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12th International Electrical engineering Congress, iEECON 2024

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University Of Technology Krungthep Department Of Electrical And Telecommunication Engineering Bangkok Thailand Thammasat School Of Engineering Thammasat University Faculty Of Engineering Department Of Electrical And Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department Of Robotic Engineering And Automation System Bangkok Thailand

ISBN: (纸本)9798350383591

Modern agriculture relies heavily on effective process monitoring, control, and remote data collection, making accurate and cost-effective sensors crucial for innovation. This research focuses on the development of a spectroscopy-based device for real-time and remote monitoring of nutrient levels in hydroponic systems. The device utilizes IoT architecture, incorporating principles of light absorbance to quantify concentrations of essential nutrients during crop growth. Three deep learning techniques, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) were adopted for data analysis. Results demonstrate that tailored implementation of deep learning can significantly enhance the performance of sensor network system (SNS). Specifically, the RNN format achieved an impressive 98% model accuracy during both training and testing phases. The technological advancements in addressing contract variation and photometric levels contribute to increased accuracy as more sensors become available. The findings serve as a comprehensive guide for collecting extensive agricultural data, encompassing environmental factors throughout the entire cultivation period, using state-of-the-art remote control sensor equipment. This work provides a practical, efficient, and cost-effective solution for precise nutrient control and continuous data monitoring in agricultural production. © 2024 IEEE.

关键词： Cost effectiveness

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Smart Food Box Design with IoT Based Android Monitoring system 8

Smart Food Box Design with IoT Based Android Monitoring Syst...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Widiarto, Hendro Koswara, Iwan Nasution, Darmeli Wahyuni, Sri Sulistianingsih, Indri Airport Electrical Engineering Politeknik Penerbangan Indonesia Curug Tangerang Indonesia University of Pembangunan Panca Budi Department of Computer System Medan Indonesia University of Pembangunan Panca Budi Department of Computer Engineering Medan Indonesia

ISBN: (纸本)9798350342604

This research focuses on the development of a Smart Food Box with an IoT-based Android monitoring system. The system necessitates the establishment of an environmental control chamber (plant) to facilitate the operation of the Peltier controller, which comprises hardware components such as the Arduino Board, Relay, LM35 Temperature Sensor, DS1307 Real-Time Clock, $16\times 2$ Display, and a Buzzer. The software design encompasses temperature readings to determine Peltier functionality as either a heating or cooling element, while also providing time-based references for breakfast, lunch, and dinner. Based on the research findings, consumers can monitor real-time food conditions, including temperature and humidity, using an Android application that is connected to an IoT-enabled smart eating city. This innovative system enhances food preservation and access through the integration of IoT and mobile technology, offering a convenient and efficient solution for modern lifestyle demands. © 2023 IEEE.

关键词： Android (operating system)

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Enhancing Internet of Vehicles Security: Advanced Intrusion Detection for Threat Detection and Mitigation 1st

Enhancing Internet of Vehicles Security: Advanced Intrusion ...

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1st International Conference on Intelligent systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Ali, Maged El-Badawy, Hesham Bahaa-Eldin, Ayman Sobh, Mohamed Giza12577 Egypt Cairo Egypt Elsewedy University of Technology Ain Shams University Cairo Egypt Department of Computer and System Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9783031823763

In this paper, a comprehensive approach to network intrusion detection data using Python is presented. Various techniques including XGB and DQN are discussed, necessitating a preprocessing phase at the beginning. This phase involves fixing data types, handling null and infinite values, dropping unnecessary columns, creating binary labels for threat detection, reducing memory usage, and balancing the dataset. Additionally, feature importance analysis and correlation visualization are explored to identify and drop highly correlated columns. Furthermore, machine learning processes, including data splitting, scaling, and computing class weights, are discussed, followed by the implementation of two machine learning methods: XGBoost and Deep Q-Network (DQN). For XGBoost, performance metrics such as F1-score, recall, precision and support are evaluated to assess model performance. For DQN, insights into its architecture, training objectives, and integration with Gym environment for reinforcement learning tasks are provided. The paper offers a comprehensive guide to preprocessing and machine learning approaches for network intrusion detection datasets, contributing to the advancement of cybersecurity research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali Language Comment on Facebook 2

Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali L...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Nobel, SM Nuruzzaman Swapno, S M Masfequier Rahman Ramachandra, A.C. Shajeeb, Hasibul Hossain Islam, Md Babul Haque, Rezaul Bangladesh University of Business and Technology Dept of Computer Science and Engineering Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh

