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The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

Orthopedic implant replacement surgery is a procedure that involves the removal of a damaged or diseased joint and the placement of a new, artificial joint. The new joint is made of metal, plastic, or ceramic materials that are designed to mimic the function of the natural joint. Orthopaedic implant replacement surgery is a highly successful procedure that can significantly improve the quality of life for patients who suffer from chronic joint pain. However, as with any surgery, there are risks associated with ortho implant replacement surgery, including infection, blood clots, and nerve damage. This may lead to aseptic loosening which will become the main cause for surgeries to happen for the second time. Therefore, it is crucially important to diagnose it clearly during the pre-operative and post-operative phases. Our main objective is to assist surgeons in identifying these illnesses accurately during the process, to avoid more serious stages. This paper reviews the methods and approaches for Orthopaedic implant replacement surgery using Computer-Aided Design (CAD), Convolutional Neural Networks (CNN), Augmedics, SCORE-CAM and deep learning algorithms. During all the three phases, various technologies are utilized to improve patient wellness. [ABSTRACT FROM AUTHOR]

Crop diseases present a substantial threat to worldwide food security, emphasizing the critical importance of timely and precise disease detection to mitigate yield losses and support sustainable agriculture. This study introduces an innovative approach that harnesses the power of Machine Learning (ML) and the Internet of Things (IoT) to revolutionize the detection of crop diseases. Within this framework, IoT sensors strategically deployed across agricultural fields gather real-time environmental data, while high-resolution crop images are captured using drones and on-site cameras. ML models, including Convolutional Neural Networks (CNNs), harness this data to enable early and accurate disease detection. By integrating IoT capabilities, we can correlate disease outbreaks with environmental factors, thereby enhancing the decision-making process. The results are promising, consistently demonstrating high accuracy in disease detection and the potential for reducing the need for chemical treatments. This research paper signifies a significant step towards promoting sustainable farming practices and enhancing global food security. [ABSTRACT FROM AUTHOR]

This paper's objective is to improve accuracy in plant leaf disease detection using Machine learning techniques such as K-Nearest Neighbors classifier (KNN) with a sample size of 10 and Novel Convolution Neural Network (CNN). This article consists of 2 groupsi.e Convolutional Neural Network (CNN) and K-Nearest Neighbors Classifier with a sample size of 10 for each group. G Power software is used to determine sample size with a pretest power value is 0.8 and alpha 0.05. The accuracy evaluated using both algorithms is compared and CNN appears to be the most significant with an accuracy of 92.48% and KNN with an accuracy of 74.14% with p=0.041. An Independent sample t-test has been used and analyzed with a confidence level of 95 percent. Results show that the CNN algorithm is significantly better than KNN in detecting plant leaf disease. [ABSTRACT FROM AUTHOR]

Multi-class image classification is one of the tasks of computer vision in which convolutional neural networks are widely used. In this paper, we create a novel ensemble learner by stacking InceptionV3, Xception, MobileNetV2, and ResNet50 convolutional neural network in an attempt to outperform the best performance of each individual models. In an ensemble learning algorithm, by combining multiple different models with different architectures, an error of prediction from a single model can be corrected by other models, thus reducing the likelihood of selecting a wrong prediction. The dataset that we used to do benchmarking is the Intel Image Classification dataset which consists of 6 classes that displays images of fully colored natural scenes. The training set consists of 14034 images, while the test set consists of 3000 images, all having 150x150 as their default size. The training process on all models involves reducing their learning rate when the performance plateaus, and early stopping if there aren't any significant improvements from an epoch to the next. The result is that each individual models have an average of 83% accuracy, yet our ensemble model managed to reach 88% on the test set. [ABSTRACT FROM AUTHOR]

