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Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

Computers, Materials & Continua 2022年第9期72卷 5301-5314页

作者： Muhamad Erza Aminanto Ibnu Rifqi Purbomukti Harry Chandra Kwangjo Kim School of Strategic and Global Studies Universitas IndonesiaDepok16424Indonesia National Institute of Information and Communications Technology(NICT) Koganei184-8795Japan Department of Electrical Engineering and Information Technology Universitas Gadjah MadaYogyakarta55281Indonesia School of Computing Korea Advanced Institute of Science and Technology(KAIST)Daejeon34141Korea

Internet of Things(IoT)networks leverage wireless communication protocols,which adversaries can *** attacks,injection attacks,and flooding are several examples of different attacks existing in Wi-Fi *** Detection System(IDS)became one solution to distinguish those attacks from benign *** learning techniques have been intensively utilized to classify the ***,the main issue of utilizing deep learning models is projecting the data,notably tabular data,into an *** study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network(CNN)models,*** define the particular sequence of placing the attribute values in a grid that would be captured as an *** the most important subset of attributes and EfficientNet,we aim for an accurate and lightweight IDS module deployed in IoT *** examine the proposed model using the Wi-Fi attacks dataset,called the AWID2 *** achieve the best performance by a 99.91%F1 score and 0.11%false-positive *** addition,our proposed model achieved comparable results with other statistical machine learning models,which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy.

关键词： Intrusion detection impersonation attack convolutional neural network anomaly detection

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Secure Traffic Data Sharing in UAV-Assisted VANETs 19th

Secure Traffic Data Sharing in UAV-Assisted VANETs

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19th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Liu, Yilin Wang, Yujue Yi, Chen Ding, Yong Yang, Changsong Wang, Huiyong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310000 China Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Institute of Cyberspace Technology HKCT Institute for Higher Education 999077 Hong Kong

ISBN: (纸本)9783031545276

Aiming at the issues of low comprehensiveness and timeliness of data, difficulty in balancing data anonymity and traceability, and challenges of securely storing massive data in traditional traffic data sharing systems, this paper proposes a UAV-VANET integrated system (UVIS) based on consortium blockchain. The UAV integrated into the VANET can promptly provide drivers and traffic managers with comprehensive traffic information and images for traffic planning, thus enhancing transportation efficiency and safety. To achieve traceability of anonymous data sharing, we introduce a proxy re-encryption mechanism to realize precise data access control, which can not only protect data and identity privacy but also trace the true identity of malicious users. Additionally, it effectively prevents the collusion between proxies and data requesters from stealing unauthorized confidential information. To alleviate the pressure of traffic data storage, we adopt a storage method that combines blockchain and IPFS, ensuring secure storage of massive data. Security analysis shows that the UVIS has achieved secure sharing of traffic data. We analyze its efficiency theoretically, and demonstrate the practicality through experiments. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Metadata

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Early Retinal Degeneration Detection with Deep Faster CNNs 2

Early Retinal Degeneration Detection with Deep Faster CNNs

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Priyadharshini, K. Balraj, Sindhusaranya Kumar, J.V. Rama Vidhate, Deepak A. Thilakavathy, P. Vanathi, P. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India SRKR Engineering College Andhra Pradesh Bhimavaram India Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Maharashtra Ahmednagar India Vels Institute of Science Technology and Advanced Studies Chennai India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (纸本)9798350383867

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included . This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration;the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large. ©2024 IEEE.

关键词： Ophthalmology

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Fast Spectral Clustering Based on Fusion Density Peaks 7

Fast Spectral Clustering Based on Fusion Density Peaks

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Gao, Yi Luo, Rong Yang, Wu Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China Southwest Jiaotong University School of Mathematics Chengdu China Southwest Jiaotong University School of Information Science and Technology Chengdu China

ISBN: (纸本)9798350352009

In recent years, Spectral Clustering and its variants have attracted much attention due to their excellent clustering performance. These algorithms first build a graph on the data based on local similarity, then compute the spectral decomposition of the Laplacian matrix of the graph, and finally achieve clustering by performing K-Means on embeddings obtained from spectral decomposition. However, the traditional spectral clustering method is difficult to deal with large-scale datasets. The large size of data will make the spectral decomposition slow and consume a lot of computational resources. To solve these problem, this paper first proposes a clustering algorithm named FGDPSC. FGDP-SC identifies global density peak points of the data as anchors in multi-scales defined by near-field, mid-field, and farfield, characterizes the data using a bipartite graph with anchors, and performs clustering efficiently through the transfer-cut of the bipartite graph. Experiments on real-world data validate the effectiveness of the proposed algorithms, especially on largescale data, by outperforming the classical Spectral Clustering algorithm and its main variants. © 2024 IEEE.

