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Journal articles on the topic 'Message prediction'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Short, Kevin M. "Signal Extraction from Chaotic Communications." International Journal of Bifurcation and Chaos 07, no. 07 (1997): 1579–97. http://dx.doi.org/10.1142/s0218127497001230.

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This paper will consider the use of nonlinear dynamic (NLD) forecasting to extract messages from chaotic communication systems. Earlier work has shown that one-step prediction methods have sometimes been able to reveal the presence of hidden messages as well as the frequency content of the hidden messages. However, recovery of the actual hidden message usually involved filtering in the frequency domain. In this paper we show that it may be possible to extract the hidden message signal without filtering in the frequency domain. The approaches which will be discussed involve either the use of mu

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Iwamoto, Yoshiyuki, Koichi Suga, Kanemitsu Ootsu, Takashi Yokota, and Takanobu Baba. "Receiving message prediction method." Parallel Computing 29, no. 11-12 (2003): 1509–38. http://dx.doi.org/10.1016/j.parco.2003.05.005.

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Huang, Boning, and Junkang Wei. "Research on Deep Learning-Based Financial Risk Prediction." Scientific Programming 2021 (December 13, 2021): 1–8. http://dx.doi.org/10.1155/2021/6913427.

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Financial text-based risk prediction is an important subset for financial analysis. Through automatic analysis of public financial comments, fundamentals on current financial expectations can be evaluated. A deep learning method for financial risk prediction based on sentiment classification is proposed in this paper. The proposed method consists of two steps. Firstly, the abstract of the financial message is extracted according to the seq2seq model. During the extraction process, the seq2seq model can cope with the situation of different input message lengths. After the abstraction, invalid i

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Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, and Ankur Narang. "Understanding Chat Messages for Sticker Recommendation in Messaging Apps." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13156–63. http://dx.doi.org/10.1609/aaai.v34i08.7019.

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Stickers are popularly used in messaging apps such as Hike to visually express a nuanced range of thoughts and utterances to convey exaggerated emotions. However, discovering the right sticker from a large and ever expanding pool of stickers while chatting can be cumbersome. In this paper, we describe a system for recommending stickers in real time as the user is typing based on the context of the conversation. We decompose the sticker recommendation (SR) problem into two steps. First, we predict the message that the user is likely to send in the chat. Second, we substitute the predicted messa

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Osipenko, Sergey, Eugene Nikolaev, and Yury Kostyukevich. "Retention Time Prediction with Message-Passing Neural Networks." Separations 9, no. 10 (2022): 291. http://dx.doi.org/10.3390/separations9100291.

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Retention time prediction, facilitated by advances in machine learning, has become a useful tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural networks and 1D-convolutional neural networks that are trained on the METLIN small molecule retention time dataset (SMRT). These approaches demonstrate accurate predictions comparable with the experimental error for the training set. The weak point of retention time prediction approaches is the transfer of predictions to various systems. The accuracy of this step depends both on the method of mapping and on the accur

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Mahendrakar, Nikhil Anand. "Next Message Prediction in a Sequence using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 152–55. http://dx.doi.org/10.22214/ijraset.2022.46959.

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Abstract: Prediction of next message after given a sequence of messages has lot of applications. It has applications in NLP (natural language processing), Log Anomaly Detection, Automating Customer Service and many more. Automating Customer Service is one such application which shall be in great focus in this paper. For any organization, automating customer service can save a lot of revenue and time. It increases the response time for customers/users, which in turn increases customer satisfaction. It reduces the load on the business. In this paper, we use machine learning algorithm (Support ve

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Yeh, Yi Hsuah, Yuan Cheng Lai, Jian Wei Lin, Ching Neng Lai, and Hui Chuan Weng. "A Tracking System with Location-Based Delivering for Reducing Short Message Transmissions." Applied Mechanics and Materials 284-287 (January 2013): 2682–86. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2682.

