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2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022

作者： Khalid, Adam Sakthivel, Usha Thangamuthu, Senthilkumaran Drieberg, Micheal Sebastian, Patrick Aziz, Azrina Abd Hiung, Lo Hai Universiti Teknologi PETRONAS Department of Electrical and Electronics Engineering Bandar Seri Iskandar Malaysia Rajarajeswari College of Engineering Department of Computer Science and Engineering Bangalore India ACS College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350334548

Internet of Things (IoT) is a promising technology that is expected to bring tremendous benefits to many application areas. IoT devices are commonly powered by batteries especially in areas where there are no electrical sources. However, these batteries have limited capacity and will eventually be depleted, which affects the lifetime of the IoT applications. The replacement of batteries are time consuming and incures additional costs. Therefore, energy saving schemes are needed to prolong the lifetime of IoT applications. This paper proposes energy saving schemes at the sensor nodes using two methods, that are low power method and shutdown and wake up method. In the former, a low-power microcontroller that uses low voltage is chosen, its low power mode puts the board to sleep and its unused peripherals are turned off during idle periods. In the latter, a Real Time Clock (RTC) module and a P-Channel MOSFET is used to to wake the microcontroller board at certain time interval, to further reduce the power consumption. A suitable low power wireless communication protocol, Zigbee, is also used for sending data between the sensor node and sink node. The current consumptions of the system are captured by a power measurement module. Experimental results show that the low power and shutdown and wake up methods can provide energy savings of 83.70% and 96.51%, respectively. The implementation of energy saving schemes is vital to ensure extended lifetime of IoT applications. © 2022 IEEE.

关键词： Sensor nodes

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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A Novel Fuzzy Semi-supervised Learning Approach for the Classification of Colorectal Cancer (FSSL-CRCC) 2nd

A Novel Fuzzy Semi-supervised Learning Approach for the Cl...

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2nd International Conference on Applied Intelligence and Informatics, AII 2022

作者： Karim, Sara Patwary, Muhammed J. A. Hossain, Mohammad Shahadat Andersson, Karl Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Department of Computer Science Electrical and Space Engineering Lulea University of Technology Skelleftea Sweden

ISBN: (纸本)9783031248009

Colorectal Cancer (CRC) is a form of cancer that develops in the colon or rectum. It is also referred as bowel cancer, colon cancer, or rectal cancer. CRC has become the second most prevailing sort of cancer in the human race. But CRC until now remains linked to a poor diagnosis in patients with severe illness. The concern for early detection drew researchers’ consciousness to a variety of machine learning-based methods. Semi-Supervised Learning (SSL) clear up this challenge in an enhanced way than every alternative Machine learning approaches through incorporating vast quantities of labeled and unlabeled data to generate superior analysis method. The paper presents a novice fuzziness-based semi-supervised learning technique within colorectal cancer diagnosis in which it uses mislabeled data alongside training classified data to improve the model’s dependability. In accustomed to other classification models such as Support Vector Machine, Random tree, Linear Regression and others, the prediction performance using this strategy on the dataset exhibited remarkable breakthroughs in enhancing the classifier’s results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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An AI-Assisted Skincare Routine Recommendation System in XR 7th

An AI-Assisted Skincare Routine Recommendation System in XR

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7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023

作者： Rajegowda, Gowravi Malalur Spyridis, Yannis Villarini, Barbara Argyriou, Vasileios Department of Network Ands and Digital Media Kingston University London United Kingdom Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom School of Computer Science and Engineering University of Westminster London United Kingdom

ISBN: (纸本)9789819990177

In recent years, there has been an increasing interest in the use of artificial intelligence (AI) and extended reality (XR) in the beauty industry. In this paper, we present an AI-assisted skin care recommendation system integrated into an XR platform. The system uses a convolutional neural network (CNN) to analyse an individual's skin type and recommend personalised skin care products in an immersive and interactive manner. Our methodology involves collecting data from individuals through a questionnaire and conducting skin analysis using a provided facial image in an immersive environment. This data is then used to train the CNN model, which recognises the skin type and existing issues and allows the recommendation engine to suggest personalised skin care products. We evaluate our system in terms of the accuracy of the CNN model, which achieves an average score of 93% in correctly classifying existing skin issues. Being integrated into an XR system, this approach has the potential to significantly enhance the beauty industry by providing immersive and engaging experiences to users, leading to more efficient and consistent skincare routines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recommender systems

