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Special Issue on InterPACK2023

JOURNAL OF ELECTRONIC PACKAGING 2024年第4期146卷 040301页

作者： Warzoha, Ronald J. Agonofer, Damena Giri, Ashutosh Shah, Jimil US Naval Acad Dept Mech & Nucl Engn Annapolis MD 21402 USA Univ Maryland Dept Mech Engn College Pk MD 20737 USA Univ Rhode Isl Dept Mech Engn Kingston RI 02881 USA Stealth Startup Palo Alto CA USA

The international Technical conference on Packaging and Integration of Electronic and Photonic Microsystems (InterPACK) is the leading international conference for the dissemination of basic and applied research in the broad field of electronics and photonics packaging. Since its inception in 1992, the InterPACK conference has served as the primary forum for members of the American Society of Mechanical Engineers Electronic and Phontonic Packaging Division (ASME EPPD) to exchange knowledge and ideas, guiding collaborative efforts between academia, national labs, and industry. The 2023 ASME InterPACK conference was held in San Diego from October 23rd through October 26th. The program contained more than 210 technical presentations, 90 original technical papers, tutorials, panel discussions, workshops, and plenary talks aligned with the following areas: (1) heterogeneous integration, (2) data centers and modular edge systems, (3) electronics packaging, (4) power/RF electronics and photonics, (5) multiscale thermal transport and energy storage, (6) flexible, wearable, and printed electronics, and (7) transportation systems, AI, and machine learning. This Special Issue of the ASME Journal of Electronic Packaging includes selected papers from the conference that represent cutting-edge research being conducted in these focus areas. All submitted manuscripts went through an independent peer review process in accordance with the editorial standards of the ASME. We expect that this series of papers will help to educate the wider EPPD and scientific communities on the exciting topics covered in the conference. We would also like to thank the authors who chose to publish their work in this Special Issue, as well as the reviewers. We would also like to thank Professor Ricky Lee and Andrew Clarke from the ASME Editorial team for their assistance with the organization of this collection of manuscripts.

关键词： Cutting Electronic packaging Teams Energy storage machine learning Packaging data centers Workshops (Work spaces) Transportation systems engineering teachers Microelectromechanical systems Electronics Mechanical engineers Photonics

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Multidisease Prediction and Causality Analysis with Hospital Management System

Multidisease Prediction and Causality Analysis with Hospital...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Abhro Kumar Roy Subham Gayen P Renukadevi Dept. Of Computer Science and Engineering (C. Tech Department) SRM Institute of Science and Technology KTR Chennai Computer Science and Engineering (C. Tech Department) SRM Institute of Science and Technology KTR Chennai

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In healthcare domain, the demand for efficient diagnosis and hospital management systems has increased unprecedentedly. This research study proposes a novel Symptom Checker integrated with a Multidisease Hospital Management System, leveraging state-of-the-art machine learning (ML) algorithms such as Random Forest Classifier and XGB Regression. Unlike existing systems, this advanced framework outperforms conventional methods by offering increased accuracy and agility in diagnosing a multitude of medical conditions. The proposed system operates as a comprehensive tool, integrating patient symptoms with ML algorithms to accurately identify the diseases. Through the analysis of symptoms given as input by patients, the Random Forest Classifier preforms well in classifying a broad spectrum of diseases, demonstrating high precision in analysing complex symptom patterns. Furthermore, the XGB Regression model enhances the system’s predictive capabilities, enabling the estimation of disease progression and facilitating proactive treatment strategies. Moreover, the proposed approach extends beyond symptom analysis. It encompasses a robust hospital management component that streamlines administrative tasks, optimizes resource allocation, and enhances patient care.

关键词： Accuracy machine learning algorithms Hospitals Computer architecture Streaming media Classification algorithms Task analysis

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Acoustic Anomaly Detection of Mechanical Failure: Time-Distributed CNN-RNN Deep learning Models 3rd

Acoustic Anomaly Detection of Mechanical Failure: Time-Distr...

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3rd international conference on Control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Yong, Lin Zhan Nugroho, Hermawan Electrical and Electronic Engineering Department University of Nottingham Semenyih Malaysia

ISBN: (纸本)9789811939228

This paper aims to perform anomaly detection on machine sound using various combinations of CNN-RNN deep learning models. The Convolutional Neural Network (CNN) was combined with three popular Recurrent Neural Network (RNN), which are Long short-term memory cell (LSTM), Gated recurrent unit (GRU), and Recurrent neural network (RNN) were used in this study. Besides, Time-distributed function was proposed in the CNN layer to perform local feature extraction from the time-series data represented by MFCC coefficients. The developed models were then evaluated with MIMII dataset. The evaluation includes confusion matrix, accuracy, precision, sensitivity, F1 score, AUC, and training speed. Results show that CNN-LSTM model outperforms the other and achieve more than 99% in the assessment parameters. CNN-GRU model however has the fastest training speed, which is 45% faster than the CNN-LSTM model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Federated learning-Based Network Intrusion Detection with a Feature Selection Approach 3

Federated Learning-Based Network Intrusion Detection with a ...

