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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Murugan, Ramalingam Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331520540

Advances in machine learning and computer vision have significantly improved the diagnostic capabilities of medical imaging. Convolutional Neural Networks (CNNs) have emerged as a crucial tool for image classification, particularly in detecting pneumonia from chest X-ray images. Despite their high accuracy, CNNs face challenges in clinical adoption due to their lack of interpretability. Medical practitioners require a clear understanding of the decision-making process to trust AI-generated diagnoses. To address the aforementioned challenge, we propose pneumonia classification model incorporating CNN with a combination of the model and Grad-CAM, for enhanced interpretability. Grad-CAM visually outlines critical regions, influencing the prediction, so the clinician can easily confirm AI-based diagnoses with a more confident and credible approach. It is exclusively for pediatric pneumonia, an accurate and explainable machine for early interventions that can also work in resource-scarce regions. Our research uses a set of 5,863 pediatric chest X-ray images, divided into Pneumonia and Normal classes. The dataset was acquired from Guangzhou Women and Children's Medical Center and divided into three subsets: training, validation, and test. By improving the accuracy and transparency of the diagnosis, this model can help bridge the gap between AI and the real world. This can help facilitate quicker and more reliable pneumonia diagnostics. © 2025 IEEE.

关键词： Convolutional neural networks

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Unveiling the Intricacies of Cyber Harassment Intentions on Social Media Platforms 3

Unveiling the Intricacies of Cyber Harassment Intentions on ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Shraya, Bejgum Reddy Dopathi, Hari Charan Anitha, G. Pattanaik, Balachandra Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Wallaga University Nekemte Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350381931

Cyberbullying is a severe issue that impacts teens and adults alike. Errors like hopelessness and suicide have resulted from it. The demand for material on social media platforms to be regulated is growing. In the work that follows, machine learning and natural language processing are used to construct a model to identify cyberbullying in text data. The program leverages data from two different types of cyberbullying: personal attacks on Wikipedia forums and tweets containing hate speech. To determine the optimal approach, three feature extraction techniques and four classifiers are examined. The method produces more than 80% accurate data from Wikipedia and more than 90% correct data from Twitter. © 2024 IEEE.

关键词： Machine learning

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Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment 2

Transformer-Based Classification Outcome Prediction for Mult...

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2nd IEEE International Conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Ma, Danqing Wang, Meng Xiang, Ao Qi, Zongqing Yang, Qin Computer Science Stevens Institute of Technology HobokenNJ United States University of Sofia Department of Computer Science Palo AltoCA United States School of Computer Science & Engineering University of Electronic Science and Technology of China Sichuan China School of Integrated Circult Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Stevens Institute of Technology Computer Science HobokenNJ United States

ISBN: (纸本)9798350373646

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects. © 2024 IEEE.

关键词： computerized tomography

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An Efficient Data Aggregation Solution for Smart Meters Based on Cloud-Edge Collaboration 20th

An Efficient Data Aggregation Solution for Smart Meters Bas...

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20th International Conference on Services Computing, SCC 2023 held as Part of the Services Conference Federation, SCF 2023

作者： Xia, Zhuoqun Zhang, Li Sangaiah, Arun Kumar The School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410000 China Department of Electrical and Computer Engineering Lebanese American University Byblos83990 Lebanon

ISBN: (纸本)9783031516733

Smart meters are an important component of the smart grid, and the large-scale deployment of meters on the user side generates a large amount of data that brings huge expenses to the smart grid. In addition, attackers can monitor users’ electricity consumption based on the transmitted data, which poses a privacy threat. To solve these problems, this paper proposes a secure and efficient data aggregation scheme for smart meters based on cloud-side collaboration. Based on cloud-edge collaboration, the cloud server uses double Z-Score standardization to process the electricity consumption feature data, and at the same time uses improved Euclidean distance to obtain the number of clusters and classification results of the feature distinctions, and interacts with the edge device to respond. Based on the response information, the power provider generates confusion parameters and perturbation factors with classification information, which are sent to the meter for generating convergence values to minimize Gaussian noise and attached to the meter data to achieve the confusion processing of meter data. The aggregator receives the meter and cloud information for classification and aggregation and generates an aggregation signature to transmit the data. The results show that the scheme has high privacy security and operational efficiency. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart meters