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Combining Short Message Service (SMS) and Global Position System (GPS), this paper proposes a novel method, called Location-based Delivering (LBD), and further develops a realistic system to tracking target's moving. LBD can reduce the number of short message transmissions while maintaining location accuracy within an acceptable range. LBD mainly adopts two proposed techniques: location prediction and dynamic threshold. Location prediction utilizes the current target's location, moving speed, bearing to predict its next location. When the distance between the predicted location and the actual

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Geschke, Daniel, Kai Sassenberg, Georg Ruhrmann, and Denise Sommer. "Behavior Speaks Louder than Traits." Zeitschrift für Psychologie / Journal of Psychology 215, no. 4 (2007): 248–54. http://dx.doi.org/10.1027/0044-3409.215.4.248.

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The impact of message abstractness on recipients is controversial. Research on person perception suggests that abstract messages are more influential, whereas research on communication and its outcomes suggests that concrete, vivid communication has greater impact. It was predicted that the latter also applies to the domain of person perception if the message contains unexpected (i.e., stereotype inconsistent) information. This prediction was tested experimentally. It was found that a message about a target person with unexpected content exerts more influence when it is concrete than when it i

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Kim, Hyun Suk, Sijia Yang, Minji Kim, Brett Hemenway, Lyle Ungar, and Joseph N. Cappella. "An Experimental Study of Recommendation Algorithms for Tailored Health Communication." Computational Communication Research 1, no. 1 (2019): 103–29. http://dx.doi.org/10.5117/ccr2019.1.005.sukk.

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Abstract Recommendation algorithms are widely used in online cultural markets to provide personalized suggestions for products like books and movies. At the heart of the commercial success of recommendation algorithms is their ability to make an accurate prediction of a target person’s preferences for previously unseen items. Can these algorithms also be used to predict which health messages an individual will evaluate favorably, and thereby provide effective tailored communication to the person? Although there is evidence that message tailoring enhances persuasion, little research has examine

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Zhang, Cui, Wang, Ding, and Xia. "Distributed Joint Cooperative Self-Localization and Target Tracking Algorithm for Mobile Networks." Sensors 19, no. 18 (2019): 3829. http://dx.doi.org/10.3390/s19183829.

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: Location information is a key issue for applications of the Internet of Things. In this paper, we focus on mobile wireless networks with moving agents and targets. The positioning process is divided into two phases based on the factor graph, i.e., a prediction phase and a joint self-location and tracking phase. In the prediction phase, we develop an adaptive prediction model by exploiting the correlation of trajectories within a short period to formulate the prediction message. In the joint positioning phase, agents calculate the cooperative messages according to variational message passing

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Cao, Yumei, Peng Li, Tianmian Liang, Xiaojun Wu, Xiaoming Wang, and Yuanru Cui. "A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model." Applied Sciences 13, no. 8 (2023): 5217. http://dx.doi.org/10.3390/app13085217.

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Opportunities networks’ message transmission is significantly impacted by routing prediction, which has been a focus of opportunity network research. The network of student nodes with smart devices is a particular type of opportunity network in the campus setting, and the predictability of campus node movement trajectories is also influenced by the regularity of students’ social mobility. In this research, a novel Markov route prediction method is proposed under the campus background. When two nodes meet, they share the movement track data of other nodes stored in each other’s cache in order t

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Li, Yuntao, Yan Zhang, and Jiazhe Lin. "Message spreading modeling from the perspective of social psychology, differential equation dynamics, and deep learning technique." Nonlinear Analysis: Modelling and Control 30 (May 3, 2025): 1–14. https://doi.org/10.15388/namc.2025.30.41797.

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To gain a deeper understanding of the characteristics of message spreading, it is crucial to explore various methods of modeling this phenomenon. Given that message spreading is significantly influenced by social media, we propose a modified spreading model informed by social psychology analysis. This approach also incorporates differential equation dynamics and deep learning technique. The proposed model accounts for a cross-transmission mechanism between individuals and social media platforms, as well as a nonlinear spreading rate, to effectively characterize the saturation effect of message

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Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, and Ankur Narang. "Large Scale Multilingual Sticker Recommendation In Messaging Apps." AI Magazine 42, no. 4 (2022): 16–28. http://dx.doi.org/10.1609/aimag.v42i4.15098.

