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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Naik, Mude Nagarjuna Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bangalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions. © 2025 IEEE.

关键词： Smart city

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Secure Dynamic Spectrum Access in Internet-of-Things Based on Machine Unlearning

Secure Dynamic Spectrum Access in Internet-of-Things Based o...

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

作者： Feng Li Shen Shui Kwok-Yan Lam Bowen Shen Li Wang School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Dynamic spectrum access (DSA) has become a key technology to improve spectrum efficiency in Internet-of-Things (IoT) networks. However, traditional DSA methods require collection of user behaviour data and location information, hence posing concerns for leakage of privacy data from the AI models. In scenarios where strict privacy regulations require the deletion of privacy-sensitive user data, existing DSA mechanisms only have the AI models and without access to the training processes or training data. This paper proposes a DSA method for IoT by integrating Deep Q-Network (DQN) reinforcement learning with zero-shot machine unlearning. This novel approach addresses the dual challenges of efficient spectrum utilization and user privacy protection in IoT environments. By leveraging on knowledge transfer techniques in machine learning, our solution can effectively protect user privacy without directly accessing the original training data while maintaining the model's effectiveness on retained data. Furthermore, this method optimizes the learning process, ensuring that learning efficiency and model convergence speed are comparable to, and in some cases superior to, traditional reinforcement learning methods. Through a series of experimental validations, this paper demonstrates the effectiveness and efficiency of the proposal in IoT DSA scenarios. This research provides a new solution for IoT spectrum management and opens up new opportunities for privacy-enhanced machine learning applications.

关键词： Training data privacy Privacy Adaptation models Machine learning algorithms Dynamic spectrum access Training data data models Protection Radio spectrum management

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Secure Dynamic Spectrum Access in Internet-of-Things Based on Machine Unlearning

Secure Dynamic Spectrum Access in Internet-of-Things Based o...

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7th International Conference on Artificial Intelligence in information and Communication, ICAIIC 2025

作者： Li, Feng Shui, Shen Lam, Kwok-Yan Shen, Bowen Wang, Li School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9798331507022

关键词： Zero-shot learning

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Wearable Wellness: Depression Screening via Fitbit data Collected During COVID-19 Pandemic

Wearable Wellness: Depression Screening via Fitbit Data Coll...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Nikola Grozdani America Muñoz Alexander Pietrick Ricardo Flores Avantika Shrestha Xingtong Guo Shichao Liu Elke A. Rundensteiner Computer Science Worcester Polytechnic Institute Worcester MA USA Computer Science/Information Systems National Louis University Chicago IL USA Computer Science/Data Science Worcester Polytechnic Institute Worcester MA USA Data Science Worcester Polytechnic Institute Worcester MA USA Architectural Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

The COVID-19 pandemic has greatly increased depression among adolescents. The current depression diagnosis process requires significant patient effort and can be costly. Prior research through passively collected data has shown promising depression screening results but is limited by complex data collection and privacy concerns. In this research, we create multiple machine learning models to screen physiological data collected from Fitbit, a wearable biomarker, and depression screening surveys across 166 college students. The highest-scoring model on these physiological modalities achieved an F1-score of 0.92. Our research findings highlight the potential impact of digital technology development in current clinical practices.

关键词： COVID-19 Training Surveys Wearable Health Monitoring systems Pandemics Biological system modeling Machine learning

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A Discussion of the Mapping from the Unified Search Space to the Search Space of the Optimized Task in Evolutionary Multitasking Optimization

A Discussion of the Mapping from the Unified Search Space to...

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2021 China Automation Congress, CAC 2021

作者： Yao, Shuangshuang Dong, Zhiming Wang, Xianpeng Liu, Qingqing Lou, Hangyu Zhao, Yumeng College of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Institute of Industrial and Systems Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665426473

Multitasking evolutionary algorithms enable the communication of information between different tasks and the mining of valuable knowledge by encoding all individuals into a unified search space. The mapping of decision variables of the same dimension for different tasks in the unified search space has the same dimensional index, i.e., the same encoded position. In this paper, we disrupt this mapping relationship so that decision variables of the same dimension for different tasks correspond to different encoded positions in the unified search space, to analyze and discuss the effect of this mapping relationship on the performance of the algorithm. A series of experimental results suggest that the mapping relationship between the decision variables of different tasks in a multitasking evolutionary algorithm has a significant impact on the performance of the algorithm. © 2021 IEEE

关键词： Mapping

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Inductive Meta-path Learning for Schema-complex Heterogeneous information Networks

arXiv

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arXiv 2023年

作者： Liu, Shixuan Fan, Changjun Cheng, Kewei Wang, Yunfei Cui, Peng Sun, Yizhou Liu, Zhong Laboratory for Big Data and Decision College of Systems Engineering National University of Defense Technology Hunan China National Key Laboratory of Information Systems Engineering College of Systems Engineering National University of Defense Technology Hunan China University of California Los Angeles United States Department of Computer Science and Technology Tsinghua University Beijing China

Heterogeneous information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm. Copyright © 2023, The Authors. All rights reserved.

