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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Qian Liu, Lijun Yang, Xiaobing Liu, Li Peng, Wei Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming650500 China

ISBN: (纸本)9798350386226

Medical Visual Question Answering (Med-VQA) aims at answering clinical questions related to medical imaging, providing important reference for medical imaging diagnosis. Gastrointestinal visual question answering can deeply understand and analyze gastrointestinal medical images, provides supporting information to answer questions about the diagnostic process, and improves diagnostic accuracy and efficiency. However, most existing Med-VQA can only handle simple and general questions, struggling to tackle visual question answering related to complex diseases. Therefore, this paper proposes a Med-VQA model called CACL, which uses cross-guidance and improved combined loss. The model focuses on gastrointestinal image analysis and achieves efficient and accurate feature extraction for gastrointestinal images. A cross-guided attention module is also designed to enhance the model's reasoning ability when dealing with complex cross-modal tasks. In addition, a multi-task composite loss function is designed to balance the loss of segmentation task and classification task, and improve the overall performance. Experimental results show that the proposed method can effectively improve the accuracy of gastrointestinal visual question answering. © 2024 IEEE.

关键词： Multi-task learning

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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The Impact of AI-Driven Educational Transformation in Sri Lanka's Higher Education 6

The Impact of AI-Driven Educational Transformation in Sri La...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Aflal, Saroon Mohammed Shamugarajah, Sutharshan Thiruthanigesan, Kanagasabai Balasubramaniam, Balarajan Samarakoon, Uthpala Ragel, Roshan G. Sri Lanka Institute of Information Technology Northern Uni Faculty of Computing Jaffna Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka University of Peradeniya Department of Computer Engineering Sri Lanka

ISBN: (纸本)9798331517878

This study investigates the impact of artificial intelligence (AI) tools on undergraduate education in Sri Lanka, focusing on various categories of AI tools, including chatbots, grammar checkers, CV generators, and more. Through a comprehensive survey conducted across multiple higher education institutions in Sri Lanka, the study assesses the frequency of AI tool usage and students' interest levels. The research utilizes k-means clustering to identify distinct segments of students based on their AI tool usage patterns and interest levels. Findings reveal high adoption rates of AI tools among Sri Lankan undergraduates, with significant reliance on chatbots like ChatGPT. The study highlights the diverse usage of AI tools, their impact on academic activities, and the varying levels of student engagement. The results suggest that while AI tools are widely used, targeted interventions and additional support are necessary to maximize their benefits. The study provides valuable insights for policymakers and educators, emphasizing the need to strategically integrate AI tools in higher education to enhance learning outcomes and prepare students for a technology-driven future. © 2024 IEEE.

关键词： Students

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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Image Acquisition and Deep Convolutional Neural Network Incorporated Cost-Effective Automated Nursery Disease Detection Method for the Tea Industry 10

Image Acquisition and Deep Convolutional Neural Network Inco...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Weerasekara, Dinithi Bhagya Kahatapitiya, Nipun Shantha Sanjeewa, Nuwan Wathudura, Vimukthi Madushan Prasanna, Nuwan Kalupahana, Deshan Wijenayake, Udaya Silva, Bhagya Nathali Wijethunge, Akila Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Department of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka IOT Solutions Pvt Ltd Homagama Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331529048

Tea cultivation, a globally significant industry across over 60 countries, requires effective management of tea nurseries for robust plant growth and disease control. Implementing automated disease detection in tea nurseries using advanced technologies enhances efficiency, precision, and sustainability compared to manual inspection methods. An image acquisition system incorporating a Deep Convolutional Neural Network (DCNN) was developed as a cost-effective automated solution for nursery disease detection. First, images of Blister Blight (BB) disease in tea plants were collected primarily from various estates. These images were then preprocessed, labeled, and augmented to create a robust dataset. Two models based on the YOLOv5 architecture were trained, with the second model benefiting from dataset augmentation and enhanced labeling. The Improved Model achieved a mean Average Precision (mAP) of 80.6% at a 0.5 threshold, demonstrating its high accuracy in BB detection in tea nurseries. It was integrated with the image acquisition system to enable continuous monitoring of tea leaves in nursery plantations and facilitate the immediate detection of BB diseases. This system holds significant potential for further enhancement in disease management across the tea industry, promoting healthier crops, reducing costs, and minimizing environmental impact. © 2024 IEEE.