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Stickers are popularly used while messaging to visually express nuanced thoughts. We describe a real-time sticker recommendation (SR) system. We decompose SR into two steps: predict the message that is likely to be sent, and substitute that message with an appropriate sticker. To address the challenges caused by transliteration of message from users’ native language to the Roman script, we learn message embeddings by employing character-level CNN in an unsupervised manner. We use them to cluster semantically similar messages. Next, we predict the message cluster instead of the message. Except

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Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, and Ankur Narang. "Large Scale Multilingual Sticker Recommendation In Messaging Apps." AI Magazine 42, no. 4 (2022): 16–28. http://dx.doi.org/10.1609/aaai.12023.

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Stickers are popularly used while messaging to visually express nuanced thoughts. We describe a real-time sticker recommendation (SR) system. We decompose SR into two steps: predict the message that is likely to be sent, and substitute that message with an appropriate sticker. To address the challenges caused by transliteration of message from users’ native language to the Roman script, we learn message embeddings by employing character-level CNN in an unsupervised manner. We use them to cluster semantically similar messages. Next, we predict the message cluster instead of the message. Except

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Zhang, Sheng, Houzhong Liu, Caisen Chen, Zhaojun Shi, and William Wei Song. "Activity-based routing algorithm in opportunistic mobile social networks." International Journal of Distributed Sensor Networks 17, no. 9 (2021): 155014772110412. http://dx.doi.org/10.1177/15501477211041272.

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In opportunistic mobile social networks, nodes are clustered according to their interests or hobbies and take part in different activities regularly. We delve into the temporal and spatial mobility characteristics of network nodes and put forward an activity-based message opportunistic forwarding algorithm. The main idea of the algorithm is that we choose different message forwarding methods according to the situation of nodes participating in activities. If the source node and the destination node are both attend in the same activities, we select the best relay node which has the biggest deli

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Choudhary, Esha. "Spam SMS Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6868–76. http://dx.doi.org/10.22214/ijraset.2023.53235.

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Abstract: As the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. I

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Mai, Sijie, Shuangjia Zheng, Yuedong Yang, and Haifeng Hu. "Communicative Message Passing for Inductive Relation Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4294–302. http://dx.doi.org/10.1609/aaai.v35i5.16554.

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Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are limited to a transductive setting and cannot process previously unseen entities. The recent proposed subgraph-based relation reasoning models provided alternatives to predict links from the subgraph structure surrounding a candidate triplet inductively. However, we observe that these methods often neglect the directed nature of the extracted subgraph and weaken the role of relation information in the subgraph modeli

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Liebeskind, Chaya. "Emoji Identification and Prediction in Hebrew Political Corpus." Issues in Informing Science and Information Technology 16 (2019): 343–59. http://dx.doi.org/10.28945/4372.

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Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning

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Kwon, Hongwook, Ho Heon Kim, Jaeil An, Jae-Ho Lee, and Yu Rang Park. "Lifelog Data-Based Prediction Model of Digital Health Care App Customer Churn: Retrospective Observational Study." Journal of Medical Internet Research 23, no. 1 (2021): e22184. http://dx.doi.org/10.2196/22184.

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Background Customer churn is the rate at which customers stop doing business with an entity. In the field of digital health care, user churn prediction is important not only in terms of company revenue but also for improving the health of users. Churn prediction has been previously studied, but most studies applied time-invariant model structures and used structured data. However, additional unstructured data have become available; therefore, it has become essential to process daily time-series log data for churn predictions. Objective We aimed to apply a recurrent neural network structure to

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Chen, Siyuan, Jiahai Wang, and Guoqing Li. "Neural Relational Inference with Efficient Message Passing Mechanisms." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7055–63. http://dx.doi.org/10.1609/aaai.v35i8.16868.

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Many complex processes can be viewed as dynamical systems of interacting agents. In many cases, only the state sequences of individual agents are observed, while the interacting relations and the dynamical rules are unknown. The neural relational inference (NRI) model adopts graph neural networks that pass messages over a latent graph to jointly learn the relations and the dynamics based on the observed data. However, NRI infers the relations independently and suffers from error accumulation in multi-step prediction at dynamics learning procedure. Besides, relation reconstruction without prior

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Kwon, Youngchun, Dongseon Lee, Youn-Suk Choi, Myeonginn Kang, and Seokho Kang. "Neural Message Passing for NMR Chemical Shift Prediction." Journal of Chemical Information and Modeling 60, no. 4 (2020): 2024–30. http://dx.doi.org/10.1021/acs.jcim.0c00195.

