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Enhanced pilot allocation in MIMO-OFDM systems using the enhanced shuffled frog leaping algorithm

Discover Electronics 2025年第1期2卷 1-14页

作者： Singh, Harjeet Department of Computer Science and Engineering Baba Farid College of Engineering and Technology Bathinda India

This paper presents a method of pilot allocation based on Enhanced Shuffled Frog Leaping Algorithm (ESFLA) for channel estimation optimization in high-mobility and large-scale MIMO-OFDM systems. Building upon channel sparsity, the algorithmic method of ESFLA will reduce pilot overhead while also increasing the accuracy compared to current approaches like ISLFA, RLPA, and DLPA. Simulation results show that ESFLA outperforms others in minimizing BER and MSE for a wide range of SNRs with robust recovery guarantees. Despite its high computational complexity, the ability of ESFLA to significantly enhance channel estimation makes it an attractive candidate for next-generation wireless communication systems.

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Expression Analysis of Tweets using NLP in Machine Learning

Expression Analysis of Tweets using NLP in Machine Learning

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sabeenian, R.S. Abishiek, A. Harivignesh, S. Dinesh, P.M. Lakshmi, G. Prasanna Alagarsamy, Manjunathan Sona College of Technology Electronics and Communication Engineering Salem India Sandip University School of Computer Science and Engineering Maharashtra Nashik India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621112 India

ISBN: (纸本)9798350390049

Nowadays the social media has become one of the most essential platforms for reviewing and commenting their own opinions and perspectives. Sentiment analysis is the study of analyzing reviews of the people from texts, products and also analyzing the customer opinions. Expression or Sentiment analysis uses machine learning concepts particularly Natural Language Processing and various ML algorithms. Mining of the data helps to draw conclusions from various fields. We have proposed a paper where we analyze the sentiments of twitter data, identify and segregate positive tweets and negative tweets using machine learning and displaying the number of positive and negative tweets, plotting them using histogram and displaying the classification report. We have done this project since it is one of the most important tasks of classifying the positive and negative texts and analyze the sentiments which is very crucial for any organization's growth © 2024 IEEE.

关键词： Tweets

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Electricity Generation From Food Waste Using IOT 3

Electricity Generation From Food Waste Using IOT

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Arunkumar, M. Agalya, S. Bhuvanaeshwari, M. IFET College of Engineering Dept. of Information Technology Villupuram India Surya Group of Institutions Dept. of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350372120

One inventive solution is to turn food waste into power using the Internet of Things (IoT) to meet the growing need for sustainable energy sources. It turns thermal energy from trash into electricity by utilizing thermoelectric devices and the Seebeck effect. Real-time monitoring and control are made possible with IoT integration. By improving the extraction of energy from liquid or semi-solid waste, this technique reduces waste and promotes a circular energy economy. The system makes use of hardware components such as transformers and voltage regulators. The outcomes of experiments demonstrate its efficacy and possible role in renewable energy. This creative idea, which provides a sustainable route ahead for the production of power from food waste, highlights the importance of IoT in improving waste-to-energy systems. © 2024 IEEE.

关键词： Internet of things

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A Robust Airport Detection Method Based on Environment-Insensitive Saliency Analysis 21st

A Robust Airport Detection Method Based on Environment-Inse...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Chen, Hongtao Zhong, Baojiang Ma, Kai-Kuang School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789819601271

Image color saliency has been popularly exploited to detect airports from remote sensing images (RSIs). However, in a complex environment, many non-airport image regions could also produce high saliency, leading to poor robustness of the airport detection result. To address this fundamental problem, a novel airport detection method based on an environment-insensitive saliency analysis process is proposed in the present paper. In our saliency analysis process, an iterative scheme is developed to compute the color saliency of the input RSI, by which environmental disturbances (e.g. mountains, oceans) can be progressively suppressed from the saliency computation though iterative refinement. Since the saliency of non-airport areas is reduced, the target airport can be identified with high robustness. By further fusing the color saliency with the RSI structural saliency, airport support region can be obtained. To capture the airport with high completeness, a region growing algorithm is also proposed, which first selects a set of seed pixels from the saliency map and then greedily adds neighboring pixels with about the same gray-scale value to form a complete profile of the airport region as the final detection output. Extensive experimental results show that our proposed airport detection method can outperform the current state-of-the-art methods by a large margin. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Remote sensing

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Blockchain Based Voting System for Secure and Transparent Electoral Processes 10

Blockchain Based Voting System for Secure and Transparent El...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sumathy, S. Murugan, S. Karthick Latha, J. Mageshwari, G. Andrews, Allen Joseph Gilbert Kumar, M. Hema Sir Theagaraya College-Shift Ii Pg Department of Computer Science Tamilnadu Chennai India R. M. D. Engineering College Department of Computer Science and Engineering Tamilnadu Thiruvallur India R. M. K. Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvallur India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Tiruvallur India Deloitte consulting Llp 200 Sterling road MechanicsburgPA United States Sona College of Technology Department of Ece Tamil Nadu Salem India

ISBN: (纸本)9798350384369

This study describes an innovative approach to blockchain-based voting systems. The proposed method guarantees the integrity, security, and resistance to manipulation of elections conducted on a decentralized network through the utilization of smart contracts and cryptographic principles. Emphasizing participant confidentiality, vote integrity, and real-time auditability, the experimental outcomes demonstrate the system's scalability, dependability, and security. Within the domain of theoretical democratic discourse, presents a prospective scenario in which voting methods based on blockchain technology. The results indicate that the approach can be easily implemented on a large scale, representing a significant milestone in the struggle for democratic, secure, and transparent voting procedures. With highly efficient, boasting a transaction speed of 500 TPS, scalability to accommodate over a thousand users, and a block confirmation time of less than 10s. Encryption efficiency and digital signature dependability are indicators of the system's high level of security. Equipped with an accuracy rate that is 97.2%. © 2024 IEEE.