ISBN: (纸本)9798350317558

This study addresses the challenge of sentiment analysis in Bengali Facebook comments, where users express their opinions on various topics. Existing sentiment analysis techniques have difficulty achieving high accuracy in this situation, which has inspired the creation of a fresh methodology. To improve sentiment analysis of Bengali Facebook comments, we suggest a model that combines a hybrid convolutional neural network (CNN) and a long short-term memory network (LSTM). Our research adds to the expanding area of sentiment analysis, especially for languages with few resources, such as Bengali. The success of the Hybrid CNN LSTM model opens the door for more precise and context-aware sentiment analysis on social media platforms, allowing for a better understanding of user sentiments and boosting user experiences. Our study focused on dividing comments into five groups according to their emotional content: troll, not bully, sexual, religious, and threat. We carefully generated the handmade dataset and ensured the quality and relevance of a dataset of 50,000 Bengali comments before using it to train and test our algorithm. The Hybrid CNN LSTM model outperforms conventional approaches and achieves an outstanding accuracy rate of 92.09%, demonstrating amazing efficiency in sentiment recognition. This dramatic increase in precision indicates that machine learning techniques are successful in sentiment analysis of Bengali Facebook comments. © 2024 IEEE.

关键词： Bengali Language CNN Comment Facebook Fully Connected Layer Hybrid LSTM NLP Predict Sentiment

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Industrial Automation PLC Implementation of MPPT Using P&O Algorithm for PV system Applications 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Yatimi, Hanane Ouberri, Youness Marah, Rim Aroudam, Elhassan Modeling and Simulation of Mechanical System Team Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco Computer Science and Engineering System Laboratory Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031352454

ISBN: (纸本)9783031352447

One of the most important concerns about the control of the photovoltaic system is the maximum power point tracking (MPPT) algorithms and their implementation in various electronic devices. This paper presents a programmable logic controller (PLC) software design for a standalone photovoltaic system based on the Perturb and Observe (P&O) MPPT algorithm. The entire photovoltaic system is simulated in Siemens total integration automation interface (TIA portal) and the results are plotted using Siemens Human Machine Interface WinCC flexible. The main contributions of the present paper are: 1) development of a simulated PV system block using automation PLC software for simulation test before hardware implementation, 2) development of a new automated design based on P&O-MPPT algorithm for Photovoltaic system control, 3) Validate the applicability and the effectiveness of using PLCs as maximum power point controller based on MPPT algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Cyclic Transfer Learning for Mandarin-English Code-Switching Speech Recognition

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IEEE Signal Processing Letters 2023年 30卷 1387-1391页

作者： Nga, Cao Hong Vu, Duc-Quang Luong, Huong Hoang Huang, Chien-Lin Wang, Jia-Ching National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Thai Nguyen University of Education Department of Computer Science and Information System Thai Nguyen7905218 Viet Nam Fpt University Department of Information Technology Can Tho900000 Viet Nam National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Transfer learning is a common method to improve the performance of the model on a target task via pre-training the model on pretext tasks. Different from the methods using monolingual corpora for pre-training, in this study, we propose a Cyclic Transfer Learning method (CTL) that utilizes both code-switching (CS) and monolingual speech resources as the pretext tasks. Moreover, the model in our approach is always alternately learned among these tasks. This helps our model can improve its performance via maintaining CS features during transferring knowledge. The experiment results on the standard SEAME Mandarin-English CS corpus have shown that our proposed CTL approach achieves the best performance with Mixed Error Rate (MER) of 16.3% on testman, 24.1% on testsge. In comparison to the baseline model that was pre-trained with monolingual data, our CTL method achieves 11.4% and 8.7% relative MER reduction on the testman and testsge sets, respectively. Besides, the CTL approach also outperforms compared to other state-of-the-art methods. © 1994-2012 IEEE.

关键词： Speech recognition

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Alzheimer’s Disease and Frontotemporal Dementia: Differential Diagnosis Using Electroencephalogram Signal 1

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13th National Technical Symposium on Unmanned system Technology, NUSYS 2023