The reality is that the population and number of automobiles on the street are growing day by day. The efficient monitoring of continuously increasing traffic is a critical challenge that traffic operators are facing. Traffic control is generally achieved using stationary video recorders, observers or manual counters. Hence, Unmanned Aerial Vehicles or drones are the high-quality answers to this trouble as they lessen the price of the set-up of numerous cameras and sensors. This paper's principal ideology is split into phases. The first one is the monitoring of traffic using a drone which helps in analyzing the traffic in a particular area and making interpretations. The second is fire detection and extinguisher. Here the drone is used to detect fire with the aid of a fire sensor as it helps the fire-fighters to analyze the affected area and prioritize their work. This project also up-skills in designing a quad-copter. Our motivation for this project lays both personal interests in a better understanding of object detection and academic research. A drone consists of a camera to cover a wider area which helps in traffic analysis. Here, the number of vehicles are estimated. Drones have recently emerged as an outstanding technology in the business market and are geared up with a camera, geo-positioning sensors, and communications hardware that may relay statistics. This project is implemented by using artificial intelligence and the Internet of Things for Fire Detection. To be more specific traffic detection is achieved using Deep Learning algorithm which is the Convolutional Neural Networks Algorithm using python. Fire sensors are mounted on the drone, and it flies on a flight path within the building and sends fire alert data to the system controller upon sensing a fire condition via fire sensor and also puts off the fire using the water pump. For fire detection the sensor used in the drone basically detects IR (Infra-Red) light wavelength that is emitted from the fire flame which helps fire-fighters to quickly assess and monitor dangerous areas which results in more safety and limits the time it takes to scan the whole area. [ABSTRACT FROM AUTHOR]

With the upcoming large-scale surveys like LSST, we expect to find approximately 105 strong gravitational lenses among data of many orders of magnitude larger. In this scenario, the usage of non-automated techniques is too time-consuming and hence impractical for science. For this reason, machine learning techniques started becoming an alternative to previous methods. In our previous work [1], we have proposed a new machine learning architecture based on the principle of self-attention, trained to find strong gravitational lenses on simulated data from the Bologna Lens Challenge. Self-attention-based models have clear advantages compared to simpler CNNs and highly competing performance in comparison to the current state-of-art CNN models. We apply the proposed model to the Kilo Degree Survey, identifying some new strong lens candidates. However, these have been identified among a plethora of false positives, which made the application of this model not so advantageous. Therefore, throughout this paper, we investigate the pitfalls of this approach, and possible solutions, such as transfer learning, are proposed. [ABSTRACT FROM AUTHOR]

Cervical cancer is one of the most known health problems faced by women around the world. Early detection of cervical cancer may reduce the mortality rate. Pap smear images are new techniques used for screening cervical cancer. This paper excludes the nucleus of pap smear images, and the resultant images are classified into seven classes based on the surrounding region nucleus. Automated features are extracted using three pre-trained convolutional neural networks (CNN). The resultant features are twenty-one. The principal component analysis reduces the dimensionality and selects the most significant features into ten features. These features are fed to two types of machine learning algorithms: support vector machine (SVM) classifier and random forest classifier. The support vector machine classifier achieved the highest accuracy for seven classes, reaching 93.1%. This method will help the physicians in the diagnosis of cervical cancer depending on the tissues, not the nucleus. Furthermore, the result can be enhanced using a huge amount of data. [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]

Detection of diseases in crops is an important aspect to deal with as it affects the productivity of the agricultural yield. The farmer may not realize that the crop is suffering from a disease by looking at the leaves. In this paper, we will be performing Convolutional Neural Networks (CNN) where data needs to be pre-processed and then we need to divide the data into training dataset and test dataset. We will use the tomato leaves dataset to predict whether a tomato plant is healthy or diseased. Finally, we will be giving a user input image and using this model we will be detecting whether the plant is healthy or diseased and which disease it is suffering from which will help in enhancing the life of a farmer by increasing his productivity. Based on this detected information the farmer will be cautious if the plant is diseased and take appropriate actions. [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 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]

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]

The paper proposed an automatic image annotation (AIA) model that is assigned method semantic labels to the new image automatically. It is also proposed a scheme for implementation an AIA-system based on deep learning method by using a Convolutional Neural Network (CNN) model that raw dataset (Corel-5K, ESP Game, IAPR TC-12, NUSWIDE, and MS-COCO). The photos are recorded and accessible for searching, categorization, and retrieval purposes. These photographs must be tagged with appropriate words, phrases, or keywords in order for the required action to be conducted properly. These operations are facing challenges in assigning labels to an image which is in form of keyword tagging or a very short description of the image, that lead to speed up the image's retriever so that the aims of paper are to develop a multilayered tagging model that has common interaction layer by using deep learning method, CNN-RNN model. Although Back Propagation Through Time (BPTT) adjusts the weights of the neural network, and the model is used it to increase the quality of retrieved images that have a high level of relative keywords. In addition, it is a supervised learning algorithm that allows the network to be corrected concerning the errors that have occurred. Finally, the proposed model implemented by Python 3.7 because it has useful models and is change some parts to enhance the results of this paper. [ABSTRACT FROM AUTHOR]