关键词： Convolutional neural networks

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Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

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computers, Materials & Continua 2023年第7期76卷 687-709页

作者： Muhammad Saad Maaz Bin Ahmad Muhammad Asif Muhammad Khalid Khan Toqeer Mahmood Elsayed Tag Eldin Hala Abdel Hameed College of Computing and Information Sciences Karachi Institute of Economics&TechnologyKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835Egypt Faculty of Computer and Information Systems Fayoum UniversityFaiyumEgypt Khaybar Applied College Taibah UniversityMedinaSaudi Arabia

Pandemics have always been a nightmare for humanity,especially in developing *** lockdowns are considered one of the effective ways to deal with spreading such ***,developing countries cannot afford such solutions because these may severely damage the country’s ***,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations *** novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled *** to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is *** use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding ***,this study also emphasizes information security,end-to-end solution,and experimental ***,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing ***,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication ***,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine *** step-by-step methodology and test results are proposed to ensure contact tracing and quarantine *** existing research studies,the security aspect is also considered in the realm of *** practical implementation of the proposed solution also obtains the *** results indicate the successful implementation of blockchain-enabled contac

关键词： Blockchain contact tracing distributed ledger technology geo-fencing internet of things industrial internet of things isolation management social distancing ultra-wideband

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Security and Reliability Performance Analysis of Cognitive NOMA Network Under Outage Constraint of Multiple Primary Users 23

Security and Reliability Performance Analysis of Cognitive N...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Pham Huu, Tung Tran, Hung Tran, Duc-Tan Dang, Viet-Hung Nhan Vo, Van Nguyen Dinh, Viet Faculty of Information Technology Hanoi University of Civil Engineering Hanoi Viet Nam Faculty of Computer Science Phenikaa Univerity Hanoi Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa Univerity Hanoi Viet Nam Faculty of Information Technology Duy Tan University Da Nang Viet Nam Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798400708916

In this paper, we conduct an investigation into the security and reliability aspects of a cognitive non-orthogonal multiple access (NOMA) network operating under outage constraints imposed by multiple primary users (PUs). Specifically, we examine a scenario where secondary users (SUs) have the capability to operate in NOMA mode, allowing them to harness the licensed spectrum band of multiple PUs, provided that the interference generated by SUs remains below a predefined threshold. Given that context, both SUs and PUs are susceptible in this spectrum-sharing environment, a potential threat arises in the form of an eavesdropper who seeks to exploit the secrecy of messages exchanged among SUs. Given this setting, we derive a power allocation policy for the SUs, formulate closed-form expressions for outage probability (OP) and intercept probability (IP) to assess system performance, and analyze the trade-off between reliability and security. Finally, we supplement our findings with numerical examples and discussions to illustrate the proposed concepts. © 2023 ACM.

关键词： Economic and social effects

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EOG acquisition system based on ATmega AVR microcontroller

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16589-16605页

作者： Abdel-Samei, Abdel-Gawad A. Shaaban, Abdel-Salam Brisha, Ayman M. El-Samie, Fathi E. Abd Ali, Ahmed S. Department of Electronics Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Process of Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Mechanical Engineering Mechatronics Division Faculty of Engineering Assiut University Assiut Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes. © 2023, The Author(s).

关键词： Electrodes

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Improved Accuracy of Decision Tree with XGBoost Technique in Cat Skin Disease Diagnosis 4

Improved Accuracy of Decision Tree with XGBoost Technique in...

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4th International Conference of Science and information technology in Smart Administration, ICSINTESA 2024

作者： Safrina, Nanda Lydia, Maya Silvi Nasution, Benny Benyamin Universitas Sumatera Utara Master of Informatics Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350376111

This research aims to develop an expert system for initial diagnoses of skin diseases in cats using the Decision Tree method. It assists cat owners in identifying skin diseases based on observed symptoms. Data from expert veterinarian drh. Ismi Azima, including various types of cat skin diseases and their symptoms, were used. The research employs 80% training data and 20% testing data to develop the classification model. Testing results indicate the Decision Tree model achieves up to 90% average accuracy in diagnosing cat skin diseases based on 50 datasets. This expert system aids in diagnosis and provides treatment and prevention solutions, beneficial for cat owners, especially in areas with limited veterinary access. In conclusion, this Decision Tree-based expert system effectively provides initial diagnoses of skin diseases in cats with high accuracy. © 2024 IEEE.

关键词： Cat Skin Diseases Decision Tree Expert System XGBoost

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Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification

Contrastive Masked Autoencoders for Character-Level Open-Set...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Jiang, Xiaowei Ma, Wenhao Duan, Yiqun Do, Thomas Lin, Chin-Teng Australian Ai Institute School of Computer Science University of Technology GrapheneX-UTS Human-centric Ai Centre Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9781665410205

In the realm of digital forensics and document authentication, writer identification plays a crucial role in determining the authors of documents based on handwriting styles. The primary challenge in writer-id is the 'open-set scenario', where the goal is accurately recognizing writers unseen during the model training. To overcome this challenge, representation learning is the key. This method can capture unique handwriting features, enabling it to recognize styles not previously encountered during training. Building on this concept, this paper introduces the Contrastive Masked Auto-Encoders (CMAE) for Character-level Open-Set Writer Identification. We merge Masked Auto-Encoders (MAE) with Contrastive Learning (CL) to simultaneously and respectively capture sequential information and distinguish diverse handwriting styles. Demonstrating its effectiveness, our model achieves state-of-the-art (SOTA) results on the CASIA online handwriting dataset, reaching an impressive precision rate of 89.7%. Our study advances universal writer-id with a sophisticated representation learning approach, contributing substantially to the ever-evolving landscape of digital handwriting analysis, and catering to the demands of an increasingly interconnected world. © 2024 IEEE.

关键词： Contrastive Learning

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