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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Sugimura, Hiroshi Isshiki, Masao Kanagawa Institute of Technology Dept. of Home Electronics Kanagawa Japan Kanagawa Institute of Technology Graduate School of Engineering Kanagawa Japan

ISBN: (纸本)9798350340181

This paper proposes a fast and stable method to discover and get properties of ECHONET Lite protocol-based IoT appliances in a smart home. We develop three methods step by step. Finally, we propose a technique that acquires all of the information, re-acquires the data in case of errors, and completes the acquisition of the unacquired information. © 2023 IEEE.

关键词： Domestic appliances

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Challenges and Solutions for Practical Implementation of Process Bus Based Substation Protection and Automation system

Challenges and Solutions for Practical Implementation of Pro...

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2023 IEEE Asia Meeting on Environment and Electrical engineering, EEE-AM 2023

作者： Soetomo, Kevin Hamamatsu, Koichi Thuan, Dinh Huu Linh, Nguyen Manh Quang, Lai Vu Okai, Makoto Power Grid System Solutions Toshiba Energy Systems & Solutions Corporation Engineering Dept. Kawasaki Japan Technical Department Ho Chi Minh City Viet Nam Ltd. Engineering Dept. Hanoi Viet Nam Ltd. Sales & Engineering Division Hanoi Viet Nam Toshiba Energy Systems & Solutions Corporation Protection and Control Dept. Fuchu Japan

ISBN: (纸本)9798350381061

The protection and control system in substations has undergone significant evolution since the era of hardwired systems. In recent years, the increasing trend of digital transformation (DX) across various fields, including protection and automation systems, has led to the promotion of IEC 61850 process bus-based digital substations. However, recent analysis and evaluation have revealed numerous challenges in various aspects, such as technical issues, system performance, test methods, maintenance, and economic considerations. Moreover, many of these challenges lie outside the scope of international standards like IEC 61850 and IEC 61869. As a result, the worldwide implementation of digital substations has been hindered. This paper aims to describe the issues encountered during the implementation of process bus technology and propose solutions that focus on optimizing system configuration based on our experience in implementing digital substation projects and developing MU and IED. Addressing these issues is crucial to fully unlocking the potential of digital substations. One of the most promising advantages is the availability of digital data collected from substation equipment. By utilizing this data, overall power system operational efficiency can be enhanced, and equipment management can be optimized. Predictive maintenance can be implemented, enabling the early detection of potential equipment failures, minimizing unplanned outages, and reducing the need for emergency repairs, which contribute to unnecessary carbon emissions and equipment downtime. Ultimately, digital substations empower end-users to reduce greenhouse gas emissions, improve energy efficiency, and seamlessly integrate power systems, thereby supporting the carbon-neutral initiative and facilitating the transition towards a sustainable energy future. © 2023 IEEE.

关键词： Energy efficiency

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Optimizing Healthcare Accessibility and Effectiveness through the Implementation of AI Solutions for Remote Patient Monitoring 3

Optimizing Healthcare Accessibility and Effectiveness throug...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Sahu, Dinesh Prasad Kumar, Ashwini Ahmad, Salman Khursheed Majumder, Jayeeta Gangopadhyay, Sourav Bennett University School of Computer Science Engineering & Technology Greater Noida India Camellia Institute of Technology & Management Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology & Management Dept. of Applied Computational Science and Engineering Greater Noida India Brainware University Department of Hospital Management Kolkata India

ISBN: (纸本)9798350384598

This study investigates the optimization of healthcare accessibility and effectiveness by integrating artificial intelligence (AI) solutions within Internet of Things (IoT) systems for remote patient monitoring. Embracing a holistic approach, we deploy medical sensors, including temperature, pressure, heart rate, and oxygen sensors, to collect patients' continuous health metrics (vital signs). These sensed data are then transmitted to an Arduino controller, responsible for managing data communication with the cloud infrastructure using a Wi-Fi module that facilitates data storage and analysis. Stored data are accessed by authorized healthcare professionals, who exploit machine learning models, including Artificial Neural Networks (ANN), Logistic Regression (LR) and Decision Tree (DT), to forecast patients' health outcome in advance using the sensed data. A rigorous evaluation demonstrated the IoT system's effectiveness, based on testing with sensed data from ten patients over a month, where 70% of the data were used for training the models, and the rest for testing. The proposed IoT system demonstrates the success;ANN exhibited the highest accuracy at 97.88%, followed by LR at 93.43%, and DT at 91.20%. Precision, recall, and F1 scores further validated the models' ability to predict health outcome accurately. Evaluation of the accuracy and loss curves showed the convergence of the models towards optimal performance. This study contributes to remote healthcare monitoring and provides a promising IoT paradigm for realizing ubiquitous remote monitoring, optimized healthcare effectiveness, and patient outcome through AI-enabled IoT systems. © 2024 IEEE.

