Helmet violation detection is a crucial aspect ofroad safety, as it can significantly reduce the number of fatalities and injuries caused by motorcycle accidents. In recent years, computer vision techniques have been widely used to develop automated systems for helmet violation detection. This project proposes a helmet violation detection system using image processing and machine learning techniques. The proposed system employs computer vision algorithms to detect whether a motorcyclist is wearing a helmet or not. The (...) system is based on a deep learning model, specifically Convolutional Neural Networks (CNN), to classify the input images into two classes, i.e., helmet and nonhelmet. The system is trained on a large dataset of images with differentlighting conditions, backgrounds, and helmet types to enhance its accuracy and generalization ability. The proposed system can be implemented on existing surveillance cameras installed at strategic locations on the road. This system has the potential to increase road safety and reduce the number of motorcycle accidents caused bythe violation of helmet-wearing rules. The system involves person detection, helmet, vs. no-helmet, classification using YOLO algorithm. Convolutional neural network with sequential model is implementing for number plate detection process CNN classification model proposes for classify the number plate in image and extract the user details. Then calculate the fine amount. Finally making SMS services to send alert the users too preventing motorcycle accident. (shrink)

Traffic sign detection and recognition (TSDR) is a critical aspect of autonomous driving and intelligent transportation systems. Traditional methods of traffic sign detection rely on handcrafted features and classical machine learning algorithms, which often struggle to achieve high accuracy in complex real-world environments. In contrast, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable performance in both detecting and recognizing traffic signs in diverse conditions. This paper reviews the application of deep learning methods for TSDR, focusing (...) on recent advancements in the use of CNNs, Region-based CNNs (R-CNNs), and YOLO (You Only Look Once) for real-time detection and recognition of traffic signs. We discuss the challenges, including the variability of traffic signs, environmental factors, and computational requirements, and present solutions proposed in the literature. The paper also highlights future directions for improving the robustness, accuracy, and speed of traffic sign detection and recognition systems, with an emphasis on real-time applications. (shrink)

The coronavirus disease has spread throughout the world and its fear has made people to be more cautious in public places. Since precautionary measures are the only reliable protocol to defend ourselves, social distancing is the only best approach to defend against the pandemic situation. The reproduction number i.e. R0 factor of COVID-19, can be slowed down only through the physical distancing norms. This research proposes a deep learning approach for maintaining the social distance by tracking and detecting (...) the people present indoor and outdoor scenarios. Surveillance video is taken as the input and applied into you only look once (YOLO) V3 algorithm. The persons in the video are identified based on the segmentation algorithm present within the framework and then using Euclidean distance the image is evaluated. The bounding box algorithm helps to segregate the humans based on the minimum distance threshold. The proposed method is evaluated for images with peoples in the market, availing essential commodities and students entry inside a campus. Our proposed region-based convolutional neural network (RCNN) algorithm gives a better accuracy over the traditional models and hence the service can be implemented in general for places where social distancing is mandatory. (shrink)

One key aspect in the design of autonomous vehicles is the implementation of real-time object detection which includes identifying and classifying obstacles present in the environment of the vehicle . The system will also be able to capture live or recorded videos and accurately identify and classify the objects within the video streams in very active situations requiring low turnaround times so that the vehicle is able to make the necessary safe driving critical choices . more recent state of the (...) art deep learning frameworks that tackle the object detection problem are Region Based Convolutional Nural network R-CNN and its variants fast R-cnn and faster R-cnn more approximately Fixes models’ Execution Speed. Another object detection system presented in the year 2015 is known as the YOLO system which is focused on fast predictive capabilities and for use in motion pictures. BDD100K dataset for video based deep driving understanding in 100k videos and 10 tasks which are designed to assess how far the algorithm gets in its objective of Image Recognition in driverless cars. The dataset has geographical, environment and weather variability, which is beneficial for training models that have a high likelihood of encountering drastic changes. (shrink)