Chronic liver disease (CLD) is a general term that refers to a number of different hepatic disease processes that consume a significant amount of healthcare and financial resources worldwide. Cirrhosis develops when CLD progresses to permanent end-stage liver fibrosis. For the course and prognosis of liver fibrosis, early detection and intervention are crucial. Ultrasound provides a variety of advantages as one of the most commonly utilized methods for detecting liver fibers, including ease, accuracy, and resilience. However, getting subjective and consistent diagnoses is challenging since ultrasound images are inevitably influenced by device characteristics, ultrasound interactions with body tissues, operation procedures, and other uncontrollable elements. The goal of paper is to develop a new proposed liver fibrosis detection model for ultrasound shear wave elastography (SWE) images using deep learning algorithm to distinguish between normal and fibrosis tissue. This paper provides a convolutional neural network-based deep learning feature extraction algorithm for binary classification and detection of liver fibrosis. The dataset used to develop these models was locally collected from an Iraqi hospital. The results obtained revealed that the final model (CNN) was able to classify normal and abnormal liver tissue from SWE with an accuracy of 97.45%, Sensitivity 98.27%, Specificity 96.66%, Precision 96.61% and F1-score of 97.17%. This is accurate results for assist diagnosis liver fibrosis. [ABSTRACT FROM AUTHOR]

The objective of this paper is to identify the signature verification system which can overcome individual presence for verification and recognition task of handwritten signature. Amid corona duration it has been found that there is a need of automatic online system which can omit physical presence of person for signature verification. This paper has proposed signature verification model with better accuracy. Novel Architecture of convolution neural network is proposed which exhibited 99% training accuracy on GPDS synthetic Signature database. Writer independent Features of signature were extracted through CNN and these features were fed into machine learning model for classification. Novel architecture is proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, Convolutional Neural Network Segmentation (CNNS) by Dual-Interface Reign Architecture is proposed to figure out the condition of breast tumor. The existing novel approaches from machine learning may not be voluntarily useful as they require a huge training data bit with segmented pixels. This architecture is built upon a CNN and a dual mode network which is used to segment and can non-linearly maps the input data into a deep latent space respectively. the two paths of dual-interface networks are locality preserving learner and a conditional graph learner, both are used for the extraction of images and exploit the input features. The complemented dual-interface architecture is further used to improve the rate of classification. This proposed method can overwhelm the restriction of some benchmarks for the classification in breast imageries. In addition, to perform extensive error analysis, an analytical model is developed for mammograms and tested for varying pixel widths and input probabilities. The analytical model is validated through simulation. The results of our proposed method demonstrate that it can effectively detect the different kinds of mammograms in database by segmenting them more precisely. When comparing with previous works the precision is improved up to 5% and 8% than the GSA-ANN and ANN respectively. [ABSTRACT FROM AUTHOR]

Face masks are used to prevent the spread of disease. Covid19 is a major example, where individuals are required to wear masks in public places to stop the sickness from spreading. The SARS-CoV-2 virus outbreak, which killed people, began in 2019. Tiny droplets from the lips or nose of a person who has COVID-19 can pass the illness to another person when they cough or breathe. There were limited medical resources and no vaccines to cure the disease. This led to hundreds of thousands of deaths worldwide. The only non-pharmaceutical way suggested by WHO to prevent the spread of this virus was to wear masks. Nowadays it has been asked by Many providers of public assistance have to wear masks in order to provide their services. Face mask identification has therefore become a meticulous effort to aid the worldwide civilization. This paper provides a solution to solve this problem of face mask detection using machine learning algorithms such as CNN (Convolutional Neural Network), TensorFlow, Keras, and OpenCV. This model detects the face from the image and identifies whether a person is wearing a face mask properly or not. Accuracy of the model is 96%. Using the Sequential Convolutional Neural Network model values of parameters is optimized in order to detect whether a person is wearing a face mask properly or not these parameters are also used to prevent our model from over-fitting. [ABSTRACT FROM AUTHOR]

