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2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Chen, Rongrong College of Business Administration Chongqing Telecommunications Vocational College Chongqing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

This paper proposes an intelligent recommendation method for electronic commerce. This paper aims to study the problem that the MAE value in the existing e-commerce recommendation algorithm is too large, which affects the prediction accuracy of the system. In terms of hardware, PC104 industrial computer is used as the processing CPU. DSP and memory chip MPC565 are used as processing center. The clock signal, reset circuit and power supply circuit are designed by means of signal fiber compensation. The software calculates the personalized characteristics of e-commerce according to its characteristics, and then uses the association rules algorithm to process the personalized characteristics data. Use Java tools to convert it into code, use different functions of the code to achieve intelligent recommendation and then complete the design of the software part. The experiment proves that the e-commerce intelligent recommendation model constructed by using individual characteristics has less sample set and higher prediction accuracy and practicability than the conventional e-commerce intelligent recommendation method. © 2025 SPIE.

关键词： Marketplaces

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Overview of Accent Conversion Techniques Based on Deep Learning 6

Overview of Accent Conversion Techniques Based on Deep Learn...

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6th ieee international conference on Image processing, Applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Panahi, Issa The University of Texas at Tyler Department of Electrical and Computer Engineering TylerTX United States The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798331506520

In modern communication, speech technology plays a vital role, with accent being one of the key features that reflects the nuances of spoken technology. However, differences in accent can make communication difficult, especially in global contexts like language learning and human computer interaction. Accent conversion approaches often solve these issues by transforming the accent of speaker to that of the other speakers, while maintaining linguistic information and speaker identity. This technology holds great applications to speech recognition, language learning, and cross-lingual communication. This paper explores the use of advanced deep learning techniques in accent conversion systems which have improved both the naturalness and intelligibility of converted speech. Additionally, we discuss the methods generally used for performance evaluation of these systems. © 2025 ieee.

关键词： Deep learning

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5G communication architecture and key technologies 5

5G communication architecture and key technologies

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5th international conference on Signal processing and computer Science, SPCS 2024

作者： Gao, Ruihan School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Under the background of the rapid development of big data and artificial intelligence, the rapid development of mobile communication has accelerated the development process of 5G technology. 5G has comprehensively affected people's production and life, and brought opportunities and challenges to intelligent network systems. From the perspective of 5G mobile communication network architecture and key technologies, this paper presents the core points and advantages of 5G through the newly improved and upgraded 5G network architecture and various updated and iterative key technologies, and looks forward to the future development trend of 5G. © 2025 SPIE.

关键词： 5G mobile communication systems

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Active Sensing for Multiuser Beam Tracking With Reconfigurable intelligent Surface

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ieee TRANSACTIONS ON WIRELESS communicationS 2025年第1期24卷 540-554页

作者： Han, Han Jiang, Tao Yu, Wei Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Marvell Semicond Canada Inc Toronto ON M5H 1J9 Canada Qualcomm Technol Inc Santa Clara CA 95051 USA

This paper studies a beam tracking problem in which an access point (AP), in collaboration with a reconfigurable intelligent surface (RIS), dynamically adjusts its downlink beamformers and the reflection pattern at the RIS in order to maintain reliable communications with multiple mobile user equipments (UEs). Specifically, the mobile UEs send uplink pilots to the AP periodically during the channel sensing intervals, the AP then adaptively configures the beamformers and the RIS reflection coefficients for subsequent data transmission based on the received pilots. This is an active sensing problem, because channel sensing involves configuring the RIS coefficients during the pilot stage and the optimal sensing strategy should exploit the trajectory of channel state information (CSI) from previously received pilots. Analytical solution to such an active sensing problem is very challenging. In this paper, we propose a deep learning framework utilizing a recurrent neural network (RNN) to automatically summarize the time-varying CSI obtained from the periodically received pilots into state vectors. These state vectors are then mapped to the AP beamformers and RIS reflection coefficients for subsequent downlink data transmissions, as well as the RIS reflection coefficients for the next round of uplink channel sensing. The mappings from the state vectors to the downlink beamformers and the RIS reflection coefficients for both channel sensing and downlink data transmission are performed using graph neural networks (GNNs) to account for the interference among the UEs. Simulations demonstrate significant and interpretable performance improvement of the proposed approach over the existing data-driven methods with nonadaptive channel sensing schemes.

