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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Panimalar Kathiroli Kanmani. S Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of science and technology Chennai Department of Information Technology Puducherry Technological University Puducherry

Nature-inspired algorithms have become increasingly prevalent in engineering and mathematical optimization due to their efficacy and adaptability. Among these algorithms, the Monarch Butterfly Optimization Algorithm (MBOA) is a population-based metaheuristic, characterized by its simple implementation and the utilization of the Migration Operator (MO) and Butterfly Adjusting Rate (BAR) to guide the search path of butterflies. However, the algorithm can suffer from susceptibility to local optima traps, early convergence, and inferior performance due to a lack of random factors. To address these challenges, we propose an Enhanced Monarch Butterfly Optimization Algorithm (E-MBOA) that incorporates Ranking-based Selection (RBS) and a Random Jumping strategy. The RBS prioritizes the search direction based on individuals' ranks in the population, enhancing the search process by encouraging the exploration of feasible areas with lower objective function values. By employing the Random Jumping strategy, the process can effectively investigate the search space and avoid local optima, thus mitigating early convergence issues. The proposed model is assessed on a variety of mechanical and engineering design problems. Results are contrasted to other optimization approaches published in the literature, demonstrating that the proposed model offers faster convergence to global optima and effectively avoids local optima traps. E-MBOA's performance validates its feasibility and effectiveness as a powerful tool for resolving complex engineering optimization problems.

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Smart Building Indoor Temperature Prediction Using the IoT and Machine Learning 2

Smart Building Indoor Temperature Prediction Using the IoT a...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Vincent, Robin Rohit Sarkar, Swagata Irence, Shalom Prakash, R.B.R. Senthil Rama, R. Suganthi, D. School of Computer Science & Engineering and Information Science Computer Science & Engineering Presidency University Bengaluru560064 India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai 44 India EEE Karunya Kits / CBE India Koneru Lakshmaiah Education Foundation Electrical and Electronics Engineering Vaddeswaram522302 India DMI College of Engineering EEE Chennai India Saveetha College of Liberal Arts and Sciences Department of Computer Science Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350397635

Due to fast expansion and improved lifestyle, the need for useable energy has skyrocketed in recent decades, notably in the construction industry. Several factors, such as local climate, building makeup, and energy consumption habits, affect a structure's efficiency in using energy. In this paper, we aim for forecasting the interior temperature of smart buildings by combining IoT with popular machine Learning algorithms. This prediction model has been developed using online learning techniques to increase its adaptability to novel inputs. This paper details a methodology for creating an analytical model that may be utilized for smart building interior temperature forecasting by merging the IoT with some popular machine learning methods. This prediction model was built with an online-learning strategy, so it may be used for a dataset with which the developers are unfamiliar. To ensure the precision of the method, this article uses Machine Learning to test it using real-world sensor data. In order to verify the methodology, the study conducts experiments using Machine Learning on collected, real-world sensor data. The study then proposes incorporating the following method into an Edge Computing based IoT architecture in order to make the building operational in an energy-efficient manner. The learning framework was validated with data collected from an Internet of Things network installed in a home over the course of a summertime. The learnt model's efficacy has been evaluated, and the model's predictability of interior temperature changes has been quantified. The paper demonstrates the viability of the proposed Learning framework for thermal models based on the Internet of Things and provides a pragmatic answer to the problem of supplying accurate thermal models for use in the temperature controls of smart buildings of the future. © 2023 IEEE.

关键词： Energy utilization

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Dimensionality and dynamics for next-generation artificial neural networks

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Patterns 2025年

作者： Wang, Ge Fan, Feng-Lei Department of Biomedical Engineering Department of Electrical Computer and Systems Engineering Department of Computer Science Center for Computational Innovations Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Data Science City University of Hong Kong Kowloon Hong Kong

The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms. © 2025 The Author(s)

关键词： Neural network models

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Electro-optical phase-locked loop for hybrid integrated external cavity laser

Electro-optical phase-locked loop for hybrid integrated exte...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Liu, Chuxin Guo, Yuyao Xu, Ruiyang Lu, Liangjun Chen, Jianping Zhou, Linjie Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We implement an analog EO-PLL for a III/V-Si3N4 hybrid integrated ECL to generate a highly-linear FMCW signal over multiple wavelengths. The ranging resolution is improved from 5 m to 35 cm for a 100-m target. © ... 详细信息

ISBN: (纸本)9781957171180

关键词： Phase locked loops

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FIXED-BUDGET DIFFERENTIALLY PRIVATE BEST ARM IDENTIFICATION

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arXiv 2024年

作者： Chen, Zhirui Karthik, P.N. Chee, Yeow Meng Tan, Vincent Y.F. The Department of Industrial Systems Engineering and Management National University of Singapore Singapore The Institute of Data Science National University of Singapore Singapore The Department of Mathematics The Department of Electrical and Computer Engineering National University of Singapore Singapore

