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Thin film ferroelectric photonic-electronic memory

Light(science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Recognition of Wh-Question Sign Gestures in Video Streams using an Attention Driven C3D-BiLSTM Network 2

Recognition of Wh-Question Sign Gestures in Video Streams us...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Dey, Arnab Biswas, Samit Le, Dac-Nhuong Dept. of Computer Science and Technology Indian Institute of Engineering Science and Technology Howrah Shibpur711103 India Faculty of Information Technology Haiphong University Haiphong180000 Viet Nam

Human sign language is a visual and gestural means of communication used by people with hearing impairments to interact with others. It has the potential to enable interaction between individuals who struggle with verbal communication and those who lack the essential proficiency to comprehend sign language. Wh-questions constitute a substantial portion of daily sign language interactions. Automatic recognition of Wh-question words in sign language, encompassing interrogative words, has received limited attention in gesture recognition. In this context, a novel dataset named the American Question Sign Video dataset (AQSVd) has been introduced as a significant contribution. Recognizing Wh-question signs in video streams empowers individuals to effectively convey messages through hand movements and gestures, fostering inclusivity and accessibility in communication for deaf and hearing-impaired individuals. The paper proposes a Deep Convolutional-3D BiLSTM Multi-head Attention network for recognizing American Wh-Question word sign gestures in video streams. Incorporating Multi-head attention in the proposed model enhances its ability to capture intricate spatial and temporal features, essential for accurate gesture recognition. The proposed model is thoroughly evaluated across a range of datasets, such as INCLUDE50, WLASL-100, and our AQSVd dataset, achieving remarkable results. Specifically, our model demonstrated an outstanding 98.91% validation accuracy on the AQSVd dataset. The C3D-BiLSTM MHAttention model outperforms other state-of-the-art models, demonstrating its superiority in sign language recognition tasks. The proposed dataset and C3D-BiLSTM Multi-head Attention model contribute significantly to this field, offering potential benefits in education, human-robot interaction, and overall communication for the hearing-impaired community. © 2024 Elsevier B.V.. All rights reserved.

关键词： Gesture recognition

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Intelligent Detection system for Multi-Step Cyber-Attack Based on Machine Learning 15

Intelligent Detection System for Multi-Step Cyber-Attack Bas...

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15th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Jasim, Omar Hammad Al-Dosary, Duaa Ahmed, Hamsa M. Al-Ani, Muzhir Shaban University of Anbar Computer Networking Systems Department Ramadi Iraq Al Baha University Faculty of Computer Science and Information Technology Al Baha Saudi Arabia Human Resources Department Ramadi Iraq College of Computer Sciences and Information Technology University of Anbar Anbar Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah Iraq

ISBN: (纸本)9798350335149

Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms. © 2023 IEEE.

关键词： Network security

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IALS: Innovative Approach for Lung Segmentation Applying Artificial Intelligence and Deep Learning

IALS: Innovative Approach for Lung Segmentation Applying Art...

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Hossain, Mohammad Alamgir Shahi, Fazal Imam Husain, Sadia Suliman, Abdelrazig Kamal, Mohammad Shahid Ahmad, Yasir Talukdar, Mohammad Mazedul Huq Martin, R. John Jazan University College of Engineering & Computer Science Department of computer Science Jazan Saudi Arabia Jazan University ELearning Center Jazan Saudi Arabia University of Bisha Faculty of Computers and Information Technology Department of Information Technology Saudi Arabia Jazan University College of Arts and Humanities Department of Foreign Languages Jazan Saudi Arabia

ISBN: (纸本)9798331513894

The automated segmentation of lungs within chest CT scans remains crucial for many medical applications that require disease diagnosis and treatment design. The presented deep incremental learning (DIL) system addresses fundamental limitations that arise during lung segmentation processes. The exceptional segmentation outcomes result from the neural network structure which includes masked RCNN+UNet for tissue segmentation and GAN+VARMA for classification. Medical literature identifies lung tissue structures as difficult to study because they present irregular forms and complex structures. The task of obtaining precise segmentation proves very challenging. The application of masked RCNN+UNet enables shape control and improves segmentation precision of the lung system. The DIL function helps maintain improved features within segmented images. The applied framework enables improved performance through DSC and MAE and JI metrics evaluation. Experimental evidence shows that the new framework achieves superior performance compared to current top-ranking methods for lung segmentation. The proposed framework exhibits excellent precision and detection efficiency with a DSC value of 0.96 and JI value of 0.93 and a low MAE © 2025 IEEE.

