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5th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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Interval prediction strategy for the remaining useful life of lithium-ion battery pack considering cell inconsistency

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Journal of Power Electronics 2025年 1-13页

作者： Pang, Xiaoqiong Li, Xiao Kim, Jonghoon Lee, Miyoung Zhao, Zhen Wen, Jie Zeng, Jianchao School of Computer Science and Technology North University of China Taiyuan China Department of Electrical Engineering Chungnam National University Daejeon Korea Republic of Institute of Network Technology Yantai China School of Electrical and Control Engineering North University of China Shanxi Taiyuan China

Accurate and robust remaining useful life (RUL) prediction of lithium-ion battery packs is critical for ensuring system operation reliability and safety. However, the inconsistency accelerates battery pack degradation, making RUL prediction challenging. Furthermore, traditional prediction methods offering specific values cannot meet varying safety and urgency requirements. Aiming at the above problems, this study proposed an RUL interval prediction scheme considering cell inconsistency for lithium-ion battery packs. First, the degradation and inconsistency information of the battery pack is captured by extracting health indicators from the measured data. Second, fuzzy information granulation is introduced to transform the time series data into fuzzy granules, providing upper and lower bounds for interval prediction modeling. Then, long short-term memory is employed for modeling, generating interval prediction results that support decision adjustment for different safety and emergency levels. Finally, the experiment results show that the proposed strategy can effectively cover the real value and achieve reliable interval prediction. It also indicates that the impact of inconsistency on the prediction results becomes more remarkable as the starting point moves back. With 60% training data, the interval prediction evaluation criterion P of the model with inconsistency information is approximately 43% higher than the model without. © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2025.

关键词： Battery Pack

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Empowering Dysarthric Communication: Hybrid Transformer-CTC-Based Speech Recognition System

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IEEE Access 2025年 13卷 82479-82491页

作者： Vinotha, R. Hepsiba, D. Anand, L. D. Vijay Bruntha, P. Malin Dinh, Linh Dang, Hien Karunya Institute of Technology and Sciences Department of Robotics Engineering Tamil Nadu Coimbatore641114 India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore641114 India Karunya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641114 India Suffolk University Department of Information Systems BostonMA02108 United States Thuyloi University Faculty of Computer Science and Engineering Hanoi116705 Viet Nam Molloy University Department of Mathematics and Computer Science Rockville CentreNY11570 United States

Dysarthria, a motor speech disorder, impairs the muscles involved in speech production, leading to challenges in articulation, pronunciation, and overall communication. This results in slow, slurred speech that is difficult to understand. Augmentative and Alternative Communication (AAC) aids integrated with speech recognition technology offer a promising solution for individuals with dysarthria. However, Automatic Speech Recognition (ASR) systems trained on typical speech data often struggle to recognize dysarthric speech due to its unique speech patterns and limited training data. To address these challenges, a hybrid Transformer-CTC model has been proposed for improving ASR performance on dysarthric speech. The Transformer architecture employs a self-attention mechanism that models complex dependencies between speech features, enabling it to identify and emphasize important patterns even when training data is limited. This ability is particularly crucial for dysarthric speech, where speech signals often exhibit high variability. On the other hand, Connectionist Temporal Classification (CTC) acts as an effective transcription layer. It aligns speech features with character sequences without requiring precise input-output alignment, making it well-suited for handling the inconsistencies and distortions present in dysarthric speech. The integration of these components creates a powerful architecture capable of learning nuanced speech patterns and delivering accurate transcriptions for dysarthric speech. The model was trained using the UA speech corpus, containing 13 hours of speech from 15 speakers with varying dysarthria levels. The proposed hybrid system achieves an impressive Word Recognition Accuracy (WRA) of 89%, demonstrating its effectiveness in accurately transcribing dysarthric speech. This innovative approach significantly advances the development of ASR technologies tailored to diverse and variable speech patterns, ultimately enhancing communication for in

关键词： Speech analysis

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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ATOM: An Automatic Topology Synthesis Framework for Operational Amplifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1193-1198页

作者： Shen, Jinyi Yang, Fan Shang, Li Yan, Changhao Bi, Zhaori Zhou, Dian Zeng, Xuan Fudan University State Key Laboratory of Integrated Chips and Systems School of Microelectronics Shanghai200433 China Fudan University State Key Laboratory of Integrated Chips and Systems The School of Computer Science Shanghai200433 China Fudan University State Key Laboratory of Integrated Chips and Systems Microelectronics Department Shanghai200433 China University of Texas at Dallas Department of Electrical Engineering DallasTX75080 United States