关键词： Remote patient monitoring

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Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment 2

Multinomial Naive Bayes Based Machine Learning Analysis of T...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Saravanan, U.K.Balaji Vijay, M. Shreedhar, T. Rajasekar, G. Yashwanth, R. Shakthipriya, P. Sona College of Technology Information Technology Salem India Vmkv Enginerring College Dept. of Computer Science and Engineering Salem India

ISBN: (纸本)9798350347579

Sentiment analysis identifies and categorizes thoughts and feelings expressed in the source text. Social media uses tweets, status updates, and blog columns to generate sensitive data. SA on user-generated data helps understand crowd opinion. Due to slang and spelling errors, Twitter sentiment analysis (TSA) is much more complex than general sentiment analysis. Every day, millions of tweets are generated on various topics. Machine learning algorithms can be used to solve this problem-the proposed machine learning algorithm includes preprocessing, weight calculation, feature extraction, and classification. First, we collected a Kaggle dataset from Twitter sentiment analysis. In the second step, the data preprocessing steps mainly include stop words, emoji, and repeated characters in words and URLs due to their low importance. Then, we used tokenization, normalization, stemming, and lemmatization to achieve better results. In the third step, we computed weights using word2vec to represent the tweets. In the fourth step, we use a feature extraction method consisting of TF-IDF and word embedding to check based on word and noun counts. Finally, TSA can be used to explore sentiment using Multinomial Naïve Bayes (MNB) and Maximum Entropy classification. The proposed machine learning algorithm achieves better results with TSA in terms of recognition accuracy, recall, precision and F-measure. © 2023 IEEE.

关键词： Sentiment analysis

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A Multi-Agent Approach for Protection Coordination in a Microgrid Using JADE Platform

A Multi-Agent Approach for Protection Coordination in a Micr...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Jayant Mani Tripathi Nitin Yadav Sanjeev Kumar Mallik Aseem Chandel Dept. of Electrical Engineering Rajkiya Engineering College Mainpuri India Dept. of Electrical Engineering National Institute of Technology Patna India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

A protection strategy utilizing agent technology for adaptive relaying of Directional Overcurrent Relays (DOCRs) has been proposed in this paper. In this approach, communication plays a vital role by providing real time information of the changing system conditions. In this study, communication channel has been simulated in Java Agent Development Framework (JADE) platform. The change in topology has an adverse impact on the protection coordination of the DOCRs and hence, the settings of DOCRs should be updated. In this study, for 9-bus test system communication channel flow has been established between different agents of the system, namely, relay agents, DG agents, equipment agents etc through Agent Communication Language (ACL) standard. Different test cases have been considered, namely, DG connection/disconnection, line outage, change in microgrid operation and relay coordination upon occurrence of fault for enhancing the protection strategy. In this study communication channel between different agents has been carried out through series of simulation on JADE platform for different contingencies in the quest to validate the sequence of events that occur upon fault inception. Hence, the DOCRs adaptively update their settings utilizing the communication channel and mitigate any maloperation of the relay. This ensures greater reliability and efficiency in mitigating the relay coordination problem.

关键词： Java Communication channels Microgrids Real-time systems Topology Reliability Protection Relays Sustainable development Standards

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Diabetes Mellitus Prediction Based on Enhanced K Strange Points Clustering and Classification 7

Diabetes Mellitus Prediction Based on Enhanced K Strange Poi...

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7th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2022

作者： Johnson, Terence Narvekar, Anup Vaz, Jude Haldankar, Aryan Hubli, Shivani Naik, Omkar Padre Conceicao College of Engineering Verna Dept. of Information Technology Goa India Dept. of Computer Engineering Padre Conceicao College of Engineering Verna Goa India

ISBN: (纸本)9781665496483

The Diabetes Mellitus prediction system projected in this paper employs the Enhanced K-Strange Points Clustering Algorithm (EKSPCA) and the Naïve Bayes Classifier for clustering and classification respectively. The Enhanced K-Strange Points Clustering Algorithm is employed for its benefits over other clustering algorithms in that it takes lesser time compared to the previously used clustering algorithms, with higher accuracy rate. The outcomes proved that the Diabetes Mellitus prediction system projected in this paper produced better results than the existing systems with respect to execution speed. © 2022 IEEE.