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Phy-CoCo: Physical Constraint-Based Correlation Learning for Tropical Cyclone Intensity and Size Estimation 27

Phy-CoCo: Physical Constraint-Based Correlation Learning for...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Yan, Hanting Mu, Pan Huang, Cheng Zhang, Jinglin Bai, Cong College of Computer Science Zhejiang University of Technology China School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781643685489

Tropical Cyclone (TC) estimation aims to estimate various attributes of TC in real-time to alleviate and prevent disasters caused by violent TCs. As artificial intelligence technology advances, various deep learning-based multi-task estimation approaches have been proposed. However, most of them only focus on extracting common features of tasks, disregarding potential negative transfer and task interactions between different tasks. This paper is thus motivated to propose a Physical Constraint-based Correlation (Phy-CoCo) learning framework from the perspective of Multi-Task Learning (MTL). Specifically, for task-specific feature learning, we introduce Correlation Modeling (CoM) based on Centrally Expanded Pooling (CEP). Furthermore, for cross-task interaction, we propose a Multi-Domain Recurrent Convolution (MDRC) module to incorporate physical constraints into MTL. These physical constraints enable the transformation of different task features by simulating the physical relations among different attributes of TC. Lastly, in combination with a task-shared network that leverages the hybrid fusion of multi-modal data, our MTL framework accurately estimates various TC attributes. Extensive experiments conducted on our constructed dataset demonstrate that the proposed Phy-CoCo outperforms previous methods in TC estimation in terms of estimation error, verifying the potential of the physics-incorporated MTL model. © 2024 The Authors.

关键词： Tropical cyclone

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Persuasive AI Feedback: Enhancing Student Emotions and Engagement in Higher Education 19

Persuasive AI Feedback: Enhancing Student Emotions and Engag...

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19th International Conference on Persuasive Technology Adjunct, PERSUASIVE-ADJ 2024

作者： Alsaiari, Omar Baghaei, Nilufar School of Electrical Engineering and Computer Science The University of Queensland St Lucia4072 Australia Department of Computer Science College of Science and Arts Najran University Sharurah68341 Saudi Arabia

This study focuses on the employment of persuasive technology with artificial intelligence (AI) to enhance student’s emotions and engagement in educational environments. By integrating persuasive elements such as praise, personalization, reminders, and emojis into AI-driven feedback, we aim to bridge the gap in understanding their effects on students’ emotions and engagement levels. Anchored in the control and value theory of achievement emotions, our mixed-methods research will assess these impacts using the Achievement Emotions Questionnaire-Short (AEQ-S) and engagement data from the RiPPLE platform. We hypothesize that such AI feedback strategies will significantly improve students’ emotional experiences and engagement with learning tasks. This inquiry contributes to educational technology by offering insights into designing emotionally intelligent AI feedback systems, potentially enriching the learning experience for university students. Expected outcomes include practical guidance for leveraging AI in creating more engaging and supportive educational settings. © 2024 Copyright for this paper by its authors.

关键词： Students

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Detecting cross-site scripting attacks using machine learning: A systematic review

Detecting cross-site scripting attacks using machine learnin...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Baniya, D. Amrita Chaudhary, A. Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

In today s age, many daily tasks are performed through the Internet using various web applications. While using the web application, the information and data are stored in the database of the network which can easily be attacked by the attacker. The attacker used different types of attack patterns to steal the victim s information or any details like their bank information, and business details. Among the different types of attacks over the web application vulnerabilities, the most prominent category of attack is a cross-site scripting (XSS) attack. In the XSS attack, an attacker transmits the script to the victim s web Browser. As the victims are unaware that they were being attacked leading to stealing the valuable information like personal details, bank details, and cookies. Using the cookies of the victim, the attacker can easily access their account without using a username and password. Early, different types of approaches have been done to shoot out the vulnerabilities related to XSS. This paper purports to be a survey on XSS. The primary contribution of this study to the current state-of-the-art XSS is a systematic review of various types of research done to detect XSS vulnerabilities using Machine Learning. This paper also provides the background related to XSS, techniques, difficulties and future guidelines in the field of XSS to provide researchers with significant knowledge for further work. © 2024 The Author(s).