作者： Rostamikia, Mehran Sarbaz, Yashar Farzamnia, Ali Biomedical System Modeling Lab Biomedical Engineering Department Electrical and Computer Engineering Faculty University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Dementia is a neurological disorder that affects a person’s cognitive and social skills, leading to a decline in their overall mental functioning. Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) are two common types of dementia. Frontotemporal dementia mostly affects the frontal and temporal lobes of the brain. These areas are responsible for executive functions, decision-making, language, behavior regulation, and personality traits. Alzheimer’s disease primarily damages the cerebral cortex, which is responsible for higher cognitive functions such as memory, language, and perception. The electroencephalogram (EEG) signal has many advantages in diagnosing these disorders, including low cost and high temporal resolution. This study compares AD and FTD patients with healthy subjects by extracting specific features from EEG signals. Two machine learning algorithms were used for the separation, Support Vector Machines (SVMs) and k-Nearest Neighbors (KNNs), and 10-Fold Cross-Validation was applied to validate the performance of this method and an accuracy of 91.2% (sd = 7.8) was achieved using the SVM classifier for diagnosing the disease and 71.1% (sd = 8.6) for classifying AD and FTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Simultaneous Speech Extraction for Multiple Target Speakers Under Meeting Scenarios

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Journal of Shanghai Jiaotong University (Science) 2024年 1-7页

作者： Zeng, Bang Suo, Hongbin Wan, Yulong Li, Ming School of Computer Science Wuhan University Wuhan430027 China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan Jiangsu215316 China Data & amp AI Engineering System OPPO Beijing100125 China

The common target speech separation directly estimates the target source, ignoring the interrelationship between different speakers at each frame. We propose a multiple-target speech separation (MTSS) model to simultaneously extract each speaker’s voice from the mixed speech rather than just optimally estimating the target source. Moreover, we propose a speaker diarization (SD) aware MTSS system (SD-MTSS). By exploiting the target speaker voice activity detection (TSVAD) and the estimated mask, our SD-MTSS model can extract the speech signal of each speaker concurrently in a conversational recording without additional enrollment audio in advance. Experimental results show that our MTSS model achieves improvements of 1.38 dB signal-to-distortion ratio (SDR), 1.34 dB scale-invariant signal-to-distortion ratio (SISDR), and 0.13 perceptual evaluation of speech quality (PESQ) over the baseline on the WSJ0-2mix-extr dataset, separately. The SD-MTSS system makes a 19.2% relative speaker dependent character error rate reduction on the Alimeeting dataset. © Shanghai Jiao Tong University 2024.

关键词： Speech analysis

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Multi Attributes Recognition from Human Gait Analysis using MotionSense Dataset

Multi Attributes Recognition from Human Gait Analysis using ...

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2022 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2022

作者： Ibrar, Kainat Shaikh, Anam Zafar, Shakeel University of Wah Wah Department of Computer Science Pakistan Mehran University of Engineering and Technology Department of Computer System Engineering Jamshoro Pakistan UET Taxila Department of Computer Engineering Taxila Pakistan

ISBN: (数字)9781665475198

ISBN: (纸本)9781665475198

Human Gait analysis is a very prodigious and flourishing field of research nowadays, due to its immense importance in clinical and medical studies, rehabilitation, security and surveillance, crime investigation, health, sports, development of marketing applications and product optimization etc. Every human has a distinctive gait pattern, which with critical scrutiny may exhibit a lot of information about his identity and personal traits. Although researchers have made remarkable efforts in this field of research but there is a lack of work regarding sensorial gait analysis for identifying multi-attributes of a person. This paper proposes a novel framework to recognize multi-attributes i.e., user, gender, age and weight of a person based on gait analysis using smartphone built-in sensors including accelerometer, gyroscope and motion sensor. We have used an existing dataset named 'MotionSense' for human activity and attributes recognition. Multi-class machine learning algorithms are applied for training the dataset. We have achieved the accuracy of 99.75% for User, 99.74% for gender, 99.61% for age and 99.74% for weight recognition respectively. Experimental results and performance evaluation of the applied machine learning classifiers reveals the efficacy of the proposed scheme. © 2022 IEEE.

关键词： Classification (of information)

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Real-Time Object Detection with IOT Using a Smart Cart 18

Real-Time Object Detection with IOT Using a Smart Cart

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed Muhammad Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of AI System Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350331011

With Industry 4.0's technological advancements, the convenience and affordability of Artificial Intelligence (AI) and IoT have increased. However, customers still face long waiting times in payment queues during the smart shopping experience. Current solutions rely on expensive sensor-based RFIDs or simple barcodes, making them impractical and limited in scalability. This paper proposes an economically smart solution with two cameras on NVIDIA JETSON NANO for realtime object monitoring and automated shopping. Communication between modules is facilitated by TCP/IP and HTTP protocols. By utilizing the YOLO v7, real-time monitoring is achieved. This innovative approach revolutionizes the customer experience by providing a seamless interface and time-saving features. Additionally, it offers potential integration with e-commerce businesses and payment gateways, further enhancing the overall customer and vendor experience. © 2024 IEEE.

关键词： Graphical user interfaces

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