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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 ieee.

关键词： Fruits

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Injecting Visual Features into Whisper for Parameter-Efficient Noise-Robust Audio-Visual Speech Recognition

Injecting Visual Features into Whisper for Parameter-Efficie...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Zhao Yeo, Yue Heng Jiang, Rui Fu, Xiao Chen, Weiguang Xi, Wei Zhao, Jizhong School of Computer Science and Technology Xi'an Jiaotong University China School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9798350368741

Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or fully finetuning a pre-trained model, both of which incur significant computational costs and are often impractical for large-scale speech foundation models. This gap highlights the need for more efficient methods to leverage visual and acoustic information in AVSR tasks. To address this challenge, we propose AVWhisper, a parameter-efficient model that integrates visual and acoustic representations by injecting visual features from the AV-HuBERT encoder into the pre-trained Whisper model. Our approach leverages the existing attention mechanisms in Whisper to facilitate cross-modal interaction and integrates auxiliary visual information through lightweight adapters based on Low-Rank Adaptation (LoRA) and prompt-based techniques. Furthermore, a two-phase training strategy is adopted to effectively handle cross-domain differences and visual information injection problems respectively. Extensive experiments on the LRS3-TED dataset demonstrate that AVWhisper consistently outperforms state-of-the-art methods across various noise conditions, offering a more efficient and scalable solution for audio-visual speech recognition. © 2025 ieee.

关键词： Audio-Visual Speech Recognition LoRA Noise-Robust Parameter-Efficient Prompt Whisper

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Research on Optimization Algorithm and Simulation Model of On-Board Battery Energy Management 5

Research on Optimization Algorithm and Simulation Model of O...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Hu, Yinquan Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9798331533694

This paper proposes an on-board battery energy management optimization algorithm based on deep reinforcement learning (DRL). By adjusting the battery charging and discharging strategy in real time, it optimizes energy usage and prolongs battery life. The algorithm models the battery charging and discharging process as a Markov decision process (MDP) and designs an adaptive reward function, which can dynamically optimize battery performance under different driving environments. Experimental simulation results show that the system using this optimization algorithm has significant advantages in energy efficiency, charging cycle and battery life compared with the traditional PID control algorithm. In various driving scenarios such as simulated urban roads and highways, the optimization algorithm improves energy efficiency by 12.6%, reduces charging cycles by 15%, and prolongs battery life by 18%. These experimental results verify the application potential of deep reinforcement learning in on-board battery energy management and demonstrate its advantages and feasibility in practical applications. © 2025 ieee.

关键词： Battery management systems

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Gradient Descent based Polarization Channel Estimation in Extremely Largescale MIMO systems 17

Gradient Descent Based Polarization Channel Estimation in Ex...

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17th ieee international conference on computer Research and Development, ICCRD 2025

作者： Liu, Shuo Wang, Hongmei Li, Shiyin Wang, Junze Li, Hanxiao Wang, Faguang China University of Mining and Technology School of Information and Control Engineering Xuzhou China

ISBN: (纸本)9798331531881

Accurate channel model and channel estimation are crucial for achieving extremely large-scale multiple-inputmultiple-output (XL-MIMO) in 6G networks with ultra-high spectral efficiency. As the aperture of the antenna array increases dramatically in XL-MIMO scenarios, the electromagnetic propagation field will change from far-field to near-field. In this paper, we propose a gradient descent-based near-field XL-MIMO channel model (GD-SOMP), an algorithm that makes the discrete adoption points more continuous to improve the estimation accuracy to match the actual near-field XL-MIMO scenarios. Numerical simulation results show that the GD-SOMP algorithm outperforms the conventional near-field channel estimation algorithm in terms of mean square error performance. © 2025 ieee.

关键词： Channel estimation

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IMC based PID Controller for Spherical Tank Liquid Level Control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 ieee.

关键词： Three term control systems

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Classifying Human Emotions through EEG data with Machine Learning 1

Classifying Human Emotions through EEG data with Machine Lea...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kaur, Gaganjot Gupta, Meenu Kumar, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798331529246

The Psychological and neurobiological phenomenon is called an emotion. The capability of human beings to present different expressions clashes with the emotional state of mind. Therefore, it is challenging to identify a human's real emotional state by observing physical appearance. Nowadays, using emotional engineering to predict human emotions is one of the major attempts of Machine Learning (ML). This study aims to investigate the usage of techniques of machine learning for efficient emotion prediction that relies on electroencephalography (EEG) signals. EEG dataset is complex and sometimes noisy, making it difficult to interpret using traditional methods. So, in this research the Kaggle dataset collected through a Muse headband, five distinct ML methods are applied to classify emotions. Performance metrics such as precision, recall, F1 score, support, and accuracy are employed to differentiate between different classification techniques. Random Forest turned out to be the best method due to its high accuracy 99%. Three emotions are predicted based on the results of performance metrics. This study benefits mental health monitoring, neurofeedback therapy, stress management, human-computer interaction, etc. This study lays the groundwork for exploring this field and helps develop more sophisticated control systems in the future. © 2025 ieee.

