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arXiv 2024年

作者： Shem-Tov, Eliad Sipper, Moshe Elyasaf, Achiya Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover leverages the capabilities of deep reinforcement learning and an encoder-decoder architecture to select offspring genes. BERT mutation masks multiple gp-tree nodes and then tries to replace these masks with nodes that will most likely improve the individual's fitness. We show the efficacy of both operators through experimentation. © 2024, CC BY.

关键词： Genetic programming

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Teeth and Jaw Segmentation from CBCT images Using 3D Deep Learning Models 18

Teeth and Jaw Segmentation from CBCT images Using 3D Deep Le...

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18th International computer engineering Conference, ICENCO 2022

作者： Abdo, Yassmina Mohamed, Nader Alsawaf, Maryam Elsaeed, Mohamed Ain Shams University Faculty of Computers and Information Sciences Scientific Computing Department Cairo Egypt Higher Institute of Engineering and Technology Mechatronics Engineering Department Cairo Egypt Kafr Elshiekh University Faculty of Engineering Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9781665458955

The outcome of oral surgery is dependent on precise preoperative planning. In order to properly plan for dental surgery or suitable implant placement, it is necessary for accurate segmentation of the jaw tissues: the teeth, the cortical bone, the trabecular core, and overall, the inferior alveolar nerve. Achieving 3D virtual surgical planning is very hard and is a major drawback because most of the existing jaw segmentation methods require a large amount of expert knowledge for manual or partially automatic segmentation. What is more, due to the lack of experienced doctors and experts, high-quality expert knowledge is hard to achieve in practice. Furthermore, metal artifacts and large variations in their shape and size among individuals influence the segmentation of the jaw in CBCT scans seriously. Therefore, this paper presents a new automatic jaw segmentation in CBCT scans dataset. This approach is divided into two main phases;the first phase adopts the use of image processing techniques to extract the region of interest (ROI) which is the area containing the jaws. It is an important phase as it prepares the 3D image data before using it in the second phase as it makes the training more efficient rather than training the model overall 3D head of CBCT scans. In the second phase, we use several models to segment each jaw separately and then gets a 3D segmentation view of both jaws. We implement such a segmentation approach on head CBCT scans given as Dicom Numpy files datasets and then evaluate the performance. Our proposed approach has been tested in a validation set that has been evaluated using Dice's coefficient and the Attention Unet model achieved promising results with a 91 © 2022 IEEE.

关键词： Deep learning

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An Analysis of Machine Learning Algorithms for Smart Healthcare systems 1

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Alduailij, Mai Mishra, Anupama Zamzami, Ikhlas Fuad Psannis, Konstantinos Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Faculty of Business King Abdulaziz University Rabigh Saudi Arabia University of Macedonia Thessaloniki Greece

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

The epidemic taught us to keep digital health records. It also showed us how wearable observing gear, video conferences, and AI-powered chatbots may give good treatment remotely. Real-time data from health care devices across the globe helped attack and track the infection. Biomedical imaging, sensors, and machine learning have improved health in recent years. Medical care and biomedical sciences are now information science sectors that demand enhanced data mining approaches. Biomedical data have high dimensionality, class irregularity, and few tests. AI uses information and computations to imitate how people learn, continually improving its accuracy. ML is an important part of information science. Calculations utilising measurable methods reveal essential information about information mining operations. This paper explains and compares ML algorithms that can detect diseases sooner. We summarise ML’s algorithms and processes to extract information for a data-driven society. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Performance Improvements of Vehicular PKI Protocol for the Security of V2X Communications 46

Performance Improvements of Vehicular PKI Protocol for the S...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Farran, Hassan Khoury, David Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services BudapestH-1111 Hungary American University of Science and Technology Department of Computer Science and Information and Communication Technology Beirut Lebanon

ISBN: (纸本)9798350303964

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system. © 2023 IEEE.

关键词： Vehicle to Everything

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Comp-RPKI: A Decentralized Protocol for Full Route Origin Validation

Comp-RPKI: A Decentralized Protocol for Full Route Origin Va...

