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Twitter sentiment analysis using conditional generative adversarial network

International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Predicting Cervical Cancer Using Deep Learning Mutation-based Atom Search Optimization (MASO) Algorithm 2

Predicting Cervical Cancer Using Deep Learning Mutation-base...

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2nd IEEE International Conference on Trends in quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Mayuri, A.V.R. Selvekumar Subash, K. Aeron, Anurag Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India Bahir Dar University Bahir Dar Institute of Technology Department of Electrical and Computer Engineering Ethiopia Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350384277

Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected early enough, treatment can be effective. Presently, it is difficult for medical workers to detect this kind of cancer before it spreads rapidly. This study used a number of machine learning classification algorithms with risk markers to predict cervical cancer. These study suggestions mutation-based Atom Search Optimization (MASO) with a Deep Convolutional Neural Network (DCNN) to offer a original method for automated diagnosis of uterine cervical cancer. Though DCNN is used to extract features from medical imaging data, MASO is used to increase the optimization of the diagnostic model. Composed, these technologies have the potential to rise cervical cancer diagnosis accuracy and efficiency, offering an automated approach for early detection and prompt intervention. Our proposed model established a strong 95% accuracy rate for generalization. This study explores the difficulties caused by missing values and class imbalance in the specific dataset with the goal of assisting medical professionals in the early detection of cervical cancer and improving treatment for people that are impacted by the illness. © 2024 IEEE.

关键词： Convolutional neural networks

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Temperature-Dependent Hydrogen Modulations of Ultra-Scaled a-IGZO Thin Film Transistor Under Gate Bias Stress

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2024年 5卷 9-16页

作者： Aslam, Muhammad Chang, Shu-Wei Chuang, Min-Hui Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan701401 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic electrical characterization of a-IGZO TFT related to reliability issues, such as positive bias temperature stress (PBTS) and negative bias temperature stress (NBTS), would entitle its integration into novel electronics systems. Unexpectedly, PBTS is characterized by the transition of positive Vth shift to negative Vth shift (ΔVth, the positive shift followed by the stress and temperature activated negative shift). This transition is attributed to charge trapping/trap-site generations and hydrogen migration to the active layer. The ΔVth shift mechanism depends on the temperature and voltage stress. On the other hand, a negative ΔVth shift has been observed during the NBTS operation and could be attributed to the hole trapping at the interface of GI/IGZO. An effective suppression of the gate leakage current has also been observed during reliability tests. Simulation results reveal a pronounced potential at the edges of source and drain regions, and considered the origin of hydrogen migration into the IGZO layer. Thermal image results also reveal the strong temperature/potential distribution at the edges of the source/drain regions, indorsing the simulation results. © 2020 IEEE.

关键词： Oxide semiconductors

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Experimental observation of coherent-perfect-absorber and laser points in anti-PT symmetry

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Physical Review A 2024年第3期110卷 033504页

作者： Minye Yang Qi Zhong Zhilu Ye Şahin K. Özdemir Mohamed Farhat Ramy El-Ganainy Pai-Yen Chen State Key Laboratory for Manufacturing Systems Engineering School of Electronic Science and Engineering Department of Electrical and Computer Engineering Department of Engineering Science and Mechanics The Materials Research Institute Computer Electrical and Mathematical Science and Engineering Division Department of Physics Henes Center for Quantum Phenomena

The coexistence of coherent perfect absorber and laser or amplifier (CPAL) point is a peculiar spectral singularity associated with the scattering matrices of non-Hermitian systems. While the potential of CPAL systems for sensing application has been highlighted recently, the extreme sensitivity of parity-time (PT)-symmetric CPAL devices to the input signal deviations has so far impeded their practical utilization. Here we explore a strategy for implementing CPAL circuits by exploiting another type of non-Hermitian symmetry, namely anti-PT (APT) symmetry. We demonstrate that the condition for building CPAL in our proposed APT-symmetric electronic circuits additionally requires parity symmetry, which simplifies the circuit design and implementation. Additionally, we show that this newly proposed structure is 1.85 times more robust compared to previous CPAL devices studied in the literature.

关键词： Noise quantum sensing Non-Hermitian systems PT-symmetry

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Spontaneous strong symmetry breaking in open systems: Purification perspective

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Physical Review B 2024年第15期110卷 155150页

作者： Pablo Sala Sarang Gopalakrishnan Masaki Oshikawa Yizhi You Department of Physics and Institute for Quantum Information and Matter Walter Burke Institute for Theoretical Physics Department of Electrical and Computer Engineering Institute for Solid State Physics Department of Physics

We explore the effect of decoherence on many-body mixed states from a purification perspective. Here, quantum channels map to unitary transformations acting on a purified state defined within an extended Hilbert space. First, we exploit this approach to analyze the phenomenon of spontaneous strong-to-weak symmetry breaking (sw-SSB) triggered by strongly symmetric local quantum channels. We find that sw-SSB for mixed states relates to proximity to a symmetry-protected topological (SPT) order in the purified state. Remarkably, the measurement-induced long-range order in the purified SPT state, as characterized by the Rényi-2 correlator, mirrors the ensuing long-range order in the mixed state due to sw-SSB. Moreover, we establish a 1-to-1 correspondence between various sw-SSB order parameters for the mixed state, and strange correlators in the purification, which signify the SPT order. This purification perspective is further extended to explore intrinsic mixed-state topological order and decoherent symmetry-protected topological phases.

