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Enhancing the Production of White Charcoal and Syngas from a Stove with an IoT Airflow Controller

Enhancing the Production of White Charcoal and Syngas from a...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Charnyut Karnjanapiboon Nattapon Wichan Chakkraphan Thawonngamyingsakul Kiattisak Sornjai Kanyaphorn Chaiwong Faculty of Engineering Rajamangala University of Technology Lanna Nan Thailand Faculty of Engineering Rajamangala University of Technology Lanna Tak Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

The aim of this study is to improve the production of white charcoal and syngas using a stove equipped with an IoT airflow controller. The airflow regulation system for a gasification furnace including a double-burner, utilized for syngas production alongside white charcoal. The temperature range for a double-burner gasification furnace, utilizing the prescribed IoT air flow control system, is between 800 and 1,250 °C. The gasification reaction at the stove's exterior generates syngas, while the pyrolysis process at the stove's core produces white charcoal. The experimental air flow rate ratio ranged from 2.5 to 4 m/s to examine the yield of syngas and the characteristics of white charcoal derived from rice husk. The implementation of an IoT air flow control system revealed that the air flow rate could be adjusted and the furnace temperature could be continually modulated in response to variations in reactivity within the furnace.

关键词： Temperature sensors Temperature measurement Temperature distribution Flow production systems Furnaces Production Regulation Temperature control Monitoring Syngas

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Enhancing Software Quality with Fault Detection and Prediction based on AI-Driven Model

Enhancing Software Quality with Fault Detection and Predicti...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Ramesh Bishukarma Sumeet Mathur Puneet Matapurkar Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Mathematical Sciences & Computer Application Bundelkhand University Jhansi India Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA’s software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance.

关键词： Support vector machines Accuracy Fault detection Software algorithms Software quality Predictive models Nearest neighbor methods Software measurement Classification tree analysis Software engineering

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Conv-TasNet Adaptive Noise Cancellation Model Enhanced by WaveNet

Conv-TasNet Adaptive Noise Cancellation Model Enhanced by Wa...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Kun Zhao Yaojie Hou College of Computer Science and Technology (College of Big Data) Taiyuan University of Technology Jinzhong China College of Electrical and Automation Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Traditional speech denoising methods often struggle with low signal-to-noise ratios (SNR) and poor clarity in complex noise environments. To address these limitations, this study introduces an adaptive denoising model that integrates Conv-TasNet with WaveNet enhancements. The proposed model incorporates causal convolution within the Conv-TasNet separator to better capture long-term dependencies and employs WaveNet's gating activation mechanism to effectively handle nonlinear speech features. Additionally, a mask weighting strategy is implemented to emphasize target speech signals while suppressing background noise. Experimental results demonstrate that the enhanced model significantly outperforms the original Conv-TasNet, achieving an SNR improvement from –5 dB to 33.74 dB and reducing mean squared error (MSE) to nearly zero. These findings confirm the model's superior performance and robustness in complex noisy environments, highlighting its potential for advanced speech enhancement applications.

关键词： Adaptation models Convolution Computational modeling Particle separators Noise reduction Speech enhancement Feature extraction Computational efficiency Noise measurement Signal to noise ratio

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Deep Belief Networks and LSTM for Precise Weather Forecasting

Deep Belief Networks and LSTM for Precise Weather Forecastin...

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international conference on ent Computing and Control systems (ICICCS)

作者： J. Rama Devi Pavitra Pullela Upputholla Nandini Nagur Baji Shaik Pondugula Ajithesh Reddy Computer Science Dept PVPSIT Vijayawada A.P India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Heavy rainfall often leads to various natural hazards, making accurate rainfall forecasting crucial for implementing effective preventive measures. This study proposes a novel three-phase forecasting model comprising Pre-processing, Feature Extraction, and Forecasting to improve prediction accuracy. The model leverages advanced deep learning algorithms, specifically Deep-Belief Networks (DBN) and Long Short-Term Memory (LSTM), to analyze historical rainfall data and detect complex patterns. The primary objective of this work is to enhance forecasting precision by identifying intricate relationships within meteorological data, thereby providing valuable insights for disaster preparedness and risk mitigation. The proposed model’s performance is systematically evaluated against existing approaches using multiple metrics, demonstrating its superior predictive capabilities. By enabling early interventions, this method contributes to proactive disaster management, reducing the impact of extreme weather events.

