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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Waheed, Muhammad Fasih Bernadin, Shonda Hassan, Ali Francois, Jayson FAMU-FSU College of Engineering Electrical and Computer Engineering Department Tallahassee United States

ISBN: (纸本)9798331504847

The reliability and quality of 3D printing processes are critically dependent on the timely detection of mechanical faults. Traditional monitoring methods often rely on visual inspection and hardware sensors, which can be both costly and limited in scope. This paper explores a scalable and contactless method for the use of real-time audio signal analysis for detecting mechanical faults in 3D printers. By capturing and classifying audio data during the printing process, we aim to identify common faults such as nozzle clogging, filament breakage, pully skipping and various other mechanical faults. Utilizing Convolutional neural networks, we implement algorithms capable of real-time audio classification to detect these faults promptly. Our methodology involves conducting a series of controlled experiments to gather audio data, followed by the application of advanced machine learning models for fault detection. Additionally, we review existing literature on audio-based fault detection in manufacturing and 3D printing to contextualize our research within the broader field. Preliminary results demonstrate that audio signals, when analyzed with machine learning techniques, provide a reliable and cost-effective means of enhancing real-time fault detection. © 2025 IEEE.

关键词： Model predictive control

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Does Partner Asymmetry Facilitate Innovation Speed? Organizational Learning and Social Exchange Perspectives

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IEEE Transactions on engineering Management 2024年 71卷 4144-4159页

作者： Hsiao, Yung-Chang Wu, Ming-Ho National University of Tainan Department of Business and Management College of Management Tainan700301 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science Kaohsiung824005 Taiwan

The right partner and high innovation speed are crucial for a successful research and development (R&D) alliance in the high-tech industry. Does homogeneity or heterogeneity between partners benefit innovation speed? This study examined the links between partner asymmetries (technological heterogeneity and network resource asymmetry) and innovative speed from both economic and social dual perspectives by focusing on biopharmaceutical industries. The results revealed an inverse U-shaped relationship between technology heterogeneity and innovation speed. In addition, a positive linear relationship exists between network resource asymmetry and innovation speed for 1991-2000 alliances and for biotech-biotech partner-type alliances;significantly negative moderating effects of time to market occurred on these linear relations. Finally, this study makes important theoretical and practical contributions to partner selection literature on R&D alliances in the biopharmaceutical industry. © 1988-2012 IEEE.

关键词： Behavioral research

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A Low-Power 12-Bit SAR ADC for Analog Convolutional Kernel of Mixed-Signal CNN Accelerator

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computers, Materials & Continua 2023年第5期75卷 4357-4375页

作者： Jungyeon Lee Malik Summair Asghar HyungWon Kim Department of Electronics College of Electrical and Computer EngineeringChungbuk National UniversityCheongju28644Korea Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad CampusUniversity RoadTobe CampAbbottabad22044Pakistan

As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power *** overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator *** is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in *** work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)*** most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power *** SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase *** was implemented in a test chip using a 55 nm CMOS *** demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional *** ADC test chip shows a chip size of 4600μm^(2)with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits.

关键词： Convolution neural networks split-capacitor-based digital-toanalog converter(DAC) SAR analog-to-digital converter artificial intelligence system-on-chip analog convolutional kernel

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Bridging the Gap Between Central and Local Decision-Making: The Efficacy of Collaborative Equilibria in Altruistic Congestion Games 63

Bridging the Gap Between Central and Local Decision-Making: ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ferguson, Bryce L. Paccagnan, Dario Pradelski, Bary S. R. Marden, Jason R. University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States Imperial College Department of Computing London United Kingdom France

ISBN: (纸本)9798350316339

Congestion games are popular models often used to study the system-level inefficiencies caused by selfish agents, typically measured by the price of anarchy. One may expect that aligning the agents' preferences with the system-level objective-altruistic behavior-would improve efficiency, but recent works have shown that altruism can lead to more significant inefficiency than selfishness in congestion games. In this work, we study to what extent the localness of decision-making causes inefficiency by considering collaborative decision-making paradigms that exist between centralized and distributed in altruistic congestion games. In altruistic congestion games with convex latency functions, the system cost is a super-modular function over the player's joint actions, and the Nash equilibria of the game are local optima in the neighborhood of unilateral deviations. When agents can collaborate, we can exploit the common-interest structure to consider equilibria with stronger local optimality guarantees in the system objective, e.g., if groups of k agents can collaboratively minimize the system cost, the system equilibria are the local optima over k-lateral deviations. Our main contributions are in constructing tractable linear programs that provide bounds on the price of anarchy of collaborative equilibria in altruistic congestion games. Our findings bridge the gap between the known efficiency guarantees of centralized and distributed decision-making paradigms while also providing insights into the benefit of inter-agent collaboration in multi-agent systems. © 2024 IEEE.

