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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Zhu, Zengjie Huang, Xiaoxia Li, Pan Lin, Phone Sun Yat-sen University School of Electronics and Communication Engineering China Hangzhou Dianzi University School of Computer Science China National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350351255

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology for network performance enhancement by intelligently manipulating the propagation environment. In contrast to conventional reflecting-only RIS, the simultaneous transmitting and reflecting RIS (STAR-RIS) extends coverage to 360 degrees through transmission and reflection, offering more flexibility in channel reconfiguration. The application of STAR-RIS in wireless networks holds tremendous potential for significantly improving network connectivity by fostering cascaded paths. However, it remains open on the deployment strategy for STAR-RIS to guarantee network connectivity cost-effectively, especially when considering random blockages. In this paper, we focus on investigating the impact of STAR-RIS on network connectivity in blockage-prone scenarios. Leveraging percolation theory, we derive upper and lower bounds for the critical density of nodes in both no-STAR-RIS and STAR-RIS-aided networks. Our results quantify the impact of STAR-RIS on network connectivity, demonstrating its significant enhancement on network connectivity and providing valuable insights into cost-effective deployment schemes for STAR-RIS. © 2024 IEEE.

关键词： Cost effectiveness

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4257-4272页

作者： Aslam, Naila Alzamzami, Ohoud Xia, Kewen Sadiq, Saima Umer, Muhammad Bisogni, Carmen Ashraf, Imran School of Electronics and Information Engineering Hebei University of Technology Tianjin China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science Information Technology The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano Italy Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

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computers, Materials & Continua 2023年第1期74卷 881-895页

作者： Nitin Sharma Mohd Anul Haq Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Electronics&Communication Engineering DCRUST Murthal131039India Department of Transportation Engineering IITKanpur208016Uttar PradeshIndia Department of Computer Science Applications PIETSmalkha132102India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile *** is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight *** the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency *** Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic *** study is based on a comprehensive and detailed literature evaluation in the field of *** major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car *** research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for *** properties of characters are employed to recognise the Indian licence plate in this *** licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were *** comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

关键词： Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine

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Microsphere-assisted super-resolution polarized light microscopy for polarization-sensitive materials

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Optics Letters 2025年第3期50卷 908-911页

作者： Ye, Ran Zhang, Xiangyu Zhi, Lingjie Cao, Yurong Melinte, Sorin Wang, Zengbo Yang, Songlin School of Computer and Electronic Information Nanjing Normal University Nanjing210046 China Institute of Information and Communication Technologies Electronics and Applied Mathematics Université Catholique de Louvain Louvain-la-Neuve1348 Belgium School of Computer Science and Engineering Bangor University BangorLL57 1UT United Kingdom

Polarization sensitivity is a fundamental phenomenon observed in nature, and its application is vital for advancing scientific discoveries. Here, we present a microsphere-assisted polarized light microscopy method that enables high-quality, label-free super-resolution imaging of polarization-sensitive materials. We investigated the imaging performance of this method using various samples, including colloidal crystals and grating structures. Both simulations and experiments were performed to explore the underlying physical mechanisms driving the enhancement in imaging quality. The results of this work have potential values for the fields of polarized light microscopy and super-resolution imaging. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Microspheres

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Efficient Real-Time Human Activity Recognition with IoT Sensor Data Using Conditionally Parametrized Convolutions 4

Efficient Real-Time Human Activity Recognition with IoT Sens...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Lovanshi, Mayank Tiwari, Vivek Singh, Anurag Ingle, Rajesh International Institute of Information Technology Department of Computer Science Engineering Naya Raipur India ABV-Indian Institute of Information Technology Department of Computer Science Engineering Gwalior India International Institute of Information Technology Department of Electronics Communication Engineering Naya Raipur India Symbiosis Skills of Professional University Department of Computer Science Engineering Pune India

ISBN: (纸本)9798331518981

Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises computational demands. The high operational costs present challenges for real-time HAR on sensor-based devices. Shallow learning approaches, while computationally efficient, generally fail to deliver comparable performance. Consequently, there is a critical need for deep learning methods that can effectively balance accuracy and computational cost, a topic that remains largely underexplored. This paper introduces a computationally efficient CNN that utilizes conditionally parametrized convolution, designed specifically for real HAR on sensor-based devices. The model is tested on the OPPORTUNITY dataset, achieving existing accuracy without increasing computational load. Through extensive ablation studies, we show that this high-capacity network surpasses baseline models while maintaining a similar level of computational efficiency. The proposed method can directly replace existing deep HAR frameworks and be easily implemented on sensor-based platforms for real-time applications. © 2024 IEEE.