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Zhu, Xingyu. "A Directed Message Passing Neural Network Model for Predicting True Solubility in Drug Discovery." Applied and Computational Engineering 166, no. 1 (2025): 135–40. https://doi.org/10.54254/2755-2721/2025.tj24494.

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This study presents a novel directed message-passing neural network (DMPNN) model for predicting true solubility (logS) in drug discovery. Traditional methods such as high-throughput screening and QSAR modelsexemplified by the RuleofFivehave historically guided early discovery efforts but often fall short in handling modern high-dimensional, complex chemical datasets. Recent advances in integrating machine learning, including support vector machines, random forests, and deep learning, have improved prediction accuracy. However, high-dimensional data and accurate error estimation remain signifi

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Eslamieh, Pegah, Mehdi Shajari, and Ahmad Nickabadi. "User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks." Mathematics 11, no. 13 (2023): 2950. http://dx.doi.org/10.3390/math11132950.

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Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks as their primary sources of information. In this article, we propose User2Vec, a novel approach to improve stock market prediction accuracy, which contributes to more informed investment decision making. User2Vec is a unique method that recognizes the unequal impact of different user opinion

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Memon, Ambreen, Sardar M. N. Islam, Muhammad Nadeem Ali, and Byung-Seo Kim. "Enhancing Energy Efficiency of Sensors and Communication Devices in Opportunistic Networks Through Human Mobility Interaction Prediction." Sensors 25, no. 5 (2025): 1414. https://doi.org/10.3390/s25051414.

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The proliferation of smart devices such as sensors and communication devices has necessitated the development of networks that can adopt device-to-device communication for delay-tolerant data transfer and energy efficiency. Therefore, there is a need to develop opportunistic networks to enhance energy efficiency through improved data routing. A sensor device equipped with computing, communication, and mobility capabilities can opportunistically transfer data to another device, either as a direct recipient or as an intermediary forwarding data to a third device. Routing algorithms designed for

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Takahashi, Narumi. "Message from the Winner." Journal of Disaster Research 18, no. 2 (2023): 82. http://dx.doi.org/10.20965/jdr.2023.p0082.

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We are very honored to receive the prestigious JDR Award for the Most Cited Paper 2022. The winning paper, “Real-Time Tsunami Prediction System Using DONET,” discusses a system that uses data from the Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET), which was installed in the rupture areas of the 1944 Tonankai and 1946 Nankai earthquakes in order to instantly generate and visualize tsunami prediction information. The forecast information from this system consists of tsunami arrival time, maximum tsunami height, tsunami inundation area, and inundation depth distribution. Th

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Ao, Zhen, Feng Li, Qiang Ma, and Guiqing He. "Voice and Position Simultaneous Communication System Based on Beidou Navigation Constellation." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 5 (2020): 1010–17. http://dx.doi.org/10.1051/jnwpu/20203851010.

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Considering China Beidou has unique two-way communication capability for short messages that are not available in other navigation systems such as GPS, a 600bps vocoder adapted to the short message channel of Beidou is developed. The sinusoidal excitation linear prediction algorithm is adopted by the vocoder to achieve voice communication with clear communication quality. Furthermore, a coordinate compression algorithm for processing positioning information is designed to provide more transmission space for speech encoded data. Based on the above-mentioned results, a communication system that

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Cao, Yuwei, Hao Peng, Zhengtao Yu, and Philip S. Yu. "Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8255–64. http://dx.doi.org/10.1609/aaai.v38i8.28666.

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As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more inform

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Jordan, Amy, Jessica Taylor Piotrowski, Amy Bleakley, and Giridhar Mallya. "Developing Media Interventions to Reduce Household Sugar-Sweetened Beverage Consumption." ANNALS of the American Academy of Political and Social Science 640, no. 1 (2012): 118–35. http://dx.doi.org/10.1177/0002716211425656.

