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Audio Denoising: Speech Enhancement Using Convolutional Neural networks 5th

Audio Denoising: Speech Enhancement Using Convolutional Neur...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Aruna, T. Vaidya, Shreya Raikar, Rakshita Chikkamath, Satish School of Electronics and Communication Engineering KLE Technological University Hubballi India

ISBN: (纸本)9789819788644

Imagine someone talking on a busy street and their words suppressed by chatter and traffic sound. Our research sets out to tackle such noisy audio recordings and how CNNs can rise to this challenge, restoring clarity to communication. this paper presents an innovative pipeline for the enhancement of speech, that focuses on improving the quality of speech signals which are corrupted by background noise. Our method combines the Denoising Autoencoder (DAE) and Convolutional Neural Network (CNN) which is integrated with classical signal processing techniques. When trained on a sufficient amount of noisy speech frames, the DAE outperforms traditional ideal ratio mask (IRM) approaches when it comes to estimating in clean speech spectrum, resulting denoised speech is analyzed by using the STFTShor (tTime Fourier Transform) and then reconstructed using the overlap-add technique, providing a refined output. this entire pipeline can adapt to diverse acoustic scenarios, thus presenting an all-purpose solution for real-world applications starting from communication systems to voice- activated technologies. Our research demonstrates exceptional performance and a significant improvement in speech enhancement. the solution of integrating DAE-CNN answers existing difficulties in noise reduction and also contributes significantly to Machine learning advancements related to audio processing. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech enhancement

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Based on the Application and Research of Artificial Intelligence in Medical Diagnosis and Treatment

Based on the Application and Research of Artificial Intellig...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Zhuyu Chen Xuliang Yang Kaixin Zheng Beibei Wang Dongguan City University Dongguan Guangdong China Jiaxing Nanhu University Jia xing Zhejiang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With the rapid development of artificial intelligence technology, its application in medical diagnosis and treatment is increasingly extensive. In this paper, an intelligent diagnosis system based on convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed to solve the problems of high misdiagnosis rate and low data processing efficiency in traditional medical diagnosis methods. By optimizing the convolutional layer structure of CNN and the hidden layer design of LSTM, the accuracy of the proposed system on the MNIST medical image dataset and MIME-III electronic medical record dataset reached 95.6% and 91.5%, respectively, which was significantly higher than the traditional method (85.4 % and 84.7%)[1]. the experimental results show that the system in this paper is superior to the existing studies in accuracy, recall rate and AUC value, especially when dealing with complex cases. In addition, this paper further improves the training efficiency and generalization ability of the model by introducing data enhancement and early stop method. this study provides a new technical means for the application of artificial intelligence in the medical field, which has important theoretical significance and practical application value.

关键词： Training Accuracy Feature extraction Data models Convolutional neural networks Medical diagnosis Resource management Electronic medical records Long short term memory Medical diagnostic imaging

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A High Efficient and Cost Effective Waste Sorting Solution using Advanced sensors and AI

A High Efficient and Cost Effective Waste Sorting Solution u...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Kannan E Yuvarani B Sanjaykumar S Tamilvanan T Vasanth J Manikandan S Department of Electrical and Electronics Engineering K.S.R. College of Engineering Tamilnadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the rapid increase in global waste, including plastic, metal, electronic waste (e-waste), and hazardous materials, necessitates an efficient and automated waste management system. Traditional manual waste sorting is labor-intensive, error-prone, and poses health risks due to exposure to hazardous materials. Existing automated solutions, such as IoT-based waste monitoring and basic sensor-based segregation, suffer from low classification accuracy, high misclassification rates, and limited adaptability to complex waste types. this research addresses these challenges by proposing an AI-driven waste segregation system integrating machine vision, YOLO (You Only Look Once) object detection, and Convolutional Neural networks (CNNs) for real-time waste classification with improved precision. the system utilizes an ESP32-CAM microcontroller to capture and process images, metal sensors to distinguish ferrous and non-ferrous metals, and gas sensors to detect hazardous gases such as methane (CH 4 ), carbon monoxide (CO), and ammonia (NH 3 ). Motorized actuators then autonomously direct waste into designated bins, reducing human intervention and increasing sorting efficiency. Experimental results demonstrate that the proposed system achieves a classification accuracy of 97-98%, significantly outperforming manual sorting (83-85%). Additionally, misclassification rates were reduced by fivefold, particularly in hazardous waste detection, where errors dropped from 1.5 kg to 0.3 kg per 100 kg of waste processed. the integration of advanced AI models and sensor technologies ensures precise waste categorization, optimizing recycling processes, minimizing landfill waste, and enhancing workplace safety. this scalable and cost-effective solution supports smart city initiatives and sustainable waste management by automating waste segregation with high efficiency, adaptability, and minimal operational costs.