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Mixed-Signal Building Blocks for Communication Systems Using Flexible Oxide TFT Technology

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2022年第4期1卷 223-230页

作者： Tiwari, Bhawna Bahubalindruni, Pydi Ganga Shrivastava, Suyash Goes, Joao Bhopal Department of Electrical Engineering and Computer Science Bhopal462066 India Caparica2829-516 Portugal Caparica2829-516 Portugal

This article reports two important mixed-signal building blocks using unipolar oxide thin-film transistors (TFTs) on a 30- μ m -thick polymide substrate, which find potential application in communication systems. These blocks include a low-voltage bootstrapped (BS) switch and a latch-type comparator designed using a device channel length of 2 μ m. Functional demonstration of these blocks has been carried out from measurements under normal ambient conditions with a load of 10 M Ω ||12 pF at a supply voltage ( VDD ) not exceeding 3 V. While the BS switch shows a linear ON-resistance of 40 k Ω , at a VDD of 2 V, without any voltage drop across it, the proposed comparator has shown a very low offset of 10 mV with a decision time of 4 μ s. These blocks help in expanding the application of oxide TFT technology in communication systems to design the binary amplitude-shift keying (BASK) modulator and demodulator circuits. © 2022 IEEE.

关键词： Comparators (optical)

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dLeak: An IoT-Based Gas Leak Detection Framework for Smart Factory

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SN computer science 2022年第4期3卷 273页

作者： Rajbanshi, Anamika Das, Debanjan Udutalapally, Venkanna Mahapatra, Rajarshi Department of Electrical Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering IIIT Naya Raipur India Department of Computer Science and Engineering Engineering IIIT Naya Raipur India

Gas industries very often suffer from leakage. This paper proposes an IoT-enabled acoustic-based leak detection system to detect leaks more accurately and rapidly that can improve the safety of such gas industries. Acoustic Emission (AE) signals are collected from a test setup of an industry, which is mimicked by a working kitchen where a pressure cooker whistle was used in the place of a leak. Each AE signal is segmented using a hamming window and features are extracted from every segment. According to our data set, a best fit model has been implemented. Naive Bayes, SVM and KNN classifiers are giving acceptable testing accuracy to detect the leaks. Signals are collected from the spot where the model has to be implemented. Then, these signals are transported to the supervisor’s device using the cloud. The signals are finally tested in the implemented model which successfully predicts the presence of a leak in that spot. The Naive Bayes, SVM and KNN models gave high testing accuracy of 92.2%, 92.4% and 94.44% respectively, of which KNN gave the best performance. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Acoustic sensing IoT Leak detection Machine learning Naive bayes Smart factory Support vector machine (SVM)

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Integrated Platform for Spin-Photon Interfaces in Silicon

Integrated Platform for Spin-Photon Interfaces in Silicon

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Song, Hanbin Zhang, Xueyue Zhi, Yiyang Komza, Lukasz Sipahigil, Alp Department of Materials Science and Engineering University of California Berkeley BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California Berkeley BerkeleyCA94720 United States

We demonstrate optical initialization and readout of electron spins of a single T center in a silicon nanophotonic waveguide. We present progress for coherent control of the waveguide single spin for silicon spin-phot... 详细信息

ISBN: (纸本)9781957171395

关键词： Electron optics

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Tour Spot Recommendation System via Content-Based Filtering 16

Tour Spot Recommendation System via Content-Based Filtering

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Muneer, Mishal Rasheed, Uzair Khalid, Sadia Ahmad, Muhammad University of Lahore Department of Computer Science and It Lahore Pakistan College of Electrical and Mechanical Engineering Nust Department of Computer and Software Engineering Islamabad Pakistan University of Engineering and Technology Department of Computer Engineering Lahore Pakistan