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In 2010, the city of Philadelphia launched a media campaign to reduce the consumption of sugar-sweetened beverages (SSBs) in homes with children as a strategy to combat obesity. Using the integrative model (IM) of behavioral change and prediction, a theory-based precampaign survey of Philadelphia parents was conducted to determine the most effective message content. Results indicated that intention to eliminate SSB consumption is attitudinally driven and suggested that effective messages should highlight feelings of nurturing and concern about child weight gain. Focus group testing led to the

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Ghaleb, Fuad A., Waleed Ali, Bander Ali Saleh Al-Rimy, and Sharaf J. Malebary. "Intelligent Proof-of-Trustworthiness-Based Secure Safety Message Dissemination Scheme for Vehicular Ad Hoc Networks Using Blockchain and Deep Learning Techniques." Mathematics 11, no. 7 (2023): 1704. http://dx.doi.org/10.3390/math11071704.

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Vehicular ad hoc networks have emerged as the main building block for the future cooperative intelligent transportation system (cITS) to improve road safety and traffic efficiency and to provide passenger comfort. However, vehicular networks are decentralized, characterized by high mobility and dynamicity, and vehicles move in a hostile environment; such characteristics make VANET applications suffer many security and communication issues. Recently, blockchain has been suggested to solve several VANET issues including the dissemination of trustworthy life-threatening information. However, exis

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Wen, Yean-Fu, Ko-Yu Hung, Yi-Ting Hwang, and Yeong-Sung Frank Lin. "Sports lottery game prediction system development and evaluation on social networks." Internet Research 26, no. 3 (2016): 758–88. http://dx.doi.org/10.1108/intr-05-2014-0139.

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Purpose – The purpose of this paper is to establish a social-network sp.orts lottery system to support users in predicting and simulating sports lottery betting. The community data were generated to support user decision and featured instant game records and odds data synchronisation. Furthermore, the next development cycle were evaluated through a questionnaire. Design/methodology/approach – An extended prototype website development methodology was applied to develop the system. An online sample was collected to evaluate the function, interface, operation, and prediction designs. The χ2 test

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Afsahi, Ahmad, and Nikitas J. Dimopoulos. "Efficient communication using message prediction for clusters of multiprocessors." Concurrency and Computation: Practice and Experience 14, no. 10 (2002): 859–83. http://dx.doi.org/10.1002/cpe.697.

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Busk, Jonas, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, and Tejs Vegge. "Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks." Machine Learning: Science and Technology 3, no. 1 (2021): 015012. http://dx.doi.org/10.1088/2632-2153/ac3eb3.

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Abstract Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are important for assessing confidence in predictions and enabling decision making. However, machine learning models can produce badly calibrated uncertainty estimates and it is therefore crucial to detect and handle uncertainty carefully. In this work we extend a message passing neural network designed specifically for predicting properties of molecules and materials with a calibrated probabilistic predictive distr

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Hu, Jiwei, Yuan Gao, Qiwen Jin, Guangpeng Zhao, and Hongyang Lu. "An Efficient Lossless Compression Algorithm for Maritime Safety Information Using Byte Encoding Network." Journal of Marine Science and Engineering 12, no. 7 (2024): 1075. http://dx.doi.org/10.3390/jmse12071075.

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The short message function of the BeiDou satellite system, due to its strong concurrent processing capabilities, can quickly and accurately send information to the target location in emergency situations. However, because of data redundancy and limitations on message length, a single piece of information often requires multiple transmissions through BeiDou short messages to be completed, resulting in limited transmission capacity. To improve the transmission capacity of information, it is necessary to compress the information during transmission using BeiDou’s short message communication funct

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Zhang, Guomin, Lihua Song, Haitao Wang, Chao Hu, and Na Wang. "Location-assistant content distribution scheme for emergency rescue." International Journal of Distributed Sensor Networks 14, no. 9 (2018): 155014771880046. http://dx.doi.org/10.1177/1550147718800464.