关键词： Federated learning

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SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Nfissi, Alaa Bouachir, Wassim Bouguila, Nizar Mishara, Brian Data Science Laboratory Montréal Canada Concordia University Concordia Institute for Information Systems Engineering Montréal Canada Centre for Research Intervention on Suicide Ethical Issues End-of-Life Practices Montréal Canada University of Québec at Montréal Psychology Department Montréal Canada

In the field of human-computer interaction and psychological assessment, speech emotion recognition (SER) plays an important role in deciphering emotional states from speech signals. Despite advancements, challenges persist due to system complexity, feature distinctiveness issues, and noise interference. This paper introduces a new end-to-end (E2E) deep learning multi-resolution framework for SER, addressing these limitations by extracting meaningful representations directly from raw waveform speech signals. By leveraging the properties of the fast discrete wavelet transform (FDWT), including the cascade algorithm, conjugate quadrature filter, and coefficient denoising, our approach introduces a learnable model for both wavelet bases and denoising through deep learning techniques. The framework incorporates an activation function for learnable asymmetric hard thresholding of wavelet coefficients. Our approach exploits the capabilities of wavelets for effective localization in both time and frequency domains. We then combine one-dimensional dilated convolutional neural networks (1D dilated CNN) with a spatial attention layer and bidirectional gated recurrent units (Bi-GRU) with a temporal attention layer to efficiently capture the nuanced spatial and temporal characteristics of emotional features. By handling variable-length speech without segmentation and eliminating the need for pre or post-processing, the proposed model outperformed state-of-the-art methods on IEMOCAP and EMO-DB datasets. The source code of this paper is shared on the Github repository: https://***/alaaNfissi/SigWavNet-Learning-Multiresolution-Signal-Wavelet-Network-for-Speech-Emotion-Recognition. © 2010-2012 IEEE.

关键词： Discrete wavelet transforms

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Investigating Imperceptibility of Adversarial Attacks on Tabular data: An Empirical Analysis

arXiv

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arXiv 2024年

作者： He, Zhipeng Ouyang, Chun Alzubaidi, Laith Barros, Alistair Moreira, Catarina School of Information Systems Queensland University of Technology Brisbane Australia Center for Data Science Queensland University of Technology Brisbane Australia School of Mechanical Medical and Process Engineering Queensland University of Technology Brisbane Australia Data Science Institute University of Technology Sydney Sydney Australia INESC-ID Instituto Superior Técnico University of Lisboa Lisboa Portugal

Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks have been extensively studied in unstructured data like images, applying them to structured data, such as tabular data, presents new challenges. These challenges arise from the inherent heterogeneity and complex feature interdependencies in tabular data, which differ from the characteristics of image data. To account for this distinction, it is necessary to establish tailored imperceptibility criteria specific to tabular data. However, there is currently a lack of standardised metrics for assessing the imperceptibility of adversarial attacks on tabular data. To address this gap, we propose a set of key properties and corresponding metrics designed to comprehensively characterise imperceptible adversarial attacks on tabular data. These are: proximity to the original input, sparsity of altered features, deviation from the original data distribution, sensitivity in perturbing features with narrow distribution, immutability of certain features that should remain unchanged, feasibility of specific feature values that should not go beyond valid practical ranges, and feature interdependencies capturing complex relationships between data attributes. We evaluate the imperceptibility of five adversarial attacks, including both bounded attacks and unbounded attacks, on tabular data using the proposed imperceptibility metrics. The results reveal a trade-off between the imperceptibility and effectiveness of these attacks. The study also identifies limitations in current attack algorithms, offering insights that can guide future research in the area. The findings gained from this empirical analysis provide valuable direction for enhancing the design of adversarial attack algorithms, thereby advancing adversarial machine learning on tabular data. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Ontology-Based Method for Semantic Association Rules

Ontology-Based Method for Semantic Association Rules

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IEEE India Conference (INDICON)

作者： Abhilash C B Kavi Mahesh Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad Dharwad India Dept. of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad India

ISBN: (纸本)9781665452724

Finding interesting association rules is a popular and current topic in data mining. The Apriori family of algorithms is built around two rule extraction measures: support and confidence. Even though these two measures are easy to compute, they yield many rules, the majority of which are redundant and may not be of interest to the user. Also, by themselves, support and confidence do not generate strong rules. Additional measures are required to mine interesting facts from data. Ontologies have become the fundamental building blocks for structuring and formalizing data. With the semantic structuring of information, the implicit relationship between data elements makes the analyst get important facts from the data. Our study proposes a novel framework for interestingness in data by combining domain ontology with semantic interestingness measures. The ontology-based method infers rules that are semantically enriched and strong. We analyze the quality of the rule considering the factors defined by the domain experts. It is observed that our methodology generates semantically enriched rules that are more acceptable to domain experts.

关键词： COVID-19 Current measurement Semantics Knowledge based systems Influenza Ontologies Resource description framework

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