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3rd international conference on Electrical, Communication and Computer engineering, ICECCE 2021

作者： Qin, Yang Kondo, Masaaki The University of Tokyo Tokyo Japan

ISBN: (纸本)9781665438971

With the increase and diversity of network attacks, machine learning has shown its efficiency in realizing intrusion detection. Federated learning (FL) has been proposed as a new distributed machine learning approach, which collaboratively trains a prediction model by aggregating local models of users without sharing their privacy-sensitive data. Recently, the approach is applied to optimize intrusion detection for resourced-constrained environments. However, since the attacks are becoming more sophisticated and targeted, there is also a growing need to enhance detection models according to the characteristics of attack type;meanwhile, choosing effective feature sets from the network traffic characteristics is considered one of the most important technologies in data analysis. In this paper, we first proposed a federated learning-based intrusion detection system with feature selection technology. Firstly, a greedy algorithm is suggested to select features that achieve better intrusion detection accuracy regarding different attack categories. Afterward, multiple global models are generated by the server in federated learning, according to the decided features of edge devices. For evaluating the effectiveness of the proposed approach, simulation experiments based on the latest on-device neural network for anomaly detection are conducted over the NSL-KDD dataset. Experimental results demonstrate greatly improved accuracy of our method. © 2021 IEEE.

关键词： Intrusion detection

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A Comparative Analysis of machine learning Models for Fake News Detection

A Comparative Analysis of Machine Learning Models for Fake N...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Dhuriya Ankit Subhash Natasha Sharma Anoop Kumar Sudhanshu Kumar Jha Ishica Rajneesh Pandey Computer Science and Engineering Chandigarh University Mohali Punjab

The rapid increase in the sharing of fake news poses a major threat in this modern digital world to information integrity. This issue can be addressed by using the machine learning (ML) models. This research is based on comparative study of four such ML models- Random Forest Classifier, Logistic Regression, Decision Tree, and Gradient Boosting Classifier. Various performance metrics such as precision, accuracy, recall, Fl-score are employed for model evaluation. The dataset is being used to train the features based on textual content and metadata. This particular analysis produces the results like Random Forest Classifier, which exhibits highest accuracy and provides reliable outcomes in differentiating fake news and actual news. Additionally, the research underscores the importance of data preprocessing, feature selection, and model hyper-parameter tuning in optimising the efficiency in fake news detection. This study provides valuable insights to combat the spreading of fake news in this digital age.

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Classification of Structures and Monuments based on Indian Architectural Styles

Classification of Structures and Monuments based on Indian A...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Ananya Ganapathi Deivanai Saravanan Sneha T Raghavan Jyotsna C Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic value. The study addresses the gap between technology & heritage by developing a framework for classifying 8 architectural styles: Buddhist, Indo Islamic, Rajput, Dravidian, Hindu, Sikh, British & Modern. Using the HOG method for feature extraction, the models capture intricate architectural details. Classification is done using machine learning models like Logistic Regression, Random Forest & XG-Boost, as well as advanced deep learning models such as DenseNet & InceptionV3. DenseNet achieves the highest accuracy of 86%, followed by InceptionV3 at 79%, showing the capability of deep learning in handling complex visual data. The study not only compares model performance but also provides a scalable method for architectural style classification that contributes to the heritage preservation.

关键词： Deep learning Logistic regression Visualization Accuracy Computational modeling Computer architecture Feature extraction Cultural differences Intelligent systems Random forests

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Prediction of Diabetes using machine learning Algorithms

Prediction of Diabetes using Machine Learning Algorithms

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Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Priyanka Kanaparthi Janjhyam Venkata Naga Ramesh M V B Murali Krishna M U Ganesh Naidu V S Divya Sundar Anantharamaiah Vengala Department Of CSBS B V Raju Institute of Technology NarsapurMedak India Department of CSE Graphic Era Hill University Dehradun Department of CSE Aditya University Surampalem India Department of CSE Saveetha Engineering College Chennai India Department of BES-1 K L Deemed To Be University Vaddeswaram A.P India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In this work, we utilize machine learning to build a diabetes risk prediction model which is uniquely suited to a dataset of only females. The dataset focuses on important health measurements of women, such as the frequency of pregnancies and blood tests for insulin and glucose. We develop a Support Vector machine (SVM) algorithm using NumPy, Pandas, and other Python packages. SVM is a good fit for the complex interplay of factors in diabetes risk prediction because of its ability to handle high-dimensional datasets and non-linear interactions. The approach uses historical data to train our SVM model, which enables the computer identify the patterns and correlations inside the dataset. Through the identification of minor associations between physiological indicators and pregnancy history, the model gains proficiency in forecasting the probability of developing diabetes.