关键词： Medical applications

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An Electricity Usage Monitoring system Using Internet of Things (IoT) for Small Business Stalls 1

An Electricity Usage Monitoring System Using Internet of Thi...

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1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023

作者： Aisuwarya, Ratna Yolanda, Desta Talmera, Annisa Triana Universitas Andalas Faculty of Information Technology Computer Engineering Department Padang Indonesia

ISBN: (纸本)9798350306484

In this proposed research, an IoT -based electrical power monitoring system is developed to monitor small business stalls by adding voltage and output sensors. The system displays electrical parameters and estimated costs on a web interface and provides notifications through an instant messaging service for the stall owners. The study aims to display the results of monitoring electricity usage to users via a web server and provide notifications if there is excess usage based on the rental rules for the stall tenants. Based on the implementation design and system testing, the system can collect data on the use of electrical power at the electrical terminal of a stall using the current sensor SCT013 and the voltage sensor ZMPT101B with an average error of 0.69% - 1.41 %. The system also successfully performs real-time power and cost calculations and shows the information on the website. Additionally, a notification in the form of an excess usage warning is sent via Telegram Bot if there is an excess of 1105 watts of electricity. © 2023 IEEE.

关键词： Microcontrollers

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computer aided detection of mammographic mass using exact Gaussian–Hermite moments

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1139-1147页

作者： Eltoukhy, Mohamed Meselhy Elhoseny, Mohamed Hosny, Khalid M. Singh, Amit Kumar Computer Science Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Faculty of Computers and Information Mansoura University El Mansûra Egypt Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig Egypt Waknaghat Himachal Pradesh Solan India

Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Medical informatics

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Digital Touchpoints: Generating Synthetic Data for Elderly Smartphone Interactions 11

Digital Touchpoints: Generating Synthetic Data for Elderly S...

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11th International Conference on information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Maqbool, Bilal Herold, Sebastian Department of Mathematics and Computer Science Faculty of Health Science and Technology Karlstad University Karlstad Sweden

ISBN: (纸本)9789897587436

Context: Ensuring smartphone interfaces are usable and accessible is essential for elderly users, particularly those with motor impairments, who face challenges with touchscreen interactions. Problem: Hand tremors and limited motor control can hinder touchscreen accuracy and efficiency. Meanwhile, recruiting elderly participants for usability studies can be challenging, often resulting in limited interaction data. Objectives: This study aimed to investigate elderly users’ smartphone interaction patterns, identify key challenges, and generate synthetic data to address data scarcity for usability research. Method: A custom-designed mobile app collected interaction data from 51 elderly participants performing tapping, dragging, and tracing tasks. Hand steadiness was assessed using accelerometer data. Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) models were used to generate synthetic datasets replicating user interaction patterns. Results: Users with shaky hands struggled with precision tasks, especially involving smaller GUI elements, while larger elements improved performance. Continuous control was also found to be challenging in tracing tasks. Synthetic datasets successfully replicated spatial, temporal, and distributional metrics, demonstrating potential utility in future usability evaluation research. Conclusions: Inclusive GUI designs and adaptive features can improve accessibility for the elderly with limited motor control. Synthetic data can offer a potential solution for further usability evaluation research in building AI-driven design evaluation tools, reducing reliance on resource-intensive participant recruitment in earlier prototypes. Future work should examine diverse tasks and scenarios and involve people with severe motor impairments. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Usability engineering

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education 5

Enhancing Early Childhood Learning using Image Processing fo...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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