Farmers are often concerned about plant disease since it can greatly affect crop productivity and quality. Expert manual inspection is required in traditional techniques of identifying plant diseases, which can be time- and money-consuming. Deep learning algorithms have made automated plant disease detection systems more practical. Convolutional neural networks (CNNs) are used in our proposed deep learning- based technique for the diagnosis of plant diseases. The suggested system uses plant photos as input to determine the presence of illnesses (...) in the plants and then provides treatment recommendations. The CNN can learn the visual patterns connected to various diseases because it is trained using a sizable dataset of photos of healthy and diseased plants. This system also includes IOT so that after the result is acquired, it will communicate via IOT. Several publicly accessible datasets of plant photos were used in our studies to gauge the performance of the suggested approach. The experimental results show that the suggested approach detects several plant diseases, such as tomato bacterial spot, potato early blight, and apple black rot, with high accuracy. Overall, our approach shows the promise of deep learning methods for automatically detecting plant diseases, which can assist farmers in identifying and treating plant diseases more quickly and successfully. (shrink)

This study explores the application of deep learning and image processing techniques for the detection of skin cancer. Leveraging convolutional neural networks (CNNs) and advanced image processing algorithms, the proposed system aims to accurately identify and classify skin lesions. The model is trained on a diverse dataset, encompassing various skin conditions, to enhance its diagnostic capabilities. Results demonstrate the potential for automated and reliable skin cancer detection, offering a promising approach for early diagnosis and improved patient outcomes. The (...) class='Hi'>deep learning model is trained on a comprehensive dataset, including various types of skin lesions and conditions, to ensure robust performance across a spectrum of cases. Image preprocessing techniques are employed to enhance feature extraction and improve the model's ability to discern subtle patterns indicative of skin cancer. The study further investigates the interpretability of the deep learning model, employing techniques to visualize and understand the decision-making process. This transparency aids in building trust in the system's predictions and facilitates collaboration between AI and medical practitioners. As the landscape of healthcare continues to evolve, the combination of deep learning and image processing offers a scalable and efficient solution for skin cancer detection, fostering advancements in early intervention and personalized patient care. (shrink)

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down (...) the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities (...) and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)

there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of this paper (...) is By attempting to translate sign language using deep learning models, we focused in this paper a comparing between Inception and Xception ¬¬¬¬¬ algorithm and compared it to previous papers in which we used VGG19[1], mobileNet [2], where we get in the Inception and Xception algorithm on the degree of Accuracy 99.32%, 99,43% respectively. number of images (29000 in the dataset in size 75*75 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

The increase in number of vehicles on the road is being observed day by day, and the responsibilities that should be upheld by vehicle owners are frequently neglected. To ensure that the rules defined by the RTO are adhered to by every vehicle owner, the "Vehicle Surveillance System using deep learning" is proposed. This system is designed to capture vehicle data through web-app, with the identification of vehicles being achieved through the recognition of their number plates, and (...) the extraction of characters from the plates. Once the vehicle's number plate is recognized, the vehicle's information and owner's details will be verified with RTO data using Application programming interface (API). If any unfulfilled criteria are identified by the system, a notification will be generated. Vehicle detection is carried out using the Common Objects in Context Single Shot MultiBox Detection (COCOSSD) model, while the Russian model is utilized for number plate recognition. The extraction of characters from the number plate is executed through Optical Character Recognition (OCR), which includes character extraction and segmentation. (shrink)

The COVID-19 outbreak has had a significant influence on the health of people all across the world, and preventing its further spread requires an early and correct diagnosis. Imaging using X-rays is often used to identify respiratory disorders like COVID-19, and approaches based on machine learning may be used to automate the diagnostic process. In this research, we present a deep learning approach for COVID-19 identification in X-ray pictures utilizing global thresholding. Our framework consists of two main components: (1) (...) global thresholding to preprocess X-ray images and extract features, and (2) a Convolutional Neural Network (CNN) for classifying the images as positive (indicating COVID-19) or negative (indicating no COVID-19). Global thresholding is used to convert X-ray images into binary images, which can highlight the features of interest, such as lung opacity. The Network is instructed to use a dataset consisting of X-ray pictures that have been labeled with the COVID-19 status of each image. The usefulness of our approach was shown by experimental findings obtained using a dataset of X-ray pictures that were made accessible to the public. We achieve high accuracy in COVID-19 detection, outperforming traditional machine learning techniques. Our framework is also able to correctly identify COVID-19 in images that were misclassified by human experts. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