The paper intends to build a drone detection system using machine learning and deep learning algorithms. The system could differentiate between a drone and other flying objects like a different type of bird and recognize it. It also covers the various algorithms implemented for the development of drone detection systems. The research work uses YOLO algorithm as it is very efficient and widely used algorithm. Using the YoloV4 model, 85% of accuracy is obtained by classifying the images of the military drones in the 'aeroplane' category. It is one of the classes 'coco' dataset files. YoloV4 algorithm detects the drones which are based on deep convolutional neural networks. YoloV4 model is one of the most tested and stable versions of the YOLO. Once the drone is identified by the model, it will switch on the alarm to indicate drone detection. [ABSTRACT FROM AUTHOR]

The Arabic Sign Language is the way of communicating between people who suffer from hearing impairment in Arabic countries. According to the reports of the World Health Organization, the number of people suffering from hearing impairment in the Arab region was increased, and that produces a gap between society and hearing-impaired people in education and learning, work, using social media, communicating with others, etc. So, automatic sign language interpreters became a pressing necessity to reduce this gap and minimize their isolation. In this paper, a hybrid combination of modified Convolution Neural networks and machine learning classifiers is suggested. This proposal recognizes both the sign language alphabets and the symbolic sign language (words). A new dataset was created from the current dataset images and images that stemmed from video or captured by a camera. Images are preprocessed and then the features extracted by using Linear Discriminant Analysis are used as input to one dimension convolution neural network that uses one of the three machine learning classifiers (Naive Bayes, Decision Trees, and Random Forest) instead of a neural network. The proposal algorithm performance was tested with various challenges and gives the very promised accuracy reached up to 99.9% for recognition of alphabets and words, and the possibility to work efficiently in real-time. [ABSTRACT FROM AUTHOR]

continuing care is referred to as a chronic disease. The most widespread and expensive medical illnesses worldwide are chronic diseases. Chronic diseases can result in hospitalization, long-term impairment, worse quality of life, and even death. These conditions include cancer, diabetes, hypertension, stroke, heart disease, respiratory conditions, and kidney diseases. In reality, the greatest cause of mortality and disability worldwide is chronic illnesses. In this paper, we present deep-based and machine-based models to diagnose chronic diseases, this system includes several stages, namely the stage of data pre-processing and the stage of disease detection, which is carried out in two ways, the first depending on a deep Convolution Neural Network (CNN) and the second based on five machine learning algorithms: Stochastic Gradient Descent (SGD), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The proposed model works on three data sets, namely (Pima Indians Diabetes Dataset, Cardiovascular Disease dataset, and UCI Heart Disease Data) to classify heart, diabetes, and kidney diseases. The experimental results proved the capability of the suggested system to classify the aforementioned diseases with an ideal accuracy of 100% using the CNN in the first model, and an accuracy of 94% in the second model using the SGD and LR algorithms. [ABSTRACT FROM AUTHOR]

Real-time face detection and recognition have become a very important issue in the past two decades. Current security methods based on passwords or fingerprints are risky due to the COVID-19 crisis that can be spread by touch. Face recognition methods are the best choice for security applications. It has proven to be one of the most reliable biometric security systems because frames can be captured from cameras without touching or interacting with them, and these images are automatically recorded and validated using existing databases. CNN's deep network has made it easy to build complex models to extract facial features at a higher resolution than humans can recognize. CNN models are produced to solve complex tasks but with high memory and computational requirements, due to application concerns and as well as the difficulty, this presents to artificial vision algorithms. They must be able to handle the large contrast in aspects of the same faces as well as differences in shooting parameters (position, lighting, haircut, expression, background, etc.). This paper seeks to create a real-time face recognition model to be used as a surveillance camera and works to store images of known and unknown faces instead of watching camera footage, and also for a biometrics system based on convolutional neural networks. It proposes an architecture for a deep learning model which it works to improve the current accuracy and processing time. [ABSTRACT FROM AUTHOR]