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International Conference on Big Data Computing and Communications (BIGCOM)

作者： Yanwei Wu Yun Wang Jack DeJulius Riad Abboud Melvin Woitel Bohdan Osadchyy Kyree Richardson Computer Science and Software Engineering University of Wisconsin Platteville WI USA Computer Science and Information Systems Bradley University Peoria IL USA

The Resource Public Key Infrastructure (RPKI) is an essential technology for securing the Border Gateway Protocol (BGP) routing on the Internet. Although the global RPKI-ROV (Route Origin Validation) rate of unique Prefix-Origin Pairs(IPv4) has been improved from 17% in June 2019 (pre-pandemic) to 41.12% in March 2023 (post-pandemic) [1] – [3], the overall RPKI-ROV rate remains below 50%. This indicates a significant insufficiency in RPKI-ROV deployment for ensuring secure BGP routing and persistent security challenges continue to persist in the Internet. To address this, we first develop software to analyze and visualize the relationships among Autonomous systems (ASes), degree distribution, and their RPKI validation status, given a region in IL. Next, we investigate the RPKI validation rates along real-time routing paths from Peoria, IL to all 50 states in the USA. Based on the analysis, we propose a novel lightweight, decentralized, easy-to-deploy, offline protocol called Comp-RPKI, which aims to complement and address the partial deployment of RPKI. Comp-RPKI aims to ensure every segment of a routing path is validated either through online RPKI or secured by offline Comp-RPKI, thereby achieving full RKPI-ROV coverage and enhancing BGP security.

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Defensive Auto-Updatable and Adaptable Bot Recommender System (DAABRS): A New Architecture Approach in Cloud Computing systems 6

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Khwaldeh, Sufian Mohit, Y. Khushwant, S. Department of Network and Information Security College of lnformation Technology University of Fujairah 1207 United Arab Emirates Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba77110 Jordan Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak124001 India

ISBN: (纸本)9798350394634

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults. © 2024 IEEE.

关键词： Containers

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Sea Cense - A Commercially Valued Sea Cucumber Analyzer 5

Sea Cense - A Commercially Valued Sea Cucumber Analyzer

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Hettiarachchi, Thavishi Herath, Gagani Jayasekara, Kasuni Thushamini, Malmi Attanayaka, Buddhima Gamage, Narmada Nishanthan, G. Dissanayake, D.C.T. SLIIT Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka SLIIT Faculty of Computing Department of Information Technology Malabe Sri Lanka South Easetern University Faculty of Technology Department of BST Oluvil Sri Lanka University of Sri Jayawardanapura Department of Zoology Nugegoda Sri Lanka

ISBN: (纸本)9798350358131

In Sri Lanka, sea cucumbers are a highly sought-after marine product that is traded abroad. It has gained significant attention in recent years due to its high demand in the seafood market and its medicinal properties. Yet, the Sri Lankan Sea cucumber industry has not been able to fully realize its potential due to barriers like inaccurate methods for identifying and predicting the quality of sea cucumber species and the inability to provide a proper market value. This study offers an innovative solution that automates the evaluation and classification of sea cucumbers utilizing advanced computer vision and machine learning approaches. The proposed analyzer incorporates a multi-step approach, including image preprocessing, feature extraction, and classification. A set of preprocessed images are used to extract textural and distinguished color-based characteristics. These characteristics are then used to accurately classify sea cucumber species and grade them according to quality criteria using a machine learning algorithm. This project introduces a novel approach to analyzing sea cucumber length to calculate the price using a mobile application, making the task more efficient and accessible. The proposed solution 'Sea Cense' comprises 4 models that are developed using the CNN algorithm. These models' experimental findings demonstrated accuracy levels exceeding 85%. © 2023 IEEE.

关键词： Forecasting

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Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

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computers, Materials & Continua 2022年第2期70卷 3281-3294页

作者： Nazar Hussain Muhammad Attique Khan Usman Tariq Seifedine Kadry Muhammad Asfand E.Yar Almetwally M.Mostafa Abeer Ali Alnuaim Shafiq Ahmad Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Data Science Noroff University CollegeNorway Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543KSASaudi Arabia Department of natural and engineering sciences College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11421Saudi Arabia Faculty of Engineering Industrial Engineering DepartmentKing Saud UniversityRiyadh11421Saudi Arabia

Agriculture is an important research area in the field of visual recognition by *** diseases affect the quality and yields of ***-stage identification of crop disease decreases financial losses and positively impacts crop *** manual identification of crop diseases,which aremostly visible on leaves,is a very time-consuming and costly *** this work,we propose a new framework for the recognition of cucumber leaf *** proposed framework is based on deep learning and involves the fusion and selection of the best *** the feature extraction phase,VGG(Visual Geometry Group)and Inception V3 deep learning models are considered and *** fine-tuned models are trained using deep transfer *** are extracted in the later step and fused using a parallel maximum fusion *** the later step,best features are selected usingWhale Optimization *** best-selected features are classified using supervised learning algorithms for the final classification *** experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%.A comparison with recent techniques shows the significance of the proposed method.

关键词： Cucumber diseases database preparation deep learning parallel fusion features selection

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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

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Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

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Ontological Model for Cohesive Smart Health Services Management

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computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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