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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jain, Alok Sangeeta, K. Swathi, B. Sadim, Mohd. Dwivedi, Shashi Prakash Albawi, Ali Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P. Bhopal India Lloyd Institute of Engineering & Technology Knowledge Park Ii Uttar Pradesh Greater Noida India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350305463

Bad actors threaten medical imaging AI systems' dependability and safety, affecting patient care and diagnosis accuracy. A novel defense against this expanding threat is adversarial defense via ensemble integration. Explainable Feature-Based Defense (XFBD), Adversarial Training with Transfer Learning (ATTL), and Robust Classifier Augmentation (RCA) are three innovative techniques that have never been combined. RCA enhances training with controlled aggression. This allows the model to distinguish authentic medical imaging data from altered sources. ATTL makes transfer-learning-taught models more resistant to topic-specific hostile approaches. XFBD simplifies the defensive process, helping us comprehend how the model picks and fights different methods. A comparison indicates that ADEI always outperforms tried-and-true approaches. ADEI outperforms typical approaches in accuracy, sensitivity, precision, stability, interpretability, private protection, and computing cost. Strong safeguards are needed as AI-powered healthcare research becomes increasingly popular. As a lighthouse, ADEI protects everything from attack. The math behind each section and how they work together to strengthen the system make it valuable. ADEI advances AI-assisted healthcare's future. Testing instruments must be accurate and dependable in this industry. © 2024 IEEE.

关键词： medical imaging

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Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net

Brain Tumor Classification and Segmentation Using Dual-Outpu...

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21st International Conference on informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Zargari, Seyed Aman Kia, Zahra Sadat Nickfarjam, Ali Mohammad Hieber, Daniel Holl, Felix Electrical Engineering Department Faculty of Electrical and Computer Engineering Semnan University Semnan Iran Department of Health Information Management and Technology School of Allied Medical Sciences Kashan University of Medical Science Kashan Iran Health Information Management Research Center Kashan University of Medical Science Kashan Iran Institute DigiHealth Neu-Ulm University of Applied Sciences Neu-Ulm Germany

ISBN: (纸本)9781643684000

We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net's up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net's up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors. © 2023 The authors and IOS Press.

关键词： Tumors

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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

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computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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Comparative Analysis of the Use of Variational Autoencoders and Generative Adversarial Networks for Anomalous Image Detection Tasks 7

Comparative Analysis of the Use of Variational Autoencoders ...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Lytvynenko, Volodtmyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution Kundiiev Institute of Occupational Health National Academy of Medical Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331534141

This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the models is conducted using metrics such as AUC, Precision, Recall, and F1-Score. The results indicate that VAEs provide high Recall in anomaly detection but with low Precision, whereas WGANs demonstrate more balanced results with fewer false positives. Recommendations are proposed for selecting an appropriate model depending on the specific task requirements and the need to minimize false alarms or achieve high detection completeness. © 2024 IEEE.

关键词： Generative adversarial networks

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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Hoomod, Haider K. Al-Hamami, Mohammad Alaa Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Niaf, Jolan Rokan College of Education Mustansiriyah University Baghdad Iraq Applied Science University Manama Department of Management Information Systems Manam Bahrain Imam Al-Kadhim College Computer Techniques Engineering Department Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9798350369106

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network. © 2024 IEEE.

关键词： Middleware

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Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer:a comprehensive comparative study

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Intelligent Medicine 2024年第2期4卷 114-127页

作者： Ao Chen Chen Li Md Mamunur Rahaman Yudong Yao Haoyuan Chen Hechen Yang Peng Zhao Weiming Hu Wanli Liu Shuojia Zou Ning Xu Marcin Grzegorzek Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110169China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Arts and Design Liaoning Petrochemical UniversityFushunLiaoning 113001China Institute of Medical Informatics University of LübeckLüebeckGermany

Background With the gradual increase of infertility in the world,among which male sperm problems are the main factor for infertility,more and more couples are using computer-assisted sperm analysis(CASA)to assist in the analysis and treatment of ***,the rapid development of deep learning(DL)has led to strong results in image classification ***,the classification of sperm images has not been well studied in current deep learning methods,and the sperm images are often affected by noise in practical CASA *** purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm *** The SVIA dataset is a publicly available large-scale sperm dataset containing three *** this work,we used subset-C,which provides more than 125,000 independent images of sperms and impurities,including 121,401 sperm images and 4,479 impurity *** investigate the anti-noise robustness of deep learning classification methods applied on sperm images,we conducted a comprehensive comparative study of sperm images using many convolutional neural network(CNN)and visual transformer(VT)deep learning methods to find the deep learning model with the most stable anti-noise *** This study proved that VT had strong robustness for the classification of tiny object(sperm and impurity)image datasets under some types of conventional noise and some adversarial *** particular,under the influence of Poisson noise,accuracy changed from 91.45%to 91.08%,impurity precison changed from 92.7%to 91.3%,impurity recall changed from 88.8%to 89.5%,and impurity F1-score changed 90.7%to 90.4%.Meanwhile,sperm precision changed from 90.9%to 90.5%,sperm recall changed from 92.5%to 93.8%,and sperm F1-score changed from 92.1%to 90.4%.Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods;the robustness with regard to noise of VT methods based

关键词： computer-assisted sperm analysis Anti-noise Robustness Deep learning .Image classification Sperm image Conventional noise Adversarial attacks Convolutional neural network Visual transformer

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Optimizing Query Performance in Big Data Systems Using Machine Learning Algorithms 2