关键词： Sensors Reconfigurable intelligent surfaces Reflection coefficient Vectors Downlink Correlation Data communication Adaptation models Reflection Long short term memory Active sensing beam tracking deep learning reconfigurable intelligent surface graph neural network recurrent neural network long short-term memory (LSTM)

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Machine Learning based Heatwave Modeling for Akola, Maharashtra 3

Machine Learning based Heatwave Modeling for Akola, Maharash...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Mandal, Nabanita Gajria, Abhinav Rangwala, Hussain Sarode, Tanuja Computer Engineering Department Thadomal Shahani Engineering College Bandra Mumbai India

ISBN: (纸本)9798331527549

Heatwaves are a serious challenge in India, claiming many lives yearly. The heatwaves occur when the temperature rises above the normal continuously for a few days. This paper proposes a Stacked LSTM model that predicts the maximum temperature. The study region is Akola in Maharashtra, India which is prone to heatwaves. It is observed that the Proposed Method gives less Mean Absolute Error as compared to other techniques like Multiple Linear Regression, Random Forest, and Support Vector Regression. After data collection, appropriate data pre-processing technique is applied. Model is built considering the pre-processed data which does not have any anomalies. The reason for improved result of Proposed model is its ability to handle the complex pattern present in the data and learn from it to give better predictions. Maximum temperature is selected as the target variable for prediction and the other features are humidity, dew point etc. are considered here. © 2025 ieee.

关键词： Multiple linear regression

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Automail AI: intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 ieee.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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Dual-Plate Verification System for Vehicle Identifcation using IoT and Embedded Systems 3

Dual-Plate Verification System for Vehicle Identifcation usi...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Sampath, A.V.S.S. Malagati, Nischal S. Kanapathi, V. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research uses a dual-plate verification system for improving vehicle security by using IoT and embedded systems. The system compares front and rear license plates to detect any anomaly, which signals unauthorized modifications. Using real-time image processing and IoT-based data communication, this solution gives us an accurate plate matching system, making sure of reliable vehicle identity verification. Its possible applications include traffic monitoring, parking management, and detecting any unlawful activity. This project is unique because it integrates the dual plate verification system with an Arduino IoT setup. For hassle-free text recognition, this system uses deep learning frameworks combined with a wireless mobile setup that serves as a camera. The system’s accuracy is shown through experiments, displaying its value in improving transportation security with automation. The number plates were detected, and the respective penalties for the vehicles were imposed accordingly. © 2025 ieee.

关键词： Anomaly detection

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The Future of Traffic Management: Embracing AI for Sustainable Urban Mobility

The Future of Traffic Management: Embracing AI for Sustainab...

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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Sharma, Prashant Sharma, Siddharth Pratap, Raj Chauhan, Rosey Verma, Shikha Sharda University Greater Computer Science and Engineering Noida India

ISBN: (纸本)9798331529833

The management of traffic in urban environments has become increasingly complex due to rapid urbanization and the growing number of vehicles on the road. This literature review synthesizes research on various traffic management systems and technologies, focusing on solutions such as real-time image processing, IoT-based systems, wireless sensor networks, and deep learning models. The review highlights key advancements, including intelligent traffic controllers utilizing real-time video analytics, IoT for congestion control, and AI-enabled traffic monitoring systems. © 2025 ieee.

关键词： Traffic congestion

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Enhancing Occluded Face Recognition Through Device-Edge Collaboration and Cross-Domain Feature Fusion with Heatmaps 3

Enhancing Occluded Face Recognition Through Device-Edge Coll...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Prasanth, Ashwin Annie Micheal, A. Department of Computer Science & Engineering Sathyabama Institute of Science & Technology Chennai India

ISBN: (纸本)9798331527549

Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a novel device-edge collaborative framework that advances occluded face recognition through an innovative cross-domain feature fusion technique. Our method employs a software-generated, occlusion-aware heatmap created from standard visible-light images, which emphasizes key facial landmarks even when regions of the face are partially covered. This heatmap provides a complementary data layer that enhances traditional visible facial features, allowing the cross-domain feature fusion process to synthesize a more comprehensive set of features for reliable recognition under occlusion. The combined feature set leverages both visible and simulated heatmap information to improve model robustness, even in complex occlusion scenarios. Additionally, a delay-optimized task scheduling method is applied on the edge, ensuring efficient resource allocation, faster processing, and minimal latency. Although this project is ongoing, preliminary evaluations indicate the potential effectiveness of this approach, with expected outcomes to include high recognition accuracy and reduced latency. This framework provides a scalable solution for occluded face recognition by integrating complementary data sources without requiring specialized hardware. © 2025 ieee.

关键词： Face recognition

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Plant Leaf Disease Identification based on Machine and Deep Learning Methods 3

Plant Leaf Disease Identification based on Machine and Deep ...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Kalaiarasi, P. Ramalakshmi, R. Kannan, V. Bhuvanesh, A. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are labor-intensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models. © 2025 ieee.

关键词： Deep learning

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