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A Deep Learning-Based Spectral Efficiency Maximization in Multiple Users Multiple STAR-RISs Massive MIMO-NOMA Networks

A Deep Learning-Based Spectral Efficiency Maximization in Mu...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Science and Engineering International University - Vietnam National University HCM City Vietnam Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we investigate a deep learning-based spectral efficiency maximization in multiple users multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks, where multiple STAR-RISs are deployed to assist a base station (BS) in communicating with multiple near and far users simultaneously. To improve spectral efficiency (SE), we formulate a joint optimization of the precoding matrix, the phase shift of reflection and transmission of the STAR-RIS problem subject to the individual quality of services (QoS) of each user, maximum power budget at BS and the discrete phase shift. The formulated problem belongs to the class of mixed-integer non-convex optimization problem, which is difficult to solve optimally. We first relax the formulated problem and then transform it into an equivalent non-convex one but with a more tractable form. We then apply the inner approximation method to approximate the non-convex parts iteratively. Towards real-time optimization, we propose a novel deep learning framework with a convolutional neural network (CNN) model to predict the downlink SE based on users’ position information and channel gains. Numerical results show that the proposed approach improves the SE compared to the massive MIMO systems with conventional RIS. Besides, the DL framework for the proposed scheme predicts the optimal SE within a short time compared to the conventional approach.

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IsoGAN: A Gan Based Method for Isometric View Images Generation From Three Orthographic Views Contour Drawings

IsoGAN: A Gan Based Method for Isometric View Images Generat...

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Image Processing Challenges (ICIPC), IEEE International Conference on

作者： Thao Nguyen Phuong Hidetomo Sakaino Vinh Nguyen Duy Data Solution Dept. (FCJ.ABC) FPT Consulting Japan AI-Image Group FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

Reconstructing the 2D or 3D shape of an object from several 2D drawings is a crucial problem in computer-aided design (CAD). Despite the advancement of deep neural networks, automatic isometric image generation from three orthographic views line drawings using deep learning remains unresolved. Existing image-to-image translation techniques often generate images from just one input image. In this paper, we propose a novel method for the above task using a GAN-based model, namely IsoGAN. This method takes three images of object’s front, side, and top view as input, then analyzes the spatial and geometrical relations between each view and finally generates the corresponding isometric view image of the object. Extensive experiments on SPARE3D dataset show promising results of IsoGAN on isometric view generation task, demonstrating the effectiveness of the proposed IsoGAN.

关键词： Deep learning Solid modeling Design automation Three-dimensional displays Image synthesis Shape Transforms Vectors Generators Image reconstruction

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Enhancing Choroidal Nevus Position Identification through CNN-Based Segmentation of Eye Fundus Images

Enhancing Choroidal Nevus Position Identification through CN...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mohammadmahdi Eshragh Emad A.Mohammed Behrouz Far Trafford Crump Ezekiel Weis Dept of Electrical & Software Engineering University of Calgary Calgary Canada Dept of Physics and Computer Science Wilfrid Laurier University Waterloo Canada Dept of Surgery University of Calgary Calgary Canada Dept of Ophthalmology & Visual Science University of Alberta Edmonton Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effective in detecting and analyzing such abnormalities with high accuracy and efficiencyThis research is part of a larger project to develop a decision support system for choroidal nevus diagnosis, focusing on creating a segmentation algorithm to identify key areas in color fundus images. The study evaluates and compares the efficacy of various convolutional neural network (CNN) segmentation models, a crucial step for improved image analysis accuracyFundus images from the Alberta Ocular Brachytherapy Program, including healthy and choroidal nevus-affected eyes, were used. An ocular oncologist provided a ground truth mask dataset for training the models. Preprocessing improved image features, and multiple CNN models segmented the images to detect lesions. Model performance was compared to find the most accurate and efficient approach, with external validation using a separate test set and ophthalmology expertsFour CNN models – U-net, Residual U-net, Attention U-net, and a voting-based Ensemble – were developed for segmentation. Their effectiveness was measured by accuracy metrics, achieving Dice Coefficient scores of 85.02%, 85.66%, 86.89%, and 87.7% respectively.

关键词： Training Measurement Image segmentation Accuracy Machine learning algorithms Image color analysis Machine learning Ophthalmology Convolutional neural networks Lesions

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Analyzing the Cause of Students Reluctant to Participate in the Classroom: Machine Learning Approach 10

Analyzing the Cause of Students Reluctant to Participate in ...

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10th International Conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Islam, Majharul Islam, Md. Sanzidul Siddik, Md. Abu Bakkar Mousumi, Sadia Sultana Sharmin Daffodil International University Dpt. of Software Engineering Dhaka Bangladesh Daffodil International University Dept. Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665439701

in the twenty-first century, seems many students are reluctant to participate in classroom matters day by day. Some days ago, a course teacher is providing a lecture, on the most important topics that will be implemented in every step in life that class majority students were nonexistent. We got motivation why it's happened and find out a lot of reasons behind uninterested in class after interrogation. Who are belonging age range 20-25, most of the time they face an obstacle and suffer and other teenagers. The fundamental purpose of this research to take action based on behind reasons, so that students can move in classroom learning matters. In this article, solve will be described and how to motivate students. Then it will be much helpful and far informative for teacher, students, guardians and also education specialist [8]. In this research shown causes, and also recommended how to overcome and solve those reluctant and spread knowledge and enlighten the nation. The aim of the research to help and inform provide to students avoid his/her predicted absent in classroom using Machine learning, particularly we used Liner Algorithm. Then, students will participate in classroom lectures as well as social activity and world would be more peaceful. This research achieved more than the accuracy of 95.9%. © 2021 IEEE.

