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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Bormon, Sajib Prova, Jebin Ahmed Ahmad, Md Hasan Rimon Sohag, Sohanur Rahman Shishir, Hasanur Rahman Daffodil International University Department of Computer Science and Engineering Ashulia Dhaka Bangladesh Noakhali Science and Technology University Department of Software Engineering Noakhali Bangladesh

ISBN: (纸本)9798331529765

False news spreads quickly due to the extensive distribution of incorrect or misleading information across digital channels, which is a global problem. This bias undermines the credibility of information, promotes the spread of misleading information. Utilizing machine learning and deep learning models this study examines the detection of inaccurate information. Natural Language Processing methods are used for data preprocessing on the text datasets. Five machine learning models are used in this study: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Passive Aggressive Classifier. After preprocessing, the accuracy values of these models are 95%, 99.7%, 99.3%, 98.9%, and 99.5%, respectively. The study demonstrates that the decision tree model has superior performance, with the LSTM algorithm following closely after. The PAC and LSTM model have also demonstrated commendable results of 99.5% accuracy. The accuracies offer valuable information regarding the constraints of each model, aiding researchers in selecting suitable ways for identifying counterfeit news. The findings assist individuals in making informed decisions on the establishment of precise and effective ways for identifying false information, which is essential to maintaining the integrity of information distribution. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of different pseudo-linear estimators for vision-based target motion estimation

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Control Theory and technology 2023年第3期21卷 448-457页

作者： Zian Ning Yin Zhang Shiyu Zhao Department of Computer Science and Technology Zhejiang UniversityHangzhou 310058ZhejiangChina School of Engineering Westlake UniversityHangzhou 310024ZhejiangChina

Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target *** this paper,we focus on a problem that is very specific yet we believe *** is,from the vision measurements,we can formulate various *** and how the measurements should be used?These problems are very fundamental,but we notice that practitioners usually do not pay special attention to them and often make *** by this,we formulate three pseudo-linear measurements based on the bearing and angle measurements,which are standard vision measurements that can be *** estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical *** is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based *** the variance of the original measurement noise is unknown,the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.

关键词： Pseudo-linear measurements Kalman filter Least-squares estimator Vision-based target motion analysis Fisher information

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NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

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Blockchain and Machine Learning Integration for Secure Data Exchange in Modern Digital Ecosystems

Blockchain and Machine Learning Integration for Secure Data ...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Chandhok, Gunita Arun Vijayakumar, N. Pundir, Sumit Patjoshi, Pramod Kumar Swaroop, Rupayali Chaya Kumari, H.A. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Karpagam College of Engineering Department of Computer Science and Technology Tamil Nadu Coimbatore India Graphic Era University Department of Computer Science and Engineering Uttarakhand India Centurion University of Technology and Management School of Management Odisha Khurda India Jecrc University Department of Computer Science Engineering Rajasthan Jaipur India Vidyavardhaka College of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9798350309140

In response to the growing demand for safe data exchange in modern digital ecosystems, the study analyzes the combination of blockchain with machine learning, proposing a unique framework to solve the limitations of existing systems. Existing systems have drawbacks such as compromised data integrity, vulnerability to illegal access, and a lack of fundamental trust mechanisms. The proposed system combines the reliability of blockchain with the analytical capabilities of machine learning, strengthening data security and privacy. Using blockchain's decentralized ledger provides transparency and integrity, while machine learning algorithms contribute predictive analytics and anomaly identification. Through extensive testing and analysis, the proposed system indicates significant improvements in avoiding unwanted access and maintaining data accuracy. The results highlight the revolutionary potential of the integration across multiple industries, minimizing the constraints of outdated technologies and building a stronger and trustworthy environment for data exchange. The study offers critical insights to the growing environment of secure data exchange, paving the way for companies to embrace a future where privacy and dependability are essential. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid UNet Model Incorporating Attention Mechanism for Adaptive Brain Tumor Magnetic Resonance Segmentation 7

A Hybrid UNet Model Incorporating Attention Mechanism for Ad...

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7th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： He, Weihua Gopalakrishnan, Anilkmar Kothalil Vincent Mar School of Engineering Science and Technology Assumption University Department of Computer Science Bangkok10240 Thailand

ISBN: (纸本)9798350370782

The segmentation of medical images is crucial, particularly in brain tumor MR imaging, as it aids doctors in accurate diagnosis and treatment planning. However, conventional UNet models often face limitations due to their inability to capture intricate features, resulting in suboptimal segmentation performance. This study addresses these challenges by proposing a UNet model enhanced with a Convolutional Block Attention Module (CBAM). This advanced network leverages spatial and channel attention mechanisms that work together to focus more effectively on important image regions, refining feature representation. Experiments conducted on the TCGA brain tumor MR dataset demonstrate that our proposed UNet+CBAM model outperforms the standard UNet, achieving a 2.5% improvement in the Dice coefficient. This significant enhancement underscores the efficacy of integrating attention mechanisms into medical image segmentation. We conclude that our UNet+CBAM model offers an improved and reliable solution for brain tumor segmentation, potentially advancing its clinical application. © 2024 IEEE.