关键词： measurement Rain Accuracy Computational modeling Weather forecasting Predictive models Feature extraction Data models Forecasting Long short term memory

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Adaptive Chunking for VideoRAG Pipelines with a Newly Gathered Bilingual Educational Dataset

Adaptive Chunking for VideoRAG Pipelines with a Newly Gather...

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international CSI computer conference, CSICC

作者： Arshia Hemmat Kianoosh Vadaei Melika Shirian Mohammad Hassan Heydari Afsaneh Fatemi dept. Computer Engineering University of Isfahan Isfahan Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

This paper introduces an innovative approach to Retrieval-Augmented Generation (RAG) for video question answering (VideoQA) through the development of an adaptive chunking methodology and the creation of a bilingual educational dataset. Our proposed adaptive chunking technique, powered by CLIP embeddings and SSIM scores, identifies meaningful transitions in video content by segmenting educational videos into semantically coherent chunks. This methodology optimizes the processing of slide-based lectures, ensuring efficient integration of visual and textual modalities for downstream RAG tasks. To support this work, we gathered a bilingual dataset comprising Persian and English mid- to long-duration academic videos, curated to reflect diverse topics, teaching styles, and multilingual content. Each video is enriched with synthetic question-answer pairs designed to challenge pure large language models (LLMs) and underscore the necessity of retrieval-augmented systems. The evaluation compares our CLIP-SSIM-based chunking approach against conventional video slicing methods, demonstrating significant improvements across RAGAS metrics, including Answer Relevance, Context Relevance, and Faithfulness. Fur-thermore, our findings reveal that the multimodal image-text retrieval scenario achieves the best overall performance, emphasizing the importance of integrating complementary modalities. This research establishes a robust framework for video RAG pipelines, expanding the capabilities of multimodal AI systems for educational content analysis and retrieval 1 1 The dataset is publicly available at: https://***/datasets/uIAICIEduViQA.

关键词： measurement Visualization Large language models Pipelines Retrieval augmented generation Education Question answering (information retrieval) Multilingual Standards Context modeling

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Analyzing of the Alignment between Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) Using Textual Similarity-Based Methods

Analyzing of the Alignment between Course Learning Outcomes ...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), international conference on

作者： Afra Mahendra Prasetyawati Werihisfenli Saragih Mariana Frima Ade Romadhony School of Computing Telkom University Bandung Indonesia Secretariat & Strategic Planning Directorate Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Universities are the pinnacle of education in Indonesia to prepare the younger generation before entering the workforce. For example, in Indonesia, there is a government regulation (Permendikbudristek Number 53 of 2023) that concerning the Assurance of Higher Education Quality, each study program must define Program Learning Outcomes (PLOs) to achieve the university's objectives, which will then be translated into Course Learning Outcomes (CLOs) at the course level. Defining CLOs that align with specific PLOs is quite challenging. Therefore, in our study, we conducted an analysis of the alignment of PLOs and CLOs based on a text similarity approach. We modeled the PLOs and CLOs texts using TF-IDF and fastText representations, with cosine similarity as the similarity measurement. We evaluated the PLOs and CLOs from the computer science program at a university in Indonesia. The experimental results show that the fastText representation performs better in identifying the alignment between PLO and CLO, demonstrating its effectiveness in capturing semantic meaning beyond simple lexical similarities. This study highlights the importance of leveraging advanced text representation techniques to improve curriculum mapping and ensure a more structured and coherent learning outcome framework.