Abstract: Lemons are an important fruit that have a wide range of uses and benefits, from culinary to health to household and beauty applications. Deep learning techniques have shown promising results in image classification tasks, including fruit quality detection. In this paper, we propose a convolutional neural network (CNN)-based approach for detecting the quality of lemons by analysing visual features such as colour and texture. The study aims to develop and train a deep learning model to classify lemons (...) based on their quality, evaluate the model's performance, compare it to traditional machine learning approaches, and identify key factors that affect the model's performance. The dataset used in the study consists of approximately 2533 images of lemons and an empty surface, divided into training, test, and validation data, and includes images of both good and bad quality lemons under different lighting conditions. The deep learning model achieved an accuracy of 99.74% on the test dataset and outperformed traditional machine learning approaches. The developed model has the potential to improve efficiency and accuracy in lemon quality control in the agriculture and food industry. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (...) (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) and Convolutional Neural (...) Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi- cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used (...) as a technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve (...) response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion (...) of synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

he health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high (...) accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

Facial recognition is a computer vision task that involves identifying or verifying individuals based on their facial features. It has widespread applications in security, authentication, and human-computer interaction. Supervised learning techniques have become the foundation for facial recognition systems, as they enable the model to learn from labeled data to recognize patterns and make predictions. This paper explores the use of supervised learning algorithms, such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and k-Nearest Neighbors (k-NN), for facial recognition (...) tasks. We will demonstrate how these algorithms can be used to accurately identify individuals by learning from training datasets with labeled facial images. (shrink)

A face reveals a lot of information about a person's identity, age, sex, race, and emotional as well as psychological state. Facial expressions are often used in the behavioral interpretation of emotions and play a key role in social interactions. Due to its potential applications such as HCI, behavioral science, automatic facial emotion detection is one of the most intriguing and challenging areas in computer vision. Our Facial Emotion Recognition system performs detection and location of faces in a cluttered scene, (...) facial feature extraction, and facial expression classification. Numerous models were experimented such as decision trees and SVM’s before arriving at a Convolutional Neural Networks (CNN) model. Due to large number of filters, CNNs are efficient for image recognition tasks since they can capture spatial features of the inputs. After observing unbelievable performance with deep learning models, we used deep convolution features to better represent the given image instead of using the traditional handcrafted features. We have proposed and developed a Deep Convolutional Neural Network for classifying human emotions from dynamic facial expressions in real-time. Kaggle’s FER-2013 dataset with seven facial emotions labels as happy, sad, surprise, fear, anger, disgust, and neutral is used to train the model. An overall training accuracy of 92.86% and test accuracy of 64.42% are achieved. Finally, a live video stream connected to a face detector feeds images to the neural network. The network subsequently classifies an arbitrary number of faces per image in real-time simutaneously, wherein appropriate emotions are displayed over the subject’s faces and their probabilities are displayed over a real-time bar graph on a separate window. (shrink)

Computer vision, or image recognition, analyses and interprets visual data in real-world scenarios like images and videos. AI and ML research focusses on object, scene, action, and feature identification because of its usefulness in image processing. Neural networks and deep learning have improved image recognition systems significantly in recent years. Early image recognition used template matching to identify objects. A photo is compared to a stored template using similarity measures like correlation to get the best match. There are several (...) constraints, especially with distorted, scaled, or noisy images. Template matching is computationally expensive. CNNs are the most common deep learning architecture for computer vision. CNNs automatically learn visual input hierarchies. They include pooling, convolutional, and fully connected layers. Regional patterns like shapes, textures, and edges in the input image are filtered by convolutional layers. Pooling layers reduce feature map spatial dimensions, making feature extraction consistent and robust. Thick layers, or fully linked layers, classify or regress using learning features. CNN training requires a big labelled dataset. This research article introduces a deep learning model for the classification and recognition of images. The input data for this model is the Images Data Set. The GenNet Algorithm is employed to derive critical features. The accuracy of classification results is enhanced through the use of preprocessing and feature extraction. The classification model is generated using Convolution Neural Network, AlexNet, ResNet, and VGG. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with (...) a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using (...) deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high (...) accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. A. (shrink)