ISBN: (纸本)9781665464772

Online recommendation systems have gained prominence. The online recommendation systems provide a better and faster way to choose Tour Spots for traveling and transactions. Recommender systems are powerful emerging technologies that help consumers to find the best places for touring according to their interests. The recommendation system recommends the most appropriate destinations for the end users. Internet travel portals also compete with each other regularly by taking into consideration several characteristics. Recommendation systems are one of the best methods to raise income and attract consumers. Established systems retrieved irrelevant information that resulted in low customer satisfaction and disappointment of customers. In this research, the Tour Spot recommendation system through content-based filtering is proposed and designed. It recommends the best picnic spot according to the budget and interest of the user. The proposed system improves the tourist experience and recommends the best place according to the things they would like;most significantly it is budget friendly as it suggests different places according to the interests and needs of the user. Data collection, including user information, consolidated user engagement reports, and tour spot data, is the primary challenge of implementing a travel recommendation system. User information originates mainly from information entered by the user during the registration process. When the system has the data of user likes and dislikes, then the system compares the user preferences to the dataset that the author makes for the tour recommendation systems in the dataset first identifies all the features that must be considered at the time of selection of travel destination. This Article also considers user likes and dislikes and the previous history of users to recommend a similar tour spot. © 2022 IEEE.

关键词： Recommender systems

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DeAFusion: Detail-Aware Image Fusion for Whitematter Hyperintensity Segmentation

DeAFusion: Detail-Aware Image Fusion for Whitematter Hyperin...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jo, Gyeongdeok Kim, Jonghun Park, Bo-Yong Park, Hyunjin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Institute for Basic Science Center for Neuroscience Imaging Research Suwon Korea Republic of Korea University Department of Brain and Cognitive Engineering Seoul Korea Republic of

ISBN: (纸本)9798350386226

White matter hyperintensities (WMHs) are critical indicators of cerebral small vessel disease and are linked to stroke and cognitive decline. Accurate WHM segmentation is challenging due to limited contrast and small size, particularly for deep WMHs where T1-weighted and FLAIR MRI are routinely used. Image fusion offers a method to combine two modalities into one fused modality useful for WHM segmentation. We propose DeAFusion: Detail-Aware Image Fusion, a novel image fusion framework. This framework introduces a Pixel-wise Information Preservation Degree map to enable detailed control of fusion at the pixel level and incorporates segmentation loss to embed semantic information. Our model outperforms existing fusion methods in enhancing lesion visibility and preserving structural details, as demonstrated on both public and in-house datasets. DeAFusion shows promise for improving clinical assessments of WMHs. Our code is available at https://***/Gyeongdeok-Jo/DeAFusion. © 2024 IEEE.

关键词： Image fusion

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DialogNTM: Context Reconstruction in Multi-Turn Dialogue Generation using Neural Turing Machines

DialogNTM: Context Reconstruction in Multi-Turn Dialogue Gen...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Haohao Liu, Meiling Zhou, Jiyun Fu, Kaiquan College of Computer and Control Engineering Northeast Forestry University Harbin China Lieber Institute Johns Hopkins University Baltimore United States South Dakota State University Department of Electrical Engineering and Computer Science BrookingsSD United States

ISBN: (纸本)9781665410205

In actual conversational scenarios, we can often determine which parts of the previous dialogue are more critical based on the current inquiry. However, the existing contextual modeling methods often encode the query sentence and the dialogue history in a unified manner, which fails to effectively highlight the inference effect of the query sentence. Moreover, these methods typically process the dialogue history only at the information extraction level, neglecting the treatment of the context itself. In this paper, we propose a novel conversational context modeling technique called DialogNTM. Based on the guidance of the query sentence, the technology can effectively eliminate redundant information by reconstructing the representation of the context. Specifically, we have tweaked the memory and input flow of the Neural Turing Machine (NTM) to encode contextual information in memory and guide the read, write, and erase operations of memory through query sentence. This design simulates the human brain's dynamic retrieval and renewal mechanism of previous memories when dealing with current problems. We have conducted extensive experiments on three publicly available datasets to verify the effectiveness of the DialogNTM model. Compared to the benchmark model, DialogNTM showed significant performance improvements ranging from 11% to 73% across multiple automated evaluation metrics (3.52% to 8.68% in absolute terms). © 2024 IEEE.

关键词： Turing machines

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