关键词： Multi agent systems

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computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

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computer Systems Science & engineering 2023年第8期46卷 1811-1826页

作者： M.Uvaneshwari M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of Computing Technologies School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign *** benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in *** recognition of BT is highly significant to protecting the patient’s ***,the BT can be identified through the magnetic resonance imaging(MRI)scanning *** the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the ***,ML has prevailed against standard image processing *** studies denote the superiority of machine learning(ML)techniques over standard ***,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)*** accomplish the detection of brain tumor effectively,a computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research ***,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull ***,mayfly optimization with the Kapur’s thresholding based segmentation process takes *** feature extraction proposes,local diagonal extreme patterns(LDEP)are *** last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification *** accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research *** experimental validation of the proposed model demonstrates its promising performance over other existing methods.

关键词： Brain tumor machine learning segmentation computer-aided diagnosis skull stripping

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Advancing Federated Learning: A Comprehensive Solution for Model Aggregation, Heterogeneity, Privacy, and Security

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SN computer Science 2025年第5期6卷 1-36页

作者： Bari, Bifta Sama Yelamarthi, Kumar Department of Electrical and Computer Engineering College of Engineering Tennessee Tech University Cookeville 38505 TN United States

Secure vehicular communication is one of the challenges that is crucial for Intelligent Transportation Systems (ITS). Although Federated Learning (FL) enhances data privacy compared to Centralized Learning (CL), it faces challenges with data heterogeneity and resource allocation. To address these challenges, the Hybrid Federated-Centralized Learning (HFCL) framework improves efficiency and reduces communication overhead but still lacks comprehensive solutions to existing key issues including FL model aggregation, client heterogeneity, privacy-preserving communication, and enhanced security protocols. This paper proposes a unified HFCL framework incorporating the existing key issues that aims to develop and analyze model aggregation ensuring equitable client contributions, secure communications, and protect against various security threats. The implementation with FedAvg aggregation algorithm on diverse clients and datasets ensures robust and efficient communication, while Differential Privacy (DP) in HFCL enhances security and resilience against adversaries including poisoning attacks. Performance analysis reveals that HFCL maintains high accuracy and stability across varying learning rates and client configurations. Notably, the proposed unified privacy preserving HFCL achieves nearly 99.15% accuracy on the MNIST dataset and 68.4% on the CIFAR10 dataset, demonstrating its efficacy in different scenarios. By addressing the existing critical challenges, the proposed framework enhances the scalability, performance, and security of distributed ML systems, with a primary focus on vehicular communication. It highlights the significant advantages of HFCL for data processing and decision-making in applications like collision avoidance and autonomous driving. Additionally, the framework’s applicability could be extended to healthcare and finance, enabling secure and efficient analysis of sensitive data. © The Author(s), under exclusive licence to Springer Nature Singapore P

关键词： Centralized machine learning Convolutional neural networks Edge computing Federated learning Vehicular communication

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Classification of Fetal Cardiac Arrhythmia Using Heart Rate Variability and Machine Learning 5

Classification of Fetal Cardiac Arrhythmia Using Heart Rate ...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Shahid, Muhammad Arslan Shaukat, Arslan Akram, Muhammad Usman Butt, Wasi Haider College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350394764

Fetal arrhythmias can lead to cardiac failure or death;thus, early detection is crucial but challenged by noise and artifacts. This paper investigates fetal arrhythmia detection using time, frequency, and non-linear Heart Rate Variability (HRV) features extracted from fetal ECG signals in the NIFEEG dataset. Machine learning classifiers including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, XGBoost, Naive Bayes, and Convolutional Neural Network (CNN) are evaluated. The HRV features provide insights into fetal heart rate patterns. While SVM, KNN, Decision Tree, and XGBoost show promise, CNN demonstrates the highest performance with 98.16% accuracy, 100% sensitivity, 96.15% specificity, and 98.11% F1-score. This highlights CNN's potential for early and reliable fetal arrhythmia detection. Statistical analysis supports the significance of HRV features, paving the way for improved prenatal monitoring. To further improve model generalizability, future work should focus on expanding the dataset size, exploring advanced deep neural network architectures such as LSTMs, and integrating supplementary physiological inputs like uterine contractions. Overall, this study demonstrates the capabilities of HRV-based machine learning approaches, especially CNNs, for robust fetal arrhythmia detection, offering valuable clinical implications for early diagnosis and management. © 2024 IEEE.

