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Research on Sarcasm Detection technology Based on Image-Text Fusion

Computers, Materials & Continua 2024年第6期79卷 5225-5242页

作者： Xiaofang Jin Yuying Yang YinanWu Ying Xu School of Information and Communication Engineering Communication University of ChinaBeijing100024China Cable Television Technology Research Institute Academy of Broadcasting ScienceBeijing100053China

The emergence of new media in various fields has continuously strengthened the social aspect of social *** tend to express emotions in social interactions,and many people even use satire,metaphors,and other techniques to express some negative emotions,it is necessary to detect sarcasm in social comment *** sarcasm,the more reference data modalities used,the better the experimental *** paper conducts research on sarcasm detection technology based on image-text fusion *** effectively utilize the features of each modality,a feature reconstruction output algorithm is *** algorithm is based on the attention mechanism,learns the low-rank features of another modality through cross-modality,the eigenvectors are reconstructed for the corresponding modality through weighted *** only the image modality in the dataset is used,the preprocessed data has outstanding performance in reconstructing the output model,with an accuracy rate of 87.6%.When using only the text modality data in the dataset,the reconstructed output model is optimal,with an accuracy rate of 85.2%.To improve feature fusion between modalities for effective classification,a weight adaptive learning algorithm is *** algorithm uses a neural network combined with an attention mechanism to calculate the attention weight of each modality to achieve weight adaptive learning purposes,with an accuracy rate of 87.9%.Extensive experiments on a benchmark dataset demonstrate the superiority of our proposed model.

关键词： Sentiment analysis sarcasm detection feature fusion feature reconstruction

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Development of Human Activity Prediction Systems in Smart Homes 1

Development of Human Activity Prediction Systems in Smart Ho...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Sukanya, K. Prashanth, Addagatla Addagatla, Ugendhar TKR College of Engineering and Technology Electronics and Communication Engineering Hyderabad Meerpet India Institute of Aeronautical Engineering Electronics and Communication Engineering Hyderabad Dundigal India Mvsr Engineering College Computer Science and Engineering Hyderabad Nadergul India

ISBN: (纸本)9798350377002

Human activity prediction using machine learning in smart homes is an emerging research area with significant potential for enhancing the functionality and efficiency of residential environments. This study explores the application of machine learning techniques to predict human activities within smart homes and highlights the benefits it brings to residents. By analyzing data collected from various sensors and devices, such as motion sensors, smart appliances, and environmental sensors, machine learning models can accurately forecast activities like occupancy patterns, usage of appliances, and daily routines. Predicting user activity aids in energy efficiency by optimizing consumption patterns, enhances recommender systems for personalized suggestions, and boosts anomaly detection for identifying irregularities in various domains. Leveraging predictive analytics, organizations can streamline operations, improve user experience, and strengthen security measures effectively. The findings of this study demonstrate the potential of human activity prediction using machine learning in smart homes to create intelligent, efficient, and personalized living spaces. © 2024 IEEE.

关键词： Smart homes

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Design and Analysis of Square Fractal Shaped Planar Antenna

Design and Analysis of Square Fractal Shaped Planar Antenna

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Ramesh Babu, B.S.S.V. Gullala, Deekshitha Srinivas, M.S.S.S. Gupta, Akash Kumar Duvvarapu, Pavani Chimirala, Chandrasekhar Jaka, Syamala Raghu Engineering College Department of Electronics and Communication Engineering Andhra Pradesh Visakhapatnam India Raghu Institute of Technology Department of Electronics and Communication Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350358926

This paper presents the design, analysis and simulation of microstrip patch antenna in the field of wireless communication networks. This antenna is used for the design optimization of a microstrip patch antenna tuned over a frequency range of 14 GHz with a return loss of -19dB. The gain over this microstrip patch antenna is over a range of 9dB. This paper mainly focuses on how the analysis and development of a square fractal-shaped planar antenna is done. This research provides the feasibility and advantages of how the fractal-shaped geometries in planar antennae is enhanced for wireless communication systems.. © 2024 IEEE.