关键词： Students

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning

Phoneme Classification in Imagined Speech Using Explainable ...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Sejin Kim Hyunwook Kang Ji-Hoon Jeong Minji Lee Dept. Artificial Intelligence The Catholic University of Korea Bucheon South Korea Dept. Electrical and Computer Engineering Inha University Incheon South Korea Dept. Computer Science Chungbuk National University Cheongju South Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon South Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36 %, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke's area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke's area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future.

关键词： Accuracy Machine learning algorithms Closed box Machine learning Predictive models Brain modeling Brain-computer interfaces Classification algorithms Reliability Next generation networking

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Rare and Common Types of Retinal Disease Recognition Using Ensemble Deep Learning

Rare and Common Types of Retinal Disease Recognition Using E...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Al Shahriar Uddin Khondakar Pranta Arafath Bin Mohiuddin Amira Hossain Anamul Sakib Md. Moniruzzaman Shanto Shamim Forhad Abdullah Al-Sakib Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Computer Science Wright State University Dayton USA Department of Collage of Engineering and Technology Westcliff University CA USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Detecting retinal diseases is crucial for preventing vision loss, but it comes with challenges, such as data imbalance and accurately classifying rare conditions. This study introduces a new ensemble transfer learning model that combines EfficientNetB7, SE-ResNet, DenseNet-169, and NASNet to improve the classification of common and rare retinal diseases. The ensemble uses a probability averaging approach to maximise each architecture’s strengths. Essential preprocessing techniques such as Global Contrast Normalization (GCN) and Total Variation Denoising (TVD) were used to enhance image quality. The model was trained and evaluated on the Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0, achieving 98.65% accuracy and an F1-score of 0.96% for common diseases and 99.10% accuracy with an F1-score of 0.97 for rare diseases, outperforming individual models significantly. The ensemble has been integrated into a web-based application that allows clinicians to upload retinal images for instant diagnostic feedback, enabling real-time analysis and visual insights. Despite its strengths, challenges such as high computational demands and reliance on high-resolution photos remain. Future work will improve computational efficiency through model compression techniques and extend the model to other imaging modalities, such as Optical Coherence Tomography (OCT). In conclusion, this study presents a robust and scalable solution for retinal disease classification, potentially improving early detection and providing real-time support in clinical settings, particularly in resource-limited environments.

关键词： Deep learning Visualization Accuracy Computational modeling Optical coherence tomography Transfer learning Imaging Retina Real-time systems Diseases

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Innovative Poverty Estimation through Machine Learning Approaches 18

Innovative Poverty Estimation through Machine Learning Appro...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mashhood, Ahmad Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim Khan, Ammad Aslam Khan, Bahram BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences M.A. Road Satellite Town Rawalpindi Pakistan

ISBN: (纸本)9798350328172

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges. © 2023 IEEE.

关键词： Forecasting

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： M. Ashok Kumar Laith H. Jasim Sudhakar K R. Archana Reddy N. Naga Saranya Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%).

关键词： Geometry Deformable models Visualization Three-dimensional displays Sensitivity Event detection Feature extraction

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Hybrid ConvLSTM with Attention for Precise Driver Behavior Classification

Hybrid ConvLSTM with Attention for Precise Driver Behavior C...

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Artificial Intelligence and Knowledge engineering (AIKE)

作者： Ghulam Mustafa Seong-Je Cho Youngsup Hwang Dept. of Computer Engineering Sun Moon University Asan-si South Korea Dept. of Software Science Dankook University Yongin South Korea

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences.

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SemParser: A Semantic Parser for Log Analytics

SemParser: A Semantic Parser for Log Analytics

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International Conference on software engineering (ICSE)

作者： Yintong Huo Yuxin Su Cheryl Lee Michael R. Lyu Computer Science & Engineering Dept. The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China

Logs, being run-time information automatically generated by software, record system events and activities with their timestamps. Before obtaining more insights into the run-time status of the software, a fundamental step of log analysis, called log parsing, is employed to extract structured templates and parameters from the semi-structured raw log messages. However, current log parsers are all syntax-based and regard each message as a character string, ignoring the semantic information included in parameters and templates. Thus, we propose the first semantic-based parser SemParser to unlock the critical bottleneck of mining semantics from log messages. It contains two steps, an end-to-end semantics miner and a joint parser. Specifically, the first step aims to identify explicit semantics inside a single log, and the second step is responsible for jointly inferring implicit semantics and computing structural outputs according to the contextual knowledge base of the logs. To analyze the effectiveness of our semantic parser, we first demonstrate that it can derive rich semantics from log messages collected from six widely-applied systems with an average F1 score of 0.985. Then, we conduct two representative downstream tasks, showing that current downstream models improve their performance with appropriately extracted semantics by 1.2%-11.7% and 8.65% on two anomaly detection datasets and a failure identification dataset, respectively. We believe these findings provide insights into semantically understanding log messages for the log analysis community.

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