关键词： Magnetic resonance imaging

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AI-Driven Mycology: Transfer Learning for Automated Mushroom Species Identification

AI-Driven Mycology: Transfer Learning for Automated Mushroom...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Ahmad, Md Hasan Rahman Sohag, Sohanur Rimon Bormon, Sajib Shishir, Hasanur Rahman Hossain Antar, Sabbir Daffodil International University Department of Computer Science and Engineering Ashulia Dhaka Bangladesh Noakhali Science and Technology University Department of Software Engineering Noakhali Bangladesh

ISBN: (纸本)9798331529765

Mushroom categorization is a difficult process since there are so many different species and they all have different aesthetic qualities. In this paper, we are to investigate the use of transfer learning techniques for precise mushroom classification. We have examined the performance of EfficientNetB0, ResNet50, InceptionV3, and MobileNet, four well-known transfer learning models. The mushroom dataset is first preprocessed to make sure that the images are properly scaled and normalized. After initializing and fine-tuning the pre-trained weights of the above-mentioned models on our mushroom dataset, we then apply transfer learning techniques. Each model is trained using a carefully chosen training set, and its performance is assessed using a different test set. Our findings reveal that all four transfer learning models successfully categorize mushrooms, achieving outstanding classification accuracies. With a 97% accuracy rate, EfficientNetB0 surpasses the other models when we compare them. Its sophisticated design, which combines compound scaling and AutoML approaches to attain the best accuracy and efficiency, is responsible for its exceptional performance. The results of this study demonstrate the value of transfer learning in tasks involving mushroom categorization. Additionally, they stress the benefit of using cutting-edge models like EfficientNetB0, which produce greater accuracy compared to conventional transfer learning architectures. The development of automated methods for mushroom identification and subsequent breakthroughs in the mycology field can be facilitated by the use of these findings by academics and professionals working on mushroom classification. © 2024 IEEE.

关键词： Adversarial machine learning

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The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

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Neural Computing and Applications 2024年第11期36卷 5757-5797页

作者： Amiri, Zahra Heidari, Arash Navimipour, Nima Jafari Esmaeilpour, Mansour Yazdani, Yalda Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Computer Engineering Department Hamedan Branch Islamic Azad University Hamedan Iran Immunology Research Center Tabriz University of Medical Sciences Tabriz Iran

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. © The Author(s

关键词： Internet of things

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Faster AMEDA-A Hybrid Mesoscale Eddy Detection Algorithm

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computer Modeling in engineering & sciences 2024年第11期141卷 1827-1846页

作者： Xinchang Zhang Xiaokang Pan Rongjie Zhu Runda Guan Zhongfeng Qiu Biao Song Organization Department Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Teacher Education Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics&Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.

关键词： Mesoscale eddy detection object detection contour detection remote sensing

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Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

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computers, Materials & Continua 2022年第2期70卷 2563-2579页

作者： D.Stalin David S.Arun Mozhi Selvi S.Sivaprakash P.Vishnu Raja Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan Department of Computer Science and Engineering IFET College of EngineeringVillupuram605108Tamil NaduIndia Department of Computer Science Engineering DMI St.John the Baptist UniversityMangochiLilongweMalawi Department of Information Technology CMR Engineering College(Autonomous)Hyderabad501401TelanganaIndia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Irretrievable loss of vision is the predominant result of Glaucoma in the ***,multiple approaches have paid attention to the automatic detection of glaucoma on fundus *** to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed *** Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary *** research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting *** SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed *** is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood *** ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative *** terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.

关键词： Glaucoma and diabetic retinopathy detection ensemble convolutional neural network spatially based ellipse fitting curve optic disk optic cup

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Enhancing Crypto Transaction Security: A Machine Learning Approach 10

Enhancing Crypto Transaction Security: A Machine Learning Ap...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mekala Rani, T.M.S. Suresh, A. Bhargavi, S. Vardhan Reddy, M. Harsha Sai Nikhil, K. Priya, G. Chandu Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India Andhra Pradesh India

ISBN: (纸本)9798350353778

The project aims to enhance the security of cryptocurrency transactions through the implementation of advanced machine learning methodologies that are RNN(recurrent neural networks),LSTM,VGG16. We aim to create a strong and flexible system that can recognize and stop fraudulent activity in real-time by utilizing machine learning techniques. By utilizing dataset containing the features related to average time between sent and recived transactions, amount involved. This project focuses on creating a proactive defense mechanism that can analyze transaction patterns, detect anomalies, and promptly alert users or intervene to mitigate potential threats. By combining cutting-edge machine learning techniques with the ever-evolving landscape of cryptocurrency security, this project aims to safeguard digital assets and promote trust in the rapidly growing crypto ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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