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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Habib, Md Selim Department of Data Science and Business Analytics Florida Polytechnic University LakelandFL33805 United States Department of Aerospace and Geodesy Technical University of Munich Munich80333 Germany Department of Electrical Engineering and Computer Science Florida Institute of Technology FL32901 United States

A supervised machine learning framework is implemented to predict the propagation loss of randomly structured nested hollow-core anti-resonant fiber for the first time. The random forest classifier outperforms other m... 详细信息

关键词： Adversarial machine learning

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CS2023: computer science Curriculum Guidelines: A New Liberal Arts Perspective

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ACM Inroads 2025年第1期16卷 40-52页

作者： Holland-Minkley, Amanda Barnard, Jakob E. Barr, Valerie Braught, Grant Davis, Janet Reed, David Schmitt, Karl Tartaro, Andrea Teresco, James D. Department of Computing and Information Studies Washington & Jefferson College 60 S. Lincoln St. WashingtonPA15301 United States Department of Computer Science and Technology University of Jamestown 6401 College Ln. JamestownND58405 United States Department of Computer Science Bard College 30 Campus Road Annandale-on-HudsonNY12504 United States Department of Computer Science Dickinson College P.O. Box 1773 CarlislePA17013 United States Department of Computer Science Whitman College 345 Boyer Ave. Walla WallaWA99362 United States Department of Computer Science Design & Journalism Creighton University Hitchcock 209 OmahaNE68178 United States Department of Computing and Data Analytics Trinity Christian College 6601 West College Drive Palos HeightsIL60463 United States Department of Computer Science Furman University 3300 Poinsett Hwy GreenvilleSC29690 United States Department of Computer Science Siena College 515 Loudon Rd. LoudonvilleNY12211 United States

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Prediction models

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Enhanced Multimodal Hate Video Detection via Channel-wise and Modality-wise Fusion

Enhanced Multimodal Hate Video Detection via Channel-wise an...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yinghui Zhang Tailin Chen Yuchen Zhang Zeyu Fu Department of Computer Science University of Exeter UK Institute for Analytics and Data Science University of Essex UK

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

The rapid rise of video content on platforms such as TikTok and YouTube has transformed information dissemination, but it has also facilitated the spread of harmful content, particularly hate videos. Despite significant efforts to combat hate speech, detecting these videos remains challenging due to their often implicit nature. Current detection methods primarily rely on unimodal approaches, which inadequately capture the complementary features across different modalities. While multimodal techniques offer a broader perspective, many fail to effectively integrate temporal dynamics and modality-wise interactions essential for identifying nuanced hate content. In this paper, we present CMFusion, an enhanced multimodal hate video detection model utilizing a novel Channel-wise and Modality-wise Fusion Mechanism. CMFusion first extracts features from text, audio, and video modalities using pre-trained models and then incorporates a temporal cross-attention mechanism to capture dependencies between video and audio streams. The learned features are then processed by channel-wise and modality-wise fusion modules to obtain informative representations of videos. Our extensive experiments on a real-world dataset demonstrate that CMFusion significantly outperforms five widely used baselines in terms of accuracy, precision, recall, and F1 score. Comprehensive ablation studies and parameter analyses further validate our design choices, highlighting the model’s effectiveness in detecting hate videos. The source codes will be made publicly available at https://***/EvelynZ10/cmfusion.

关键词： Adaptation models Video on demand Correlation Source coding Hate speech Feature extraction data models Web sites data mining Streams

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Research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Maize Plant Disease Prediction of UAV Images for Precision Agriculture Using Fusion of Multimodal 3

Maize Plant Disease Prediction of UAV Images for Precision A...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Mishra, Vinay Naik, Nenavath Srinivas Kumar, Santosh Alsamhi, Saeed Hamood Saif, Abdu Curry, Edward -Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Galway Insight Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

The agriculture sector is vital to a country's economic and productive development. With technological advancements, we can identify plant diseases in their early stages, similar to how human diseases are diagnosed, and avoid significant financial loss. For example, a new technique has been developed to detect diseases in maize crops, a widely used raw product in the food industry and precision agriculture. Furthermore, farmers ensure the fields do not remain wet for extended periods to prevent fungus infection. Currently, machine learning and deep learning approaches are being utilized, making agriculture one of the most researched topics in AI. This study proposes a novel framework for predicting maize plant disease using a fusion of multi-deep learning models. The proposed framework uses a four-stage pipeline for detecting disease in maize plants by fusion of Xception, DenseNet-121, and ensembling of Xception and DenseNet-121 models to achieve excellent results on the OSF drone image dataset, which consists of 1821 high-resolution images of maize plants taken from unmanned aerial vehicles (UAVs). The accuracy of our proposed approach is 96.95%, which is highly effective, considering the challenges posed by drone images. © 2023 IEEE.

关键词： Drones

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A Survey on Intrusion Detection System for IoT Networks Based on Artificial Intelligence

A Survey on Intrusion Detection System for IoT Networks Base...

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2023 IEEE International Conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Checker, Saksham Yadav, Madhuri Katarya, Rahul Delhi Technological University Department of Applied Physics New Delhi India Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science New Delhi India

ISBN: (纸本)9798350305111

Internet of Things(IoT) devices is the prime factor in automating the real world in the past few years. These networks provide efficient utilization of resources, quality data, and benefits with reduced human input as well as have secured processing which results in a high potential for automating the healthcare and defence systems. Noticing the large number of attacks on these networks in previous years, there is a vast study to detect the intrusion by attackers to ease the use of these devices without the fear of losing data. This survey is a deep study of works on detecting the intrusion in IoT networks and devices revolving around the use of Machine Learning and Deep Learning based algorithms on real-world datasets like 'Bot-IoT' and 'KDD'. The survey provides a building block in understanding the basics of Intrusion and also the benefits of Intrusion detection systems and thus promotes the researchers to focus more on prevention of such attacks. Furthermore, it compiles the studies from the year 2016 and compares the models of Machine Learning and Deep Learning segregated based on data sets used in training;also, summarizes the performance of such mechanisms. © 2023 IEEE.

关键词： Intrusion detection

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Image Fusion Using Wavelet Transformation and XGboost Algorithm

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computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

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Advanced VANET Technology for Emergency Vehicle Safety in Time-Critical Scenario 8

Advanced VANET Technology for Emergency Vehicle Safety in Ti...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions. © 2023 IEEE.

关键词： Object detection

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A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning Information System Using IOT Based Traffic Prediction Model

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Ear...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Murali, N. David, D. Beulah Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350334357

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design. © 2023 IEEE.

关键词： Machine learning

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