关键词： Graph algorithms

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Automotive RADAR Human Classification Algorithm Through Simulation Analysis: Basics and Practical Challenges 2nd

Automotive RADAR Human Classification Algorithm Through Simu...

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2nd International Conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2021

作者： James, Anto Jeyaprabu Jayavel, Kayalvizhi Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Information Technology School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789811698842

In the automotive domain, Advanced Driver-Assistance System (ADAS) sensors are game changers which bring science fiction movies into real life with the help of Automotive Radar and imaging sensors. This paper will summarize the types of radar, algorithms, and its features from the Advanced Signal Processing (ADSP) concepts. Especially in the urban roads, vehicle drivers need to make a quick decision on pedestrian classification behind other objects. Also, the pedestrians’ unique actions need to be predicted while crossing the road. There are multiple reasons where radars are unable to make a right decision, but behind the obstacles/objects scenario the Emergency Brake Assist (EBA) feature is more sophisticated to make a perfect decision better than humans (drivers). This paper will assist the researchers to visualize the open points behind the pedestrian nature and possible ways to improve the EBA feature. Also, this paper will help researchers to find the suitable algorithms and hardware from the automotive market. Hope this paper will also bridge the gap between academic researchers and industrial trends. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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A Novel Iris Segmentation Approach Using Spindle Ternary Tree 12

A Novel Iris Segmentation Approach Using Spindle Ternary Tre...

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12th International Conference on information technology in Medicine and Education, ITME 2022

作者： Lin, Yuhui Ye, Zubin Xiao, Baosen Fuzhou Institute of Technology School of Computing and Information Science Fuzhou China Xiamen University Tan Kah Kee College School of Information Science and Technology Zhangzhou China

ISBN: (纸本)9798350310153

This paper presents an accurate and novel iris segmentation approach. First rough iris region is extracted by boosted Local Binary Pattern features classifier. Follow Intensity gradient matrix is then built though gray gradient operator under polar coordinate. Then Special Ternary Tree model is proposed to handle the matrix for tracking iris boundaries by searching the best route as iris boundary. Comparatively, experimental results on the popular iris databases demonstrate that the proposed approach is accurate, efficient and robust. © 2022 IEEE.

关键词： Local binary pattern

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Binary Classification of Mucormycosis Infection Severity using Transfer Learning with VGG16 2

Binary Classification of Mucormycosis Infection Severity usi...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Sugunadevi, C. Maheswari, B. Uma Cmr Institute of Technology Bengaluru Department of Information Science and Engineering Karnataka India Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350308846

Mucormycosis, one of the significant fungal illnesses caused by the fungus Mucormycetes, is a rare and fatal disease. It is found all over the environment. People with weakened immune systems are more prone to get sick rapidly, including those with diabetes or COVID-19 history. Mucormycosis can affect the eyes and quickly spread to the brain if it enters through the nose, sinuses, or lungs. Existing research in the medical domain shows that deep learning techniques are a promising solution for assisting medical practitioners in making quick decisions. This study aims to use transfer learning with a predefined VGG16 model to create a classifier that can distinguish between mild and severe Mucormycosis illness symptoms. The neural network is trained, validated, and tested by loading black fungus dataset. The accuracy is calculated by varying the number of images. The results show that the proposed model gives an accuracy of 92% for 520 input images. © 2023 IEEE.

关键词： Deep Learning Mucormycosis Transfer Learning VGG16

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Caption Royale: Exploring the Design Space of Afective Captions from the Perspective of Deaf and Hard-of-Hearing Individuals

Caption Royale: Exploring the Design Space of Afective Capti...