关键词： computer science Translation Electronic learning Computational modeling Semantics Education Government Data science Regulation Information systems

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VisiCompanion: Comparative Analysis of Yolov8 and Yolov9

VisiCompanion: Comparative Analysis of Yolov8 and Yolov9

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Disruptive Technologies (ICDT), international conference on

作者： Payal Chhabra Tanmay Arora Vibhore Jain Neeraj Gandhi Swapnil Bhatnagar Nitin Arora Department of (CSE-AIML) KIET group of institutions Ghaziabad India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

There are approximately 21 million visually im-paired individuals who depend on other people for their basic work. This research adds to the current understanding of assistive technology by introducing a method for choosing and optimizing the best object detection models suited for accessi-bility environments. This research enhances the Visicompanion functions and assists visually impaired individuals in doing crucial daily tasks independently by selecting a suitable model. This research compares the analysis of two models i.e. Y oLov8 [1] and YOLOV9 [2] and identifies which algorithm is best for the application. YOLOv8 is known for its rapid inference speed which is beneficial for real-time systems whereas YOLOv9 introduces advanced detection capability that improves accuracy but may increase problems for processing time and computational resource requirements. This research uses evaluation metrics like precision, recall, size of model, and resource utilization across different hardware configurations. The results showed thatYOLOv9 is slightly better than YOLOv8 with mAP reaching 95% and precision 96.8%.

关键词： measurement computer vision Computational modeling Object detection Assistive technologies Real-time systems Inference algorithms Hardware Resource management Disruptive technologies

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Design Process and Development of Automation for Smart Buildings

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

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

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

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

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.

关键词： Adaptation models Smart buildings Automation Uncertainty Costs Computational modeling Energy measurement Sustainable development MATLAB Space heating

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A Study on Intelligibility, Naturalness and Fluency of Text-to-Speech systems

A Study on Intelligibility, Naturalness and Fluency of Text-...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Priyadharshini S Rumana Nachiyar M Usharani K Computer Science and Business System Ramco Institute of Technology India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Text-to-Speech (TTS) technology advancements have resulted in the developing of sophisticated models like Tacotron and WaveNet. This research evaluates these models based on clarity, naturalness, and flow. Tacotron employs a comprehensive architecture that converts text into mel-spectrograms, excelling in prosody and fluency. WaveNet, a probabilistic model, directly generates raw audio waveforms, delivering exceptional sound quality. The LJSpeech dataset assessed performance through metrics such as Word Error Rate (WER) and Mean Opinion Score (MOS). Tacotron achieved a WER of 6.2%, indicating strong fluency with some alignment challenges. WaveNet outperformed Tacotron with a WER of 4.7% and a MOS of 4.6, demonstrating greater naturalness but some minor fluency issues. Tacotron is ideal for expressive applications, while WaveNet excels in realism. Future research could explore hybrid models to combine these strengths and address current limitations..

关键词： measurement Error analysis Computational modeling computer architecture Probabilistic logic Text to speech Informatics

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Intelligent Road Condition Monitoring and Analysis for Safer Highways

Intelligent Road Condition Monitoring and Analysis for Safer...

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Innovation in Computing and Engineering (ICE), international conference on

作者： M. Mohamed Iqbal Nandhana S Vijay V Sreehari Veeramachaneni Information Technology Sri Sivasubramaniya Nadar College Of Engineering Chennai Tamilnadu

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

Road damages are one of the greatest threats to the safety of road users as they contribute to mechanical breakdowns and eventual accidents. This project addresses the problem by presenting a possible solution of how to develop an efficient road damage detection system using the YOLOv5 object detection algorithm. The system makes use of the images from the road damage datasets to create a YOLOv5 model that is made to detect potholes, joint cracks, and alligator cracks and outlines where the potholes are with bounding boxes. Furthermore, GPS coordinates of the detected damage are logged and stored in cloud database and this can be used to provide road management authorities information on hazardous road conditions for action. The system also investigates pothole depth prediction through MiDaS, an emerging method in the area of Depth Estimation. The proposed system attains an average of 78.1% detection accuracy and a mAP@50 of 76.6% on average. These results validate the system’s real-time pothole and crack detection and classification performance while setting the foundation for enhanced detection sensitivity, depth prediction, and navigation integration for real-time pothole warning to drivers. By integrating geo-tagging based on IoT, the effectiveness of performing anticipatory repair and maintenance of road infrastructure is highly increased, therefore road safety is improved.

关键词： YOLO Technological innovation Solid modeling Sensitivity Depth measurement Volume measurement Road safety Real-time systems Maintenance Vehicles

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