The main factor causing blindness in countries like India and others is Diabetic Retinopathy. Millions of people around the world are affected by DR, which causes vision loss and blindness. Detecting diabetes mellitus early plays an essential role in preventing the loss of vision that can result from diabetes mellitus being left untreated for a considerable amount of time. It is becoming increasingly more important to detect diabetic retinopathy early through automated systems instead of manual screening procedures like fluorescein angiography, optical coherence tomography etc., as it can lead to blindness among patients with uncontrolled diabetes. There are several published studies on machine learning and deep learning-based DR detection systems. In this paper, we explore the basics of advanced AI technologies used in DR analysis and early detection. The purpose of this study is to review DR detection methods are examined from a variety of perspectives. It includes fundus datasets, pre-processing of images, methodologies, machine learning and deep learning-based methodologies, and performance metrics. As well as presenting the review findings, it also presents the authors' observations. There were numerous public datasets for DR detection that are readily available. The Artificial Neural Network proved to be the better classifier over other machine learning methods based on shape, texture, and statistical features for DR detection. The purpose of this study is to propose the Convolutional Neural Network (CNN) through GoogLeNet for betterment based on the previous CNN model which was implemented using deep learning. Furthermore, the scientific community dedicated to developing automated DR investigative techniques. This review offers a thorough overview of DR detection methods. [ABSTRACT FROM AUTHOR]

Computer vision is an area of machine learning that enables computers and systems to take actions and make recommendations based on information from digital images, videos, and other visual inputs. The purpose of this paper is to discuss the selection of optimal parameters in car classification training. This paper discusses computer vision training using the CNN algorithm on vehicle miniature classification. One of the factors that affect the performance of Convolutional Neural Network (CNN) is the selection of appropriate parameters. The data we use comes from the primary data we collect. We took photos of 3 types of vehicles, namely buses, cars, and motorcycles with a total of 486 instances. Next we do data preprocessing by resizing 256*256px. Then we conducted various trials of data augmentation i.e: including "horizontal", "vertical", "horizontal and vertical". We experimented with different augmentations to get the highest accuracy. We present several variations of the epoch to identify the best model of CNN. The accuracy results obtained are 97.34%. [ABSTRACT FROM AUTHOR]

An objective function is one strategy to achieve a fitting model in machine learning. The target of this paper is to acquire more information about the objective function and to observe of it application in CNN. Various CNN architecture has proposed to achieve high accuracy by apply of the objective function. We used the CNN framework as a method to explain the content of architecture. To achieve a good model, every CNN used an objective function as a parameter to measure the closeness between the learning dataset and the actual dataset. As a pre-trained model to extract the critical feature, many scholars proposed a pre-trained CNN model to get high accuracy and a significant model. One of the ablation studies in CNN is a reformulation of the objective function. An objective function has often shown by a matrix operation known as a loss function or loss entropy. As a result, from this research is various CNN architecture models that tailor to many different objects. We can review from architecture, formulation, filter, and dense layer to achieve a good feature extraction as a feature map. Many parameters can observe on every step of CNN. Impact of this review, we can get a baseline model as beginning research to develop a new CNN architecture that can compare with the baseline model. [ABSTRACT FROM AUTHOR]

Due to the development of technology, it is easy to integrate this into many of applications such as automatic security door lock system. As presented in this paper, we proposed a voice recognition for the user authentication in order to open the door by using the word in Bahasa. In order to recognize the user voice, the convolution neural network (CNN) deep learning algorithm will be utilized as a proposed method. We also provide a simple web based which is allowed to recognize the user voice directly when the user spoke. Some experiments have been carried out to verify the performance of the system by using the work "Buka" and some words which has the similarity in pronunciation. Based on the experiment results, our system was able to recognize the trained voice accurately even the user used another word which was similar to say. [ABSTRACT FROM AUTHOR]