关键词： Feature extraction

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SAR Analysis in an Anatomical Head Model Using CFL-Optimized Yee Cells and an Accurate Dipole Model at 700-MHz for 5G Mobile Radiation

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IEEE Access 2025年 13卷 82718-82731页

作者： Jariyanorawiss, Terapass Kanjanasit, Komsan Chongburee, Wachira Sornsungnoen, Nattapon Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok10900 Thailand Prince of Songkla University Phuket Campus College of Computing Phuket83120 Thailand

Electromagnetic radiation (EMR) exposure from fifth-generation (5G) mobile technology influences human health. This article proposes a numerical analysis to investigate the specific absorption rate (SAR) using an anatomical human head and accurate dipole model to examine the EMR effect at 700 MHz, a low-band frequency for 5G mobile communication. A three-dimensional (3D) digital model of a realistic human head is reconstructed using a series of magnetic resonance imaging (MRI) scans. This model encompasses various tissue types with their respective bio-material properties. The radiation from a mobile terminal is modeled using a dipole-based structure. In this study, the Unsplit-Field Finite-Difference Time-Domain (UF-FDTD) algorithm is modified for numerical analysis, with a perfectly matched layer (PML) serving as the boundary condition. The electromagnetic (EM) simulation is conducted to study the interaction between the two computational objects in the artificial absorbing environment. The Yee cell is utilized for a structured lattice in the UF-FDTD approach, characterized by its uniformity and consistent application throughout the model. The research problem involves correlating the size of the human head model with the unit cell size, which is associated with the average weight of the human brain for adults. The Yee cell is optimized based on the Courant-Friedrichs-Lewy (CFL) condition. This work enhances simulation accuracy twofold. First, the optimization of the unit cell size relative to the wavelength is refined in comparison to other unit cells without a phase error. This optimization leads to an accurate time-step parameter via the CFL criterion, ensuring the stability of the computer simulation. Second, the optimized grid structure of the UF-FDTD algorithm closely approximates the infinitesimal feeding gap, ensuring precise alignment with the exact dimensions of the feed-gap point in the dipole antenna configuration and accurately reflecting the effects

关键词： Antenna radiation

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AlFloos: a balanced dataset for the sixth issue of the Saudi Arabian currency banknote*

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Journal of Umm Al-Qura University for engineering and Architecture 2024年第4期15卷 446-454页

作者： Bati, Ghassan F. Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

Artificial Intelligence, especially machine learning, is considered by many researchers the new electricity and an important building block of the fourth industrial revolution. Saudi Arabia is amongst the first nations worldwide to realize this importance and apply it in its school systems at various levels. This interest motivates researchers to create Arabic datasets for research and educational purposes, particularly with the popularity of English sources and the absence of Arabic sources. This study attempts to lessen this gap by creating for the first time ever a dataset for the sixth issue of the Saudi Arabian currency and analyzing it using the famous machine learning tools: Orange Data Mining, Google Teachable Machine, and ***, which require no coding. The importance of the study to the community is as follows: 1—It provides the first work ever to establish a balanced dataset for the sixth issue of the Saudi Arabian currency banknote, which contains images for the banknote denominations and the tabular data generated using deep learning. 2—It is the first scientific work that uses shallow machine learning and deep learning models to create good-performing models for classifying the sixth issue of the Saudi Arabian currency without coding. This work paves the way for researchers and those interested from various fields and backgrounds to develop machine learning applications to classify the sixth issue of the Saudi Arabian currency, especially in mobile phones or in microcontrollers, to inspire multiple IoT and Tiny machine learning applications like currency recognition and classification for special needs people, automatic counting and sorting of banknotes in banks and stores, and fake currency detection. © The Author(s) 2024.

关键词： Deep learning Image classification Image embedding Saudi Arabian Currency Classification Supervised machine learning

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