关键词： Fractals

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Design and Development of an e-Health Package for Voice Pathology Detection 5th

Design and Development of an e-Health Package for Voice P...

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5th International Conference on Computing and Network communications, CoCoNet 2023

作者： Rai, Shiksha Rishita, S. Lasya, Kandalam Srihari Vekkot, Susmitha Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819747108

In this paper, a speech-based detection system is developed for voice pathologies like GERD, laryngitis, bulbar palsy, vocal fold leukoplakia, cyst, dysarthria, and aphasia. The e-health set-up comprises three modules for the detection of specific voice disorders leading to degradation in voice quality. Classification of these pathologies is performed using various machine learning algorithms before and after data balancing using SMOTE. The maximum performance is obtained for CatBoost with an accuracy of 56% for the detection of voice disorders before data balancing and 93% post-data balancing. An accuracy of 86.5% is obtained by random forest for detection of dysarthria and an accuracy of 88% is obtained for aphasia using the CatBoost classifier. Further, to make an improvement in the selected features we have customized a generalized feature set for the classification of voice disorders. All the components are effectively incorporated into a user-friendly e-health package, ensuring non-invasive, and precise diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Cryptanalysis in e-commerce: A novel arrangement procedure using DHEC-SAT upon elliptic curve cryptography

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Multimedia Tools and Applications 2025年第8期84卷 5377-5395页

作者： Fauziyah Wang, Zhaoshun School of Computer and Communication Engineering University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing China

Cryptanalytic innovations are considered the most economical and useful method for guaranteeing information security across shared networks. Despite the tinier key magnitude of Elliptic Curve Cryptography (ECC) compared to common cryptanalysis schemes, the majority of scholars have employed cryptanalysis methods to deliver data reliability and verification. Besides, the computation attempt needed by Diffie Hellman-Elliptic Curve (DH-EC) for equal key dimensions is similar and delivers extra security advantages. The research offers a novel key arrangement procedure using DH-EC in the secure automatic transactions (SAT) setting (DHEC-SAT) to enhance data safety. The DHEC-SAT procedure effectively applied the keys in the SAT setting for online payment and was employed to decode and encode the validation data. The DHEC-SAT procedure is implemented for an entry of e-commerce payments to deliver great-degree security examination, fast verification, and the safety of the suggested Cryptanalytic scheme. The suggested DHEC-SAT generates highly secure systems regarding data size (168 bytes), computational period (4.34 ms), and communication costs (1271 bits), as contrasted to extant ECC procedures and PIN-enabled key arrangements. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic commerce

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Resilient Framework for Next-Generation Heterogeneous Wireless Networks in Disaster Management 4

Resilient Framework for Next-Generation Heterogeneous Wirele...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mathias, Ajisha Raj, J. Relin Francis Raja, S.R. Krishnan, R. Santhana Balamurugan, S. Rajkumar, G.Vinoth Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Hindustan Institute of Technology and Science Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Mathematics Madurai India J.P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798331540364

The growing occurrence and concentration of natural and artificial disasters necessitate the development of robust communication systems capable of maintaining reliable and efficient connectivity under adverse conditions. Heterogeneous wireless networks (HSNs) have emerged as a promising solution, offering the flexibility to integrate various wireless technologies and adapt to changing environments. However, the inherent complexity and dynamic nature of HSNs present significant challenges in ensuring resilience and reliability, particularly in disaster scenarios where network infrastructure may be severely compromised. This research investigates a Resilient Framework for Next Generation Heterogeneous Wireless Networks in Disaster Management, focusing on enhancing communication reliability and efficiency during critical scenarios. The study introduces Network Coding Enhanced COPE (NC-COPE), a novel algorithm integrating network coding principles into the COPE framework. Simulation analysis compares NC-COPE with existing algorithms: Optimized Link State Routing (OLSR), Coding-Aware Routing (CAR), and Dynamic Source Routing (DSR) using metrics such as Packet Delivery Ratio (PDR), Routing Overhead, and Resilience to Node Failures. NC-COPE demonstrates superior performance in maintaining high PDR, reducing routing overhead, and enhancing network resilience under node failures. The findings underscore NC-COPE's potential to mitigate communication challenges in disaster management scenarios, highlighting its role in future wireless network deployments. © 2024 IEEE.

