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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Historical Study of the Development of the Misuse of Biological Weapons Technology 8

Historical Study of the Development of the Misuse of Biologi...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Agustin, Dian Mantoro, Teddy Yulivan, Ivan State Intelligence College Medical Intelligence Post-graduate Program Bogor Indonesia Sampoerna University and State Intelligence College Media-Tech Lab Department of Computer Science Jakarta Indonesia Indonesia Defence University Department of Information Systems Jakarta Indonesia

ISBN: (纸本)9781665453899

Technology will continue to be an important thing in life, technological developments can be found in everyday life. In medical intelligence technology, to identify bio-Terrorism actions, and aspects that can threaten national security, such as targets that may be the object of attack, identify indicators early, build biosecurity, and analyze pathogen communities. The threat that hit the world is when resources are used by irresponsible people or groups and are used as biological weapons (bioweapons), bioterrorism, and others. These biological weapons are not only used for war but can also be used for terrorism purposes. The involvement of biological weapons in the history of war has been used, various weapons were created before the 20th century biological weapons in the form of biological toxins or bacteria from decaying animals and plants, after the 20th-century weapons switched to bacteria (anthrax), viruses (smallpox), and poison (botulinum). Regarding what is used as a biological weapon, this is regulated by various policies, both national policies, and defense policies. Anticipatory steps and efforts to overcome the threat of bioterrorism need to involve all parties, including the community, security forces, health workers, and scientists. Legal reforms can be carried out to perfect and complement existing legal loopholes as a result of controlling bioterrorism which widens the opportunities or loopholes for bioterrorism attacks in Indonesia that should receive state attention and formulate bioterrorism regulations. © 2022 IEEE.

关键词： Bioterrorism

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A Comparative Study of Machine Learning Models for Sentiment Analysis on User Reviews of the Desa Wisata Nusantara Application in the Google Play Store

A Comparative Study of Machine Learning Models for Sentiment...

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International Conference on ICT For Smart Society (ICISS)

作者： Fairuz Iqbal Maulana Anita Rahayu Vandha Pradwiyasma Widartha Puput Dani Prasetyo Adi Agung Purnomo Andi Pramono Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Information System Pukyong National University Busan South Korea Research Center for Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

In the swiftly changing digital environment, user reviews on platforms like the Google Play Store have emerged as a crucial factor influencing an application's success. These reviews, abundant in user experiences and perceptions, serve as a valuable asset for sentiment analysis. However, the sheer volume of these reviews poses a challenge for manual analysis, thereby necessitating the deployment of automated sentiment analysis methods. We utilize the Bernoulli Naive Bayes (BNB) and Support Vector Machine (SVM) models, selected for its simplicity, efficiency, and capability to handle large datasets with high dimensionality, characteristic of text data such as user reviews. Based on the analysis and conclusions derived from the research inquiries in this study, it is evident that a significant proportion of customer reviews, classifying 42.39% (or 142 reviews) as positive, 45.07% (or 151 reviews) as negative, and 12.54 % (or 42 reviews) as Neutral sentiments regarding the Desa Wisata Nusantara Application on the Google Play Store. We found that the Bernoulli Naive Bayes model had better accuracy than the Support Vector Machine. The accuracy level of the Bernoulli Naive Bayes model is 73 % and the Support Vector Machine is 70%. We present the outcomes of our analysis, discuss their implications, and propose directions for future research. Our research signifies a promising advancement towards harnessing machine learning for sentiment analysis, setting the stage for future investigations in this intriguing field.

关键词： Support vector machines Sentiment analysis Analytical models Accuracy Reviews Data models Vectors User experience Bayes methods Internet

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Internet of Things for Smart Furniture: A Systematic Review

Internet of Things for Smart Furniture: A Systematic Review

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International Conference on ICT For Smart Society (ICISS)

作者： Fairuz Iqbal Maulana Bambang Kartono Kurniawan Andi Pramono Agung Purnomo Muhamad Ezran Bin Zainal Abdulah Mustafa Mohamed Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Design and Creative Technology Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan Bachok Kelantan School of Technology of the Arts Republic Polytechnic Singapore

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

The research entitled “Internet of Things for Smart Furniture: A Systematic Review” explores integrating IoT technology into smart furniture to improve functionality and user experience. By synthesizing existing literature, this research aims to discover how the Internet of Things (IoT) is leveraged to meet specific user needs with smart furniture and what hardware and sensor technologies are proposed for smart furniture design, considering factors such as scalability and need for users. Current research trends in securing user communications and data in the context of IoT-enabled smart furniture. This research involved a literature search which included journal articles and reviews from the Web of science (WOS) and the Scopus database (core collection) from 2014 to 2023, and the final results were 9 documents that would be reviewed. Several impactful means must be used to meet specific user needs, such as health monitoring, enhanced security, enhanced convenience and mobility, integration with smart home systems, and personalization and adaptability. Drawing insights from IoT data quality, machine learning applications in IoT, and smart furniture design for elderly care, this research provides a comprehensive picture of the current IoT-enabled smart furniture landscape. This research can explore existing IoT solutions for smart furniture, with a focus on cutting-edge technologies and design principles that can shape the future of this domain.

