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CoML: Machine Learning based approach for COVID-19 related Suicidal Ideation detection

CoML: Machine Learning based approach for COVID-19 related S...

引用

Intelligent Systems and computer Vision (ISCV)

作者： Salah Bouktif Akib Mohi Ud Din Khanday Ali Ouni Dept. of Computer Science and Software Engineering CIT United Arab Emirates University Al Ain UAE Dept. of Software Engineering and IT École de Technologie Supérieure University of Quebec Montreal Canada

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

The pervasive use of online social media platforms has changed the communication habits and subsequently the content and the frequency of the human exchanges. People share their thoughts on social media more frequently and freely. In particular and due to the societal stigma, people find less obstacle to discuss mental health problems on social media compared to face-o-face discussion. Mining social media for the sake of analysing suicidal intention is an attractive and at the same time challenging task especially during COVID-19 era. The main objective is to leverage machine learning to classify the social media user suicidal behavior while considering the impact of COVID-19 circumstances. We propose a rigorous feature engineering technique based on TF/IDF and Bag of Words. Machine learning classifiers and ensemble models are compared to find that the Neural Network(NN) classifier outperforms the benchmarks with a precision of 94%, a Recall of 94%, an F1 Score of 94% , and an overall accuracy of 94%. In the future, more feature selection techniques and deep learning can be used.

关键词： COVID-19 Accuracy Social networking (online) Neural networks Mental health Media Feature extraction

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An Efficient Detection Model for Brain Stroke Based on Transfer Learning

An Efficient Detection Model for Brain Stroke Based on Trans...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Faroq H. M. Mohammed Abdulfattah Ba Alawi Ferhat Bozkurt M. Dilruba Geyikoğlu Electrical and Electronic Engineering Dept. Faculty of Engineering Ataturk University Erzurum Turkey Computer Engineering Dept. Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Dept. Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Brain stroke is one of the most common causes of death, ranking as the second leading cause worldwide. With the advancements of deep learning, the detection of brain strokes from CT images becomes possible. In this study, we propose a method for classifying brain stroke images and predicting the presence of a stroke using convolutional neural networks (CNNs), which are particularly effective in image classification tasks. These techniques involve training different CNN models (EfficientNetB0-B7, InceptionResNetV2, ResNet50, ResNet152, and VGG19) on a dataset of labeled brain strokes and normal brain images. Through this process, the model learns to identify patterns and features that are indicative of a stroke. The results show that the different CNN models were able to classify stroke images with high accuracy and predict the presence of stroke with an overall accuracy of 99.95%, which is a significant improvement compared to similar studies. This demonstrates the potential of deep learning-based approaches using CNNs in assisting the diagnosis of strokes and aiding in early-stage treatment. Finally, we provide an analysis of model performance, highlighting each CNN model used, and providing insights for future research.

关键词： Training Analytical models Accuracy Transfer learning Stroke (medical condition) Brain modeling Convolutional neural networks

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models

Improving Fake News Detection with a Mixture of Experts: Lev...

引用

Advancements in Computing (ICAC), International Conference on

作者： Tharindu Gunasekara Disira Thihan Sansika Kodithuwakku Dinuka Dissanayake Harinda Fernando Samitha Vidhanaarachchi Archchana Sindhujan dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Science and Electronic Engineering University of Surrey United Kingdom

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods.

关键词： Sentiment analysis Analytical models Accuracy Bidirectional control Logic gates Linguistics Transformers Encoding Vectors Fake news

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Yushintia Pramitarini Duy H. N. Nguyen Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea Department of Electrical and Computer Engineering San Diego State University San Diego CA USA Dept. of Software and Communications Engineering Hongik University Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance.

关键词： Array signal processing Precoding Simulation Quality of service Programming Search problems Prediction algorithms Energy efficiency Real-time systems Optimization

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Three Levels of Agile Planning in a software Vendor Environment 32

Three Levels of Agile Planning in a Software Vendor Environm...

引用

32nd Australasian Conference on Information Systems: Information Systems for a Sustainable Future, Connectedness, and Social Good, ACIS 2021

作者： Lal, Ramesh Clear, Tony Dept. of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Dept. of Computer Science and Software Engineering University of Technology Auckland New Zealand

There is a misconception that agile development requires minimal planning effort. In reality, an agile approach for market-driven software development requires highly disciplined, reliable, and accurate planning practices to swiftly plan and develop high-value innovations in a software vendor environment. This study investigated a highly successful international software vendor in Melbourne, Australia, to provide a case study on agile planning practices. Five planning practices were identified which underly successful agile software development for software vendors. These planning practices were driven by agile concepts such as adaptation, self-organizing, cross-functional collaboration, and empowerment/delegation. We constructed a conceptual framework for agile planning practices, the APP (Agile Planning Practices) Framework illustrating the three levels of agile planning. Copyright © 2021 authors.