关键词： communication Reliability Disaster Management Heterogeneous Wireless Networks Network Coding Packet Delivery Ratio Resilient Framework Routing Overhead

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Recent technology Advancements in Smart City Management:A Review

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Computers, Materials & Continua 2024年第12期81卷 3617-3663页

作者： Chiranjeevi Karri JoséJ.M.Machado Joao Manuel R.S.Tavares Deepak Kumar Jain Suresh Dannana Santosh Kumar Gottapu Amir H.Gandomi Department of Mechanical Engineering Faculty of EngineeringUniversity of PortoPorto4200-465Portugal Department of Electronics and Communication Engineering Andhra University College of EngineeringAndhra UniversityVisakhaptnam530003Andhra PradeshIndia Laboratory of lntelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Artificial IntelligenceDalian University of TechnologyDalian116024China Department of Electronics and Communication Engineering GMR Institute of TechnologyRajamVizianagaram532127Andhra PradeshIndia Department of Civil Engineering Gayatri Vidya Parishad College of Engineering(GVPCE)Visakhapatnam530048Andhra PradeshIndia Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) óbuda UniversityBudapest1034Hungary

The rapid population growth,insecure lifestyle,wastage of natural resources,indiscipline behavior of human beings,urgency in the medical field,security of patient information,agricultural-related problems,and automation requirements in industries are the reasons for invention of *** cities aim to address these challenges through the integration of technology,data,and innovative *** a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living,improve resource efficiency,and create sustainable *** review presents five of the most critical technologies for smart and/or safe cities,addressing pertinent topics such as intelligent traffic management systems,information and communications technology,blockchain technology,re-identification,and the Internet of *** challenges,observations,and remarks of each technology in solving problems are discussed,and the dependency effects on the technologies’performance are also *** deep learning models for various applications are *** models performance,their dependency on dataset size,type,hyper-parameters,and the non-availability of labels or ground truth are discussed.

关键词： Smart cities intelligent traffic management systems internet of things deep learning blockchain technology

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Study of Electrical Characteristics and Charge Storage Behaviour of Gold Embedded Floating Gate MOS Structure 3

Study of Electrical Characteristics and Charge Storage Behav...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Banerjee, Amrita Chakraborty, Gargi Roy, Sunipa Heritage Institute Of Technology Department Of Electronics&Communication Engineering Kolkata India Future Institute Of Engg & Management Department Of Electronics&Communication Engineering Kolkata India Guru Nanak Institute Of Technology Department Of Electronics&Communication Engineering Kolkata India

ISBN: (纸本)9798350374643

Non-volatile memory(NVM) devices are of great interest because of their memory capacities in the form of charge storage, scalability and retention capabilities. A Metal-Oxide Semiconductor (MOS) structure with a tunnel oxide, control oxide and a composite gate dielectric embedded with Gold (Au) nano-crystals(nc) has been studied in this paper. This work represents a theoretical analysis of leakage current in the form of field emission or the Fowler-Nordheim (F-N) tunneling current in the designed structure. The lowering of the onset voltage in the F-N tunneling current expressed the high retention capacity or low writing voltage of the NVM structure. The proposed device structure shows a considerable memory window when the voltage sweeps from -6V to + 15V back and forth. Additionally, the transfer characteristics of the nanoparticles-embedded MOS transistor have been studied well in this paper. This kind of proposed NVM structure, owing to its charge storing capacity and scalability can find immense application in the field of nonvolatile memory design. © 2024 IEEE.

关键词： Gate dielectrics

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Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

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Australian Journal of Electrical and electronics engineering 2025年第2期22卷 271-282页

作者： Karambir Yadav, Ashwani Kumar Madan, Charanjeet Department of Electrical Engineering ASET Amity University Jaipur India Department of Electronics & Communication Engineering ASET Amity University Jaipur India Department of Electrical Engineering Guru Jambheshwar University of Science & Technology Hisar India

PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm. ©, Engineers Australia.

关键词： Optimization

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