Optimizing Query Performance in Big Data Systems Using Machi...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thirupurasundari, D.R. Rajesh Kumar, K. Palani, Hari Kumar Ilangovan, S. Senthilvel, P. Gururama Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India Madha Engineering College Kundrathur Department of Electronics and Communication Engineering TamilNadu Chennai India School of Engineering Ajeenkya D y Patil University India K.L.N. College of Engineering Department of Information Technology Tamilnadu Pottapalayam630612 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350369960

The retrieval of information from extensive datasets in an efficient manner is critical in the era of big data. Conventional optimization methods encounter difficulties in managing the multitude of intricate and varied queries that big data systems confront. To optimize query efficacy in big data systems, this article introduces an innovative strategy that uses machine learning (ML) algorithms. To forecast optimal query execution strategies, our methodology entails training machine learning models using historical query execution data. The methodology consists of two components: First, characteristics are extracted from query plans, encompassing query patterns and statistics pertaining to the underlying data. Furthermore, to forecast the efficacy of a specific strategy, ML models, including decision trees, neural networks, and gradient-boosting machines, are trained using these characteristics. The findings were persuasive. In particular, for intricate queries, our ML-based optimizer enhanced query performance by as much as 60 % when compared to conventional optimizers. Changing duties and data distributions were also accommodated by our system, demonstrating its robustness and adaptability. Our research establishes a foundation for an emerging class of adaptive query optimizers capable of acquiring knowledge and adjusting to the dynamic characteristics of data and queries. By integrating machine learning capabilities with big data systems, we present a novel solution that significantly enhances the speed and efficiency of data retrieval. © 2023 IEEE.

关键词： Adaptive Query Planning Big Data Machine Learning Performance Improvement Query Optimization

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SECURING COMPLEX IOT PLATFORMS WITH TOKEN BASED ACCESS CONTROL AND AUTHENTICATED KEY ESTABLISHMENT

SECURING COMPLEX IOT PLATFORMS WITH TOKEN BASED ACCESS CONTR...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： William, P. Kotiyal, Arnav Al-Jawahry, Hassan M. Shrivastava, Anurag Yadav, Kanchan Aswini, Kilaru Department of Information Technology Sanjivani College of Engineering SPPU Kopargaon Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Electrical Engineering GLA University Mathura India Institute of Aeronautical Engineering Dundigal Hyderabad India

ISBN: (纸本)9798350355338

The functionality of a security architecture for advanced Internet of Things (IoT) devices is discussed in this article. Validated key configuration and access control that is based on tokens are absolutely necessary in order to guarantee the safety of this idea. In order to keep up with the rapid proliferation of the IoT ecosystem, it is imperative that the availability, integrity, and security of data be maintained at all times. Token-based access control is an opportunity to provide a workable solution to one of the most common concerns raised about the safety of the internet of things (IoT). This article's objective is to improve the safety of the Internet of Things (IoT) by analyzing the guiding principles, implementation tactics, and benefits offered by a variety of security measures that may be included into complex IoT systems. In this article, we explain how token-based access control and verified key generation may be merged into a single, all-encompassing method to strengthen the safety of platforms used for the internet of things (IoT). Token-based access control provides flexibility and adaptability when it comes to regulating rights in dynamic settings that are linked to the Internet of Things. In order to shield oneself from potential dangers such as man-in-the-middle attacks, one must take the necessary precautions, such as generating authorized keys to keep the privacy of one's communication routes secret. This research uses performance measurements, comparisons of code sizes, and statistical analysis to evaluate and examine the efficacy and efficiency of a variety of different security methods. The findings demonstrate how readily the recommended methodology may be adapted to meet the requirements of a wide variety of Internet of Things (IoT) systems. This paper is a helpful resource for enterprises and developers who work in dynamic situations and are interested in strengthening the security of Internet of Things deployments. © 2024 IEEE.

关键词： Access control

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Classification of diabetes by kernel based SVM with PSO

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2021年第4期14卷 1242-1255页

作者： Choubey, Dilip K. Tripathi, Sudhakar Kumar, Prabhat Shukla, Vaibhav Dhandhania, Vinay K. Department of Computer Science & Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Information Technology Rajkiya Engineering College Ambedkar Nagar India Depatment of Computer Science and Engineering National Institute of Technology Patna India Department of Computer Science and Engineering Tech Mahindra Mumbai Maharasthra India Department of Diabetology Bombay Medical Hall Upper Bazar Ranchi India

Background: The classification method is required to deduce possible errors and assist the doctors. These methods are used to take suitable decisions in real world applications. It is well known that classification is an efficient, effective and broadly utilized strategy in several applications such as medical diagnosis, etc. The prime objective of this research paper is to achieve an efficient and effective classification method for Diabetes. Methods: The proposed methodology comprises two phases: The first phase deals with t h e description of Pima Indian Diabetes Dataset and Localized Diabetes Dataset, whereas in the second phase, the dataset has been processed through two different approaches. Results: The first approach entails classification through Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel and Linear Kernel SVM on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, PSO has been utilized as a feature reduction method followed by using the same set of classification methods used in the first approach. PSO_Linear Kernel SVM provides the highest accuracy and ROC for both the above mentioned dataset. Conclusion: The present work consists of a comparative analysis of outcomes w.r.t. performance assessment has been done PSO and without PSO for the same set of classification methods. Finally, it has been concluded that PSO selects the relevant features, reduces the expense and computation time while improving the ROC and accuracy. The used methodology could be implemented in other medical diseases. © 2021 Bentham science Publishers.

关键词： Classification (of information)

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