Now a day there is exponential growth in music files, so the need for automatic music genre classification approach is also increasing. Current classification systems of music genres developed using machine learning algorithms and some of the limitations of existing music genre systems are doesn't work with large database, low accuracy etc. In this paper, presented a large music dataset namely GTZAN with 10 distinct music genres like jazz, rock, hip-hop etc. In order train the classification model of music genre system, a deep learning based technique called CNN (Convolution Neural network) is applied. The proposed method performs the classification of music genre into innumerable genre with the help of feature vector extraction, which plays a vital role in training the model. The feature vector called MFCC (Mel Frequency Cepstral Coefficient) considered in classifying the raw files which are converted to respective music genres or types. The performance of various classification techniques are analyzed compared with respect to evaluation metrics. [ABSTRACT FROM AUTHOR]

Incremental Learning refers to the capability of the machine learning mode to adjust to new data of previously unseen classes with forgetting the previously learned knowledge Neural networks, like most learning architectures, suffer from the problem of catastrophic failure, i.e. the characteristics of a Machine Learning architecture to adjust its parameter to better evaluate itself over the newer data, while completely forgetting all of what it had learnt from the data it had seen previously. The requirements of such a system in the data-rich world that we find ourselves in, and more specifically, how we can implement a simple algorithm to let a CNN learn in an incremental manner, without destroying the past learning that it has undergone. Many of the previous incremental learning techniques used distillation loss functions, which were not found to be effective in large numbers of incremental classes. In this paper, we aim at developing the idea of Incremental Learning in general, which uses the simple technique of reinforcement learning and weighted ensemble to incrementally train the network. Further to check the efficacy of our proposed algorithm, we implemented our algorithm for CNN network and evaluated it on MNIST dataset, which showed desirable performance on accuracymetric. [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]

Nowadays social media plays a significant role in all sorts of our activities ranging from analysing the attitude of a person for the job, getting opinions towards buying a product, acting as a forum for exchanging thoughts about the current events of various domains, creating awareness to the public about the natural calamities, educating the public about the fraudulent news spread by the fakers, initiating the young aspirant to protest against any societal issues, etc. Grasping the opinions shared by the experienced people towards a product, film, event, news, or politics like any subject of matter is one among the worth noting applications for a common man. It extends its application to making decisions about our day-to-day activities. The text reviews consist of enormous, sparse, non-uniform distribution of words represented as features. Text mining is the backend process for those applications. It includes techniques such as feature representation, sentiment classification, feature optimization, etc. Analysing the opinions suggested by the experienced people as positive and negative reviews is a challenging process and it is the baseline of our work. This paper contributes to the related processes involved in analysing the sentiments from the text reviews and accurately classifying them based on their polarity. In the proposed work, we particularly focus on feature representation techniques that have a major effect on enhancing the performance of sentiment classification. We explore different feature representation models such as TF-IDF vectorizer, word2vec vectorizer, and glove vectorizer as these word embedding models are interpreting the words and their syntactic and semantic relationships differently from the corpus. Also, we employ machine learning algorithms and a deep convolution neural network to perform comparative studies in classifying the sentiments. The word2vec in combination with Deep Convolution Neural Network provides the accuracy of 85.7%, precision of 84.4%, recall of 87%, and F-measure of 85.7% compared to other models. [ABSTRACT FROM AUTHOR]

A major cause of vision loss for humans in worldwide is diabetic retinopathy (DR). This disease should be treated in earlier to reduce the defect. However low-cost settings some ophthalmologists are available to care patients with diabetes, the clinical diagnosis of diabetic retinopathy will a great challenge for doctors. This paper reviews the recent studies on the detection of diabetic retinopathy by using most efficient algorithms of deep learning that is convolutional neural network (CNN) and ALEXNET which is highly used to detect diabetic retinopathy feature from the pictures. CNN and ALEXNET approach to diabetic retinopathy detection which will save time, expensive. And also, it is more efficient. Hence it is more beneficial for diabetic retinopathy detection. [ABSTRACT FROM AUTHOR]