关键词： Support vector machines Pregnancy machine learning algorithms Computational modeling Medical services Predictive models Prediction algorithms Diabetes Frequency measurement Risk management

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machine learning in Local Positioning Systems

Machine Learning in Local Positioning Systems

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Informatics, Electronics, and Radio engineering (PIERE), international conference Problems of

作者： Elena V. Kokoreva Yuliya S. Lizneva Ksenia I. Shurygina Ilya V. Doshchinsky Digital Broadcasting and Radio Communication Systems Department Siberian State University of Telecommunication and Information Science Novosibirsk Russia Infocommunication Systems and Networks Department Siberian State University of Telecommunication and Information Science Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

In the modern circumstances related to the public life's informatization and digitalization, as well as the development of infocommunication technologies, the greatest interest lies in the services based on determining the object's location. The developers are facing the topical challenge of introducing the artificial intelligence to the geolocation technologies. Local positioning systems allow navigating or searching for objects indoors, where the radio signal from satellite (global) navigation systems reaches being greatly distorted and does not provide acceptable geolocation accuracy. The authors have developed the software intended for determination of the mobile object's coordinates with the use of neural networks. Two different approaches to this issue are compared in this article - firstly, the use of Neural Network Toolbox from the MATLAB application package, and secondly, the development of software modules in the Python programming language using the TensorFlow and Keras libraries. The authors analyzed the obtained results by such criteria as, first of all, the highest accuracy (least error) of positioning, as well as ease of implementation and adaptability of the proposed solutions.

关键词： Accuracy Geology Neural networks Radio navigation machine learning Satellite navigation systems Search problems Software Wireless fidelity Python

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Predictive Maintenance Analysis Using Multivariate machine Performance data for Industrial Downtime Reduction

Predictive Maintenance Analysis Using Multivariate Machine P...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Rajeswara Reddy Saddala D. Radha V. S. Kirthika Devi Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Tool downtime prediction requires efficient monitoring because it helps maximize industrial operation quality while maintaining uninterrupted plant operations. The analysis utilizes machine learning algorithms with Decision Trees to identify machine downtime events through operational measurements which include hydraulic pressure and coolant temperature and spindle speed and vibration levels. data normalization alongside Forward Selection feature selection methods were used on a balanced dataset to boost model performance for prediction purposes. The Decision Tree model functioned to differentiate machine defects into two discrete categories-failure and no failure-thus proving its competence in such predictive classification activities. The study conducted a comparative evaluation between Decision Trees and Random Forest along with other algorithms which included SVM and Logistic Regression and Adaboost in addition to CatBoost and XGBoost and Naive Bayes and KNN using KD-Tree and Ball-Tree variations. The experimental findings show that Decision Trees provide clear predictions coupled with high precision and fair runtimes to support predictive maintenance implementations. The research delivers fundamental findings that help improve downtime prediction methods to boost operational effectiveness.

关键词： Vibrations Temperature measurement Support vector machines Accuracy Predictive models Prediction algorithms Maintenance Decision trees Random forests Predictive maintenance

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Anomaly-Based Intrusion Detection Systems by using machine learning based Stacking Ensemble Model

Anomaly-Based Intrusion Detection Systems by using Machine L...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： P R Buvaneswari Madhura G K Heba Alasady K. Alagarraja Mukesh Soni School of Electrical and Electronics Engineering VIT Bhopal University Bhopal India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering University Centre for Research & Development Chandigarh University Mohali India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, network intrusion refers to unauthorized access to systems or networks by external attackers or insiders aimed at stealing information or deploy malicious software. Traditional approaches for anomaly-based intrusion detection systems have faced several challenges which include high computational overhead and limited capability in detecting zero-day attacks due to reliance on predefined attack signatures or handcrafted features. To address these limitations, this research proposes a machine learning-based Stacking Ensemble Model (ML-SEM) for anomaly-based intrusion detection, effectively reducing computational overhead and enhancing detection of previously unseen attacks. Initially, data is collected from University of Nevada, Reno Intrusion Detection dataset (UNR-IDD) which consists of network traffic data and various types of attacks. Then, the collected data is preprocessed by using standard scaler normalization which ensures that all features have a mean of zero and a standard deviation of one. After that, the features are selected using Principal Component Analysis (PCA) which reduces dimensionality and enhances model efficiency by eliminating redundant and less significant features. Finally, anomaly-based intrusion detection is done by using proposed ML-SEM. The proposed ML-SEM achieved better results in terms of accuracy (98.6%), precision (95.5%) and recall (98.3%) when compared with existing Random Forest (RF).

关键词： Radio frequency Analytical models Adaptation models Accuracy Stacking Intrusion detection Telecommunication traffic Integrated circuit modeling Standards Principal component analysis

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