关键词： Machine learning

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Emo-Spots: Detection and Analysis of Emotional Attributes Through Bio-Inspired Facial Landmarks

Emo-Spots: Detection and Analysis of Emotional Attributes Th...

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International Conference on IoT, Intelligent Computing and Security, IICS 2021

作者： Bakkialakshmi, V.S. Sudalaimuthu, T. Umamaheswari, B. Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Engineering Bengaluru India

ISBN: (纸本)9789811981357

Modern engineering society requires qualified solutions to many practical problems, to meet accurate outcomes. Emotion detection through face landmarks has unique coordination toward improving the feedback system. Bio-inspired learning methods are an interesting area of research. Face expressions are detected through various methodologies. Bio-inspired techniques are used to extract facial landmarks to detect facial expressions even better. The selection of face attributes is crucial for the prediction of deeper emotions. The proposed system is focused on implementing a bio-inspired facial landmark detection system for emotion extraction. The dataset used for the analysis is taken from the (JAFEE) Japanese female face expressions dataset. It consists of a collection of unique expressions and recorded images. Human face attributes are extracted from the Haar cascade model. The proposed novel methodology is derived from the Robust Emo-Spot Extracting technique (RESET) using point mapping model-based landmark mapping for pre-trained images and labeling spots. The face images are correlated with databases and classified the type using a deep convolutional neural network, in which appropriate Emo-spots are mapped using RESET. The adjustable Emo-Spots are randomly tuned using the randomly looped K-nearest neighbor algorithm. The novel algorithm checks for the correlation ratio (CR) 20. The proposed system is compared with the state-of-art approach in terms of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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High Order Time Shift Keying Modulation for Ambient Backscatter Communications

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IEEE Transactions on Communications 2024年第6期73卷 3792-3803页

作者： Chen, Jixiang Guan, Quansheng Rong, Yue Li, Dong Chen, Weiqi Yu, Hua South China University Of Technology School Of Electronic And Information Engineering Guangzhou510640 China Curtin University School Of Electrical Engineering Computing And Mathematical Sciences BentleyWA6102 Australia Macau University Of Science And Technology School Of Computer Science And Engineering 999078 China Guangdong University Of Finance School Of Internet Finance And Information Engineering Guangzhou China

Ambient backscatter communication (AmBC) is a newly cutting-edge technology for the Internet of Things, which utilizes the ambient radio frequency signal as the carrier to transmit information. Existing works focus on the simple on-off keying modulation which has low channel utilization. However, it is not desirable to develop the high-order modulation in the power domain due to the weak strength of the backscattered signal. In this paper, we extend the high-order modulation in the time domain instead, i.e., high-order time shift keying (TSK). Since the channel coherent time is unknown at the receiver and the tag, the detection methods with training symbols will have a huge performance degradation if the channels are changed and training symbols become outdated. To overcome this challenge, we further propose the transition-aided TSK (TA-TSK) modulation and the frequency-shifting TSK (FS-TSK) modulation, which do not need to send training symbols at the tag. These two methods can work well even if the channel coherent time is as short as one time slot. Meanwhile, the detection methods for TSK are developed and the corresponding closed-form bit-error-rate (BER) expressions are obtained. Simulation results show that a high modulation order is more suitable for the M-ary phase shift keying source than the complex Gaussian source. The high order TSK can provide at least 2 dB signal-to-noise ratio (SNR) gain at the same BER compared to the on-off keying. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Probabilistic Tube-based Control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic 63

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden University of Southern California Viterbi School of Engineering Department of Computer Science Los AngelesCA90089 United States Queen's University School of Electronics Electrical Engineering and Computer Science Northern Ireland Belfast United Kingdom

ISBN: (纸本)9798350316339

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical. © 2024 IEEE.

关键词： Stochastic systems

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