The identification of co-salient objects is a technique for identifying and segmenting an object of interest in a group of images (CSD). Numerous studies using cutting-edge machine learning techniques have concentrated on CSD. Due to their improved feature extraction capabilities, deep neural network layers(CNNs) are often used for CSD. This research paper examines the semantic segmentation abilities of a variety of popular pre-trained network such asInceptionResNet-v2, Xception, ResNets, MobileNet-v2, VGGs. These models were trained and tested using the Image Net dataset, the MSRA-10K dataset, ECSSD dataset, MSRA-B dataset, DUTS dataset, and THUR15k datasets. According to the results, ResNetsnetwork beat the other well-known CNN models, with lowest MAEs of 0.93 and 0.92, respectively. It's also worth noting that, when compared to other recent models, ResNet50 is the most noise-resistant, whereas VGG-16 is the mostsensitive. [ABSTRACT FROM AUTHOR]

Many people around the world have been affected by COVID-19. It has caused major economic and trade disruptions. Covering a person's face with a mask has become the accepted norm. Today's world, every public service provider requires their customers to wear proper face masks in order to use their assistance. As a result, the concept of identifying face masks has become a key role in assisting global civilization. A more straightforward approach to achievingthis goal is presented in this paper, which makes use of TensorFlow, Keras, OpenCV, and Scikit-Learn, as well as some fundamental machine learning packages. Using this method, you can find the face in an image and then figure out if it's covered by a mask. It should be able to detect a face as well as a mask in motion as part of its monitoring role. The approachachieves an accuracy of up to 92 percent on the dataset, respectively. The framework of Sequential Convolution Neural Networks is used to investigate optimal parameter values for detection and identification of masks without over fitting. [ABSTRACT FROM AUTHOR]

Lately, fake images have become very popular, and it is difficult for people to see them. Due to these fake images, many fields like forensics are facing problems; even social media also became a problem because of the fake images. Many forensics people are trying to overcome this problem. As new types of counterfeit images emerge quickly, the ability to customize new types of counterfeit images is an important, and even challenging, task. In this project, we explore the problem and use machine learning and image editing to overcome this problem. In this paper, we are scheming the LBP on machine learning Convolution Neural Network called LBPNET to get non-fiction images. Here we will first take LBP out of the pictures and train descriptive pictures of LBP with the Convolution Neural Network to produce a training model. Whenever we upload a new test image then that test image will be used in the training model to determine whether the test image contains a fake image or a non-fake image. [ABSTRACT FROM AUTHOR]

Every year, tens of thousands of people are declared missing in India. A significant number of people who have gone missing have yet to be found. With the aid of facial recognition, this paper suggests a unique deep learning technique application for recognizing the identified missing person from the images of a large number of people available. Photographs of suspicious people may be uploaded to a shared portal, including location and timings. The snapshot will be linked to the missing person's images in the repository. The input individual picture is classified, and the picture with the good fit is chosen from a missing person database. Using the image reported by the public, an algorithm is learned such that we can accurately find missing person in archive with missing people's images generated. Face recognition is done using Convolution Neural Network (CNN) which is a common deep learning algorithm to process image-based applications. Face features are derived from pictures using a VGG(Visual Geometry Group)-Face deep architecture CNN model that has been pre-trained. Unlike other deep learning algorithms, ours only uses a convolutional network as a high feature attribute, with the qualified KNN classifier doing the individual recognition. When properly prepared, VGG-Face, the highest performing CNN algorithm for face detection, outputs a deep learning algorithm that is distortion, lighting, and person age and exceeds previous approaches in facial recognition-based missing person identification. This recognition device classification accuracy is 90%. [ABSTRACT FROM AUTHOR]

This paper proposes an additional 'Median Layer' to the existing Convolutional Neural Network (CNN).The work primarily focuses on removing 'High Density Impulse Noise' present in an image. The proposed median layer performs median filtering on all feature channels. The developed network is an end-to-end network that does not require any non-trivial preprocessing tasks. Based on the experiments performed, results show that, by inserting a median layer to a CNN, the proposed architecture has given convincing results, when compared to the existing approaches. [ABSTRACT FROM AUTHOR]

The real estate market is becoming one of the most competitive in terms of pricing and same tends to vary significantly based on various factors. This paper focuses on identifying the type of land and estimating the price using convolutional neural network. Deep learning algorithms exhibit marvelous performance over conventional machine learning algorithms identifying the complex patterns. In this paper two Convolutional Neural Network (CNN) models are used. One is self-proposed model and another is ResNet152V2 model. Models are trained using aerial land image dataset. The ResNet152V2model showed high accuracy compared to the self proposed model. This system helps the land owner to acquire some basic essential details about the land. [ABSTRACT FROM AUTHOR]