关键词： YOLO Accuracy Metals sensor systems Real-time systems Recycling Artificial intelligence Gas detectors Intelligent sensors Sorting

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Industrial Robots and Collaborative Robots: A Comparative Study

Industrial Robots and Collaborative Robots: A Comparative St...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Mohamed Lamine Soudani Salah Nissabouri Moulay El Houssine Ech-Chhibat Mohamed Haidoury Idriss Elhirch M2S2I Laboratory ENSET Mohammedia Hassan II University (UH2) Casablanca Morocco Modeling Information Processing and Control Systems ENSAM Moulay Ismail University Meknes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

In the era of Industry 4.0 and advancements in artificial intelligence tools, the industry is increasingly moving towards automation and reducing human intervention in production processes. In this context, industrial robots play a crucial role in modern industry, not only enhancing production and improving product quality but also addressing production uncertainties and, most importantly, reducing maintenance and production costs. To this end, the present paper proposes a comparative study of industrial robots and collaborative robots, based on a review of the literature published over the past five years. this analysis revealed that both types of robots share similarities in the nature of the faults encountered. However, the type of applications and their working environments require distinct strategies: early predictive maintenance is necessary for collaborative robots, while preventive maintenance is more suitable for industrial robots. these approaches rely on classification methods such as k-Nearest Neighbors algorithms, Convolutional Neural networks, Random Forests, Support Vector Machines, and other neural networks for fault detection and effective trajectory control. the findings of this study will provide valuable information and serve as a significant resource for future researchers addressing challenges related to the use of these robots.

关键词： Industries Support vector machines Uncertainty Service robots Production Collaborative robots Industrial robots Maintenance Trajectory Artificial intelligence

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Smart Sampling and Residual Bottleneck Dense Network for Reliable and Energy-Efficient Routing in WSNs

Smart Sampling and Residual Bottleneck Dense Network for Rel...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Asma Anjum Dattathreya Salman Arafath Mohammed Dhaval Rabadiya K. B. Venkata Brahma Rao Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka Department of Electronics and Communication Engineering Alva’s Institute of Engineering and Technology Moodbidri Karnataka Department of Electrical Engineering Computer Engineering Section College of Engineering King Khalid University Abha Asir Region Saudi Arabia Faculty of Management Studies Parul University Dist. Vadodara Gujarat Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the fundamental requirement for WSNs made up of power-efficient and inexpensive sensor nodes depends on short-distance multi-hop communication. the potential of WSNs expands through new digital electronics and wireless communications technology despite the ongoing critical challenge related to energy efficiency. the main energy issues in WSNs stem from the excessively high control signal overhead between base stations and sensor nodes together with packet losses that require retransmissions to reduce energy efficiency. the proposed routing technique solves energy inefficiencies by employing RBDN-HMOA as an energy-efficient routing method. the proposed approach lowers system power consumption through its algorithm which picks distance and energy residual-based optimum forwarding nodes and adapts sampling rates automatically. the algorithm adopts Human Memory Optimization Algorithm (HMOA) to optimize network parameters. Research data shows that experimental results demonstrate both power efficiency and enhanced data transfer rate. the network demonstrates scalability because both throughputs reach 1100 kbps and energy usage reduces to 0.08 J when using 10 nodes. the RBDN-HMOA creates a practical method which extends network duration in practical deployments by diminishing WSN energy utilization without compromising data integrity.