关键词： Open quantum systems & decoherence quantum correlations in quantum information Topological phases of matter Symmetries in condensed matter

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Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum Access in Sensor Ad Hoc Networks 15

Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Geetha, B. Kansal, Amit Sudhamsu, Gadug Gawali, Piyush P. Amudha, A. Samrat, Bhavuk Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Research Center Quantum University Uttar Pradesh India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Cognitive Radio-primarily based Spectrum Sensing (CRSS) is a crucial generation for permitting dynamic spectrum access in sensor ad hoc networks (SAHNs). This era allows customers to successfully make use of to be had spectrum whilst keeping off interference from existing customers. CRSS makes use of sensing and optimization strategies to identify free frequency bands to enable verbal exchange in SAHNs. Moreover, the CRSS additionally consists of protocols and techniques to locate and defend against malicious behavior, which include spectrum jamming and false sensing reports. Subsequently, CRSS may be blended with superior technology, such as multi-agent structures and system gaining knowledge of, to create an efficient and sturdy gadget for dynamic spectrum access. © 2024 IEEE.

关键词： Frequency Generation Malicious Permitting Primarily Spectrum Techniques Technology

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Comparison of SVM and Random Forest for Multimodal Hand Gesture Recognition

Comparison of SVM and Random Forest for Multimodal Hand Gest...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Rahman, Farin Samuel, Joshua McFarlane, Nicole University of Tennessee Min H. Kao Department of Electrical Engineering and Computer Science KnoxvilleTN United States Golisano College of Computing and Information Sciences Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9798331504847

Machine learning has been implemented to detect and identify sign language hand gestures (letters and numbers) using the Photoplethysmography (PPG) and impedance. The sensor data was collected from commercially available wristbands and evaluation boards. Two supervised machine learning algorithms: Random Forest and Support Vector Machine were used for categorizing the data points. The accuracy for each of the algorithms for the letters and numbers was within the range of either 70-75% or 20-28% for the PPG data. The accuracy was between 33-40% and 55-66% using Random Forest. The accuracy was between 61-76% to 80-90% for when using Support Vector Machine for impedance data. Between the two algorithms, the Random forest approach has better consistency of 70-75% with PPG data whereas the Support Vector Machine approach is more consistent for impedance data with 61-76% accuracy to detect the hand gestures. © 2025 IEEE.

关键词： Random forests

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Distributed exact Grover’s algorithm

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Frontiers of physics 2023年第5期18卷 255-279页

作者： Xu Zhou Daowen Qiu Le Luo Institute of Quantum Computing and Computer Theory School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China School of Physics and Astronomy Sun Yat-sen UniversityZhuhai 519082China QUDOOR Co Ltd.Beijing 100089China

Distributed quantum computation has gained extensive *** this paper,we consider a search problem that includes only one target item in the unordered *** that,we propose a distributed exact Grover’s algorithm(DEGA),which decomposes the original search problem into■n/2■***,(i)our algorithm is as exact as the modified version of Grover’s algorithm by Long,which means the theoretical probability of finding the objective state is 100%;(ii)the actual depth of our circuit is 8(n mod 2)+9,which is less than the circuit depths of the original and modified Grover’s algorithms,1+8■π/4√2^(n)■and 9+8■π/4√2^(n)-1/2■,*** only depends on the parity of n,and it is not deepened as n increases;(iii)we provide particular situations of the DEGA on Mindquantum(a quantum software)to demonstrate the practicality and validity of our *** our circuit is shallower,it will be more resistant to the depolarization channel noise.

关键词： distributed quantum computation search problem distributed exact Grover’s algorithm(DEGA) Mindquantum the depolarization channel noise

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Deep Learning Based Multiclass Tumor Identification and Classification Using Fusion of CNN Models 17

Deep Learning Based Multiclass Tumor Identification and Clas...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Saeed, Zubair Torfeh, Tarraf Aouadi, Souha Ji, Jim Xiuquan Bouhali, Othmane Texas A&m University Department of Electrical and Computer Engineering College StationTX47080 United States Texas A&m University at Qatar Department of Electrical and Computer Engineering Doha Qatar Hamad Medical Corporation National Center for Cancer Care and Research Department of Radiation Oncology Doha3050 Qatar Hamad Bin Khalifa University Qatar Center for Quantum Computing College of Science and Engineering Doha34110 Qatar

ISBN: (纸本)9798350387384

The rising incidents of brain tumors pose significant challenges in diagnosis and treatment. Tumorous brain cancers present unique difficulties due to their critical locations and functional implications. Magnetic Resonance Imaging (MRI) offers safe and precise imaging, free from the risks associated with radiation-based methods. Deep learning, particularly Convolutional Neural Networks (CNN), has emerged as a valuable tool in identifying brain tumors in MRI scans, enabling more accurate diagnoses by fusing two well-known DL models. This study explores a fusion strategy combining AlexNet and VGG16 models, outperforming individual models trained with different learning rates. While VGG16 and AlexNet achieved accuracies of 79% and 77% respectively. Our fusion approach potentially achieved an accuracy of 81%, underscoring its efficacy in enhancing brain tumor classification accuracy. This study helps medical professionals to accurately and timely classify tumors for better outcomes for patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Creating non-Abelian gauge fields in non-Hermitian systems via photonic synthetic dimensions

Creating non-Abelian gauge fields in non-Hermitian systems v...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Pang, Zehai Wong, Bengy Tsz Tsun Hu, Jinbing Yang, Yi Department of Physics HK Institute of Quantum Science & Technology The University of Hong Kong Pokfulam Hong Kong College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781957171395

We introduce synthetic non-Abelian gauge fields to non-Hermitian systems and study their topological consequences via a non-Abelian Hatano-Nelson model, its photonic realization via a polarization-multiplexed fiber ring resonator, and broken gauge invariance of the non-Abelian Wilson loop. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Ring gages

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