关键词： Convolution

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Enhanced Cybercrime Detection on Twitter Using Aho-Corasick Algorithm and Machine Learning Techniques

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Informatica (Slovenia) 2024年第18期48卷 97-108页

作者： Rawat, Romil Raj, A. Samson Arun Chakrawarti, Rajesh Kumar Sankaran, Krishnan Sakthidasan Sarangi, Sanjaya Kumar Rawat, Hitesh Rawat, Anjali Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India MP Indore India Department od Electronics and Communication Engeneering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Utkal University Bhubaneswar India Department of Business Management and Economics University of Extremadura Spain Department of Computer and Communication Technology University of Extremadura Spain

The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature of Twitter makes cyberspace prominent (usually accessed via the dark web). The work used the datasets and considered the Scrape Twitter Data (Tweets) in Python using the SN-Scrape module and Twitter 4j API in JAVA to extract social data based on hashtags, which is used to select and access tweets for dataset design from a profile on the Twitter platform based on locations, keywords, and hashtags. The experiments contain two datasets. The first dataset has over 1700 tweets with a focus on location as a keypoint (hacking-for-fun data, cyber-violence data, and vulnerability injector data), whereas the second dataset only comprises 370 tweets with a focus on reposting of tweet status as a keypoint. The method used is focused on a new system model for analysing Twitter data and detecting terrorist attacks. The weights of susceptible keywords are found using a ternary search by the Aho-Corasick algorithm (ACA) for conducting signature and pattern matching. The result represents the ACA used to perform signature matching for assigning weights to extracted words of tweet. ML is used to evaluate Twitter data for classifying patterns and determining the behaviour to identify if a person is a terrorist. SVM (Support Vector Machine) proved to be a more accurate classifier for predicting terrorist attacks compared to other classifiers (KNN- K-Nearest Neighbour and NB-Naïve Bayes). The 1st dataset shows the KNN-Acc. -98.38% and SVM Accuracy as 98.85%, whereas the 2nd dataset shows the KNN-Acc. -91.68% and SVM Accuracy as 93.97%. The proposed work concludes that the generated weights are classified (cyber-violence, vulnerability injector, and hacking-for-fun) for further feature classification. Machine learning (ML) [KNN and SVM] is used to predict the occurrence and

关键词： Tweets

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Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks: A review

Stochastic modeling and performance analysis in balancing lo...

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作者： Ravi, Banoth Kumar, Manoj Hu, Yu-Chen Hassan, Shamsul Kumar, Bittu Department of Electronics and Communication Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Electronics and Communication Engineering CMR College of Engineering and Technology Telangana Hyderabad India

The rapid growth of vehicular applications has resulted in high demand for Internet technology, which demands an unprecedented network capacity and a high quality of service (QoS). In vehicular ad hoc networks (VANETs), since nodes (vehicles) are highly mobile. The dynamic nature of the network topology in the VANET system changes due to frequent changes in link connectivity. The vehicles-to-vehicles (V2V), vehicles-to-infrastructure (V2I), and QoS, as well as the heterogeneity of applications within the VANET. VANETs have been introduced to make driving comfortable by providing safety and support to drivers. Due to the flexibility and offloading schemes available in-vehicle applications, there are some limitations. However, there are many issues in providing optimum service provisioning and scheduling in the vehicular environment. In VANETs, BSs and roadside units (RSUs) improve QoS. However, Internet services transmit packets to vehicles using stochastic models, and it predicts the traffic on a VANET. We provide open challenges to drive stochastic models in this direction. © 2023 John Wiley & Sons Ltd.