Bayesian optimization (BO) is more efficient in automatically synthesizing operational amplifier (opamp) topologies compared to conventional methods. However, the design space for behavior-level opamp topologies involves numerous connections that are difficult to comprehend, and evaluating each topology incurs substantial computational costs. To tackle these challenges, this brief introduces ATOM, an automatic opamp topology synthesis framework. We construct a concise design space for behavior-level opamp topologies, consisting of topologies that designers can easily understand. We propose an opamp topology optimization method that incorporates freeze-thaw BO. This method efficiently explores the design space and expedites the evaluation process. Experimental studies demonstrate that ATOM outperforms state-of-the-art topology synthesis methods in terms of success rate and optimization results while reducing the number of required simulations by up to 8.15 times. The source code for ATOM is available at https://***/Jinyi-Shen/ATOM. © 2024 IEEE.

关键词： Thawing

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Early Time Series Anomaly Prediction with Multi-Objective Optimization

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第1期9卷 972-987页

作者： Chao, Ting-En Huang, Yu Dai, Hao Yen, Gary G. Tseng, Vincent S. National Yang Ming Chiao Tung University Institute of Information Management Hsinchu300093 Taiwan Oklahoma State University Department of Electrical and Computer Engineering StillwaterOK74078 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

Anomaly prediction, aiming to predict abnormal events before occurrence, plays a key role in significantly reducing costs and minimizing potential threats to mechanical devices. Monitoring machines using fixed-length time windows faces challenges in accommodating the varying characteristics of anomaly events. The lengthy and imbalanced sequence data associated with anomaly events further complicates the resolution of these challenges. Additionally, the inherent trade-off between accurate prediction and timely alarm is a crucial concern, posing difficulties in decision-making. This study puts forward a novel framework for early anomaly prediction, named early time series Anomaly Prediction with Neighbor Over-sampling and Multi-objective Optimization (APNOMO), pronounced as 'abnormal'. The framework employs three key techniques: 1) sliding windows that divide long input sequences into segments for prediction at proper intervals, 2) over-sampling and proposed neighbor over-sampling that handle imbalanced data in a novel way, and 3) multi-objective optimization that searches optimal thresholds to balance accurately prediction and timely alarm the abnormal. Experiments on a real-world dataset demonstrate APNOMO's superior performance over some state-of-the-art designs, with higher recall, F1 score, and more suitable earliness. It can predict anomalies 0.78-3.56 hours in advance, showcasing excellent early anomaly prediction capabilities for enabling predictive maintenance. © 2017 IEEE.

关键词： Predictive maintenance

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An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

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computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

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PResCon: Physiological Response Augmented Continuous Emotion Annotation Tool 17

PResCon: Physiological Response Augmented Continuous Emotion...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Banik, Swarnali Adithya, Akhilesh Mitra, Bivas Sen, Sougata Saha, Snehanshu Ghosh, Surjya Amazon India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India BITS Pilani K K Birla Goa Campus Department of Computer Science and Information Systems India BITS Pilani K K Birla Goa Campus APPCAIR Goa India

ISBN: (纸本)9798331531195

We present an annotation tool designed to collect fine-grain continuous emotion ground truth labels and physiological responses as users watch different videos. In the existing literature, continuous emotion annotations are typically collected as self-reports based on the 2D Circumplex model through auxiliary devices (a joystick or similar tools) during video consumption. However, the users face challenges as they need to use an auxiliary device and rely on the Circumplex model's valence and arousal axis without clear indication of distinct emotion labels. Therefore, to overcome these challenges, we introduce PResCon, a tool that allows to collect emotion annotations using keyboard arrow keys only (without using any auxiliary device) and guiding users towards the discrete emotions (e.g., amused, stressed, bored, relaxed) by embedding them on the Circumplex plane. The tool encompasses two major components - (a) a web interface that displays various stimuli videos and provision of collecting emotion labels (valence and arousal) by moving the keyboard arrow keys and (b) a sensor setup (based on Arduino) that allows to collect the physiological responses (heart rate and galvanic skin response) passively during video consumption. In summary, the PResCon tool facilitates research in the domain of affective computing by allowing to collect fine-grain continuous emotion annotations and the physiological responses in real-time during video consumption. © 2025 IEEE.

关键词： Auxiliary equipment

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