关键词： Clustering algorithms

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Classification of Non-native Handwritten Characters Using Convolutional Neural Network

arXiv

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arXiv 2024年

作者： Mamun, F.A. Chowdhury, S.A.H. Sarker, H. Giti, J.E. Dept. of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

The use of convolutional neural networks (CNNs) has accelerated the progress of handwritten character classification/recognition. Handwritten character recognition (HCR) has found applications in various domains, such as traffic signal detection, language translation, and document information extraction. However, the widespread use of existing HCR technology is yet to be seen as it does not provide reliable character recognition with outstanding accuracy. One of the reasons for unreliable HCR is that existing HCR methods do not take the handwriting styles of non-native writers into account. Hence, further improvement is needed to ensure the reliability and extensive deployment of character recognition technologies for critical tasks. In this work, the classification of English characters written by non-native users is performed by proposing a custom-tailored CNN model. We train this CNN with a new dataset called the handwritten isolated English character (HIEC) dataset. This dataset consists of 16,496 images collected from 260 persons. This paper also includes an ablation study of our CNN by adjusting hyperparameters to identify the best model for the HIEC dataset. The proposed model with five convolutional layers and one hidden layer outperforms state-of-the-art models in terms of character recognition accuracy and achieves an accuracy of 97.04%. Compared with the second-best model, the relative improvement of our model in terms of classification accuracy is 4.38%. © 2024, CC BY.

关键词： Convolutional neural networks

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Deepfake Detection and Mitigation Using Advanced CNN: Ensuring Digital Content Integrity

Deepfake Detection and Mitigation Using Advanced CNN: Ensuri...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Palvi Sharma Shubham Gupta Monu Sharma Neha Gupta Dept. of CSE Model Institute of Engineering & Technology Jammu India Valley Health System Winchester Virginia School of Computer Science and Engineering Geeta University

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Deepfake technology, with its hyper-realistic fake content and backed by AI technologies, presents a serious threat to the authenticity and security of digital content. The study deployed three different machine learning-based models: Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Vision Transformers (ViT) for discrimination of real and fake faces. The model leverages the capabilities of CNN for automatic spatial feature learning from raw data, giving it exceptional performance in the discrimination between real and fake content. Performance measures were made in terms of accuracy, precision, recall, and F1 score and compared against traditional methods; the model remains a trustworthy contribution towards deepfake detection. Findings indicate that the CNN exceeded others with the highest precisions of up to 92.21%, recall of 90%, F1 score of 93%, and perfect accuracy of 97.25%. The paper further discusses the future developments, for instance, architectural improvements and real-time applications and expanding detection capacity outside visual data for a more formidable approach towards new deepfake formats, such as audio and text. This endeavor lays the groundwork for viable and scalable roads against the rising onslaught on digital media manipulation, ensuring content integrity in an AI-driven world.

关键词： Support vector machines Representation learning Deepfakes Visualization Accuracy Roads Transformers Convolutional neural networks Security Artificial intelligence

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BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network

arXiv

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arXiv 2024年

作者： Diba, N.T. Akter, N. Chowdhury, S.A.H. Giti, J.E. Dept. of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

A person’s Body Mass Index, or BMI, is the most widely used parameter for assessing their health. BMI is a crucial predictor of potential diseases that may arise at higher body fat levels because it is correlated with body fat. Conversely, a community’s or an individual’s nutritional status can be determined using the BMI. Although deep learning models are used in several studies to estimate BMI from face photos and other data, no previous research established a clear connection between deep learning techniques for handwriting analysis and BMI prediction. This article addresses this research gap with a deep learning approach to estimating BMI from handwritten characters by developing a convolutional neural network (CNN). A dataset containing samples from 48 people in lowercase English scripts is successfully captured for the BMI prediction task. The proposed CNN-based approach reports a commendable accuracy of 99.92%. Performance comparison with other popular CNN architectures reveals that AlexNet and InceptionV3 achieve the second and third-best performance, with the accuracy of 99.69% and 99.53%, respectively. © 2024, CC BY.

关键词： Convolutional neural networks

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IoT Based Risk Monitoring system 1

IoT Based Risk Monitoring System

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Joy, Salna Neethu, P.S. Babychithra, R. Anju, M.I. New Horizon College of Engineering Dept. of Electronics and Communication Bangalore India Dept. of Electronics and Communication Engineering Bangalore India New Prince Shri Bhavani College of Engineering and Technology Dept. of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665456876

The Internet of things (IoT) aims at connecting different objects, things using internet. The IoT is expanding rapidly and this motivates to apply for the food preservation domain such as preserve the standard of the veggies and fruits. In this paper we have worked on a cold storage system to analyze the environmental conditions under which the food item is being stored. The proposed system senses the temperature, moisture, gas parameters of surrounding environment as these parameters affect nutritional values of food items. An Arduino-based system is created and put into operation;it serves as both a central hub and a network layer for the refrigerated holding tank. It is also linked to the cloud, where an open-source application server supports digital storage functions. By establishing a connection to the database (DB) via its IP address, the measured variables are delivered to the base station (BS) from the cloud and stored there. Then, a cooperative sensing model that uses many observed information as input and one merged informational item or action to be performed as output is tried. As a result, numerous inputs, such as temperature and humidity, were combined and averaged to provide a tightly integrated result. Last, the system integrated an android mobile application which is used to facilitate user interaction and connect through IoT based system that is station or gateway and the internet. GPS is Used to track the remote cold storage and transport container live locations. © 2022 IEEE.

关键词： Climate change

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