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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Sheikhi, Farnaz Kowsari, Zahra K. N. Toosi University of Technology Faculty of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

COVID-19 is an infectious disease caused by the most recently discovered Coronavirus. Despite four years of study on the characteristics of this virus since its emersion in 2019, lots of questions have been left unsolved. In this study, we extend examining the predictive power of statistical models in capturing dynamics of COVID-19 in Alpha and Delta variants in the three geographical regions of country of Iran, continent of Asia, and the world. We use the models of ARIMA and Prophet to predict the number of infections and deaths due to the aforementioned variants. The results show that ARIMA is superior to Prophet in the majority of predictions. This model is further superior to the newly LSTM models proposed for predictions in the same situation. We also provide a simulation of the spread of COVID-19 pandemic in Alpha and Delta variants in a test environment, to further illustrate the differences in the nature of the two variants. © 2024 IEEE.

关键词： COVID-19

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Novel Approach to Image Similarity Estimation and Object Matching: Leveraging ViT Architecture and Euclidean Distance Metric 20

Novel Approach to Image Similarity Estimation and Object Mat...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Ardehkhani, Pegah Ardehkhani, Pouya Hooshmand, Haniyeh Sharif University of Technology Department of Industrial Engineering Tehran Iran Faculty of Engineering University of Tehran Deep Learning Research Lab Department of Computer Engineering Tehran Iran Faculty of Engineering University of Tehran Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

In addressing the challenge of image similarity estimation on the MNIST dataset, our research drives from conventional Siamese network methodologies by incorporating Vision Transformer (ViT) architecture. Departing from the standard MNIST dataset, we introduced a novel paired dataset tailored to enhance the capabilities of similarity estimation. The innovation lies in the utilization of ViT as the core foundation for extracting features, followed by the application of Euclidean distance metrics on the dual input. This departure from the traditional approach not only broadens the scope of image similarity assessment but also enhances the model's discriminative power. Notably, the model attains a commendable test accuracy of 97.14% with a patch size of 7, underscoring the efficacy of our proposed methodology. This work it not only adds to the changing scenery of the image similarity estimation while also emphasizing the importance of leveraging non-conventional architectures to achieve enhanced performance in this domain. © 2024 IEEE.

关键词： Deep learning

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Depression Detection on e-Risk 2017 using Long Short-term Memory Models 20

Depression Detection on e-Risk 2017 using Long Short-term Me...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Sheikhi, Farnaz Fakher, Laya Chekani, Danial K. N. Toosi University of Technology Faculty of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

In today's fast-paced world, individuals are increasingly vulnerable to high levels of stress, which consequently raise the risk of developing depression. Depres-sion may represent itself via various symptoms, ranging from emotional to physical pains. The symptoms appear gradually, deceiving patients to ignore their changes. The reluctance to accept the depression and the social stigma surrounding this mental disorder prevent a remarkable fraction of patients from vising the specialists and receiv- ing the treatments. Although people feel uncomfortable to reveal themselves in face-to-face connections, they have an opposite attitude towards social networks. People willingly share their opinions, thoughts, and feelings on social networks nowadays. Psychological studies have explored potential differences in the literature used by individuals with depression and those without. Hence, social network data has become a valuable source of information to study the mental health of users. Following this motivation, in this paper, we have examined the power of several deep learning algorithms for automati- cally detecting depression on eRisk 2017 dataset, obtained from the social network of Reddit. To this aim, we have studied and compared Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), and hybrid LSTM- GRU models for depression detection. The results have illustrated superiority of LSTMs in terms of accuracy and timing of detecting depression over the rest of the models. These findings contribute to the expanding body of research on using artificial intelligence for detecting mental health issues. © 2024 IEEE.

关键词： Long short-term memory

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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs computer vision Deep learning Machine learning Thoracic anomalies

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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Improving long-tail classification via decoupling and regularisation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 62-71页

作者： Shuzheng Gao Chaozheng Wang Cuiyun Gao Wenjian Luo Peiyi Han Qing Liao Guandong Xu School of Computer Science and Technology Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Harbin Institute of TechnologyShenzhenChina Faculty of Engineering and Information Technology University of Technology SydneySydneyNew South WalesAustralia

Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based *** methods mainly tackle this problem by reweighting the loss function or rebalancing the ***,one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle *** this paper,the authors shed light on the significance of the angle distribution in achieving a balanced feature space,which is essential for improving model performance under long-tailed ***,it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature *** tackle these challenges,the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components:classifier norm(*** magnitude of the classifier vector),feature norm(*** magnitude of the feature vector),and cosine similarity between the classifier vector and feature *** this way,the authors analyse the change of each component in the training process and reveal three critical problems that should be solved,that is,the imbalanced angle distribution,the lack of feature discrimination,and the low feature *** from this analysis,the authors propose a novel loss function that incorporates hyperspherical uniformity,additive angular margin,and feature norm *** component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature *** authors conduct extensive experiments on three popular benchmark datasets including CIFAR-10/100-LT,ImageNet-LT,and iNaturalist *** experimental results demonstrate that the authors’loss function outperforms several previous state-of-the-art methods in addressing the challenges posed by imbalanced and longtailed datasets,t

关键词： computer vision image classification long-tailed data machine learning

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Toward Addressing the Software Architecture Blind Spot of Information System Success Factors in the Public Health Domain 44

Toward Addressing the Software Architecture Blind Spot of In...

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44th International Conference on Information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Dang, Anthony Beydoun, Ghassan School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9781713893622

Context: In the public health domain, there is no shortage of failed Information Systems projects. In addition to overblown budgets and elapsed deadlines (ad nauseam), technical issues exist. These include poor usability, instability, system performance, and data inconsistency issues. The issues relate to Software engineering, and specifically Software Architecture. The role of Software Architecture underpins these issues. However, the enquiries and analyses of these failed Information Systems projects have focused on the perspective of stakeholders (e.g., government ministers and health practitioners) and project managers. While stakeholders provide input into project requirements and goals, the project failure or success does not emanate from these roles exclusively. For example, system instability is not specifically a stakeholder issue but rather a Software Architecture concern. Aim: To bring to bear and address public sector health Information System failure from the Software Architecture perspective. Method: A literature survey was conducted to ascertain the perceived failure and success factors within the public health domain. Results: We observed that the available literature on health Information Systems appears to lack success factors which have been noted in the Software engineering and Software Architecture literature. Software Architecture appears to be an understudied area within the public health domain. Contribution: To bring awareness to the public health domain that the Information System's success requires multi-faceted perspectives and actions. Specifically, perspectives and actions involving Software Architects and Software Engineers are required to successfully address the quality attributes of the proposed systems during development. Copyright © 2023 Anthony Dang and Ghassan Beydoun.

关键词： Software architecture

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