关键词： Decentralized finance

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Stock market prediction using deep learning algorithms

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CAAI Transactions on Intelligence technology 2023年第1期8卷 82-94页

作者： Somenath Mukherjee Bikash Sadhukhan Nairita Sarkar Debajyoti Roy Soumil De Nazrul Center of Social and Cultural Studies Kazi Nazrul UniversityAsansolIndia Department of Computer Science and Engineering Techno International New Town(Techno India College of Technology)KolkataIndia

The Stock Market is one of the most active research areas,and predicting its nature is an epic necessity *** the Stock Market is quite challenging,and it requires intensive study of the pattern of *** statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate *** machine learning and deep learning algorithms can make a firm prediction with minimised error *** Artificial Neural Network(ANN)or Deep Feedforward Neural Network and the Convolutional Neural Network(CNN)are the two network models that have been used extensively to predict the stock market *** models have been used to predict upcoming days'data values from the last few days'data *** process keeps on repeating recursively as long as the dataset is *** endeavour has been taken to optimise this prediction using deep learning,and it has given substantial *** ANN model achieved an accuracy of 97.66%,whereas the CNN model achieved an accuracy of 98.92%.The CNN model used 2-D histograms generated out of the quantised dataset within a particular time frame,and prediction is made on that *** approach has not been implemented earlier for the analysis of such *** a case study,the model has been tested on the recent COVID-19 pandemic,which caused a sudden downfall of the stock *** results obtained from this study was decent enough as it produced an accuracy of 91%.

关键词： artificial neural network convolutional neural network nifty stock market

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Personalized Alarming System for Asthma Management Based on Lung Functionality: Observational Study

Personalized Alarming System for Asthma Management Based on ...

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2023 Advances in science and engineering technology International Conferences, ASET 2023

作者： Alblooshi, Fatima Albadi, Reem Alblooshi, Khawla Ishimirwe, Emma Uwisoneye, Yvettte Alnajjar, Fady Mehiar, Dua'a College of Information Technology United Arab Emirates Universty Al Ain United Arab Emirates College of It United Arab Emirates Universty Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665454742

Asthma is a chronic respiratory disease that can be challenging to manage. To improve asthma management, we designed a personalized alarm system that utilizes lung function data to monitor and alert individuals to potentially harmful air quality. Our pilot observation included seven individuals who were exposed to two different air-quality environments, and we observed significant variations in their lung function responses. Based on these observations, we developed customized alarm systems for each participant that continuously monitored air quality and provided notifications when it reached levels that could potentially exacerbate their asthma symptoms. This personalized approach has the potential to enable individuals to proactively protect their respiratory health and avoid asthma attacks by taking timely and appropriate action based on their own specific needs. © 2023 IEEE.

关键词： Alarm systems

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A Simple Graph Contrastive Learning Framework for Short Text Classification 39

A Simple Graph Contrastive Learning Framework for Short Text...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yonghao Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University China University of Trento Italy

ISBN: (纸本)157735897X

Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited labeled data in short text classification. However, existing models have certain limitations. They rely on explicit data augmentation techniques to generate contrastive views, resulting in semantic corruption and noise. Additionally, these models only focus on learning the intrinsic consistency between the generated views, neglecting valuable discriminative information from other potential views. To address these issues, we propose a Simple graph contrastive learning framework for Short Text Classification (SimSTC). Our approach involves performing graph learning on multiple text-related component graphs to obtain multi-view text embeddings. Subsequently, we directly apply contrastive learning on these embeddings. Notably, our method eliminates the need for data augmentation operations to generate contrastive views while still leveraging the benefits of multi-view contrastive learning. Despite its simplicity, our model achieves outstanding performance, surpassing large language models on various datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Asynchronous distribution scheme of group quantum keys based on max heap in classical network

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Journal of King Saud University - computer and Information sciences 2025年第4期37卷 1-17页

作者： Zhu, Dexin Zhao, Zilong Zhang, Huanjie Zhou, Zhiqing Li, Yuanbo Zhao, Jian Song, Lijun Zheng, Jun School of Cyberspace Science and Technology Beijing Institute of Technology Beijing 100081 China College of Computer Science and Technology Changchun University Jilin Changchun 130022 China Jilin Engineering Laboratory for Quantum Information Technology Changchun Institute Of Technology Jilin Changchun 130022 China

With the advancement of the information age, various information-based products have become increasingly prevalent, leading to a corresponding rise in the volume of data requiring encrypted transmission. Consequently, secure key distribution has become particularly critical, especially in large-scale communication scenarios. Considering practical limitations such as offline availability and low performance among communication participants, we propose an asynchronous group quantum key distribution scheme based on a max heap structure and blockchain technology within classical networks. In this scheme, quantum keys serve as group communication keys, and the encrypted transmission of quantum keys is facilitated through a max heap constructed according to user information. Specifically, during the initialization phase, users upload relevant information to the Key Distribution Center (KDC), while in the key distribution phase, the blockchain stores encrypted temporary group keys, enabling asynchronous access. The proposed scheme not only reduces performance consumption for users within the group but also leverages the intrinsic properties of the max heap to dynamically balance loads. Moreover, the scheme achieves both forward and backward secrecy and demonstrates favorable performance in terms of storage efficiency and computational cost. © The Author(s) 2025.

关键词： Blockchain Group key distribution Max heap Quantum key

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