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In order to optimize the timeliness and pertinence of message delivery in emergency rescue scenarios and improve the service performance of emergency communications network, we propose a location-assistant content distribution scheme based on delay tolerant network. First of all, considering that the movement patterns of rescue teams tend to follow a predetermined course of action, we design a location-based group mobility model. Due to the intermittent network connectivity and variety of emergency service, a content-classification-based publish/subscribe architecture and a GenericSpray routin

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Zhang, Qianru, Xinyi Gao, Haixin Wang, Siu Ming Yiu, and Hongzhi Yin. "Efficient Traffic Prediction Through Spatio-Temporal Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 1093–101. https://doi.org/10.1609/aaai.v39i1.32096.

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Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations through a graph-based message-passing framework. Although GNNs have shown great promise in handling traffic datasets, their deployment in real-life applications has been hindered by scalability constraints arising from high-order message passing. Additionally, the over-smoothing problem of GNNs may lead to indistinguishable region representations as the number of layers increases, resulting in performance degradation.

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Cik, Ku Haroswati Che Ku Yahaya, Kassim Murizah, and Husni Hasnorhafiza. "Disruptive Technology: The Future of SMS Technology." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 665–71. https://doi.org/10.11591/ijeecs.v11.i2.pp665-671.

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The research illustrates a view on the current trends in the telecommunication industry focusing on the matter of Short Message Service (SMS) technology. It targets and explains disruptive technology and also introduces disruptive technology in Short Message Service (SMS). The methodology of this research is market trend analysis using data on volume of Short Message Service (SMS) sent and received through a mobile network and a survey that was conducted with questionnaires. The findings are Short Message Service (SMS) is predicted to become obsolete and be disrupted by Over the Top (OTT) mess

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Janakiraman, Sengathir. "An improved rank criterion-based NLOS node detection mechanism in VANETs." International Journal of Intelligent Unmanned Systems 9, no. 1 (2020): 1–15. http://dx.doi.org/10.1108/ijius-12-2019-0072.

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PurposeAn Improved Rank Criterion-based NLOS node Detection Mechanism (IRC-NLOS-DM) is proposed based on the benefits of a reputation model for effective localization of NLOS nodes during the dynamic exchange of emergency messages in critical situations.Design/methodology/approachThis proposed IRC-NLOS-DM scheme derives the benefits of a reputation model that influentially localizes the NLOS nodes under dynamic exchange of emergency messages. This proposed IRC-NLOS-DM scheme is an attempt to resolve the issues with the routing protocols that aids in warning message delivery of vehicles that ar

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Xie, Xiaoyun, Yahya Dorostkar Navaei, and Sajad Einy. "A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET." Wireless Communications and Mobile Computing 2023 (June 14, 2023): 1–18. http://dx.doi.org/10.1155/2023/3776815.

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In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases

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Qin, Xiao, Nasrullah Sheikh, Berthold Reinwald, and Lingfei Wu. "Relation-aware Graph Attention Model with Adaptive Self-adversarial Training." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9368–76. http://dx.doi.org/10.1609/aaai.v35i11.17129.

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This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipeline, namely heterogeneous graph representation learning and negative sampling. Existing message passing-based graph neural networks use edges either for graph traversal and/or selection of message encoding functions. Ignoring the edge semantics could have severe repercussions on the quality of embeddings, especially when dealing with two nodes having multiple relations. Furthermore, the expressivity of the learned re

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Xu, Xiaolong, Yibo Zhou, Haolong Xiang, et al. "NLGT: Neighborhood-based and Label-enhanced Graph Transformer Framework for Node Classification." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12954–62. https://doi.org/10.1609/aaai.v39i12.33413.

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Graph Neural Networks (GNNs) are widely applied on graph-level tasks, such as node classification, link prediction and graph generation. Existing GNNs mostly adopt a message-passing mechanism to aggregate node information with their neighbors, which often makes node information similar after rounds of aggregations and leads to oversmoothing. Although recent works have made improvements by combining different message aggregation methods or introducing semantic encodings as priors, these message-passing based GNNs still fail to combat oversmoothing after multiple iterations of node aggregation.

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Gao, Bibu, and Wenqiang Zhang. "A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering." Applied Sciences 11, no. 14 (2021): 6350. http://dx.doi.org/10.3390/app11146350.