The process of building new dataset and the existence of such a data followed the urgent need for the existence of datasets that are specialized in educational lectures, so this will need an accurate classification algorithm to classify it, the benefit of classify such dataset is to minimize the workload of classifying each file manually and individually. In the present paper, authors perform experimentations for conducting an empirical deep learning study, especially, convolutional neural network, for three new datasets of educational lectures which are (PDF, Word and PowerPoint datasets), The three new datasets using real data educational resources lectures collected from various document projects of different universities and institutions. The architecture has been applied to the task of the text classification in the domain of the document with documents data-sets have been obtained from a variety of projects on actual document cases. The aim of the present study is to initially test the performance of each dataset (PDF, Word, and PowerPoint dataset) through using four machine learning classification algorithms which are (Bayes Net, Random Forest, Random Committee, and OneR). Second goal is experimenting the efficiency of the approach of the deep learning in the tasks of classification and after that, comparing the efficiencies with the efficiencies of traditional machine learning classification methods. Mainly two classification techniques used to maximize the benefits of the classification process, the first one is to use the deep learning algorithm which shows an accuracy of classifying file between (95 and 96%) for three new dataset files and standard machine learning algorithms (OneR, Random forest, Bayes net, and Random Committee) these algorithm shows accuracy 91% for PDF Dataset using random forest and random committee algorithms, for Word dataset the accuracy is 46% using random committee, and for the last dataset PowerPoint the accuracy is 77% using random forest, Therefore, we will choose Deep learning algorithm because it gives higher results and accuracy than machine learning algorithms. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) will shape human civilization differently because AI is increasingly playing a key role in our lives. AI is an Art that facilitate computer, robot or machine to mimic human behaviour. Deep learning is a subfield of AI that focuses more on how machine work and mimics the human brain. Deep learning is one of the recent, trending and emerging fields of study and research. Deep learning is revolutionizing every field from robotics to medicine and has provided an easy solution to many state-of-the-art problems. The recent developments in deep learning are virtual assistants, self-driving cars, news aggregation, natural language processing, digital marketing, visual recognition and image recognition etc. Despite many developments in deep learning, there still exist some challenges. Many researchers are working on deep learning, but most of them focus on field-specific issues, while this paper presents a brief survey on deep-learning advancements from the last few decades. This work gives a clear and concise direction to new researchers and academicians. This article thoroughly discusses Deep Neural Network (DNN); it also covers different topics, i.e. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including most of the other deep learning deep algorithms. This article also covers the open research issues and recent trends in deep learning. [ABSTRACT FROM AUTHOR]

Sentiment Analysis (SA) is the most common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative, or neutral. E-Commerce portals are generating a lot of data every day in the form of customer reviews. It extracts subjective information in source material and helping a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. Many sentiment analysis techniques are available and most of them are done using a single machine learning algorithm. This paper proposes a method of extracting text features using a Convolutional neural network and uses logistic regression to analyze the sentiment polarity of the customer reviews. The result of the experimental result proves that this model effectively improves the performance of the sentimental classification. [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]

Today farmers are facing lot of problems in identifying plant diseases due to pandemic which leads to loss of Economy and they are committing suicide. As the growth of population was increasing daily, there is a increase in huge amount of pollution which affected the crop health. This paper recommends a technique where plant diseases may be detected and remedies for the disease are given to the farmer. Several plant images are identified and renamed in order to compile comprehensive database information. The Plant images are taken as datasets which are processed and trained for classification, and the outcome will be precisely predicted. Convolution Neural Network (CNN) is used in our model which consists of more layers for prediction. In order to capture photographs of plants, it is linked to a high-resolution camera. The images are given as input to the program, and the software determines whether or not the plants are healthy based on the images. Our technique not only specifies the disease but also it provides the remedies for the farmers. The combination of machine learning and deep learning techniques focuses on Societal and Economical benefits which improves national income. [ABSTRACT FROM AUTHOR]