关键词： Data privacy Protocols Shape Scalability Data integrity Smart homes Market research User experience Hardware Internet of Things

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computer Vision and Fuzzy Logic for Sustainable Indonesian Fisheries 3

Computer Vision and Fuzzy Logic for Sustainable Indonesian F...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Saputra, Adi Sakinah, Putri Purwoko, Hery Zulardi, Indra Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

Tuna fishing in Indonesia is excessive and if left unchecked, some types of tuna will become extinct within 3-10 years. There is an urgent need to help MMAF for producing more accurate fish catches data. This paper proposed a system that utilizes computer vision, fuzzy logic and Global Positioning System (GPS) for supporting a sustainableIndonesian fishery by recognizing and measuring fish length, determine fish age and where is the catch location. The system can help authority to manage sustainable fisheries management in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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International Conference on ICT For Smart Society (ICISS)

作者： Desyra Sukma Dewanthi Hari Kristopo Nunung Nurul Qomariyah Muhammad Axel Management Department Business Management & Marketing Program BINUS Business School International Undergraduate Program Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18–40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business.

关键词： Data privacy Privacy Consumer behavior Focusing Electronic commerce Security Artificial intelligence Sustainable development Interviews Recommender systems

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Microwave-Based Complementary Split-Ring Resonator for the Detection of Variations in Aqueous Media

Microwave-Based Complementary Split-Ring Resonator for the D...

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Microelectronics Technology and Devices (SBMicro)

作者： Reinaldo Velásquez Diego Tami Gilberto Medeiros-Ribeiro Cássio G. Rego Jhonattan C. Ramirez Graduate Program in Electrical Engineering UFMG Brazil Institute of Technological Sciences UNIFEI Brazil Department of Computer Science UFMG Brazil Department of Electronics Engineering UFMG Brazil

In this paper, we demonstrated a highly sensitive microwave-based resonator sensor capable of detecting variations in an aqueous medium of up to 0.94 mg/dL. In the presented device, the intrinsic resonant response (resonant amplitude, resonant frequency, and quality factor) is capacitively perturbed in reaction to the sample presence. To achieve higher sensitivity, the sample under testing generally is placed on the resonator surface, where the electric field is highly concentrated at the operating frequency. Thus, demonstrating excellent results in terms of sensitivity and reproducibility.

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Audio-Driven Facial Landmark Generation in Violin Performance using 3DCNN Network with Self Attention Model

Audio-Driven Facial Landmark Generation in Violin Performanc...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ting-Wei Lin Chao-Lin Liu Li Su Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan

In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance. The audio-driven face generation technique in music performance is still deficient. In this paper, we compile a violin soundtrack and facial expression dataset (VSFE) for modeling facial expressions in violin performance. To our knowledge, this is the first dataset mapping the relationship between violin performance audio and musicians’ facial expressions. We then propose a 3DCNN network with self-attention and residual blocks for audio-driven facial expression generation. In the experiments, we compare our methods with three baselines on talking face generation. The codes and dataset are available on the Github (https://***/kevinlin91/icassp_music2face).

关键词： Visualization Codes Avatars Signal processing Acoustics Multiple signal classification Speech processing

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SHIFTING ATTENTION TO YOU: PERSONALIZED BRAIN-INSPIRED AI MODELS

arXiv

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arXiv 2025年

作者： Zhao, Stephen Chong Hu, Yang Lee, Jason Bender, Andrew Wallace, Mark Tovar, David A. Mazumdar, Trisha Data Science Institute Vanderbilt University Nashville United States Department of Computer Science Vanderbilt University Nashville United States Neurosciences Graduate Program University of California San Diego San Diego United States Department of Psychology Vanderbilt University Nashville United States

The integration of human and artificial intelligence offers a powerful avenue for advancing our understanding of information processing, as each system provides unique computational insights. However, despite the promise of human-AI integration, current AI models are largely trained on massive datasets, optimized for population-level performance, lacking mechanisms to align their computations with individual users’ perceptual semantics and neural dynamics. Here we show that integrating human behavioral insights and millisecond scale neural data within a fine tuned CLIP based model not only captures generalized and individualized aspects of perception but also over doubles behavioral performance compared to the unmodified CLIP baseline. By embedding human inductive biases and mirroring dynamic neural processes during training, personalized neural fine tuning improves predictions of human similarity judgments and tracks the temporal evolution of individual neural responses. Our work establishes a novel, interpretable framework for designing adaptive AI systems, with broad implications for neuroscience, personalized medicine, and human–computer interaction. © 2025, CC BY-NC-ND.

关键词： Neurophysiology

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Customer churn prediction in telecommunication company

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AIP Conference Proceedings 2023年第1期2510卷

作者： Dota Biomantoro Gede Putra Kusuma Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Churn prediction methods are widely used to anticipate customer churn from services provided by a company for some reasons. This study aims to develop an optimal churn prediction model based on customer data from a telecommunication company in Indonesia. The model development and evaluation processes are performed by following the Cross-Industry Standard Process for Data Mining (CRISP-DM), which consist of business understanding, data understanding, data preparation, modelling, and evaluation. Various combination of data preparation and modelling methods have been evaluated. The evaluation results show that the combination of feature selection and prediction model yields better results compared to prediction model without feature selection. The highest accuracy is achieved by Random Forrest at 97.82%, which is followed by Decision Tree at 97.06%, and Naive Bayes at 90.62%. This result indicates that a prediction model can be reliably used to predict customer churn in a telecommunication company.

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