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As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for comba

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Xu, Gang, Xinyue Wang, Na Zhang, Zhifei Wang, Lin Yu, and Liqiang He. "A Routing Algorithm for the Sparse Opportunistic Networks Based on Node Intimacy." Wireless Communications and Mobile Computing 2021 (March 1, 2021): 1–9. http://dx.doi.org/10.1155/2021/6666211.

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Opportunistic networks are becoming more and more important in the Internet of Things. The opportunistic network routing algorithm is a very important algorithm, especially based on the historical encounters of the nodes. Such an algorithm can improve message delivery quality in scenarios where nodes meet regularly. At present, many kinds of opportunistic network routing algorithms based on historical message have been provided. According to the encounter information of the nodes in the last time slice, the routing algorithms predict probability that nodes will meet in the subsequent time slic

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Venu, Nookala, R. Swathi, Sanjaya Kumar Sarangi, et al. "Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis." Wireless Communications and Mobile Computing 2022 (July 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/2785810.

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In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. Connection damage has straight consequences on the network characteristics. For that motivation, various techniques are quick and efficient damage of connection identification using alert packet is difficult for that time to identify next connection in a mobile network. It increases end to end delay and reduces the network lifetime. The present effective connection alteration rate- (ECAR-) based communication scheme depends on the det

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Wang, Chao, Jitong Li, Yunhua He, Ke Xiao, and Haoyu Zhang. "Destination Prediction-Based Scheduling Algorithms for Message Delivery in IoVs." IEEE Access 8 (2020): 14965–76. http://dx.doi.org/10.1109/access.2020.2966494.

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Liang, Jiguang, Bo Jiang, Rongchao Yin, Chonghua Wang, JianLong Tan, and Shuo Bai. "RTPMF: Leveraging User and Message Embeddings for Retweeting Behavior Prediction." Procedia Computer Science 80 (2016): 356–65. http://dx.doi.org/10.1016/j.procs.2016.05.351.

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Oh, Hyun-Kyo, Jin-Woo Kim, Sang-Wook Kim, and Kichun Lee. "A unified framework of trust prediction based on message passing." Cluster Computing 22, S1 (2018): 2049–61. http://dx.doi.org/10.1007/s10586-018-1807-x.

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Tang, Miru, Baiqing Li, and Hongming Chen. "Application of message passing neural networks for molecular property prediction." Current Opinion in Structural Biology 81 (August 2023): 102616. http://dx.doi.org/10.1016/j.sbi.2023.102616.

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Calo, Sergio, Filippo Bistaffa, Anders Jonsson, Vicenç Gómez, and Mar Viana. "Spatial air quality prediction in urban areas via message passing." Engineering Applications of Artificial Intelligence 133 (July 2024): 108191. http://dx.doi.org/10.1016/j.engappai.2024.108191.

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Bozkurt, Prof Dr Tulay. "Message from editor." Global Journal of Psychology Research: New Trends and Issues 7, no. 1 (2017): 1. http://dx.doi.org/10.18844/gjpr.v7i1.2434.

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Message from Editor  Dear Readers,  It is a great honor for us to publish seventh volume, first issue of Global Journal of Psychology Research: New Trends and Issues (GJPR).   Global Journal of Psychology Research: New Trends and Issues welcomes original empirical investigations and comprehensive literature review articles focusing on psychological issues and related disciplines. The mission of the journal is to publish articles of professional interest for members of psychology. The scope of the journal includes, but is not limited to; the following major a

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Morikawa, Nobuyuki. "Message from the Winner." Journal of Disaster Research 18, no. 2 (2023): 84. http://dx.doi.org/10.20965/jdr.2023.p0084.

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We are very honored to receive the JDR Award for the Most Downloaded Article 2022, a new award from the Journal of Disaster Research (JDR). After the 2011 Great Tohoku Earthquake, it is now said that a magnitude 9-class earthquake could occur in the Nankai Trough, and it is necessary for us to know how strong ground motion (shaking) we will experience during such a great earthquake in order to take countermeasures. In this paper, we developed a new seismic motion prediction model that is applicable to M9-class earthquakes. We did this by adding the strong-motion records of the 2011 Great Tohok

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