关键词： Emotion Recognition

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Convolutional-Neural Network Bases Speaker Emotion Detection based on Human-computer Interaction 1

Convolutional-Neural Network Bases Speaker Emotion Detection...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Nithya, B. Saranya, R. Joseph, Mary Josphineleela, R. Thirunagalingam, Arunkumar Kodali, Arun Kumar B.S. Abdur Rahman Crescent Institute of Science and Technology Dept of Computer Science and Engineering Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Karur India St. Joseph's Institute of Technology OMR Department of IT Chennai119 India Panimalar Engineering College Department of Computer Science and Engineering India Mckesson Corporation United States Mphasis Corporation

ISBN: (纸本)9798331518578

The present investigation proposes a Convolutional Neural Network (CNN)-based method for human-computer interaction (HCI) speaker emotion identification. Optimising the user experience through clearer and sympathetic connections is made possible by emotional knowledge. Conventional techniques for detecting emotions depend on manually created characteristics, which are frequently laborious and less resilient to speech variability. By using CNNs' automatic feature extraction capabilities, the suggested system analyses speech signal spectrograms and extracts crucial emotional indicators. The method obtains notable gains in emotion classification accuracy by retraining the system on a varied collection of emotional speech samples. Important emotional states like happiness, anger, sadness, and neutrality are all successfully distinguished using the CNN model. This study demonstrates how deep learning can help develop emotion-sensitive HCI systems and promote more organic and contextually aware human-machine interactions. Applications in customer service, healthcare, and virtual assistants can benefit from the technique's adaptability and versatility. © 2025 ieee.

关键词： Convolutional neural networks

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Design Process and Development of Automation for Smart Buildings 1

Design Process and Development of Automation for Smart Build...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sethi, Gunjan Khurana, Deepika Verma, Monika Pant, Surmadhur Chauhan, Priyanka Bhatt, Bhaskar Department of Computer Science and Engineering Delhi Technical Campus Uttar Pradesh Greater Noida India IIMT College of Engineering Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Shivalik College of Engineering Department of ECE Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Mechanical Engineering Dwarahat Almora India

ISBN: (纸本)9798331518578

In order to create spaces that are secure, cozy, energy-efficient, and visually beautiful, smart buildings are becoming more and more sophisticated cyber-physical systems. However, as functional complexity, financial concerns, and market demands increase, traditional design methodologies are becoming less effective. Current framework's plan and activity have been totally changed by the presentation of automated smart building, which ensures expanded security, better inhabitant solace, and energy effectiveness. With an emphasis on MATLAB-based reproductions, this study offers a careful assessment of the plan interaction and improvement procedures for computerizing savvy structures. The study delineates how MATLAB makes it simpler to depict, reproduce, and investigate savvy building frameworks with certainty. Through the mix of numerous advancements, like sensors, regulators, and correspondence conventions, the research shows the turn of events and check of a mechanized smart building framework. The intelligent building's building automation system prioritizes a humanized interface and uses a combination of mechanical, electrical, and fire control systems, access control systems, and parking management systems to give building occupants a convenient and comfortable place to live and work. The discoveries show prominent additions in functional viability, natural adaptability, and energy utilization. The conventional heuristic and ad hoc design paradigms are becoming ineffective and inadequate for the future due to cost, the requirement to handle growing functional complexity, adaptability, supply chain fragmentation, and time-to-market pressure. © 2025 ieee.

关键词： Smart homes

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning 13

Phoneme Classification in Imagined Speech Using Explainable ...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Kim, Sejin Kang, Hyunwook Jeong, Ji-Hoon Lee, Minji Dept. Artificial Intelligence The Catholic University of Korea Bucheon Korea Republic of Dept. Electrical and Computer Engineering Inha University Incheon Korea Republic of Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36%, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke’s area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke’s area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future. © 2025 ieee.

关键词： Adversarial machine learning

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AGC-MAC: A Conceptual Model-based Platform for the Management and Analysis of Clinical Data of Retina-Macula Diseases 4th

AGC-MAC: A Conceptual Model-Based Platform for the Managemen...

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24th international conference Web engineering (ICWE)

作者： Carreno-Bolufer, Jesus Reyes Roman, Jose Fabian Cabrera Beyrouti, Ruben Casamayor Rodenas, Juan Carlos Univ Politecn Valencia Valencian Res Inst Artificial Intelligence VRAIN Valencia Spain Hosp Vega Baja Orihuela Ophtalmol Serv Orihuela Spain

ISBN: (纸本)9783031751097;9783031751103

Macular diseases are the main cause of legal blindness worldwide. The macula is a small zone of the retina responsible for central vision. The elderly are the most impacted, though anyone is susceptible. However, the efficacy of the new treatments is still unclear. Clinicians depend on their experience and knowledge to treat the patients with the best medication. Consequently, many resources are wasted, and the patient's vision may get worse. In addition, many of them do not have proper software tools that help them manage and analyze their population of patients. To fight the lack of knowledge, it is essential to get a great amount of data efficiently, which implies using a well-defined Information System (IS). With this knowledge, creating profile patients and personalizing treatments would be possible. In this work, we present AGC-MAC, a Conceptual Model-based platform that overcomes these challenges and provides a user-friendly tool for managing and analyzing patients who suffer from retina-macula diseases.

关键词： Macular Diseases Conceptual Modeling AMD Information systems

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