关键词： Vehicular ad hoc networks

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Partitioning-based Active Trust Mechanism in Cloud-Edge-Device Collaborative Data Collection 21

Partitioning-based Active Trust Mechanism in Cloud-Edge-Devi...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xia, Zhuoqun Wang, Ziyu Liu, Xiao Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China Guangxi University School of Computer Electronics and Information Nanning China

ISBN: (纸本)9798350329223

Since the open character of the Internet of Things (IoT), IoT devices are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud. Furthermore, existing trust evaluation models struggle to balance data security needs across different regions. To address this issue, the paper proposes a partition-based active trust mechanism (PATM). This PATM scheme mainly includes the following parts: First, the region is divided into subregions, and evaluations are conducted for the indicators of each subregion. Mechanism modeling and fuzzy analytic hierarchy process are used to analyze the impact of each indicator on the sensitivity of subregions. Second, the direct estimation method is utilized to estimate the demand for active detection routing based on the sensitivity of subregions. Then, the number of operations of malicious nodes is increased through the high-frequency transmission of detection routes required by each subregion. Additionally, a trust calculation method based on node operations is proposed to evaluate the reliability of the device. The experimental results show that the PATM scheme proposed in this article can quickly identify the trust degree of nodes while balancing the needs of different regions, ensuring the credibility of data collection. © 2023 IEEE.

关键词： Internet of things

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Heuristic-based Multicasting Utilizing Network Coding over a Multi-Hop Wireless Mesh Network 18

Heuristic-based Multicasting Utilizing Network Coding over a...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Neha Rathore, Prateek Shukla, Mahendra K. Pandey, Om Jee Indian Institute of Technology BHU Department of Electronics Engineering Varanasi India The LNM Institute of Information Technology Department of Communication and Computer Engineering Jaipur India ABV-Indian Institute of Information Technology Department of Information Technology Gwalior India

ISBN: (纸本)9798350391725

Multicasting over a multi-hop wireless mesh network is a challenging issue that recently received less attention. Various applications, such as distance learning, telemedicine, web radio, and online conferencing, require the same information to be sent to multiple recipients. In these applications, the destination nodes are often far from the source, necessitating the design of a multi-hop multicasting protocol. This paper proposes an efficient multi-hop multicasting approach for transmitting packets from the source to all intended destinations. By combining network coding with opportunistic routing, the proposed method significantly reduces the expected number of transmissions a packet must undergo in a lossy wireless network. Specifically, this work introduces a Heuristic-Based Multicasting (HBM) scheme that integrates network coding with opportunistic routing. First, it calculates the minimum route cost from the source node to all the destinations. Then, a novel packet transmission strategy is employed, where packets are combined using network coding and transmitted via a path derived from the Network Coding Assisted Multicasting (NCAM) route cost calculation. Extensive simulations demonstrate the effectiveness of the NCAM route cost calculation and the corresponding packet transmission strategy. The results show that the proposed HBM scheme significantly reduces the expected number of packet transmissions in the network, thereby increasing throughput compared to existing multicasting schemes such as MORE, Pacifier, and MLCAR. © 2024 IEEE.

关键词： Wireless mesh networks (WMN)

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Crop Classification using Convolutional Neural Network 15

Crop Classification using Convolutional Neural Network

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, K. Gopalakrishnan, K. Balaji, S. Jeevanantham, J. Aakhila Hayathunisa, M. Department of Information Technology Velalar College of Engineering Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Kongu Engineering College Erode India

ISBN: (纸本)9798331300579

Deep learning is a powerful machine learning technique that has the potential to revolutionize agriculture. By leveraging deep learning models, we can develop innovative solutions to critical agricultural challenges, such as crop classification, weed percentage estimation, and crop quality identification. Deep learning models can be trained on remote sensing data to accurately classify different crop types. This information can be used by farmers and agricultural stakeholders to monitor and manage their crops more effectively. For example, crop classification models can be used to identify areas of a field that are underperforming or at risk of disease. Weed infestations can significantly reduce crop yields and quality. Deep learning models can be trained on high-resolution imagery and sensor data to accurately estimate weed percentages within fields. This information can be used to develop precise and timely weed management strategies, reducing the need for excessive herbicide usage and minimizing the environmental impact. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. This information can help farmers make informed decisions about harvesting, storage, and distribution. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. The continued development and integration of deep learning technologies into agriculture hold promise for advancing global food production and agricultural sustainability. © Grenze Sc

关键词： Weed control

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