关键词： software design

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GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

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International Conference on Electronics, Information and Communications (ICEIC)

作者： BoJeong Park JongWoo Kim SeoYeong Mun Younglim Choi Hyunseok Kim dept. of Electric Engineering Dong-A University Busan South Korea dept. of Software Innovation Center Dong-A University Busan South Korea dept. of Computer Engineering Dong-A University Busan South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper aims to develop a robotic golf trainer using a wheeled robot and create a model that accurately recognizes the user's golf motion. Since existing 3D pose models show limitations in golf motion recognition [1], [2], we propose an optimized recognition model by combining various methods and preprocessing golf datasets to overcome these problems. We used HRNet to estimate 2D pose from video frames and fed these data into a transformer-based model to generate three hypotheses through residual linking and embedding layers. These were integrated to finally design a structure to predict 3D pose, and batch normalization, dropout, and LeakyReLU were used to improve the stability and nonlinearity handling of the model. Experimental results show that the proposed model effectively learns temporal information from multi-view 2D pose data without 3D annotation and performs well even when 3D data is scarce. The proposed method can be used in various applications such as human-robot interaction and sports motion analysis including golf, and further research on multiple datasets and real-world environments is expected to improve further and generalize the model's performance.

关键词： Solid modeling Three-dimensional displays Annotations Human-robot interaction Transformers Data models Real-time systems Stability analysis Mobile robots Sports

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Federated Learning-Enhanced QoS Multicast Routing to Support RIS and Edge Computing in IoT-Enabled MANETs with CF-mMIMO

Federated Learning-Enhanced QoS Multicast Routing to Support...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Amalia Amalia Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

In this paper, we propose a novel federated learning (FL)-enhanced quality of service (QoS) multicast routing, called FLQMR protocol, in IoT-enabled mobile ad-hoc networks (MANETs) with cell-free massive multiple input multiple output (CF -mMIMO). The main contributions of this paper can be summarized as follows. First, we consider the integration of cross-layer design, reconfigurable intelligent surfaces (RIS), FL, and edge computing to enhance network performance. Second, we design the FL framework to optimize routing decisions by selecting the best paths from the source node to multiple destinations. Third, we employ a cross-layer design that combines the physical layer information (i.e., mobility, position, SE) with the network layer information (i.e., route information) to establish a stable multicast tree from the source node to multiple destinations. The simulation results show that the proposed FLQMR protocol achieves a high packet delivery ratio, low routing delay, and low control overhead.

关键词： Cross layer design Simulation Quality of service Reconfigurable intelligent surfaces Routing Physical layer Ad hoc networks

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Exploiting Secure Multi-Hop Transmissions with NOMA Networks: Performance Analysis

Exploiting Secure Multi-Hop Transmissions with NOMA Networks...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we analyze the secrecy performance of secure multi-hop transmission for the NOMA system. More specifically, the multi-hop transmission can extend the coverage of wireless transmission. However, according to the principle of the wireless medium, an eavesdropper can overhear the legitimate users' transmission, which leads to serious security issues. In order to analyze the relationship between network parameters and secrecy outage probability, we derive exact closed-form expressions for the secrecy outage probability (SOP) of cell center and cell edge users, respectively. The numerical results show that the simulation and analysis results are tightly matched. Additionally, we investigate the impact of the number of hops and the distance on the secrecy performance.

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Bluetooth Low Energy-based Adaptive Scheme for IoT Services

Bluetooth Low Energy-based Adaptive Scheme for IoT Services

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Gaoyang Shan Geunkyung Choi Byeong-Hee Roh Dept. of Software and Computer Engineering Ajou University Suwon South Korea Dept. of AI Convergence Nework and Dept. of Computer Engineering Ajou University Suwon South Korea

To adapt to various IoT services, Bluetooth Low Energy (BLE) specification has defined a variety of communication modes, such as advertising, extended advertising, and periodic advertising events. As services’ environments, such as the number of users and QoS requirements, are changed, modes of protocols should be also adjusted to meet those services’ needs. This paper proposes an adaptive BLE scheme to reflect dynamically changing services’ environments. The effectiveness of the proposed scheme is verified through simulation experiments.

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A New Resource Configuring Scheme for Variable Workload in IoT Systems

A New Resource Configuring Scheme for Variable Workload in I...

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2022 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2022

作者： Nam, Sunhwa A. Cho, Kyungwoon Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of Embedded Software Research Center Ewha University Seoul Korea Republic of

ISBN: (纸本)9781665453059

With the growing data size and the increased amount of computational load in machine learning workloads, configuring the resources of IoT (Internet-of-Things) systems in an energy-saving way is becoming important. For example, the workload of an unmanned aerial vehicle (UAV) depends on weather and obstacle conditions, and reconfiguring the processor voltage and memory state for these conditions is critical to battery life. However, resource configuring in traditional IoT systems is fixed in advance to meet real-time constraints. In this paper, we suggest a new resource configuring scheme for real-time jobs that can handle workload variations in emerging IoT systems. The goal of our scheme is to minimize the energy consumed by proper resource configurations in response to workload fluctuations and eliminate the tardiness of real-time jobs. To handle workload fluctuations, we categorize real-time jobs into primary jobs and additional jobs, and pre-plan the resource configuring for various workload situations. Based on this, we start the IoT system with the configuration for the primary jobs, and when additional jobs are activated, we update the resource configurations promptly for the new situation. In particular, our resource configuring scheme optimizes the supplied voltage of the processor and memory configuration for all real-time job combinations, and reflects to the system instantly as additional jobs are activated. Based on simulation experiments under various workload conditions, we show that the suggested scheme saves the battery power by 32.1% without the tardiness of real-time jobs. © 2022 IEEE.

关键词： Machine learning

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