关键词： Wireless sensor networks Energy consumption Fluctuations Heuristic algorithms throughput Routing Energy efficiency Real-time systems Delays Optimization

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Research and Application of Intelligent Driving Navigation Voice Based on Deep Learning Technology

Research and Application of Intelligent Driving Navigation V...

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sensors and Information Technology (ICSI), international conference on

作者： Si Wu Sihui Cheng Baiqiang Gan Lishun Shen Nanfang College Guangzhou Country Guangzhou City Guangdong Province China Guangzhou Yangxin Technology Research Co. Ltd. Country Guangzhou City Guangdong Province China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With advancements in artificial intelligence, the Internet of things, and sensor technology, intelligent driving-related technologies have emerged as a central focus for the global automotive industry's upgrade. Traditional in-vehicle voice systems primarily rely on rule-based engines or simple text-to-speech (TTS), which exhibit several limitations, including mechanical and monotonous prompt voices, inadequate multimodal coordination, and a lack of context awareness. these shortcomings hinder the ability of car navigation prompts to alleviate drivers' emotional anxiety during traffic jams. this paper proposes a design methodology for in-vehicle navigation voice systems based on deep learning technology, emphasizing the perception of pleasure. By integrating traffic environment data with driver emotion data and employing multimodal technology, we construct a CBGRU + graph neural network model. this model outputs navigation prompt voices that evoke pleasure and quantify the correlation between voice features (such as timbre, speech rate, emotional expression, and semantic content) and emotional perception (including pleasure and stress relief). Experimental results demonstrate that navigation prompt voices designed to evoke pleasure during traffic jams can effectively mitigate drivers' negative emotions and enhance the overall driving experience. this research on the design of car navigation prompt sounds, oriented towards the perception of pleasure, provides both theoretical and empirical support for voice interaction design in intelligent transportation systems.

关键词： Deep learning Correlation Navigation Anxiety disorders Semantics Graph neural networks Acoustics Timbre Vehicle dynamics Heart rate variability

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Efficient Real-Time Water Distribution Network using Internet of things (IoT)

Efficient Real-Time Water Distribution Network using Interne...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Dinesh E Praveen S Parvathakrishnan P Vetri E Naveenraj V Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

In both rural and urban settings, inadequate distribution systems and water scarcity present major problems. For instance, there exists a significant problem with inconsistency of water arrival time that the residents of a locality are unaware of; this inconsistency is observed due to manual monitoring by water distributors. As there is a manual monitoring system, a leak in the pipe cannot be sensed instantly which allows for a lot of wastage as water continues to flow. In this paper, we propose an Efficient Water Distribution Network (EWDN)which integrates Arduino UNO, water level sensors, relay modules, and Real-Time clocks (RTC) in order to automate the process -divide it, store the water in form of containers, detect leakage in pipelines, and provide water supply at scheduled time intervals. It notifies residents two days ahead about the expected arrival time of water through SMS, and two hours before water supply. It automatically operates the water pump to release water for a set time. In the event a leak is detected, it immediately switches off the pump and sends out an SMS alert to the water distributor and residents, reducing further water wastage. It reduces the manual effort, provides efficient water management by minimizing the wastage of water and ensures optimal delivery of the water. the experimental results show that the proposed system is effective in enhancing water management and safety by minimizing operational inefficiencies and can be used for smart city applications.

关键词： GSM Distribution networks Manuals Real-time systems Water pumps sensor systems sensors Internet of things Leak detection Monitoring

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Self-Optimizing 6G RAN: A Unified Approach with Image and Video Analytics

Self-Optimizing 6G RAN: A Unified Approach with Image and Vi...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Rakshesh P Bhatt Divya G Nair Sivaramakrishnan Swaminathan Andrianov Anatoly Nokia Bangalore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Nokia Schaumburg USA

