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Hybrid Color Texture Features Classification Through ANN for Melanoma

Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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Gaze Tracking with Multi-Modal Neural Network in Desktop Environments Using Generic Web Camera 6

Gaze Tracking with Multi-Modal Neural Network in Desktop Env...

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6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Kohel, Karim Osama, Mohamed Mohamed, Nourhan Mokhtar, Mohamed Tamer, Omar Fakhr, Mohamed Waleed University of Northampton Faculty of Engineering Department of Computer Northampton United Kingdom Arab Academy for Science and Technology Faculty of Engineering Department of Computer Cairo Egypt

ISBN: (纸本)9798350382891

Gaze tracking is the process of estimating where a person is looking on the screen using only information from eye movement without additional input from the user, it contributes greatly in understanding and improving human-computer interactions. This paper addresses an improved gaze tracking technique building upon established approaches that are using generic web cameras instead of requiring high-cost specific hardware like infrared cameras and sensors. In the proposed method, a pipeline was constructed of various high performing AI models such as Google's MediaPipe face localization and face landmarks solutions to acquire both Iris metrics and localized eye image data. The data obtained is then passed to the proposed model that uses both image data and location metrics to predict the user's gaze position on the screen using a multi-modal architecture. Experiments done on realistic desktop environments show 8% mean distance error of the screen dimensions with a standard deviation of +/-6% from the actual gaze location. The proposed method is not affected by most logistical limitations affecting other similar approaches. © 2024 IEEE.

关键词： Eye tracking

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Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： computer vision

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Safe Return Home Solution Using a Smart Drone Scout

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Journal of Institute of Control, Robotics and systems 2024年第9期30卷 937-944页

作者： Heo, Hyeonjeong Lee, Hojun Lee, Kyuman Department of Robot and Smart System Engineering Kyungpook National University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of School of Space Engineering Sciences Kyungpook National University Korea Republic of

The women's safe return home service is short on operating hours and manpower. To address this social issue, this paper proposes an unmanned solution using a smart drone. When a user calls this system, it generates an initial safe path based on data from electronic tag wearers (i.e., obstacles) and map information. As the obstacles move, the safe path is re-planned to avoid them. The smart drone tracks and monitors the users returning home along the planned safe path at a certain distance and angle in real time. Since the raw GPS data from the user's app are unreliable, these are corrected to map-matching points for the drone to determine the points to track. Moreover, the smart drone identifies corners to avoid losing the monitored field of view at corners and adjusts its tracking accordingly. Flight test results validate the overall functionality and performance of the proposed solution, i.e., the proposed system improves user safety by effectively minimizing contact with electronic tag wearers while maintaining a certain distance and angle from the user by controlling the direction and speed of the drone. © ICROS 2024.

关键词： Drones

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From 5G to 6G: The Role of AI, Machine Learning, and Deep Learning in Wireless systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

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Search Operations With Geolocation Estimation of Missing Persons Based on Real-time Drone Images

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Journal of Institute of Control, Robotics and systems 2024年第8期30卷 890-896页

作者： Lee, Joohyuk Lee, Ho Jun Arachchige, Sasanka Kuruppu Kim, Namyoung Heo, Hyeonjeong Lee, Kyuman Department of Robot and Smart System Engineering Kyungpook National University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of School of Space Engineering Sciences Kyungpook National University Korea Republic of

The use of drones in search-and-rescue missions allows us to easily search areas that are inaccessible to humans and enables rapid and efficient mission execution with minimal manpower. In this paper, we propose a search operation method that involves automatically recognizing missing persons based on real-time images captured by a camera mounted on a drone and estimating their geolocation information. Given a particular search area, we plan a flight path while taking into consideration a cost function with constraints. Using a deep-learning model trained using cropped, generated, and augmented data, we recognize missing persons through real-time images taken by the drone following the planned path. Additionally, we estimate the geolocation of the missing persons by coordinate-transforming the reference pixels of recognized objects in the image. Based on the estimated geolocation, we identify identical objects and count the total number of objects recognized during missions. We validate the proposed search method by completing a search-and-rescue challenge using a drone. © ICROS 2024.

关键词： Cost functions

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

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Neural Computing and Applications 2024年第27期36卷 17199-17219页

作者： Hosney, Reham Talaat, Fatma M. El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Nile Higher Institute for Engineering and Technology Mansoura Egypt

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. © The Author(s) 2024.

关键词： Diagnosis

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User Interface Rendering Time in Android Applications: Revealing the Effects of Design Patterns 23

User Interface Rendering Time in Android Applications: Revea...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Paramadani, Rizky Bagus Akbar, Muhammad Aminul Pinandito, Aryo Informatics Engineering Departement Faculty of Computer Science Universitas Brawijaya Malang Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

The Google Play platform boasts a total of 2,597,819 applications. A pivotal gauge of an application's success rests on its practical utility in daily life, coupled with its demonstrated strong performance metrics. There are several performance metrics to evaluate the performance of an application, such as RAM usage, CPU utilization, and their impact on battery life. Additionally, it is crucial to consider the User Interface (UI) factor, which serves as the primary connection between users and applications. Beyond its role as a bridge between users and applications, the UI can also significantly affect the performance of an application, as rendering a UI often takes a considerable amount of time. Various approaches can be employed to enhance the rendering time of a UI, one of which involves the use of design patterns like Model-View-Controller (MVC) and Model-View-ViewModel (MVVM), which effectively separate the business logic from the UI layer. This research study compares the performance of these two design patterns in the context of UI rendering time. The findings of this study confirm that MVVM outperforms MVC significantly in rendering UI components more rapidly. This superiority is attributed to MVVM's inherent ability to isolate business logic, ultimately reducing rendering delays. Furthermore, MVVM's prominent data binding feature further strengthens its efficiency in terms of UI rendering time. © 2023 ACM.

关键词： User interfaces

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A Scalable Open-Set ECG Identification system Based on Compressed CNNs

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IEEE Transactions on Neural Networks and Learning systems 2023年第8期34卷 4966-4980页

作者： Wu, Shun-Chi Wei, Shih-Ying Chang, Chun-Shun Swindlehurst, A. Lee Chiu, Jui-Kun National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize 'open-set' identification. With the help of its scalable structure and 'transfer learning,' new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy. © 2012 IEEE.

关键词： Biometrics

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