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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Basak, Dona Miraz, Mahadi Hasan Jin, Hwang Ha Jibon, Ferdaus Anam Biswas, Satyendra Nath Sivakumar, Saaveethya Mahmud, Hasib Ahsanullah University of Science and Technology Eee Department Dhaka Bangladesh Curtin University Department of Management Digital Marketing Malaysia Sunway University Business Analytics Malaysia University of Information Technology & Sciences Computer Science Bangladesh Ahsanullah University of Science and Technology Computer Science Bangladesh Engineering Curtin University Department of Electrical & Computer Malaysia University of Barishal Department of Geology and Mining Bangladesh

ISBN: (纸本)9798350310689

This paper presents a low delay, high speed CMOS comparator in a lowest possible chip area for portable device application. The proposed comparator is designed using 90nm technology with a supply voltage of 0.8V and clock frequency of 6GHz. Delay, power consumption/conversion and offset voltage are analyzed. Transistor count is also reduced compared to several comparators mentioned in literature in order to make the chip area as compact as possible. Simulation is done using Spectre Circuit Simulator tool-Cadence. Some graphical analysis is made to characterize the proposed comparator behavior and also they are compared with other 90nm comparators for performance analysis. Power consumption is found to be 33.5 μw and delay is 54.32ps for 60% duty cycle of clock pulse. Standard deviation of offset voltage is 6.3mv from Monte Carlo method and the chip area is found to be (8.83∗8.61) m2. Also it has low parasitic effect and low noise than those conventional approaches. © 2023 IEEE.

关键词： Monte Carlo methods

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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PokéChamp: an Expert-level Minimax Language Agent

arXiv

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arXiv 2025年

作者： Karten, Seth Nguyen, Andy Luu Jin, Chi Department of Computer Science Princeton University United States Department of Electrical and Computer Engineering Princeton University United States

We introduce PokéChamp, a minimax agent powered by Large Language Models (LLMs) for Pokémon battles. Built on a general framework for two-player competitive games, PokéChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: (1) player action sampling, (2) opponent modeling, and (3) value function estimation, enabling the agent to effectively utilize gameplay history and human knowledge to reduce the search space and address partial observability. Notably, our framework requires no additional LLM training. We evaluate PokéChamp in the popular Gen 9 OU format. When powered by GPT-4o, it achieves a win rate of 76% against the best existing LLM-based bot and 84% against the strongest rule-based bot, demonstrating its superior performance. Even with an open-source 8-billion-parameter Llama 3.1 model, PokéChamp consistently outperforms the previous best LLM-based bot, Pokéllmon powered by GPT-4o, with a 64% win rate. PokéChamp attains a projected Elo of 1300-1500 on the Pokémon Showdown online ladder, placing it among the top 30%-10% of human players. In addition, this work compiles the largest real-player Pokémon battle dataset, featuring over 3 million games, including more than 500k high-Elo matches. Based on this dataset, we establish a series of battle benchmarks and puzzles to evaluate specific battling skills. We further provide key updates to the local game engine. We hope this work fosters further research that leverage Pokémon battle as benchmark to integrate LLM technologies with game-theoretic algorithms addressing general multiagent problems. Videos, code, and dataset available at https://***/view/pokechamp-llm. © 2025, CC BY.

关键词： Benchmarking

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Text Classification Accuracy Enhancement Using Deep Neural Networks

Text Classification Accuracy Enhancement Using Deep Neural N...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Najm, Mobajjal Kadhim Aljumaily, Mustafa S. Al-Khaleefa, Ahmed Ayal, A.M. Mohammed, Dheyaa Abdulameer Challoob, Aqeel Luaibi Shahid Chamran University of Ahvaz Faculty of Engineering Computer Engineering Department Ahvaz Iran University of Tennessee Electrical Engineering and Computer Science Department Tennessee United States University of Misan Faculty of Education Department of Physics Maysan Iraq University of Misan Maysan Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq Department of Computer Techniques Engineering1 Baghdad Iraq

ISBN: (纸本)9798350341881

Machine-learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), using object and learning approaches, performs well in statistical tasks, such as text classification or sentiment mining. Currently, in the field of natural language processing (NLP), various models, methods, and architectures have been proposed and a wide range of NLP tasks have been developed. Most natural language processing (NLP) problems can be formulated as classification problems (decide on the class of this object depending on the object and its context). In this field, machine learning methods and especially using neural network models are the most used. In this research, we propose a deep learning architecture for text classification that can influence model performance. The basic steps of this proposed method include text pre-processing (including sentence segmentation, punctuation, rooting, speech tagging, and vector conversion), text feature extraction, and LSTM algorithm-based classification model construction. The dataset used in this study is 20NewsGroup and IMDB. The evaluation results are provided in the form of MSE, Precision, Recall, Accuracy, and F-Measure evaluation criteria. © 2023 IEEE.

