm but also for its testing. Spark is an open-source platform to deal with lots of data. Spark's API, PySpark is coupled with Python language to enable the developers to develop a python script for the Spark processing engine. Deep learning algorithms are the most useful and appropriate for such types of tasks where a large amount of data is involved and requires high precision training and testing of the models and the algorithms. In this paper, we have discussed the selection of the tools, techniques and language for the implementation of the model for detecting the brain tumor. Tools like Google Colab and PySpark have been explored in this paper. [ABSTRACT FROM AUTHOR]

As of the exponential increase in the use of computer networks, there are now problems associated with maintaining the network's availability, integrity, and secrecy. Because of this, network administrators have no choice but to implement a wide variety of intrusion detection systems (IDS), which are designed to assist in monitoring network traffic to identify unauthorized and hostile actions. A security policy violation with the intention to harm is known as an intrusion. As a result, an intrusion detection system will monitor the traffic moving through computer systems on a network to check for malicious actions and known dangers. When it discovers such threats, the system will send up an alarm. There are two types of attacks that can be identified by an intrusion detection system: signature-based detection and misuse detection. In signature-based detection, an IDS uses information gathered from a database to analyze and compare the attack signatures to those that have been saved. The second type of detection is called anomaly detection, which considers the likelihood of a particular action happening outside the typical pattern of behavior. This paper aims to provide an overview of the various efforts being carried out to develop an effective IDS using machine learning and deep learning. The results of the study will be used to evaluate the performance of different classifiers. In addition, the paper also presents the results of the various studies that were carried out. These findings will be used to develop further improvements and enhance the performance of the IDS. [ABSTRACT FROM AUTHOR]

Cancer is a disease with a high mortality rate. Cancer is developed when the cell does not undergo a cell division process, the presence of uncontrolled cell growth turns to tumor cells and spread to various other organ systems. Early detection of such cancer improves the chances of favourable outcomes. Despite the fact that the early prognosis of cancer has made considerable improvement. Lung cancer is the second leading cancer worldwide. The identification of lung cancer and lung nodule segmentation is a crucial preliminary step. The Deep Learning network works best for medical image automation. Deep learning models and computer vision applications are expanding rapidly in the modern era. This paper includes Computed Tomography (CT) images to segment challenging lung nodules. The modified U Net introduced in this paper performs the segmentation of lung nodules. The dataset is obtained from the Radiology Department at Chennai Rajiv Gandhi General Government Hospital. The ResNodNet outproduces the segmented nodule, the model combines Respath and U Net to facilitate the categorization of multi-featured data that has been proposed as ResNodNet. The classification results produced by the suggested method are accurate at 98.6%. [ABSTRACT FROM AUTHOR]

The scope of the paper is to leverage the power of Machine Learning and Deep Learning to detect diseases in crops and to provide solutions for the diseases detected. Farmers usually detect diseases in the crops using their naked eyes, eventually leading them to make wrong predictions regarding diseases in the crops. They might use the wrong fertilizers or more than the usual dose, which could damage the plants, soil, and fields. Thus, this paper focuses on providing solutions for detecting diseases in crops and solutions for the diseases identified. Convolutional Neural Networks are used to identify various diseases in crops since it limits the number of parameters and thereby make the model more efficient. The said model is implemented by making a cross-platform code using Flutter with Dart as the programming language. [ABSTRACT FROM AUTHOR]

Object Detection has made fathomless developments in terms of deep learning with varied applications. Detection of fruits is one of the commonly used application which still has some major leaks in it. In this paper, we discuss an approach to detect the fruits on trees. The problem has a broad room for development and along with a proposed system this paper is inclined towards review for discussing all the state-of-the-art object detection algorithms in deep learning that can be used. The paper discusses ideology and approach for the fruit detection in a very concise manner. [ABSTRACT FROM AUTHOR]

Text Summarization is one of the most experimental tasks in deep learning that decreases the size of a text while it's important to keep its core and key information. Previous work in this domain is going to give remarkable results by using highly dependent data. Natural language processing-related works are trending due to complications in the implementation. Abstractive text summarization is a critical idea in natural language processing and also it's a trending, hardest problem that varies the solution or outcome based on the dataset. The objective of the paper is to rewrite into well-simplified and core information in text format that is retrieved from files or documents or datasets. Abstractive text summarization using RNN is the methodology going to be implemented in our paper. Attention mechanism and LSTM are going to be add-on advantages to the model. Both the methodologies improve the performance of the model and are also used to solve the complications problem. Keyword extracting is the one of features of this paper. State-of-art is going to be used to verify the model's efficiency. BLEU and ROUGE are going to be used to identify the accuracy and validate the model. The greatest thing about the dataset is the relationship between columns is independent of each other. Finally, the aim of the paper is to generate shortened text from whole huge data (text format). These kinds of problems are often used to solve in sequence-to-sequence passion [ABSTRACT FROM AUTHOR]

Managing livestock in large manufacturing systems is difficult, mainly in vast areas. UAVs are used to collect images of areas of interest and are rapidly becoming a possible alternative. However, extracting relevant information using good algorithms from images are still scarce. In this paper, we present a method for counting sea lions or other animals using a deep learning model for rough location, colour space control to increase the variance between sea lions and background, mathematical morphology to identify sea lions and gather them individually in clustered groups, and image matching to account for image overlap. This paper describes how to use deep learning to automatically count sea lions from aerial photographs. [ABSTRACT FROM AUTHOR]

This paper mostly focuses on real-time detection and classification of biomedical waste objects. Traditional methods for detecting the objects like biomedical waste objects in machine learning are substituted by recent technology of object detection methods in deep learning by building Convolutional Neural Network (CNN) which is nothing but the components of deep learning. This paper proposes a deep learning approach for object detection of biomedical waste material by evaluating the TensorFlow object detection API, which can used for real time application, this biomedical object detection API further we can used for automate the biomedical waste segregation system, this proposed system has been trained using more than 500 images for each type of biomedical waste object. Tensorflow is useful and easy to object detection that can form powerful image recognition software, in this proposed work we are using Faster R-CNN algorithm for developing this object detection process. [ABSTRACT FROM AUTHOR]

The importance of medicinal plants has widely increased throughout the world, especially during the COVID-19 pandemic. It is essential for a layman to identify a medicinal plant and know about its medicinal values. In this view, this paper proposes an approach that helps a person to identify the medicinal values of different plants. The paper focuses on designing a mobile application that can identify the leaf and list its medicinal values with a mere snapshot of it by performing instance segmentation, feature extraction and classification in the backend. The models were trained and tested on a custom dataset generated by collecting around 1500 images spreading over 5 medicinal plant species. The paper uses a deep learning-based segmentation approach for real-time segmentation of the leaf from the complex natural background. The segmented leaf will undergo a feature extraction process and will be given as an input to the traditional Machine Learning algorithms. Manual Feature Extraction and Deep Learning based Feature extraction processes were implemented. Various features including shape, colour and texture were extracted in the manual feature extraction process and a MobileNet pre-trained model was used for deep learning-based feature extraction. The Mask Region-based Convolutional Neural Network (Mask R-CNN) yielded the best performance for real-time segmentation with a Dice score of 0.92 and for the classification MobileNet + Logistic Regression gave the best accuracy of 0.97 and AUC score of 0.99. [ABSTRACT FROM AUTHOR]