Computed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RT-PCR) for the detection of COVID-19 illness. This article presents gray-level co-occurrence matrix (...) (GLCM) texture feature extraction and convolutional neural network (CNN)-enabled optimized diagnostic model for COVID-19 detection. In this diagnostic model, CT scan images of patients are given as input. Firstly, GLCM algorithm is used to extract texture features from the CT scan images. This feature extraction helps in achieving higher classification accuracy. Classification is performed using CNN. It achieves higher accuracy than the k-nearest neighbors (KNN) algorithm and multi-layer preceptor (MLP). The accuracy of GLCM based CNN is 99%, F1 score is 99% and the recall rate is also 98%. CNN has achieved better results than MLP and KNN algorithms for COVID-19 detection. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a (...) technique used to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...) belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

Speech Emotion Detection (SED) refers to the identification of human emotions based on speech signals. The goal of this research is to design and implement a system that can accurately classify emotions from speech using machine learning techniques. The system can be applied in various fields such as healthcare, customer service, human-computer interaction, and mental health monitoring. The paper discusses the various stages of building such a system, from collecting and preprocessing audio data to selecting machine learning models and evaluating (...) the performance of the system. A combination of feature extraction techniques and machine learning algorithms, such as Support Vector Machines (SVM) and deep learning models like Long Short-Term Memory (LSTM) networks, is employed to achieve high accuracy. (shrink)

Anamoly detection in videos plays an important role in various real-life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Nowadays, there has been a rise in the amount of disruptive and offensive activities that have been happening. Due to this, security has been given principal significance. Public places like shopping centers, avenues, banks, etc. are increasingly being equipped with CCTVs to guarantee the security of individuals. Subsequently, this inconvenience is making (...) a need to computerize this system with high accuracy. Since constant observation of these surveillance cameras by humans is a near-impossible task. It requires workforces and their constant attention to judge if the captured activities are anomalous or suspicious. Hence, this drawback is creating a need to automate this process with high accuracy. Moreover, there is a need to display which frame and which parts of the recording contain the uncommon activity which helps the quicker judgment of that unordinary action being unusual or suspicious. Therefore, to reduce the wastage of time and labour, we are utilizing deep learning algorithms for Automating Threat Recognition System. Its goal is to automatically identify signs of aggression and violence in real-time, which filters out irregularities from normal patterns. We intend to utilize different Deep Learning models (CNN and RNN) to identify and classify levels of high movement in the frame. From there, we can raise a detection alert for the situation of a threat, indicating the suspicious activities at an instance of time and spray the smoke spray. (shrink)

Abstract cantaloupe and honeydew melons are part of the muskmelon family, which originated in the Middle East. When picking either cantaloupe or honeydew melons to eat, you should choose a firm fruit that is heavy for its size, with no obvious signs of bruising. They can be stored at room temperature until you cut them, after which they should be kept in the refrigerator in an airtight container for up to five days. You should always wash and scrub the rind (...) of your melon before you cut it to remove any dirt or bacteria on the outside. In this paper, cantaloupe classification approach is presented with a dataset that contains approximately 1,312 of Cantaloupes and honeydews. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results found that CNN-driven classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.74% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. (...) Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

: COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts of expectations: (...) the number of newly contaminated cases, the number of passings, and the number of recoveries. However, it is hard to anticipate the patients' real outcomes. To address the issue, a suggested approach based on long transient memory (LSTM) forecasts the number of COVID-19 cases in the next 10 days as well as the impact of preventative measures such as social isolation and lockdown on COVID-19 spread. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network (...) with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...) helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task. (shrink)

Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. Key parameters (...) such as time, location, type of crime, and socio-economic factors are considered to build a comprehensive predictive model. Machine learning algorithms like Decision Trees, Random Forest, and Support Vector Machines are used for efficient data preprocessing and pattern recognition. Deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable advanced feature extraction and temporal analysis for enhanced prediction accuracy. The system's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy, demonstrating its reliability and scalability for real-world applications. Furthermore, the use of visualization tools allows stakeholders to comprehend crime patterns effectively, supporting proactive policing strategies. This data-driven approach not only improves crime deterrence but also aids policymakers in resource allocation and urban planning. The proposed system has the potential to revolutionize crime management by transforming traditional reactive measures into predictive, preventive, and intelligent solutions. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network (...) with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Advancements in technology, particularly in the field of artificial intelligence (AI), have opened new avenues for solving complex biological and ecological challenges. Among these, deep learning has emerged as a powerful tool for image-based classification tasks. Convolutional Neural Networks (CNNs), a subset of deep learning algorithms, are especially effective in recognizing patterns and extracting features from images. This capability makes CNNs highly suitable for applications in bird species identification. By leveraging deep learning techniques, researchers and conservationists can (...) automate the identification process, reducing human effort and significantly improving accuracy. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms (...) that allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, (...) the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

American Sign Language, or ASL as its acronym is commonly known, is a fascinating language, and many people outside of the Deaf community have begun to recognize its value and purpose. It is a visual language consisting of coordinated hand gestures, body movements, and facial expressions. Sign language is not a universal language; it varies by country and is heavily influenced by the native language and culture. The American Sign Language alphabet and the British Sign Language alphabet are completely contrary. (...) Fingerspelling is one-handed in ASL and two-handed in BSL, with the exception of the letter C. AI technologies, particularly deep learning, can play an important role in breaking down communication barriers between deaf or hearing-impaired people and other communities, significantly contributing to their social inclusion. Recent advances in sensing technologies and AI algorithms have paved the way for the development of a wide range of applications aimed at meeting the needs of the deaf and hearing-impaired communities. To that end, the purpose of this paper is By attempting to translate sign language using artificial intelligence algorithms, we focused in this paper on a transfer learning technique based on deep learning using a ResNet algorithm and compared it to previous papers in which we used VGG19[1]and mobileNet [2], where we get in the ResNet algorithm on the degree of Accuracy 93,48% e number of images (43500 in the dataset in size 64*64 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

Plant health monitoring is crucial for sustainable agriculture, as early detection of diseases can prevent significant crop losses. This study presents a deep learning-based approach using the YOLO (You Only Look Once) model for real-time plant disease detection. The model is trained on a curated dataset of diseased and healthy plant leaves, enabling accurate classification and identification of plant conditions. The methodology involves image preprocessing, feature extraction, and YOLO inference for detection. A webbased interface is developed using (...) Streamlit, allowing users to upload images of plant leaves for analysis. Upon detection of a disease, the system provides preventive measures and treatment suggestions to help farmers and agriculturists mitigate the impact. The integration of Streamlit ensures an accessible and userfriendly platform, with the potential for future deployment as a standalone application. The proposed system enhances precision in disease identification, offering an efficient, scalable, and practical solution for plant health monitoring. (shrink)

Maritime transport plays a pivotal role in global trade, yet it faces challenges due to corrosion, which deteriorates metallic surfaces of vessels, leading to potential safety hazards and financial burdens. Traditional corrosion detection methods such as visual inspections are inefficient, time-consuming, and often subjective. This paper proposes a deep learning-based solution utilizing Convolutional Neural Networks (CNNs) to detect and assess corrosion on marine vessel surfaces. Our proposed solution not only automates the detection process but also enhances accuracy, ensuring early (...) identification and effective management of corrosion. Through rigorous experimentation, the model demonstrated high accuracy, significantly improving the corrosion detection process for the maritime industry. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge (...) data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

his project aims to provide an automated system for accurately estimating the calorie content of food and beverages using advanced deep learning algorithms. With the increasing demand for health-conscious individuals, there is a need for a reliable, efficient, and easy-to-use tool that can help users make informed dietary choices. The project utilizes image processing techniques and deep learning models, such as Convolutional Neural Networks (CNN), to analyze food images and predict the corresponding calorie content. The system works by (...) first capturing an image of the food or beverage, which is then processed and passed through a pre-trained deep learning model. (shrink)