关键词： Neural network models

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A Mobile Visitor Management System Using a QR Code and PIN for Access Control 2nd

A Mobile Visitor Management System Using a QR Code and PIN f...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Awotunde, Joseph Bamidele Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Mejdoub, Youssef Abdualazizu, Zakariyya Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science Bowen University Osun State Iwo Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

Visitor management is a common activity that happens every day all around the world. Accepting visitors at home or in the office, going to the bank, or visiting a private residential area;some sort of visitor management goes on in all those activities to ensure access control and security. Insecurity is a big challenge that comes with receiving visitors and that is why visitor management is important. Traditionally, visitor management is handled physically by recording visitors’ information into a log book and asking visitors to contact their hosts before gaining access to a secured environment but this process is time-consuming and the visitor’s record is exposed to exploitation. Automated Visitor Management Systems (VMS) overcome the shortcomings of the traditional method of visitor management. VMS automates the process of recording visitors’ information, keeping a log of all visits, and ensuring some sort of access control or visitor authentication just in case of an imposter or if anything ever goes wrong. This study aims to design and model a flexible Mobile Visitor Management system that can be used to record visitors’ information and authenticate visitors for access control purposes. The study was achieved using Figma software to design and prototype the system and then using React Native and SQLite for the development of the system. The system has proven to be more convenient than the common traditional way of recording visitors’ information into a physical log book. The system protects its users from loss of information, waste of time, and insecurity. The flexible and inexpensive nature of the system makes it adaptable to almost any organization or property owner, this gives the general public a great opportunity to try out Visitor Management Technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Authentication

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Protection Scheme Analysis of LLC Series Connected Auxiliary Power Supply for HV SiC Grid-Connected Inverters

Protection Scheme Analysis of LLC Series Connected Auxiliary...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lin, Min Tolbert, Leon M. Rowden, Brian The University of Tennessee Curent Min H. Kao Department of Electrical Engineering and Computer Science United States Oak Ridge National Laboratory KnoxvilleTN United States

ISBN: (纸本)9798350376067

Auxiliary power supplies (APS) in high voltage silicon carbide (SiC) applications, such as 10 kV systems, require high insulation capability and low coupling capacitance. A common APS topology is the LLC series-connected design, where a high voltage wire runs through multiple cores to provide isolated outputs. However, faults like short circuits or open circuits at one output can disrupt other output voltages. This paper examines a potential protection scheme for this configuration, highlighting its pros and cons. The analysis aims to provide a balanced view of how the proposed scheme can improve APS performance and reliability in high voltage SiC applications, while considering the associated trade-offs. © 2024 IEEE.

关键词： Power distribution faults

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Quantum Multi-Agent Reinforcement Learning for Joint Cube-Satellites and High-Altitude Long-Endurance Aerial Vehicles in SAGIN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Kim, Gyu Seon Cho, Yeryeong Park, Soouhyun Jung, Soyi Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

Cube satellites (CubeSats) have grown into the primary non-terrestrial network capable of providing global access services in satellite-air-ground integrated networks (SAGIN). Nonetheless, the provision of genuinely global access services solely via CubeSats is challenging due to the frequent handovers and the existence of polar regions where service availability is compromised in SAGIN. To tackle these issues, the design of innovative quantum multi-agent reinforcement learning (QMARL)-based algorithm is tailored for the cooperative scheduling of multi-CubeSat/high-altitude long-endurance unmanned aerial vehicle (HALE-UAV) systems. This algorithm aims to achieve high quality of services, energy efficiency, and high capacity. Furthermore, logarithmic scale reduction in action dimensions can be realized, due to the modification in quantum measurement in QMARL. This is essential when the number of CubeSats and HALE-UAVs increases. Based on a realistic CubeSat/HALE-UAV experimental environment using real-world data, the excellence of our proposed QMARL-based scheduler is demonstrated. © 2025 IEEE.

关键词： Reinforcement learning

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Performance Evaluation and Analysis of Deep Learning Autoencoder-Based Wireless Communication System 3

Performance Evaluation and Analysis of Deep Learning Autoenc...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Ismail, Eman Ali, Abdelhay Aly, Omar A. M. Abo-Zahhad, Mohammed Assuit University Faculty of Engineering Department of Electrical Engineering Assuit Egypt Egypt-Japan University of Science and Technology Faculty of Electronics Communications and Computer Engineering Department of Electronics and Communications Engineering Alexandra Egypt

ISBN: (纸本)9798350323511

Recent advancements in deep learning have led to the emergence of autoencoder-based (AE) wireless communication systems, presenting a promising approach to tackle the challenges posed by conventional mathematical models. In this research, a thorough evaluation and analysis of the performance of deep learning AE-based wireless communication systems is conducted. Specifically, our investigation focuses on employing the additive white Gaussian noise (AWGN) channel and explores the impact of varying signal-to-noise ratio (SNR) conditions during the AE training process on system performance. The results show that training the autoencoder under diverse SNR conditions, particularly with an extended number of epochs, surpasses the performance of a fixed-trained autoencoder regarding block error rate (BLER). Additionally, a comparison of BLER between multiple AE models and traditional mathematical representations used in communication systems is conducted. The obtained findings indicate that deep learning AE-based wireless communication systems exhibit promising performance compared to conventional models, underscoring their potential as an effective solution for wireless communication systems. © 2023 IEEE.

关键词： Signal to noise ratio

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Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation 30

Dynamic Reward Adjustment in Multi-Reward Reinforcement Lear...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Min, Do June Pérez-Rosas, Verónica Resnicow, Kenneth Mihalcea, Rada Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States School of Public Health University of Michigan Ann ArborMI United States

ISBN: (纸本)9782493814104

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text properties for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DYNAOPT and C-DYNAOPT, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DYNAOPT and C-DYNAOPT, outperform existing naive and bandit baselines, demonstrating their potential for enhancing language models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Reinforcement learning

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Inverse Design of Silicon Nitride Wavelength-division Multiplexers and Frequency-selective Resonators

Inverse Design of Silicon Nitride Wavelength-division Multip...

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2024 Frontiers in Optics, FiO 2024

作者： Liu, Danxian Bostan, Egemen Zeng, Tianyi Paul, Aditya Yang, Kiyoul John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

We experimentally demonstrate inverse-designed 980/1530 nm and 1480/1530 nm wavelength-division-multiplexers on an 800 nm-thick silicon nitride platform. The devices exhibit © Optica Publishing Group 2024, ©... 详细信息

关键词： Silicon nitride

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