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2024 CHI Conference on Human Factors in computing Sytems, CHI 2024

作者： de Lacerda Pataca, Caluã Hassan, Saad Tinker, Nathan Peiris, Roshan L. Huenerfauth, Matt Computing and Information Science Rochester Institute of Technology RochesterNY United States Department of Computer Science Tulane University New OrleansLA United States Department of asl and Interpreting Education Rochester Institute of Technology RochesterNY United States School of Information Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9798400703300

Affective captions employ visual typographic modulations to convey a speaker's emotions, improving speech accessibility for Deaf and Hard-of-Hearing (dhh) individuals. However, the most effective visual modulations for expressing emotions remain uncertain. Bridging this gap, we ran three studies with 39 dhh participants, exploring the design space of affective captions, which include parameters like text color, boldness, size, and so on. Study 1 assessed preferences for nine of these styles, each conveying either valence or arousal separately. Study 2 combined Study 1's top-performing styles and measured preferences for captions depicting both valence and arousal simultaneously. Participants outlined readability, minimal distraction, intuitiveness, and emotional clarity as key factors behind their choices. In Study 3, these factors and an emotion-recognition task were used to compare how Study 2's winning styles performed versus a non-styled baseline. Based on our findings, we present the two best-performing styles as design recommendations for applications employing affective captions. © 2024 Copyright held by the owner/author(s)

关键词： Emotion Recognition

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Glioma Brain Tumor Classification using Transfer Learning 10

Glioma Brain Tumor Classification using Transfer Learning

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10th International Conference on Engineering, Applied sciences, and technology, ICEAST 2024

作者： Pilaoon, Pongsak Narkthewan, Athasart Wadlom, Noppanat Varakulsiripunth, Ruttikorn Hamamoto, Kazuhiko Maneerat, Noppadol King Mongkut's Institute Of Technology Ladkrabang School Of Engineering Bangkok10520 Thailand King Mongkut's Institute Of Technology Ladkrabang Department Of Engineering Prince of Chumphon Campus 86160 Thailand Korea Advanced Institute Of Science And Technology School Of Computing Daejeon34141 Korea Republic of Thai-Nichi Institute Of Technology Bangkok10250 Thailand Tokai University School Of Information Science And Technology Department Of Information Media Technology 4-1-1 Kitakaname Kanagawa Hiratsuka-shi259-1292 Japan

ISBN: (纸本)9798350385946

In this research the glioma brain tumor binary classification using transfer learning was introduced. The MRI image from 2 datasets comprised with REMBRANDT and BraTS2021 with increasing number of MRI images was proposed to prevent overfitting problem. MRI images were converted to JPEG format and heavily imbalanced with normal brain image is minority class. Morphological operation was used to remove skeletons and artifacts from brain region. We have introduced Contrast Limited Adaptive Histogram Equalization to preprocess and enhance contrast before classify using various CNNs. To handle imbalanced dataset problem, we proposed image augmentation to increase the number of images and obtain balanced dataset. The various CNNs transfer learning was implemented to classify glioma brain tumor. Finally, the best classifier is InceptionV3 with balanced dataset that obtained accuracy 99.19%, sensitivity 98.83%, and specificity 100% respectively, better than our past research work. © 2024 IEEE.

关键词： Brain

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Machine Learning-Based Crack Detection Methods in Ancient Buildings 24

Machine Learning-Based Crack Detection Methods in Ancient Bu...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P. Yang, Aihua Liu, Bin He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China College of Software Engineering Xiamen University of Technology Xiamen Fujian361024 China

ISBN: (纸本)9798400717147

This research paper introduces an innovative machine learning-based methodology for detecting cracks in ancient buildings, a vital aspect of architectural heritage conservation. Recognizing the limitations of conventional techniques in addressing the complex nature of historical structures, this study explores the integration of deep learning algorithms with enhanced image processing methods tailored for aged and varied materials typical of ancient architecture. The paper includes a comprehensive process involving meticulous data collection from diverse ancient structures, employing specialized preprocessing methods to handle the unique challenges of old surfaces. The development of a custom machine learning model, designed to adapt to the intricacies of historical construction, is thoroughly detailed. Experimental results demonstrate the model's superior performance in accurately identifying structural cracks compared to traditional methods. This research not only significantly contributes to the preservation of historical buildings but also exemplifies the versatile application of machine learning in the field of architectural conservation. The findings hold promise for revolutionizing the approach to maintaining and restoring our architectural heritage, ensuring longevity and integrity. © 2024 ACM.

关键词： Image enhancement

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