We study best arm identification (BAI) in linear bandits in the fixed-budget regime under differential privacy constraints, when the arm rewards are supported on the unit interval. Given a finite budget T and a privacy parameter Ε > 0, the goal is to minimise the error probability in finding the arm with the largest mean after T sampling rounds, subject to the constraint that the policy of the decision maker satisfies a certain Ε-differential privacy (Ε-DP) constraint. We construct a policy satisfying the Ε-DP constraint (called DP-BAI) by proposing the principle of maximum absolute determinants, and derive an upper bound on its error probability. Furthermore, we derive a minimax lower bound on the error probability, and demonstrate that the lower and the upper bounds decay exponentially in T, with exponents in the two bounds matching order-wise in (a) the sub-optimality gaps of the arms, (b) Ε, and (c) the problem complexity that is expressible as the sum of two terms, one characterising the complexity of standard fixed-budget BAI (without privacy constraints), and the other accounting for the Ε-DP constraint. Additionally, we present some auxiliary results that contribute to the derivation of the lower bound on the error probability. These results, we posit, may be of independent interest and could prove instrumental in proving lower bounds on error probabilities in several other bandit problems. Whereas prior works provide results for BAI in the fixed-budget regime without privacy constraints or in the fixed-confidence regime with privacy constraints, our work fills the gap in the literature by providing the results for BAI in the fixed-budget regime under the Ε-DP constraint. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 IEEE.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

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Robust Signal Temporal Logic-based Planning for Uncertain External Events

Robust Signal Temporal Logic-based Planning for Uncertain Ex...

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IEEE International Conference on Robotics and Biomimetics

作者： Shumpei Tokuda Masaki Yamakita Hiroyuki Oyama Rin Takano Department of Systems and Control Engineering Tokyo Institute of Technology Meguro Japan Data Science Laboratories NEC Corporation Kawasaki Kanagawa Japan

We propose a new nonlinear optimization-based method for signal temporal logic (STL) specification with uncertain external events. STL provides a simple way to express complex task specifications for robotic and cyber-physical systems. Event-based STL is an extension of STL and can describe the response to external events, which should be considered in practical applications. We define smooth robustness functions for event-based STL specifications and formulate a nonlinear optimization problem for event-based STL specifications. Our method introduces augmented continuous variables for external events into the optimization problem to generate robust trajectories for uncertain external events that may occur. We demonstrate the effectiveness of our method through numerical simulations.

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EmoSync: Facial Emotion Detection for Adaptive Music Playback

EmoSync: Facial Emotion Detection for Adaptive Music Playbac...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Devikanniga Devarajan K Rajkumar Computer Science and Engineering GITAM Deemed to be University Nagadenahalli Doddabalpur Bangalore Karnataka Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image directories, segmented by distinct emotions. The resulting model, equipped with convolutional, pooling, and dense layers, effectively classifies emotions from 48x48 grayscale images. In application, the model overlays detected emotions onto a webcam stream. An integrated music player module synchronizes music with the subject’s emotions. Overall, this research explores a comprehensive interactive system focused on recognizing and responding to human emotions. Without further training data, our model, to the best of our understanding, achieves modern single-network accuracy of 74.57% on FER2013

关键词： Emotion recognition Image segmentation Webcams Interactive systems Training data Streaming media Gray-scale

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A Successful Spam Detection Technique for Industrial IoT Devices based on Machine Learning Techniques 2

A Successful Spam Detection Technique for Industrial IoT Dev...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Manjunatha, B.N. Chandan, Madhavarapu Kottu, Sreekanth Rappai, Sherin Hema, Priya K Singh Rawat, Kishan Sarkar, Swagata R L Jalappa Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore561203 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India St Peter's Engineering College Department of Information Technology Telangana Medchal-Malkajgiri500100 India Kristu Jayanti College Department of Computer Science Karnataka Bengaluru560077 India Panimalar Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai600123 India Graphic Era Deemed to Be University Department of Civil Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

This research study discusses about the detection of botnets that use the Internet Relay Chat protocol. This application protocol, popularly used by programs in large chat rooms is the predominant protocol among botnet Command and Control Servers, although its use has declined in recent years. This study analyzed the use of Machine Learning techniques for the detection of Botnets based on the IRC protocol in a network, through the analysis of bidirectional flows. Similar research studies are becoming increasingly relevant to the use of networked devices that can be integrated into botnets by further increasing the power of their attacks. As a result, it is possible to conclude that this research study accomplished its objectives by investigating the application and efficiency of different algorithms, as well as the usage of characteristics employed in previous studies and explores new possibilities. According to the results, the tested classifiers were effective in completing their tasks and achieving high levels of accuracy. The best results were obtained by the Tree-based approaches., followed by the Support Vector Machine methods, and finally by the Naive Bayes method. © 2023 IEEE.

关键词： Python

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A Novel Breast Cancer Detection and Classification Using Enhanced ANN Classification

A Novel Breast Cancer Detection and Classification Using Enh...

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IoT Based Control Networks and Intelligent systems (ICICNIS), International Conference on

作者： P. Mathiyalagan G. Kiruthiga A. Rasmi M. Vadivel K.V. Mahalakshmi T.S. Poornappriya Department of CSE Sri Ramakrishna Engineering College Coimbatore Dept of Artificial intelligence and Data science Karpagam College of Engineering Coimbatore Information Science and Engineering RR Institute of Technology Bangalore Department of Information Technology Excel Engineering College Namakkal TamilNadu CSE B V Raju Institute of Technology Telangana Data and Analytics Aspire Systems Pvt Ltd Bengaluru Karnataka

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography.

关键词： Image segmentation Sensitivity Mortality Predictive models Inspection Breast cancer Reliability Lesions Intelligent systems Medical diagnostic imaging

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