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the advent of 6G wireless technology heralds a new era of network optimization driven by AI/ML algorithms across its entirety. As outlined in 3GPP TS 28.104 (Release 19), Management Data Analytics (MDA) presents diverse use cases and requirements. However, the variety of data formats and AI/ML application methods introduces complexity and limits generic implementation. Compounded by the exponential deployment of cells and a range of new devices like XR devices, UAVs, etcs., the resulting data deluge poses a formidable big-data challenge for 5G advanced, 6G, and beyond. Anticipating the unique demands of 6G, characterized by metaverse-enabled networking and AR/VR applications, there arises a need for AI/ML-driven visual data processing services. this paper presents a novel approach: representing wireless network data in the form of images and videos. Such a representation not only optimizes storage and processing requirements but also facilitates the reuse of AI/ML models across various use cases and data formats. Experimental validation is provided through two key avenues: Firstly, text-based logs are transformed into images, enabling the identification of recurrent issues through visual pattern recognition. Secondly, Comma Seperated Variables (CSV) data is converted into a series of images, effectively simulating video frames, thus extending the realm of video analytics to encompass all data types pertinent to self-optimizing networks in 6G RAN. the advantages of this image and video-based data representation paradigm are meticulously elucidated, offering a compelling proposition for the future landscape of wireless network optimization in 6G and beyond.

关键词： 6G mobile communication Data analysis Visual analytics Wireless networks Key performance indicator Data visualization Data models Pattern recognition Optimization Videos

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the Application of Deep Learning Method Combined with Attention Mechanism in Text Classification

The Application of Deep Learning Method Combined with Attent...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Jiyong Wang Jianhua Zhou Zefan Wu China University of Petroleum-Beijing at Karamay Karamay China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In this paper, we explore the application of a deep learning approach integrated with an attention mechanism for text classification, aiming to enhance the performance and robustness of complex text classification tasks. Addressing the limitations of traditional text classification methods in capturing contextual dependencies, handling long texts, and dealing with semantic ambiguity, we propose an innovative model that leverages deep learning architectures combined with a self-attention mechanism to improve feature extraction by dynamically focusing on key information within the text. the model employs word embeddings and a hierarchical network structure, with a multi-head attention mechanism at its core. Additionally, the model is optimized for specific task objectives to achieve efficient multi-class text classification. Experimental results on multiple publicly available datasets show that the proposed method achieves superior performance compared to existing state-of-the-art approaches, particularly in terms of accuracy and F1 score. this is especially evident when handling long and complex text data, where the model demonstrates enhanced capability in capturing relevant features and improving classification accuracy. Furthermore, a visual analysis of the attention weight distribution is conducted, offering insights into the model's text feature extraction mechanism. these findings provide valuable theoretical contributions and practical guidance for advancing text classification techniques in the field of natural language processing.

关键词： Deep learning Support vector machines Analytical models Visualization Attention mechanisms Recurrent neural networks Accuracy Text categorization Feature extraction Robustness

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Indian Sign Language Alphanumeric Hand Gestures Recognition Using Deep Learning Techniques

Indian Sign Language Alphanumeric Hand Gestures Recognition ...

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Advanced Research in Computing (ICARC), international conference on

作者： Shakana Mahendran Ramya Mahendran Department of Physical Science FAS Vavuniya Campus of the University of Jaffna Vavuniya Sri Lanka Information Technology Center Trincomalee Campus EUSL. Trincomalee Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Alphanumeric sign language recognition plays a vital role in enabling effective communication between individuals who are deaf and the general population. this research focuses on classifying alphanumeric hand gestures in Indian Sign Language using real-time image processing. the study collects diverse hand shape images captured under varying backgrounds and lighting conditions, making it suitable for real-world applications. In the preprocessing stage, images are cropped and resized to a standardized 128×128 pixel format to ensure uniformity in the dataset. A two-pronged approach is adopted to classify the collected images. Sequential and parallel CNNs are employed to capture spatial and channel information effectively. Additionally, a channel attention module enhances the model’s ability to identify crucial channel information. the Deep Convolutional Neural Network (DCNN) employed in this study achieved remarkable accuracy, reaching 99.976%. this outcome demonstrates the superior performance of this approach compared to other classification methods, whether utilizing real-time or non-laboratory experimental data. this research contributes to developing an efficient, accurate, and cost-effective system for classifying alphanumeric signs in Indian Sign Language. It facilitates easy and fast communication between individuals who are deaf and those who are not, promoting inclusive interactions.

关键词： Hands Training Deep learning Sign language Accuracy Shape Computational modeling Real-time systems Stability analysis Convolutional neural networks

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